code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Any =logging.get_logger(__name__)
_A : Any ={
'''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
# See all CANINE models at https://huggingface.co/models?filter=canine
}
class _lowercase ( _lowercase ):
a = """canine"""
def __init__( self: Optional[int] , UpperCamelCase__: List[Any]=768 , UpperCamelCase__: Optional[Any]=12 , UpperCamelCase__: Optional[Any]=12 , UpperCamelCase__: Optional[int]=3_072 , UpperCamelCase__: Optional[int]="gelu" , UpperCamelCase__: List[str]=0.1 , UpperCamelCase__: int=0.1 , UpperCamelCase__: Union[str, Any]=16_384 , UpperCamelCase__: List[str]=16 , UpperCamelCase__: Union[str, Any]=0.02 , UpperCamelCase__: Any=1e-12 , UpperCamelCase__: Optional[int]=0 , UpperCamelCase__: List[Any]=0xE000 , UpperCamelCase__: List[Any]=0xE001 , UpperCamelCase__: Union[str, Any]=4 , UpperCamelCase__: int=4 , UpperCamelCase__: List[str]=8 , UpperCamelCase__: List[str]=16_384 , UpperCamelCase__: int=128 , **UpperCamelCase__: str , ):
super().__init__(pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__ )
lowerCamelCase__ : Union[str, Any] = max_position_embeddings
lowerCamelCase__ : List[str] = hidden_size
lowerCamelCase__ : Optional[Any] = num_hidden_layers
lowerCamelCase__ : Optional[int] = num_attention_heads
lowerCamelCase__ : Optional[Any] = intermediate_size
lowerCamelCase__ : int = hidden_act
lowerCamelCase__ : Union[str, Any] = hidden_dropout_prob
lowerCamelCase__ : Union[str, Any] = attention_probs_dropout_prob
lowerCamelCase__ : int = initializer_range
lowerCamelCase__ : Any = type_vocab_size
lowerCamelCase__ : Tuple = layer_norm_eps
# Character config:
lowerCamelCase__ : int = downsampling_rate
lowerCamelCase__ : Tuple = upsampling_kernel_size
lowerCamelCase__ : Any = num_hash_functions
lowerCamelCase__ : Any = num_hash_buckets
lowerCamelCase__ : str = local_transformer_stride
| 41 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
snake_case : Dict = re.compile(R"\b(a|an|the)\b", re.UNICODE)
snake_case : Optional[int] = None
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : Any = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0." )
parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file." )
parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions." )
parser.add_argument(
"--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout)." )
parser.add_argument(
"--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer." )
parser.add_argument(
"--na-prob-thresh" , "-t" , type=_snake_case , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , )
parser.add_argument(
"--out-image-dir" , "-p" , metavar="out_images" , default=_snake_case , help="Save precision-recall curves to directory." )
parser.add_argument("--verbose" , "-v" , action="store_true" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def lowerCAmelCase_ ( _snake_case : Optional[Any] ) -> Tuple:
'''simple docstring'''
__magic_name__ : Optional[int] = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
__magic_name__ : str = bool(qa["answers"]["text"] )
return qid_to_has_ans
def lowerCAmelCase_ ( _snake_case : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
def remove_articles(_snake_case : List[str] ):
return ARTICLES_REGEX.sub(" " , _snake_case )
def white_space_fix(_snake_case : Optional[int] ):
return " ".join(text.split() )
def remove_punc(_snake_case : Optional[int] ):
__magic_name__ : Dict = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_snake_case : str ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_snake_case ) ) ) )
def lowerCAmelCase_ ( _snake_case : Any ) -> Optional[Any]:
'''simple docstring'''
if not s:
return []
return normalize_answer(_snake_case ).split()
def lowerCAmelCase_ ( _snake_case : str , _snake_case : Dict ) -> Tuple:
'''simple docstring'''
return int(normalize_answer(_snake_case ) == normalize_answer(_snake_case ) )
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : int ) -> str:
'''simple docstring'''
__magic_name__ : Any = get_tokens(_snake_case )
__magic_name__ : Optional[int] = get_tokens(_snake_case )
__magic_name__ : Tuple = collections.Counter(_snake_case ) & collections.Counter(_snake_case )
__magic_name__ : Tuple = sum(common.values() )
if len(_snake_case ) == 0 or len(_snake_case ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
__magic_name__ : Dict = 1.0 * num_same / len(_snake_case )
__magic_name__ : Optional[Any] = 1.0 * num_same / len(_snake_case )
__magic_name__ : List[Any] = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( _snake_case : Optional[Any] , _snake_case : List[Any] ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = {}
__magic_name__ : int = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
__magic_name__ : Union[str, Any] = qa["id"]
__magic_name__ : Any = [t for t in qa["answers"]["text"] if normalize_answer(_snake_case )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
__magic_name__ : Tuple = [""]
if qid not in preds:
print(F'''Missing prediction for {qid}''' )
continue
__magic_name__ : Any = preds[qid]
# Take max over all gold answers
__magic_name__ : List[Any] = max(compute_exact(_snake_case , _snake_case ) for a in gold_answers )
__magic_name__ : int = max(compute_fa(_snake_case , _snake_case ) for a in gold_answers )
return exact_scores, fa_scores
def lowerCAmelCase_ ( _snake_case : Optional[Any] , _snake_case : List[Any] , _snake_case : Optional[int] , _snake_case : Dict ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : str = {}
for qid, s in scores.items():
__magic_name__ : Dict = na_probs[qid] > na_prob_thresh
if pred_na:
__magic_name__ : str = float(not qid_to_has_ans[qid] )
else:
__magic_name__ : Optional[int] = s
return new_scores
def lowerCAmelCase_ ( _snake_case : List[Any] , _snake_case : List[str] , _snake_case : Tuple=None ) -> Tuple:
'''simple docstring'''
if not qid_list:
__magic_name__ : Any = len(_snake_case )
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores.values() ) / total),
("f1", 100.0 * sum(fa_scores.values() ) / total),
("total", total),
] )
else:
__magic_name__ : Tuple = len(_snake_case )
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
("f1", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
("total", total),
] )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : str , _snake_case : str ) -> Dict:
'''simple docstring'''
for k in new_eval:
__magic_name__ : int = new_eval[k]
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Dict , _snake_case : Optional[Any] , _snake_case : Union[str, Any] ) -> str:
'''simple docstring'''
plt.step(_snake_case , _snake_case , color="b" , alpha=0.2 , where="post" )
plt.fill_between(_snake_case , _snake_case , step="post" , alpha=0.2 , color="b" )
plt.xlabel("Recall" )
plt.ylabel("Precision" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(_snake_case )
plt.savefig(_snake_case )
plt.clf()
def lowerCAmelCase_ ( _snake_case : Dict , _snake_case : Any , _snake_case : Optional[int] , _snake_case : List[Any] , _snake_case : Optional[int]=None , _snake_case : int=None ) -> str:
'''simple docstring'''
__magic_name__ : Union[str, Any] = sorted(_snake_case , key=lambda _snake_case : na_probs[k] )
__magic_name__ : Optional[int] = 0.0
__magic_name__ : str = 1.0
__magic_name__ : str = 0.0
__magic_name__ : List[str] = [1.0]
__magic_name__ : str = [0.0]
__magic_name__ : Optional[Any] = 0.0
for i, qid in enumerate(_snake_case ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
__magic_name__ : List[str] = true_pos / float(i + 1 )
__magic_name__ : Any = true_pos / float(_snake_case )
if i == len(_snake_case ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(_snake_case )
recalls.append(_snake_case )
if out_image:
plot_pr_curve(_snake_case , _snake_case , _snake_case , _snake_case )
return {"ap": 100.0 * avg_prec}
def lowerCAmelCase_ ( _snake_case : Tuple , _snake_case : Optional[Any] , _snake_case : Optional[int] , _snake_case : Optional[Any] , _snake_case : Any , _snake_case : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
if out_image_dir and not os.path.exists(_snake_case ):
os.makedirs(_snake_case )
__magic_name__ : Any = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
__magic_name__ : str = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_exact.png" ) , title="Precision-Recall curve for Exact Match score" , )
__magic_name__ : Union[str, Any] = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_f1.png" ) , title="Precision-Recall curve for F1 score" , )
__magic_name__ : str = {k: float(_snake_case ) for k, v in qid_to_has_ans.items()}
__magic_name__ : str = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_oracle.png" ) , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , )
merge_eval(_snake_case , _snake_case , "pr_exact" )
merge_eval(_snake_case , _snake_case , "pr_f1" )
merge_eval(_snake_case , _snake_case , "pr_oracle" )
def lowerCAmelCase_ ( _snake_case : int , _snake_case : Optional[Any] , _snake_case : List[str] , _snake_case : Optional[Any] ) -> Dict:
'''simple docstring'''
if not qid_list:
return
__magic_name__ : Dict = [na_probs[k] for k in qid_list]
__magic_name__ : str = np.ones_like(_snake_case ) / float(len(_snake_case ) )
plt.hist(_snake_case , weights=_snake_case , bins=20 , range=(0.0, 1.0) )
plt.xlabel("Model probability of no-answer" )
plt.ylabel("Proportion of dataset" )
plt.title(F'''Histogram of no-answer probability: {name}''' )
plt.savefig(os.path.join(_snake_case , F'''na_prob_hist_{name}.png''' ) )
plt.clf()
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Tuple , _snake_case : List[str] , _snake_case : Dict ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
__magic_name__ : List[str] = num_no_ans
__magic_name__ : Dict = cur_score
__magic_name__ : Dict = 0.0
__magic_name__ : Any = sorted(_snake_case , key=lambda _snake_case : na_probs[k] )
for i, qid in enumerate(_snake_case ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
__magic_name__ : Union[str, Any] = scores[qid]
else:
if preds[qid]:
__magic_name__ : List[Any] = -1
else:
__magic_name__ : Optional[int] = 0
cur_score += diff
if cur_score > best_score:
__magic_name__ : Optional[int] = cur_score
__magic_name__ : List[Any] = na_probs[qid]
return 100.0 * best_score / len(_snake_case ), best_thresh
def lowerCAmelCase_ ( _snake_case : int , _snake_case : str , _snake_case : List[str] , _snake_case : Tuple , _snake_case : List[Any] , _snake_case : Dict ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ , __magic_name__ : List[str] = find_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case )
__magic_name__ , __magic_name__ : int = find_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case )
__magic_name__ : Optional[int] = best_exact
__magic_name__ : List[Any] = exact_thresh
__magic_name__ : Dict = best_fa
__magic_name__ : Any = fa_thresh
def lowerCAmelCase_ ( ) -> int:
'''simple docstring'''
with open(OPTS.data_file ) as f:
__magic_name__ : Optional[Any] = json.load(_snake_case )
__magic_name__ : List[Any] = dataset_json["data"]
with open(OPTS.pred_file ) as f:
__magic_name__ : Optional[Any] = json.load(_snake_case )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
__magic_name__ : Any = json.load(_snake_case )
else:
__magic_name__ : Any = {k: 0.0 for k in preds}
__magic_name__ : str = make_qid_to_has_ans(_snake_case ) # maps qid to True/False
__magic_name__ : Tuple = [k for k, v in qid_to_has_ans.items() if v]
__magic_name__ : Optional[Any] = [k for k, v in qid_to_has_ans.items() if not v]
__magic_name__ , __magic_name__ : Union[str, Any] = get_raw_scores(_snake_case , _snake_case )
__magic_name__ : Optional[Any] = apply_no_ans_threshold(_snake_case , _snake_case , _snake_case , OPTS.na_prob_thresh )
__magic_name__ : Optional[Any] = apply_no_ans_threshold(_snake_case , _snake_case , _snake_case , OPTS.na_prob_thresh )
__magic_name__ : List[Any] = make_eval_dict(_snake_case , _snake_case )
if has_ans_qids:
__magic_name__ : int = make_eval_dict(_snake_case , _snake_case , qid_list=_snake_case )
merge_eval(_snake_case , _snake_case , "HasAns" )
if no_ans_qids:
__magic_name__ : List[Any] = make_eval_dict(_snake_case , _snake_case , qid_list=_snake_case )
merge_eval(_snake_case , _snake_case , "NoAns" )
if OPTS.na_prob_file:
find_all_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , OPTS.out_image_dir )
histogram_na_prob(_snake_case , _snake_case , OPTS.out_image_dir , "hasAns" )
histogram_na_prob(_snake_case , _snake_case , OPTS.out_image_dir , "noAns" )
if OPTS.out_file:
with open(OPTS.out_file , "w" ) as f:
json.dump(_snake_case , _snake_case )
else:
print(json.dumps(_snake_case , indent=2 ) )
if __name__ == "__main__":
snake_case : int = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
main()
| 281 | 0 |
'''simple docstring'''
import math
class __UpperCAmelCase :
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = 0.0
_snake_case = 0.0
for i in range(len(lowerCAmelCase_ ) ):
da += math.pow((sample[i] - weights[0][i]) , 2 )
da += math.pow((sample[i] - weights[1][i]) , 2 )
return 0 if da > da else 1
return 0
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
for i in range(len(lowerCAmelCase_ ) ):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
def SCREAMING_SNAKE_CASE__ ( ) -> None:
# Training Examples ( m, n )
_snake_case = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
_snake_case = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
_snake_case = SelfOrganizingMap()
_snake_case = 3
_snake_case = 0.5
for _ in range(__A ):
for j in range(len(__A ) ):
# training sample
_snake_case = training_samples[j]
# Compute the winning vector
_snake_case = self_organizing_map.get_winner(__A , __A )
# Update the winning vector
_snake_case = self_organizing_map.update(__A , __A , __A , __A )
# classify test sample
_snake_case = [0, 0, 0, 1]
_snake_case = self_organizing_map.get_winner(__A , __A )
# results
print(F'Clusters that the test sample belongs to : {winner}' )
print(F'Weights that have been trained : {weights}' )
# running the main() function
if __name__ == "__main__":
main()
| 42 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
snake_case : str = "▁"
snake_case : List[Any] = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class _snake_case ( snake_case , unittest.TestCase ):
UpperCamelCase__ = BigBirdTokenizer
UpperCamelCase__ = BigBirdTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = True
def SCREAMING_SNAKE_CASE ( self ):
super().setUp()
__magic_name__ : Optional[Any] = self.tokenizer_class(_a , keep_accents=_a )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Union[str, Any] = "<s>"
__magic_name__ : Dict = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<unk>" )
self.assertEqual(vocab_keys[1] , "<s>" )
self.assertEqual(vocab_keys[-1] , "[MASK]" )
self.assertEqual(len(_a ) , 1_004 )
def SCREAMING_SNAKE_CASE ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_000 )
def SCREAMING_SNAKE_CASE ( self ):
if not self.test_rust_tokenizer:
return
__magic_name__ : Dict = self.get_tokenizer()
__magic_name__ : str = self.get_rust_tokenizer()
__magic_name__ : Any = "I was born in 92000, and this is falsé."
__magic_name__ : Dict = tokenizer.tokenize(_a )
__magic_name__ : Any = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
__magic_name__ : List[Any] = tokenizer.encode(_a , add_special_tokens=_a )
__magic_name__ : List[str] = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
__magic_name__ : str = self.get_rust_tokenizer()
__magic_name__ : Dict = tokenizer.encode(_a )
__magic_name__ : Optional[int] = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = BigBirdTokenizer(_a , keep_accents=_a )
__magic_name__ : str = tokenizer.tokenize("This is a test" )
self.assertListEqual(_a , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_a ) , [285, 46, 10, 170, 382] , )
__magic_name__ : Dict = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
__magic_name__ : Union[str, Any] = tokenizer.convert_tokens_to_ids(_a )
self.assertListEqual(
_a , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
__magic_name__ : int = tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
@cached_property
def SCREAMING_SNAKE_CASE ( self ):
return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base" )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Any = "Hello World!"
__magic_name__ : Dict = [65, 18_536, 2_260, 101, 66]
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = (
"This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
)
# fmt: off
__magic_name__ : List[str] = [65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66] # noqa: E231
# fmt: on
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@require_torch
@slow
def SCREAMING_SNAKE_CASE ( self ):
import torch
from transformers import BigBirdConfig, BigBirdModel
# Build sequence
__magic_name__ : Optional[Any] = list(self.big_tokenizer.get_vocab().keys() )[:10]
__magic_name__ : List[Any] = " ".join(_a )
__magic_name__ : Any = self.big_tokenizer.encode_plus(_a , return_tensors="pt" , return_token_type_ids=_a )
__magic_name__ : Union[str, Any] = self.big_tokenizer.batch_encode_plus(
[sequence + " " + sequence] , return_tensors="pt" , return_token_type_ids=_a )
__magic_name__ : List[str] = BigBirdConfig(attention_type="original_full" )
__magic_name__ : Optional[int] = BigBirdModel(_a )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_a )
model(**_a )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : int = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base" )
__magic_name__ : int = tokenizer.decode(tokenizer("Paris is the [MASK]." ).input_ids )
self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]" )
@slow
def SCREAMING_SNAKE_CASE ( self ):
# fmt: off
__magic_name__ : Optional[Any] = {"input_ids": [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_a , model_name="google/bigbird-roberta-base" , revision="215c99f1600e06f83acce68422f2035b2b5c3510" , )
| 281 | 0 |
from collections.abc import Callable
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , __lowercase = None) -> None:
# Stores actual heap items.
__UpperCamelCase :list = []
# Stores indexes of each item for supporting updates and deletion.
__UpperCamelCase :dict = {}
# Stores current size of heap.
__UpperCamelCase :str = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
__UpperCamelCase :int = key or (lambda __lowercase: x)
def UpperCamelCase__ ( self , __lowercase) -> int | None:
return int((i - 1) / 2) if i > 0 else None
def UpperCamelCase__ ( self , __lowercase) -> int | None:
__UpperCamelCase :Union[str, Any] = int(2 * i + 1)
return left if 0 < left < self.size else None
def UpperCamelCase__ ( self , __lowercase) -> int | None:
__UpperCamelCase :Optional[int] = int(2 * i + 2)
return right if 0 < right < self.size else None
def UpperCamelCase__ ( self , __lowercase , __lowercase) -> None:
__UpperCamelCase , __UpperCamelCase :Optional[int] = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
__UpperCamelCase , __UpperCamelCase :List[Any] = self.arr[j], self.arr[i]
def UpperCamelCase__ ( self , __lowercase , __lowercase) -> bool:
return self.arr[i][1] < self.arr[j][1]
def UpperCamelCase__ ( self , __lowercase) -> int:
__UpperCamelCase :List[str] = self._left(__lowercase)
__UpperCamelCase :Tuple = self._right(__lowercase)
__UpperCamelCase :List[str] = i
if left is not None and not self._cmp(__lowercase , __lowercase):
__UpperCamelCase :Optional[Any] = left
if right is not None and not self._cmp(__lowercase , __lowercase):
__UpperCamelCase :Optional[Any] = right
return valid_parent
def UpperCamelCase__ ( self , __lowercase) -> None:
__UpperCamelCase :Optional[int] = self._parent(__lowercase)
while parent is not None and not self._cmp(__lowercase , __lowercase):
self._swap(__lowercase , __lowercase)
__UpperCamelCase , __UpperCamelCase :Union[str, Any] = parent, self._parent(__lowercase)
def UpperCamelCase__ ( self , __lowercase) -> None:
__UpperCamelCase :List[str] = self._get_valid_parent(__lowercase)
while valid_parent != index:
self._swap(__lowercase , __lowercase)
__UpperCamelCase , __UpperCamelCase :Dict = valid_parent, self._get_valid_parent(__lowercase)
def UpperCamelCase__ ( self , __lowercase , __lowercase) -> None:
if item not in self.pos_map:
return
__UpperCamelCase :Any = self.pos_map[item]
__UpperCamelCase :Union[str, Any] = [item, self.key(__lowercase)]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(__lowercase)
self._heapify_down(__lowercase)
def UpperCamelCase__ ( self , __lowercase) -> None:
if item not in self.pos_map:
return
__UpperCamelCase :Any = self.pos_map[item]
del self.pos_map[item]
__UpperCamelCase :Any = self.arr[self.size - 1]
__UpperCamelCase :Optional[int] = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(__lowercase)
self._heapify_down(__lowercase)
def UpperCamelCase__ ( self , __lowercase , __lowercase) -> None:
__UpperCamelCase :Optional[Any] = len(self.arr)
if arr_len == self.size:
self.arr.append([item, self.key(__lowercase)])
else:
__UpperCamelCase :Optional[Any] = [item, self.key(__lowercase)]
__UpperCamelCase :List[Any] = self.size
self.size += 1
self._heapify_up(self.size - 1)
def UpperCamelCase__ ( self) -> tuple | None:
return self.arr[0] if self.size else None
def UpperCamelCase__ ( self) -> tuple | None:
__UpperCamelCase :Any = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0])
return top_item_tuple
def lowerCamelCase ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 43 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
snake_case : int = logging.get_logger(__name__)
snake_case : List[str] = {"vocab_file": "spiece.model"}
snake_case : List[str] = {
"vocab_file": {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
}
}
snake_case : Tuple = {
"albert-base-v1": 512,
"albert-large-v1": 512,
"albert-xlarge-v1": 512,
"albert-xxlarge-v1": 512,
"albert-base-v2": 512,
"albert-large-v2": 512,
"albert-xlarge-v2": 512,
"albert-xxlarge-v2": 512,
}
snake_case : List[str] = "▁"
class _snake_case ( snake_case ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , _a , _a=True , _a=True , _a=False , _a="[CLS]" , _a="[SEP]" , _a="<unk>" , _a="[SEP]" , _a="<pad>" , _a="[CLS]" , _a="[MASK]" , _a = None , **_a , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
__magic_name__ : str = (
AddedToken(_a , lstrip=_a , rstrip=_a , normalized=_a )
if isinstance(_a , _a )
else mask_token
)
__magic_name__ : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , )
__magic_name__ : Dict = do_lower_case
__magic_name__ : Tuple = remove_space
__magic_name__ : Union[str, Any] = keep_accents
__magic_name__ : Tuple = vocab_file
__magic_name__ : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_a )
@property
def SCREAMING_SNAKE_CASE ( self ):
return len(self.sp_model )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
__magic_name__ : List[str] = self.__dict__.copy()
__magic_name__ : Any = None
return state
def __setstate__( self , _a ):
__magic_name__ : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
__magic_name__ : str = {}
__magic_name__ : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE ( self , _a ):
if self.remove_space:
__magic_name__ : List[Any] = " ".join(inputs.strip().split() )
else:
__magic_name__ : str = inputs
__magic_name__ : int = outputs.replace("``" , "\"" ).replace("''" , "\"" )
if not self.keep_accents:
__magic_name__ : str = unicodedata.normalize("NFKD" , _a )
__magic_name__ : Tuple = "".join([c for c in outputs if not unicodedata.combining(_a )] )
if self.do_lower_case:
__magic_name__ : int = outputs.lower()
return outputs
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Optional[Any] = self.preprocess_text(_a )
__magic_name__ : Dict = self.sp_model.encode(_a , out_type=_a )
__magic_name__ : Any = []
for piece in pieces:
if len(_a ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
__magic_name__ : Optional[Any] = self.sp_model.EncodeAsPieces(piece[:-1].replace(_a , "" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
__magic_name__ : List[str] = cur_pieces[1:]
else:
__magic_name__ : Optional[int] = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_a )
else:
new_pieces.append(_a )
return new_pieces
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.sp_model.PieceToId(_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.sp_model.IdToPiece(_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Any = []
__magic_name__ : Union[str, Any] = ""
__magic_name__ : int = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_a ) + token
__magic_name__ : List[Any] = True
__magic_name__ : Optional[int] = []
else:
current_sub_tokens.append(_a )
__magic_name__ : Optional[Any] = False
out_string += self.sp_model.decode(_a )
return out_string.strip()
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
__magic_name__ : List[str] = [self.sep_token_id]
__magic_name__ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self , _a , _a = None , _a = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is not None:
return [1] + ([0] * len(_a )) + [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1]
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
__magic_name__ : Optional[int] = [self.sep_token_id]
__magic_name__ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__magic_name__ : List[str] = os.path.join(
_a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _a )
elif not os.path.isfile(self.vocab_file ):
with open(_a , "wb" ) as fi:
__magic_name__ : Union[str, Any] = self.sp_model.serialized_model_proto()
fi.write(_a )
return (out_vocab_file,)
| 281 | 0 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class __A ( unittest.TestCase ):
def __A ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def __A ( self ):
_lowerCAmelCase : List[str] = 1
_lowerCAmelCase : Tuple = 3
_lowerCAmelCase : Optional[int] = (32, 32)
_lowerCAmelCase : int = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(a__ )
return image
@property
def __A ( self ):
torch.manual_seed(0 )
_lowerCAmelCase : Dict = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
return model
@property
def __A ( self ):
torch.manual_seed(0 )
_lowerCAmelCase : Tuple = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
return model
@property
def __A ( self ):
torch.manual_seed(0 )
_lowerCAmelCase : Union[str, Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModel(a__ )
@property
def __A ( self ):
def extract(*a__ , **a__ ):
class __A :
def __init__( self ):
_lowerCAmelCase : Dict = torch.ones([0] )
def __A ( self , a__ ):
self.pixel_values.to(a__ )
return self
return Out()
return extract
def __A ( self ):
_lowerCAmelCase : int = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase : Optional[Any] = self.dummy_cond_unet
_lowerCAmelCase : Any = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="""scaled_linear""" , clip_sample=a__ , set_alpha_to_one=a__ , )
_lowerCAmelCase : str = self.dummy_vae
_lowerCAmelCase : Tuple = self.dummy_text_encoder
_lowerCAmelCase : Optional[Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
_lowerCAmelCase : Optional[int] = StableDiffusionPipeline(
unet=a__ , scheduler=a__ , vae=a__ , text_encoder=a__ , tokenizer=a__ , safety_checker=a__ , feature_extractor=self.dummy_extractor , )
_lowerCAmelCase : int = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
_lowerCAmelCase : Optional[Any] = """A painting of a squirrel eating a burger"""
_lowerCAmelCase : List[Any] = torch.Generator(device=a__ ).manual_seed(0 )
_lowerCAmelCase : Union[str, Any] = sd_pipe([prompt] , generator=a__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" )
_lowerCAmelCase : Any = output.images
_lowerCAmelCase : str = torch.Generator(device=a__ ).manual_seed(0 )
_lowerCAmelCase : int = sd_pipe(
[prompt] , generator=a__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , return_dict=a__ , )[0]
_lowerCAmelCase : Tuple = image[0, -3:, -3:, -1]
_lowerCAmelCase : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_lowerCAmelCase : Dict = np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __A ( self ):
_lowerCAmelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase : int = self.dummy_cond_unet
_lowerCAmelCase : Optional[int] = PNDMScheduler(skip_prk_steps=a__ )
_lowerCAmelCase : List[str] = self.dummy_vae
_lowerCAmelCase : Any = self.dummy_text_encoder
_lowerCAmelCase : Any = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
_lowerCAmelCase : str = StableDiffusionPipeline(
unet=a__ , scheduler=a__ , vae=a__ , text_encoder=a__ , tokenizer=a__ , safety_checker=a__ , feature_extractor=self.dummy_extractor , )
_lowerCAmelCase : Optional[Any] = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
_lowerCAmelCase : str = """A painting of a squirrel eating a burger"""
_lowerCAmelCase : str = torch.Generator(device=a__ ).manual_seed(0 )
_lowerCAmelCase : Tuple = sd_pipe([prompt] , generator=a__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" )
_lowerCAmelCase : Tuple = output.images
_lowerCAmelCase : Dict = torch.Generator(device=a__ ).manual_seed(0 )
_lowerCAmelCase : List[str] = sd_pipe(
[prompt] , generator=a__ , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , return_dict=a__ , )[0]
_lowerCAmelCase : Any = image[0, -3:, -3:, -1]
_lowerCAmelCase : List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_lowerCAmelCase : Union[str, Any] = np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __A ( self ):
_lowerCAmelCase : List[str] = StableDiffusionPipeline.from_pretrained(
"""hf-internal-testing/tiny-stable-diffusion-lms-pipe""" , safety_checker=a__ )
assert isinstance(a__ , a__ )
assert isinstance(pipe.scheduler , a__ )
assert pipe.safety_checker is None
_lowerCAmelCase : Any = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(a__ )
_lowerCAmelCase : str = StableDiffusionPipeline.from_pretrained(a__ )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
_lowerCAmelCase : Optional[Any] = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" )
def __A ( self ):
_lowerCAmelCase : int = self.dummy_cond_unet
_lowerCAmelCase : str = PNDMScheduler(skip_prk_steps=a__ )
_lowerCAmelCase : Any = self.dummy_vae
_lowerCAmelCase : Dict = self.dummy_text_encoder
_lowerCAmelCase : Tuple = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# put models in fp16
_lowerCAmelCase : str = unet.half()
_lowerCAmelCase : List[str] = vae.half()
_lowerCAmelCase : Tuple = bert.half()
# make sure here that pndm scheduler skips prk
_lowerCAmelCase : Dict = StableDiffusionPipeline(
unet=a__ , scheduler=a__ , vae=a__ , text_encoder=a__ , tokenizer=a__ , safety_checker=a__ , feature_extractor=self.dummy_extractor , )
_lowerCAmelCase : int = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
_lowerCAmelCase : int = """A painting of a squirrel eating a burger"""
_lowerCAmelCase : Dict = sd_pipe([prompt] , num_inference_steps=2 , output_type="""np""" ).images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class __A ( unittest.TestCase ):
def __A ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __A ( self ):
_lowerCAmelCase : List[str] = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" , safety_checker=a__ )
_lowerCAmelCase : List[Any] = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_lowerCAmelCase : Optional[int] = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
_lowerCAmelCase : Dict = (
"""portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
""" coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
""" anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
""" children from bahnhof zoo, detailed """
)
_lowerCAmelCase : List[Any] = 4003660346
_lowerCAmelCase : List[Any] = 7
# without safety guidance (sld_guidance_scale = 0)
_lowerCAmelCase : int = torch.manual_seed(a__ )
_lowerCAmelCase : Dict = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=0 , )
_lowerCAmelCase : int = output.images
_lowerCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
_lowerCAmelCase : Dict = [0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
# without safety guidance (strong configuration)
_lowerCAmelCase : Optional[int] = torch.manual_seed(a__ )
_lowerCAmelCase : List[str] = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.0_2_5 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
_lowerCAmelCase : Optional[int] = output.images
_lowerCAmelCase : Tuple = image[0, -3:, -3:, -1]
_lowerCAmelCase : str = [0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __A ( self ):
_lowerCAmelCase : Tuple = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" , safety_checker=a__ )
_lowerCAmelCase : List[str] = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_lowerCAmelCase : Optional[Any] = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
_lowerCAmelCase : List[str] = """padme amidala taking a bath artwork, safe for work, no nudity"""
_lowerCAmelCase : Tuple = 2734971755
_lowerCAmelCase : Union[str, Any] = 7
_lowerCAmelCase : Optional[int] = torch.manual_seed(a__ )
_lowerCAmelCase : Any = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=0 , )
_lowerCAmelCase : int = output.images
_lowerCAmelCase : int = image[0, -3:, -3:, -1]
_lowerCAmelCase : Dict = [0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
_lowerCAmelCase : Optional[Any] = torch.manual_seed(a__ )
_lowerCAmelCase : Any = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.0_2_5 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
_lowerCAmelCase : List[Any] = output.images
_lowerCAmelCase : List[Any] = image[0, -3:, -3:, -1]
_lowerCAmelCase : Optional[int] = [0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __A ( self ):
_lowerCAmelCase : List[str] = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" )
_lowerCAmelCase : Any = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
_lowerCAmelCase : Dict = (
"""the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
""" leyendecker"""
)
_lowerCAmelCase : int = 1044355234
_lowerCAmelCase : Tuple = 12
_lowerCAmelCase : int = torch.manual_seed(a__ )
_lowerCAmelCase : str = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=0 , )
_lowerCAmelCase : List[str] = output.images
_lowerCAmelCase : List[str] = image[0, -3:, -3:, -1]
_lowerCAmelCase : int = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-7
_lowerCAmelCase : Optional[int] = torch.manual_seed(a__ )
_lowerCAmelCase : List[Any] = sd_pipe(
[prompt] , generator=a__ , guidance_scale=a__ , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.0_2_5 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
_lowerCAmelCase : List[str] = output.images
_lowerCAmelCase : Any = image[0, -3:, -3:, -1]
_lowerCAmelCase : List[str] = np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 44 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : Tuple , _snake_case : List[Any] , _snake_case : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if isinstance(_snake_case , _snake_case ):
__magic_name__ : Union[str, Any] = np.full((len(_snake_case ), sequence_length, 2) , _snake_case )
else:
__magic_name__ : List[Any] = np.full((len(_snake_case ), sequence_length) , _snake_case )
for i, tensor in enumerate(_snake_case ):
if padding_side == "right":
if isinstance(_snake_case , _snake_case ):
__magic_name__ : Optional[Any] = tensor[:sequence_length]
else:
__magic_name__ : Union[str, Any] = tensor[:sequence_length]
else:
if isinstance(_snake_case , _snake_case ):
__magic_name__ : List[Any] = tensor[:sequence_length]
else:
__magic_name__ : Optional[Any] = tensor[:sequence_length]
return out_tensor.tolist()
def lowerCAmelCase_ ( _snake_case : Optional[int] ) -> Tuple:
'''simple docstring'''
__magic_name__ : Union[str, Any] = ord(_snake_case )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
__magic_name__ : Any = unicodedata.category(_snake_case )
if cat.startswith("P" ):
return True
return False
@dataclass
class _snake_case ( snake_case ):
UpperCamelCase__ = 42
UpperCamelCase__ = True
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = -100
UpperCamelCase__ = "pt"
def SCREAMING_SNAKE_CASE ( self , _a ):
import torch
__magic_name__ : List[str] = "label" if "label" in features[0].keys() else "labels"
__magic_name__ : Union[str, Any] = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
__magic_name__ : Optional[int] = self.tokenizer.pad(
_a , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" if labels is None else None , )
if labels is None:
return batch
__magic_name__ : Dict = torch.tensor(batch["entity_ids"] ).shape[1]
__magic_name__ : List[Any] = self.tokenizer.padding_side
if padding_side == "right":
__magic_name__ : str = [
list(_a ) + [self.label_pad_token_id] * (sequence_length - len(_a )) for label in labels
]
else:
__magic_name__ : int = [
[self.label_pad_token_id] * (sequence_length - len(_a )) + list(_a ) for label in labels
]
__magic_name__ : Dict = [feature["ner_tags"] for feature in features]
__magic_name__ : List[Any] = padding_tensor(_a , -1 , _a , _a )
__magic_name__ : Any = [feature["original_entity_spans"] for feature in features]
__magic_name__ : Any = padding_tensor(_a , (-1, -1) , _a , _a )
__magic_name__ : List[Any] = {k: torch.tensor(_a , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 281 | 0 |
"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
lowercase_ = "scheduler_config.json"
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCAmelCase : str = 1
__UpperCAmelCase : Dict = 2
__UpperCAmelCase : int = 3
__UpperCAmelCase : Tuple = 4
__UpperCAmelCase : Union[str, Any] = 5
@dataclass
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCAmelCase : jnp.ndarray
class __lowerCAmelCase :
'''simple docstring'''
__UpperCAmelCase : int = SCHEDULER_CONFIG_NAME
__UpperCAmelCase : str = ['dtype']
__UpperCAmelCase : List[str] = []
__UpperCAmelCase : Any = True
@classmethod
def __UpperCAmelCase ( cls , _a = None , _a = None , _a=False , **_a , ):
__a , __a = cls.load_config(
pretrained_model_name_or_path=_a , subfolder=_a , return_unused_kwargs=_a , **_a , )
__a , __a = cls.from_config(_a , return_unused_kwargs=_a , **_a )
if hasattr(_a , '''create_state''' ) and getattr(_a , '''has_state''' , _a ):
__a = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def __UpperCAmelCase ( self , _a , _a = False , **_a ):
self.save_config(save_directory=_a , push_to_hub=_a , **_a )
@property
def __UpperCAmelCase ( self ):
return self._get_compatibles()
@classmethod
def __UpperCAmelCase ( cls ):
__a = list(set([cls.__name__] + cls._compatibles ) )
__a = importlib.import_module(__name__.split('''.''' )[0] )
__a = [
getattr(_a , _a ) for c in compatible_classes_str if hasattr(_a , _a )
]
return compatible_classes
def lowercase ( lowerCAmelCase__ : jnp.ndarray , lowerCAmelCase__ : Tuple[int] ) -> jnp.ndarray:
assert len(lowerCAmelCase__ ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase__ ) - x.ndim) ) , lowerCAmelCase__ )
def lowercase ( lowerCAmelCase__ : int , lowerCAmelCase__ : str=0.9_99 , lowerCAmelCase__ : List[str]=jnp.floataa ) -> jnp.ndarray:
def alpha_bar(lowerCAmelCase__ : str ):
return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2
__a = []
for i in range(lowerCAmelCase__ ):
__a = i / num_diffusion_timesteps
__a = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(lowerCAmelCase__ ) / alpha_bar(lowerCAmelCase__ ) , lowerCAmelCase__ ) )
return jnp.array(lowerCAmelCase__ , dtype=lowerCAmelCase__ )
@flax.struct.dataclass
class __lowerCAmelCase :
'''simple docstring'''
__UpperCAmelCase : jnp.ndarray
__UpperCAmelCase : jnp.ndarray
__UpperCAmelCase : jnp.ndarray
@classmethod
def __UpperCAmelCase ( cls , _a ):
__a = scheduler.config
if config.trained_betas is not None:
__a = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
__a = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__a = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
__a = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
f'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''' )
__a = 1.0 - betas
__a = jnp.cumprod(_a , axis=0 )
return cls(
alphas=_a , betas=_a , alphas_cumprod=_a , )
def lowercase ( lowerCAmelCase__ : CommonSchedulerState , lowerCAmelCase__ : jnp.ndarray , lowerCAmelCase__ : jnp.ndarray , lowerCAmelCase__ : jnp.ndarray ) -> Optional[int]:
__a = state.alphas_cumprod
__a = alphas_cumprod[timesteps] ** 0.5
__a = sqrt_alpha_prod.flatten()
__a = broadcast_to_shape_from_left(lowerCAmelCase__ , original_samples.shape )
__a = (1 - alphas_cumprod[timesteps]) ** 0.5
__a = sqrt_one_minus_alpha_prod.flatten()
__a = broadcast_to_shape_from_left(lowerCAmelCase__ , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def lowercase ( lowerCAmelCase__ : CommonSchedulerState , lowerCAmelCase__ : jnp.ndarray , lowerCAmelCase__ : jnp.ndarray , lowerCAmelCase__ : jnp.ndarray ) -> Dict:
__a , __a = get_sqrt_alpha_prod(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
__a = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def lowercase ( lowerCAmelCase__ : CommonSchedulerState , lowerCAmelCase__ : jnp.ndarray , lowerCAmelCase__ : jnp.ndarray , lowerCAmelCase__ : jnp.ndarray ) -> List[Any]:
__a , __a = get_sqrt_alpha_prod(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
__a = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 45 |
import math
def lowerCAmelCase_ ( _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
return math.pow(_snake_case , 2 ) - a
def lowerCAmelCase_ ( _snake_case : float ) -> float:
'''simple docstring'''
return 2 * x
def lowerCAmelCase_ ( _snake_case : float ) -> float:
'''simple docstring'''
__magic_name__ : Optional[int] = 2.0
while start <= a:
__magic_name__ : str = math.pow(_snake_case , 2 )
return start
def lowerCAmelCase_ ( _snake_case : float , _snake_case : int = 9999 , _snake_case : float = 0.00_000_000_000_001 ) -> float:
'''simple docstring'''
if a < 0:
raise ValueError("math domain error" )
__magic_name__ : Optional[int] = get_initial_point(_snake_case )
for _ in range(_snake_case ):
__magic_name__ : int = value
__magic_name__ : str = value - fx(_snake_case , _snake_case ) / fx_derivative(_snake_case )
if abs(prev_value - value ) < tolerance:
return value
return value
if __name__ == "__main__":
from doctest import testmod
testmod()
| 281 | 0 |
"""simple docstring"""
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
if not all(char in """01""" for char in bin_string ):
raise ValueError("""Non-binary value was passed to the function""" )
if not bin_string:
raise ValueError("""Empty string was passed to the function""" )
lowerCAmelCase = """"""
while len(SCREAMING_SNAKE_CASE ) % 3 != 0:
lowerCAmelCase = """0""" + bin_string
lowerCAmelCase = [
bin_string[index : index + 3]
for index in range(len(SCREAMING_SNAKE_CASE ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
lowerCAmelCase = 0
for index, val in enumerate(SCREAMING_SNAKE_CASE ):
oct_val += int(2 ** (2 - index) * int(SCREAMING_SNAKE_CASE ) )
oct_string += str(SCREAMING_SNAKE_CASE )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 46 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class _snake_case :
UpperCamelCase__ = LEDConfig
UpperCamelCase__ = {}
UpperCamelCase__ = 'gelu'
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=False , _a=99 , _a=32 , _a=2 , _a=4 , _a=37 , _a=0.1 , _a=0.1 , _a=20 , _a=2 , _a=1 , _a=0 , _a=4 , ):
__magic_name__ : int = parent
__magic_name__ : Optional[int] = batch_size
__magic_name__ : Tuple = seq_length
__magic_name__ : List[Any] = is_training
__magic_name__ : Dict = use_labels
__magic_name__ : Optional[Any] = vocab_size
__magic_name__ : int = hidden_size
__magic_name__ : Optional[int] = num_hidden_layers
__magic_name__ : Optional[int] = num_attention_heads
__magic_name__ : Tuple = intermediate_size
__magic_name__ : Any = hidden_dropout_prob
__magic_name__ : Optional[int] = attention_probs_dropout_prob
__magic_name__ : List[str] = max_position_embeddings
__magic_name__ : Any = eos_token_id
__magic_name__ : str = pad_token_id
__magic_name__ : int = bos_token_id
__magic_name__ : Optional[int] = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
__magic_name__ : Tuple = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
__magic_name__ : Tuple = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__magic_name__ : int = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__magic_name__ : int = tf.concat([input_ids, eos_tensor] , axis=1 )
__magic_name__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__magic_name__ : Dict = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
__magic_name__ : List[str] = prepare_led_inputs_dict(_a , _a , _a )
__magic_name__ : Union[str, Any] = tf.concat(
[tf.zeros_like(_a )[:, :-1], tf.ones_like(_a )[:, -1:]] , axis=-1 , )
__magic_name__ : List[Any] = global_attention_mask
return config, inputs_dict
def SCREAMING_SNAKE_CASE ( self , _a , _a ):
__magic_name__ : Dict = TFLEDModel(config=_a ).get_decoder()
__magic_name__ : Optional[int] = inputs_dict["input_ids"]
__magic_name__ : Union[str, Any] = input_ids[:1, :]
__magic_name__ : str = inputs_dict["attention_mask"][:1, :]
__magic_name__ : int = 1
# first forward pass
__magic_name__ : Tuple = model(_a , attention_mask=_a , use_cache=_a )
__magic_name__ , __magic_name__ : str = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__magic_name__ : Optional[int] = ids_tensor((self.batch_size, 3) , config.vocab_size )
__magic_name__ : Any = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
__magic_name__ : Optional[Any] = tf.concat([input_ids, next_tokens] , axis=-1 )
__magic_name__ : List[Any] = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
__magic_name__ : List[str] = model(_a , attention_mask=_a )[0]
__magic_name__ : Dict = model(_a , attention_mask=_a , past_key_values=_a )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__magic_name__ : List[Any] = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
__magic_name__ : Union[str, Any] = output_from_no_past[:, -3:, random_slice_idx]
__magic_name__ : List[str] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_a , _a , rtol=1e-3 )
def lowerCAmelCase_ ( _snake_case : Any , _snake_case : List[Any] , _snake_case : Any , _snake_case : str=None , _snake_case : List[str]=None , _snake_case : int=None , _snake_case : Any=None , ) -> int:
'''simple docstring'''
if attention_mask is None:
__magic_name__ : str = tf.cast(tf.math.not_equal(_snake_case , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__magic_name__ : List[Any] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__magic_name__ : Dict = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__magic_name__ : Any = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class _snake_case ( snake_case , snake_case , unittest.TestCase ):
UpperCamelCase__ = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
UpperCamelCase__ = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
UpperCamelCase__ = (
{
'conversational': TFLEDForConditionalGeneration,
'feature-extraction': TFLEDModel,
'summarization': TFLEDForConditionalGeneration,
'text2text-generation': TFLEDForConditionalGeneration,
'translation': TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
UpperCamelCase__ = True
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = TFLEDModelTester(self )
__magic_name__ : List[Any] = ConfigTester(self , config_class=_a )
def SCREAMING_SNAKE_CASE ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ , __magic_name__ : int = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ : List[str] = tf.zeros_like(inputs_dict["attention_mask"] )
__magic_name__ : Optional[Any] = 2
__magic_name__ : Tuple = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict["global_attention_mask"] , )
__magic_name__ : Any = True
__magic_name__ : str = self.model_tester.seq_length
__magic_name__ : Dict = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(_a ):
__magic_name__ : str = outputs.decoder_attentions
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(_a ):
__magic_name__ : Any = [t.numpy() for t in outputs.encoder_attentions]
__magic_name__ : Tuple = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
__magic_name__ : Union[str, Any] = True
__magic_name__ : List[str] = False
__magic_name__ : Tuple = False
__magic_name__ : Optional[int] = model_class(_a )
__magic_name__ : str = model(self._prepare_for_class(_a , _a ) )
__magic_name__ : Any = len(_a )
self.assertEqual(config.output_hidden_states , _a )
check_encoder_attentions_output(_a )
if self.is_encoder_decoder:
__magic_name__ : Tuple = model_class(_a )
__magic_name__ : Optional[Any] = model(self._prepare_for_class(_a , _a ) )
self.assertEqual(config.output_hidden_states , _a )
check_decoder_attentions_output(_a )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
__magic_name__ : Dict = True
__magic_name__ : str = model_class(_a )
__magic_name__ : Any = model(self._prepare_for_class(_a , _a ) )
self.assertEqual(config.output_hidden_states , _a )
check_encoder_attentions_output(_a )
# Check attention is always last and order is fine
__magic_name__ : Union[str, Any] = True
__magic_name__ : Union[str, Any] = True
__magic_name__ : List[str] = model_class(_a )
__magic_name__ : Any = model(self._prepare_for_class(_a , _a ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_a ) )
self.assertEqual(model.config.output_hidden_states , _a )
check_encoder_attentions_output(_a )
@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing." )
def SCREAMING_SNAKE_CASE ( self ):
pass
def SCREAMING_SNAKE_CASE ( self ):
# TODO: Head-masking not yet implement
pass
def lowerCAmelCase_ ( _snake_case : int ) -> Optional[int]:
'''simple docstring'''
return tf.constant(_snake_case , dtype=tf.intaa )
snake_case : Optional[int] = 1E-4
@slow
@require_tf
class _snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[Any] = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" ).led
# change to intended input here
__magic_name__ : Optional[int] = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : str = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : Any = prepare_led_inputs_dict(model.config , _a , _a )
__magic_name__ : List[Any] = model(**_a )[0]
__magic_name__ : List[str] = (1, 1_024, 768)
self.assertEqual(output.shape , _a )
# change to expected output here
__magic_name__ : int = tf.convert_to_tensor(
[[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]] , )
tf.debugging.assert_near(output[:, :3, :3] , _a , atol=1e-3 )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Tuple = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" )
# change to intended input here
__magic_name__ : int = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : Tuple = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : Optional[Any] = prepare_led_inputs_dict(model.config , _a , _a )
__magic_name__ : Union[str, Any] = model(**_a )[0]
__magic_name__ : Optional[int] = (1, 1_024, model.config.vocab_size)
self.assertEqual(output.shape , _a )
# change to expected output here
__magic_name__ : str = tf.convert_to_tensor(
[[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]] , )
tf.debugging.assert_near(output[:, :3, :3] , _a , atol=1e-3 , rtol=1e-3 )
| 281 | 0 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class A__ ( nn.Module ):
A__ = 42
A__ = 42
A__ = 0.0
A__ = 1
A__ = 1
A__ = True
A__ = False
A__ = False
A__ = False
A__ = jnp.floataa
def A ( self : str ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =[]
_SCREAMING_SNAKE_CASE =[]
for i in range(self.num_layers ):
_SCREAMING_SNAKE_CASE =self.in_channels if i == 0 else self.out_channels
_SCREAMING_SNAKE_CASE =FlaxResnetBlockaD(
in_channels=_a , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_a )
_SCREAMING_SNAKE_CASE =FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_a )
_SCREAMING_SNAKE_CASE =resnets
_SCREAMING_SNAKE_CASE =attentions
if self.add_downsample:
_SCREAMING_SNAKE_CASE =FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Dict , _a : Union[str, Any] , _a : Union[str, Any] , _a : str , _a : Optional[Any]=True ) -> List[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =()
for resnet, attn in zip(self.resnets , self.attentions ):
_SCREAMING_SNAKE_CASE =resnet(_a , _a , deterministic=_a )
_SCREAMING_SNAKE_CASE =attn(_a , _a , deterministic=_a )
output_states += (hidden_states,)
if self.add_downsample:
_SCREAMING_SNAKE_CASE =self.downsamplers_a(_a )
output_states += (hidden_states,)
return hidden_states, output_states
class A__ ( nn.Module ):
A__ = 42
A__ = 42
A__ = 0.0
A__ = 1
A__ = True
A__ = jnp.floataa
def A ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =[]
for i in range(self.num_layers ):
_SCREAMING_SNAKE_CASE =self.in_channels if i == 0 else self.out_channels
_SCREAMING_SNAKE_CASE =FlaxResnetBlockaD(
in_channels=_a , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_a )
_SCREAMING_SNAKE_CASE =resnets
if self.add_downsample:
_SCREAMING_SNAKE_CASE =FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Dict , _a : int , _a : Tuple , _a : Union[str, Any]=True ) -> int:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =()
for resnet in self.resnets:
_SCREAMING_SNAKE_CASE =resnet(_a , _a , deterministic=_a )
output_states += (hidden_states,)
if self.add_downsample:
_SCREAMING_SNAKE_CASE =self.downsamplers_a(_a )
output_states += (hidden_states,)
return hidden_states, output_states
class A__ ( nn.Module ):
A__ = 42
A__ = 42
A__ = 42
A__ = 0.0
A__ = 1
A__ = 1
A__ = True
A__ = False
A__ = False
A__ = False
A__ = jnp.floataa
def A ( self : int ) -> Dict:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =[]
_SCREAMING_SNAKE_CASE =[]
for i in range(self.num_layers ):
_SCREAMING_SNAKE_CASE =self.in_channels if (i == self.num_layers - 1) else self.out_channels
_SCREAMING_SNAKE_CASE =self.prev_output_channel if i == 0 else self.out_channels
_SCREAMING_SNAKE_CASE =FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_a )
_SCREAMING_SNAKE_CASE =FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_a )
_SCREAMING_SNAKE_CASE =resnets
_SCREAMING_SNAKE_CASE =attentions
if self.add_upsample:
_SCREAMING_SNAKE_CASE =FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Optional[Any] , _a : Optional[Any] , _a : Dict , _a : Union[str, Any] , _a : str , _a : List[str]=True ) -> int:
'''simple docstring'''
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
_SCREAMING_SNAKE_CASE =res_hidden_states_tuple[-1]
_SCREAMING_SNAKE_CASE =res_hidden_states_tuple[:-1]
_SCREAMING_SNAKE_CASE =jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_SCREAMING_SNAKE_CASE =resnet(_a , _a , deterministic=_a )
_SCREAMING_SNAKE_CASE =attn(_a , _a , deterministic=_a )
if self.add_upsample:
_SCREAMING_SNAKE_CASE =self.upsamplers_a(_a )
return hidden_states
class A__ ( nn.Module ):
A__ = 42
A__ = 42
A__ = 42
A__ = 0.0
A__ = 1
A__ = True
A__ = jnp.floataa
def A ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =[]
for i in range(self.num_layers ):
_SCREAMING_SNAKE_CASE =self.in_channels if (i == self.num_layers - 1) else self.out_channels
_SCREAMING_SNAKE_CASE =self.prev_output_channel if i == 0 else self.out_channels
_SCREAMING_SNAKE_CASE =FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_a )
_SCREAMING_SNAKE_CASE =resnets
if self.add_upsample:
_SCREAMING_SNAKE_CASE =FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : str , _a : Dict , _a : Dict , _a : Optional[Any] , _a : str=True ) -> Optional[int]:
'''simple docstring'''
for resnet in self.resnets:
# pop res hidden states
_SCREAMING_SNAKE_CASE =res_hidden_states_tuple[-1]
_SCREAMING_SNAKE_CASE =res_hidden_states_tuple[:-1]
_SCREAMING_SNAKE_CASE =jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_SCREAMING_SNAKE_CASE =resnet(_a , _a , deterministic=_a )
if self.add_upsample:
_SCREAMING_SNAKE_CASE =self.upsamplers_a(_a )
return hidden_states
class A__ ( nn.Module ):
A__ = 42
A__ = 0.0
A__ = 1
A__ = 1
A__ = False
A__ = False
A__ = jnp.floataa
def A ( self : List[str] ) -> Any:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =[
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
_SCREAMING_SNAKE_CASE =[]
for _ in range(self.num_layers ):
_SCREAMING_SNAKE_CASE =FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_a )
_SCREAMING_SNAKE_CASE =FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_a )
_SCREAMING_SNAKE_CASE =resnets
_SCREAMING_SNAKE_CASE =attentions
def __call__( self : Union[str, Any] , _a : List[Any] , _a : Tuple , _a : Optional[Any] , _a : str=True ) -> Union[str, Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.resnets[0](_a , _a )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
_SCREAMING_SNAKE_CASE =attn(_a , _a , deterministic=_a )
_SCREAMING_SNAKE_CASE =resnet(_a , _a , deterministic=_a )
return hidden_states
| 47 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
snake_case : Optional[Any] = logging.get_logger(__name__)
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Union[str, Any]=False ) -> List[str]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = []
# fmt: off
# stem:
rename_keys.append(("cls_token", "vit.embeddings.cls_token") )
rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings") )
rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias") )
# backbone
rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias") )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''') )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''') )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
("pre_logits.fc.weight", "pooler.dense.weight"),
("pre_logits.fc.bias", "pooler.dense.bias"),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
__magic_name__ : int = [(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
# fmt: on
return rename_keys
def lowerCAmelCase_ ( _snake_case : Any , _snake_case : Any , _snake_case : Dict=False ) -> int:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
__magic_name__ : int = ""
else:
__magic_name__ : Union[str, Any] = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__magic_name__ : Optional[Any] = state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
__magic_name__ : int = state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
__magic_name__ : Dict = in_proj_weight[
: config.hidden_size, :
]
__magic_name__ : List[str] = in_proj_bias[: config.hidden_size]
__magic_name__ : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__magic_name__ : Optional[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__magic_name__ : Optional[Any] = in_proj_weight[
-config.hidden_size :, :
]
__magic_name__ : int = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase_ ( _snake_case : List[str] ) -> List[str]:
'''simple docstring'''
__magic_name__ : List[str] = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(_snake_case , _snake_case )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : int , _snake_case : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
__magic_name__ : int = dct.pop(_snake_case )
__magic_name__ : List[Any] = val
def lowerCAmelCase_ ( ) -> Dict:
'''simple docstring'''
__magic_name__ : List[str] = "http://images.cocodataset.org/val2017/000000039769.jpg"
__magic_name__ : List[str] = Image.open(requests.get(_snake_case , stream=_snake_case ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : Any , _snake_case : int=False ) -> Dict:
'''simple docstring'''
__magic_name__ : List[str] = BitConfig(
global_padding="same" , layer_type="bottleneck" , depths=(3, 4, 9) , out_features=["stage3"] , embedding_dynamic_padding=_snake_case , )
__magic_name__ : List[str] = ViTHybridConfig(backbone_config=_snake_case , image_size=384 , num_labels=1000 )
__magic_name__ : str = False
# load original model from timm
__magic_name__ : Union[str, Any] = timm.create_model(_snake_case , pretrained=_snake_case )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
__magic_name__ : List[Any] = timm_model.state_dict()
if base_model:
remove_classification_head_(_snake_case )
__magic_name__ : Tuple = create_rename_keys(_snake_case , _snake_case )
for src, dest in rename_keys:
rename_key(_snake_case , _snake_case , _snake_case )
read_in_q_k_v(_snake_case , _snake_case , _snake_case )
__magic_name__ : List[str] = "huggingface/label-files"
__magic_name__ : int = "imagenet-1k-id2label.json"
__magic_name__ : Optional[int] = json.load(open(hf_hub_download(_snake_case , _snake_case , repo_type="dataset" ) , "r" ) )
__magic_name__ : int = {int(_snake_case ): v for k, v in idalabel.items()}
__magic_name__ : List[str] = idalabel
__magic_name__ : List[str] = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
__magic_name__ : List[str] = ViTHybridModel(_snake_case ).eval()
else:
__magic_name__ : str = ViTHybridForImageClassification(_snake_case ).eval()
model.load_state_dict(_snake_case )
# create image processor
__magic_name__ : List[Any] = create_transform(**resolve_data_config({} , model=_snake_case ) )
__magic_name__ : int = transform.transforms
__magic_name__ : List[str] = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
__magic_name__ : int = ViTHybridImageProcessor(
do_resize=_snake_case , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_snake_case , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=_snake_case , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
__magic_name__ : List[Any] = prepare_img()
__magic_name__ : Any = transform(_snake_case ).unsqueeze(0 )
__magic_name__ : Tuple = processor(_snake_case , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(_snake_case , _snake_case )
# verify logits
with torch.no_grad():
__magic_name__ : Optional[int] = model(_snake_case )
__magic_name__ : List[str] = outputs.logits
print("Predicted class:" , logits.argmax(-1 ).item() )
if base_model:
__magic_name__ : List[str] = timm_model.forward_features(_snake_case )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_snake_case , outputs.pooler_output , atol=1E-3 )
else:
__magic_name__ : Any = timm_model(_snake_case )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_snake_case , outputs.logits , atol=1E-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(_snake_case ).mkdir(exist_ok=_snake_case )
print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_snake_case )
print(F'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(_snake_case )
if push_to_hub:
print(F'''Pushing model and processor to the hub {vit_name}''' )
model.push_to_hub(F'''ybelkada/{vit_name}''' )
processor.push_to_hub(F'''ybelkada/{vit_name}''' )
if __name__ == "__main__":
snake_case : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--vit_name",
default="vit_base_r50_s16_384",
type=str,
help="Name of the hybrid ViT timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
)
snake_case : List[Any] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 281 | 0 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
'JH 8S TH AH QH',
'TS KS 5S 9S AC',
'KD 6S 9D TH AD',
'KS 8D 4D 9S 4S', # pair
'8C 4S KH JS 4D', # pair
'QH 8H KD JH 8S', # pair
'KC 4H KS 2H 8D', # pair
'KD 4S KC 3H 8S', # pair
'AH 8S AS KC JH', # pair
'3H 4C 4H 3S 2H', # 2 pairs
'5S 5D 2C KH KH', # 2 pairs
'3C KH 5D 5S KH', # 2 pairs
'AS 3C KH AD KH', # 2 pairs
'7C 7S 3S 7H 5S', # 3 of a kind
'7C 7S KH 2H 7H', # 3 of a kind
'AC KH QH AH AS', # 3 of a kind
'2H 4D 3C AS 5S', # straight (low ace)
'3C 5C 4C 2C 6H', # straight
'6S 8S 7S 5H 9H', # straight
'JS QS 9H TS KH', # straight
'QC KH TS JS AH', # straight (high ace)
'8C 9C 5C 3C TC', # flush
'3S 8S 9S 5S KS', # flush
'4C 5C 9C 8C KC', # flush
'JH 8H AH KH QH', # flush
'3D 2H 3H 2C 2D', # full house
'2H 2C 3S 3H 3D', # full house
'KH KC 3S 3H 3D', # full house
'JC 6H JS JD JH', # 4 of a kind
'JC 7H JS JD JH', # 4 of a kind
'JC KH JS JD JH', # 4 of a kind
'2S AS 4S 5S 3S', # straight flush (low ace)
'2D 6D 3D 4D 5D', # straight flush
'5C 6C 3C 7C 4C', # straight flush
'JH 9H TH KH QH', # straight flush
'JH AH TH KH QH', # royal flush (high ace straight flush)
)
SCREAMING_SNAKE_CASE__ : List[Any] = (
('2H 3H 4H 5H 6H', 'KS AS TS QS JS', 'Loss'),
('2H 3H 4H 5H 6H', 'AS AD AC AH JD', 'Win'),
('AS AH 2H AD AC', 'JS JD JC JH 3D', 'Win'),
('2S AH 2H AS AC', 'JS JD JC JH AD', 'Loss'),
('2S AH 2H AS AC', '2H 3H 5H 6H 7H', 'Win'),
('AS 3S 4S 8S 2S', '2H 3H 5H 6H 7H', 'Win'),
('2H 3H 5H 6H 7H', '2S 3H 4H 5S 6C', 'Win'),
('2S 3H 4H 5S 6C', '3D 4C 5H 6H 2S', 'Tie'),
('2S 3H 4H 5S 6C', 'AH AC 5H 6H AS', 'Win'),
('2S 2H 4H 5S 4C', 'AH AC 5H 6H AS', 'Loss'),
('2S 2H 4H 5S 4C', 'AH AC 5H 6H 7S', 'Win'),
('6S AD 7H 4S AS', 'AH AC 5H 6H 7S', 'Loss'),
('2S AH 4H 5S KC', 'AH AC 5H 6H 7S', 'Loss'),
('2S 3H 6H 7S 9C', '7H 3C TH 6H 9S', 'Loss'),
('4S 5H 6H TS AC', '3S 5H 6H TS AC', 'Win'),
('2S AH 4H 5S 6C', 'AD 4C 5H 6H 2C', 'Tie'),
('AS AH 3H AD AC', 'AS AH 2H AD AC', 'Win'),
('AH AC 5H 5C QS', 'AH AC 5H 5C KS', 'Loss'),
('AH AC 5H 5C QS', 'KH KC 5H 5C QS', 'Win'),
('7C 7S KH 2H 7H', '3C 3S AH 2H 3H', 'Win'),
('3C 3S AH 2H 3H', '7C 7S KH 2H 7H', 'Loss'),
('6H 5H 4H 3H 2H', '5H 4H 3H 2H AH', 'Win'),
('5H 4H 3H 2H AH', '5H 4H 3H 2H AH', 'Tie'),
('5H 4H 3H 2H AH', '6H 5H 4H 3H 2H', 'Loss'),
('AH AD KS KC AC', 'AH KD KH AC KC', 'Win'),
('2H 4D 3C AS 5S', '2H 4D 3C 6S 5S', 'Loss'),
('2H 3S 3C 3H 2S', '3S 3C 2S 2H 2D', 'Win'),
('4D 6D 5D 2D JH', '3S 8S 3H TC KH', 'Loss'),
('4S 6C 8S 3S 7S', 'AD KS 2D 7D 7C', 'Loss'),
('6S 4C 7H 8C 3H', '5H JC AH 9D 9C', 'Loss'),
('9D 9H JH TC QH', '3C 2S JS 5C 7H', 'Win'),
('2H TC 8S AD 9S', '4H TS 7H 2C 5C', 'Win'),
('9D 3S 2C 7S 7C', 'JC TD 3C TC 9H', 'Loss'),
)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (
('2H 3H 4H 5H 6H', True),
('AS AH 2H AD AC', False),
('2H 3H 5H 6H 7H', True),
('KS AS TS QS JS', True),
('8H 9H QS JS TH', False),
('AS 3S 4S 8S 2S', True),
)
SCREAMING_SNAKE_CASE__ : Optional[int] = (
('2H 3H 4H 5H 6H', True),
('AS AH 2H AD AC', False),
('2H 3H 5H 6H 7H', False),
('KS AS TS QS JS', True),
('8H 9H QS JS TH', True),
)
SCREAMING_SNAKE_CASE__ : Optional[int] = (
('2H 4D 3C AS 5S', True, [5, 4, 3, 2, 14]),
('2H 5D 3C AS 5S', False, [14, 5, 5, 3, 2]),
('JH QD KC AS TS', False, [14, 13, 12, 11, 10]),
('9D 3S 2C 7S 7C', False, [9, 7, 7, 3, 2]),
)
SCREAMING_SNAKE_CASE__ : Dict = (
('JH AH TH KH QH', 0),
('JH 9H TH KH QH', 0),
('JC KH JS JD JH', 7),
('KH KC 3S 3H 3D', 6),
('8C 9C 5C 3C TC', 0),
('JS QS 9H TS KH', 0),
('7C 7S KH 2H 7H', 3),
('3C KH 5D 5S KH', 2),
('QH 8H KD JH 8S', 1),
('2D 6D 9D TH 7D', 0),
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = (
('JH AH TH KH QH', 23),
('JH 9H TH KH QH', 22),
('JC KH JS JD JH', 21),
('KH KC 3S 3H 3D', 20),
('8C 9C 5C 3C TC', 19),
('JS QS 9H TS KH', 18),
('7C 7S KH 2H 7H', 17),
('3C KH 5D 5S KH', 16),
('QH 8H KD JH 8S', 15),
('2D 6D 9D TH 7D', 14),
)
def A ( ) -> List[Any]:
lowerCamelCase , lowerCamelCase : Tuple = randrange(len(_SCREAMING_SNAKE_CASE ) ), randrange(len(_SCREAMING_SNAKE_CASE ) )
lowerCamelCase : Optional[Any] = ["Loss", "Tie", "Win"][(play >= oppo) + (play > oppo)]
lowerCamelCase , lowerCamelCase : Any = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def A ( _SCREAMING_SNAKE_CASE = 100 ) -> Tuple:
return (generate_random_hand() for _ in range(_SCREAMING_SNAKE_CASE ))
@pytest.mark.parametrize("hand, expected" ,_SCREAMING_SNAKE_CASE )
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> List[str]:
assert PokerHand(_SCREAMING_SNAKE_CASE )._is_flush() == expected
@pytest.mark.parametrize("hand, expected" ,_SCREAMING_SNAKE_CASE )
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> List[str]:
assert PokerHand(_SCREAMING_SNAKE_CASE )._is_straight() == expected
@pytest.mark.parametrize("hand, expected, card_values" ,_SCREAMING_SNAKE_CASE )
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
lowerCamelCase : str = PokerHand(_SCREAMING_SNAKE_CASE )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize("hand, expected" ,_SCREAMING_SNAKE_CASE )
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Any:
assert PokerHand(_SCREAMING_SNAKE_CASE )._is_same_kind() == expected
@pytest.mark.parametrize("hand, expected" ,_SCREAMING_SNAKE_CASE )
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Optional[Any]:
assert PokerHand(_SCREAMING_SNAKE_CASE )._hand_type == expected
@pytest.mark.parametrize("hand, other, expected" ,_SCREAMING_SNAKE_CASE )
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Optional[Any]:
assert PokerHand(_SCREAMING_SNAKE_CASE ).compare_with(PokerHand(_SCREAMING_SNAKE_CASE ) ) == expected
@pytest.mark.parametrize("hand, other, expected" ,generate_random_hands() )
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> int:
assert PokerHand(_SCREAMING_SNAKE_CASE ).compare_with(PokerHand(_SCREAMING_SNAKE_CASE ) ) == expected
def A ( ) -> Tuple:
lowerCamelCase : Union[str, Any] = [PokerHand(_SCREAMING_SNAKE_CASE ) for hand in SORTED_HANDS]
lowerCamelCase : int = poker_hands.copy()
shuffle(_SCREAMING_SNAKE_CASE )
lowerCamelCase : str = chain(sorted(_SCREAMING_SNAKE_CASE ) )
for index, hand in enumerate(_SCREAMING_SNAKE_CASE ):
assert hand == poker_hands[index]
def A ( ) -> List[Any]:
# Test that five high straights are compared correctly.
lowerCamelCase : List[str] = [PokerHand("2D AC 3H 4H 5S" ), PokerHand("2S 3H 4H 5S 6C" )]
pokerhands.sort(reverse=_SCREAMING_SNAKE_CASE )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def A ( ) -> Optional[int]:
# Multiple calls to five_high_straight function should still return True
# and shouldn't mutate the list in every call other than the first.
lowerCamelCase : Any = PokerHand("2C 4S AS 3D 5C" )
lowerCamelCase : Optional[int] = True
lowerCamelCase : List[str] = [5, 4, 3, 2, 14]
for _ in range(10 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def A ( ) -> Union[str, Any]:
# Problem number 54 from Project Euler
# Testing from poker_hands.txt file
lowerCamelCase : Any = 0
lowerCamelCase : str = os.path.abspath(os.path.dirname(_SCREAMING_SNAKE_CASE ) )
lowerCamelCase : Optional[Any] = os.path.join(_SCREAMING_SNAKE_CASE ,"poker_hands.txt" )
with open(_SCREAMING_SNAKE_CASE ) as file_hand:
for line in file_hand:
lowerCamelCase : Tuple = line[:14].strip()
lowerCamelCase : Any = line[15:].strip()
lowerCamelCase , lowerCamelCase : int = PokerHand(_SCREAMING_SNAKE_CASE ), PokerHand(_SCREAMING_SNAKE_CASE )
lowerCamelCase : Tuple = player.compare_with(_SCREAMING_SNAKE_CASE )
if output == "Win":
answer += 1
assert answer == 376
| 48 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
snake_case : List[str] = "facebook/wmt19-en-de"
snake_case : Dict = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
snake_case : List[str] = FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
snake_case : int = FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")
# Test
snake_case : Optional[Any] = tokenizer(["Making tiny model"], return_tensors="pt")
snake_case : List[str] = tiny_model(**batch)
print("test output:", len(outputs.logits[0]))
# Save
snake_case : Dict = "tiny-wmt19-en-de"
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F"Generated {mname_tiny}")
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 281 | 0 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __snake_case ( ):
__a , __a = 9, 14 # noqa: F841
__a = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
__a = defaultdict(_UpperCAmelCase )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
__a = mst(_UpperCAmelCase )
__a = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
__a = tuple(answer[:2] )
__a = tuple(edge[::-1] )
assert edge in result or reverse in result
| 49 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
snake_case : Optional[int] = logging.getLogger(__name__)
def lowerCAmelCase_ ( _snake_case : Dict , _snake_case : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__magic_name__ : List[str] = np.argmax(_snake_case , axis=1 )
return np.sum(outputs == labels )
def lowerCAmelCase_ ( _snake_case : Optional[Any] ) -> Dict:
'''simple docstring'''
with open(_snake_case , encoding="utf_8" ) as f:
__magic_name__ : List[str] = csv.reader(_snake_case )
__magic_name__ : List[Any] = []
next(_snake_case ) # skip the first line
for line in tqdm(_snake_case ):
output.append((" ".join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def lowerCAmelCase_ ( _snake_case : str , _snake_case : Tuple , _snake_case : Union[str, Any] , _snake_case : List[Any] , _snake_case : Tuple , _snake_case : Optional[int] ) -> int:
'''simple docstring'''
__magic_name__ : Optional[int] = []
for dataset in encoded_datasets:
__magic_name__ : Union[str, Any] = len(_snake_case )
__magic_name__ : Dict = np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
__magic_name__ : List[str] = np.zeros((n_batch, 2) , dtype=np.intaa )
__magic_name__ : Optional[int] = np.full((n_batch, 2, input_len) , fill_value=-100 , dtype=np.intaa )
__magic_name__ : int = np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(_snake_case ):
__magic_name__ : Dict = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__magic_name__ : Dict = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__magic_name__ : str = with_conta
__magic_name__ : Tuple = with_conta
__magic_name__ : Union[str, Any] = len(_snake_case ) - 1
__magic_name__ : int = len(_snake_case ) - 1
__magic_name__ : Optional[Any] = with_conta
__magic_name__ : Optional[Any] = with_conta
__magic_name__ : Optional[int] = mc_label
__magic_name__ : str = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(_snake_case ) for t in all_inputs ) )
return tensor_datasets
def lowerCAmelCase_ ( ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Any = argparse.ArgumentParser()
parser.add_argument("--model_name" , type=_snake_case , default="openai-gpt" , help="pretrained model name" )
parser.add_argument("--do_train" , action="store_true" , help="Whether to run training." )
parser.add_argument("--do_eval" , action="store_true" , help="Whether to run eval on the dev set." )
parser.add_argument(
"--output_dir" , default=_snake_case , type=_snake_case , required=_snake_case , help="The output directory where the model predictions and checkpoints will be written." , )
parser.add_argument("--train_dataset" , type=_snake_case , default="" )
parser.add_argument("--eval_dataset" , type=_snake_case , default="" )
parser.add_argument("--seed" , type=_snake_case , default=42 )
parser.add_argument("--num_train_epochs" , type=_snake_case , default=3 )
parser.add_argument("--train_batch_size" , type=_snake_case , default=8 )
parser.add_argument("--eval_batch_size" , type=_snake_case , default=16 )
parser.add_argument("--adam_epsilon" , default=1E-8 , type=_snake_case , help="Epsilon for Adam optimizer." )
parser.add_argument("--max_grad_norm" , type=_snake_case , default=1 )
parser.add_argument(
"--max_steps" , default=-1 , type=_snake_case , help=(
"If > 0: set total number of training steps to perform. Override num_train_epochs."
) , )
parser.add_argument(
"--gradient_accumulation_steps" , type=_snake_case , default=1 , help="Number of updates steps to accumulate before performing a backward/update pass." , )
parser.add_argument("--learning_rate" , type=_snake_case , default=6.25E-5 )
parser.add_argument("--warmup_steps" , default=0 , type=_snake_case , help="Linear warmup over warmup_steps." )
parser.add_argument("--lr_schedule" , type=_snake_case , default="warmup_linear" )
parser.add_argument("--weight_decay" , type=_snake_case , default=0.01 )
parser.add_argument("--lm_coef" , type=_snake_case , default=0.9 )
parser.add_argument("--n_valid" , type=_snake_case , default=374 )
parser.add_argument("--server_ip" , type=_snake_case , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=_snake_case , default="" , help="Can be used for distant debugging." )
__magic_name__ : List[Any] = parser.parse_args()
print(_snake_case )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=_snake_case )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
__magic_name__ : Dict = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
__magic_name__ : Optional[int] = torch.cuda.device_count()
logger.info("device: {}, n_gpu {}".format(_snake_case , _snake_case ) )
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True." )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
__magic_name__ : List[Any] = ["_start_", "_delimiter_", "_classify_"]
__magic_name__ : Optional[int] = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(_snake_case )
__magic_name__ : Optional[Any] = tokenizer.convert_tokens_to_ids(_snake_case )
__magic_name__ : List[str] = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(_snake_case ) )
model.to(_snake_case )
# Load and encode the datasets
def tokenize_and_encode(_snake_case : str ):
if isinstance(_snake_case , _snake_case ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_snake_case ) )
elif isinstance(_snake_case , _snake_case ):
return obj
return [tokenize_and_encode(_snake_case ) for o in obj]
logger.info("Encoding dataset..." )
__magic_name__ : Optional[int] = load_rocstories_dataset(args.train_dataset )
__magic_name__ : str = load_rocstories_dataset(args.eval_dataset )
__magic_name__ : int = (train_dataset, eval_dataset)
__magic_name__ : List[str] = tokenize_and_encode(_snake_case )
# Compute the max input length for the Transformer
__magic_name__ : Optional[Any] = model.config.n_positions // 2 - 2
__magic_name__ : Optional[int] = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
__magic_name__ : List[str] = min(_snake_case , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
__magic_name__ : List[Any] = pre_process_datasets(_snake_case , _snake_case , _snake_case , *_snake_case )
__magic_name__ , __magic_name__ : Optional[int] = tensor_datasets[0], tensor_datasets[1]
__magic_name__ : Tuple = TensorDataset(*_snake_case )
__magic_name__ : Union[str, Any] = RandomSampler(_snake_case )
__magic_name__ : Dict = DataLoader(_snake_case , sampler=_snake_case , batch_size=args.train_batch_size )
__magic_name__ : Any = TensorDataset(*_snake_case )
__magic_name__ : Optional[Any] = SequentialSampler(_snake_case )
__magic_name__ : int = DataLoader(_snake_case , sampler=_snake_case , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
__magic_name__ : Tuple = args.max_steps
__magic_name__ : List[str] = args.max_steps // (len(_snake_case ) // args.gradient_accumulation_steps) + 1
else:
__magic_name__ : List[str] = len(_snake_case ) // args.gradient_accumulation_steps * args.num_train_epochs
__magic_name__ : str = list(model.named_parameters() )
__magic_name__ : Dict = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
__magic_name__ : str = [
{
"params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
"weight_decay": args.weight_decay,
},
{"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], "weight_decay": 0.0},
]
__magic_name__ : str = AdamW(_snake_case , lr=args.learning_rate , eps=args.adam_epsilon )
__magic_name__ : List[str] = get_linear_schedule_with_warmup(
_snake_case , num_warmup_steps=args.warmup_steps , num_training_steps=_snake_case )
if args.do_train:
__magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc="Epoch" ):
__magic_name__ : List[str] = 0
__magic_name__ : Tuple = 0
__magic_name__ : Dict = tqdm(_snake_case , desc="Training" )
for step, batch in enumerate(_snake_case ):
__magic_name__ : Optional[Any] = tuple(t.to(_snake_case ) for t in batch )
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Dict = batch
__magic_name__ : Optional[Any] = model(_snake_case , mc_token_ids=_snake_case , lm_labels=_snake_case , mc_labels=_snake_case )
__magic_name__ : Optional[Any] = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
__magic_name__ : List[str] = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
__magic_name__ : int = "Training loss: {:.2e} lr: {:.2e}".format(_snake_case , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
__magic_name__ : Dict = model.module if hasattr(_snake_case , "module" ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
__magic_name__ : List[Any] = os.path.join(args.output_dir , _snake_case )
__magic_name__ : Dict = os.path.join(args.output_dir , _snake_case )
torch.save(model_to_save.state_dict() , _snake_case )
model_to_save.config.to_json_file(_snake_case )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
__magic_name__ : Dict = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
__magic_name__ : Optional[int] = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(_snake_case )
if args.do_eval:
model.eval()
__magic_name__ , __magic_name__ : Any = 0, 0
__magic_name__ , __magic_name__ : Union[str, Any] = 0, 0
for batch in tqdm(_snake_case , desc="Evaluating" ):
__magic_name__ : int = tuple(t.to(_snake_case ) for t in batch )
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] = batch
with torch.no_grad():
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Dict = model(
_snake_case , mc_token_ids=_snake_case , lm_labels=_snake_case , mc_labels=_snake_case )
__magic_name__ : Tuple = mc_logits.detach().cpu().numpy()
__magic_name__ : Any = mc_labels.to("cpu" ).numpy()
__magic_name__ : str = accuracy(_snake_case , _snake_case )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
__magic_name__ : Tuple = eval_loss / nb_eval_steps
__magic_name__ : List[Any] = eval_accuracy / nb_eval_examples
__magic_name__ : int = tr_loss / nb_tr_steps if args.do_train else None
__magic_name__ : Any = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}
__magic_name__ : int = os.path.join(args.output_dir , "eval_results.txt" )
with open(_snake_case , "w" ) as writer:
logger.info("***** Eval results *****" )
for key in sorted(result.keys() ):
logger.info(" %s = %s" , _snake_case , str(result[key] ) )
writer.write("%s = %s\n" % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 281 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = 42
class lowerCAmelCase ( __UpperCamelCase, __UpperCamelCase ):
@register_to_config
def __init__( self : List[str] , UpperCAmelCase : int = 65536 , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : int = 2 , UpperCAmelCase : int = 2 , UpperCAmelCase : int = 0 , UpperCAmelCase : str = "fourier" , UpperCAmelCase : bool = True , UpperCAmelCase : bool = False , UpperCAmelCase : float = 0.0 , UpperCAmelCase : Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , UpperCAmelCase : Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , UpperCAmelCase : Tuple[str] = "UNetMidBlock1D" , UpperCAmelCase : str = None , UpperCAmelCase : Tuple[int] = (32, 32, 64) , UpperCAmelCase : str = None , UpperCAmelCase : int = 8 , UpperCAmelCase : int = 1 , UpperCAmelCase : bool = False , ) -> List[Any]:
super().__init__()
lowerCamelCase__ : Optional[int] = sample_size
# time
if time_embedding_type == "fourier":
lowerCamelCase__ : Optional[Any] = GaussianFourierProjection(
embedding_size=8 , set_W_to_weight=UpperCAmelCase , log=UpperCAmelCase , flip_sin_to_cos=UpperCAmelCase )
lowerCamelCase__ : Any = 2 * block_out_channels[0]
elif time_embedding_type == "positional":
lowerCamelCase__ : List[Any] = Timesteps(
block_out_channels[0] , flip_sin_to_cos=UpperCAmelCase , downscale_freq_shift=UpperCAmelCase )
lowerCamelCase__ : Dict = block_out_channels[0]
if use_timestep_embedding:
lowerCamelCase__ : str = block_out_channels[0] * 4
lowerCamelCase__ : List[Any] = TimestepEmbedding(
in_channels=UpperCAmelCase , time_embed_dim=UpperCAmelCase , act_fn=UpperCAmelCase , out_dim=block_out_channels[0] , )
lowerCamelCase__ : Any = nn.ModuleList([] )
lowerCamelCase__ : Tuple = None
lowerCamelCase__ : List[str] = nn.ModuleList([] )
lowerCamelCase__ : Optional[int] = None
# down
lowerCamelCase__ : Optional[int] = in_channels
for i, down_block_type in enumerate(UpperCAmelCase ):
lowerCamelCase__ : Union[str, Any] = output_channel
lowerCamelCase__ : Tuple = block_out_channels[i]
if i == 0:
input_channel += extra_in_channels
lowerCamelCase__ : Union[str, Any] = i == len(UpperCAmelCase ) - 1
lowerCamelCase__ : Optional[int] = get_down_block(
UpperCAmelCase , num_layers=UpperCAmelCase , in_channels=UpperCAmelCase , out_channels=UpperCAmelCase , temb_channels=block_out_channels[0] , add_downsample=not is_final_block or downsample_each_block , )
self.down_blocks.append(UpperCAmelCase )
# mid
lowerCamelCase__ : Optional[int] = get_mid_block(
UpperCAmelCase , in_channels=block_out_channels[-1] , mid_channels=block_out_channels[-1] , out_channels=block_out_channels[-1] , embed_dim=block_out_channels[0] , num_layers=UpperCAmelCase , add_downsample=UpperCAmelCase , )
# up
lowerCamelCase__ : Optional[int] = list(reversed(UpperCAmelCase ) )
lowerCamelCase__ : Optional[int] = reversed_block_out_channels[0]
if out_block_type is None:
lowerCamelCase__ : List[str] = out_channels
else:
lowerCamelCase__ : Any = block_out_channels[0]
for i, up_block_type in enumerate(UpperCAmelCase ):
lowerCamelCase__ : Tuple = output_channel
lowerCamelCase__ : Union[str, Any] = (
reversed_block_out_channels[i + 1] if i < len(UpperCAmelCase ) - 1 else final_upsample_channels
)
lowerCamelCase__ : List[str] = i == len(UpperCAmelCase ) - 1
lowerCamelCase__ : Dict = get_up_block(
UpperCAmelCase , num_layers=UpperCAmelCase , in_channels=UpperCAmelCase , out_channels=UpperCAmelCase , temb_channels=block_out_channels[0] , add_upsample=not is_final_block , )
self.up_blocks.append(UpperCAmelCase )
lowerCamelCase__ : int = output_channel
# out
lowerCamelCase__ : int = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 , 32 )
lowerCamelCase__ : List[Any] = get_out_block(
out_block_type=UpperCAmelCase , num_groups_out=UpperCAmelCase , embed_dim=block_out_channels[0] , out_channels=UpperCAmelCase , act_fn=UpperCAmelCase , fc_dim=block_out_channels[-1] // 4 , )
def A_ ( self : List[Any] , UpperCAmelCase : torch.FloatTensor , UpperCAmelCase : Union[torch.Tensor, float, int] , UpperCAmelCase : bool = True , ) -> Union[UNetaDOutput, Tuple]:
lowerCamelCase__ : Optional[Any] = timestep
if not torch.is_tensor(UpperCAmelCase ):
lowerCamelCase__ : Optional[int] = torch.tensor([timesteps] , dtype=torch.long , device=sample.device )
elif torch.is_tensor(UpperCAmelCase ) and len(timesteps.shape ) == 0:
lowerCamelCase__ : List[str] = timesteps[None].to(sample.device )
lowerCamelCase__ : Optional[int] = self.time_proj(UpperCAmelCase )
if self.config.use_timestep_embedding:
lowerCamelCase__ : str = self.time_mlp(UpperCAmelCase )
else:
lowerCamelCase__ : List[str] = timestep_embed[..., None]
lowerCamelCase__ : str = timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
lowerCamelCase__ : str = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )
# 2. down
lowerCamelCase__ : str = ()
for downsample_block in self.down_blocks:
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = downsample_block(hidden_states=UpperCAmelCase , temb=UpperCAmelCase )
down_block_res_samples += res_samples
# 3. mid
if self.mid_block:
lowerCamelCase__ : Optional[Any] = self.mid_block(UpperCAmelCase , UpperCAmelCase )
# 4. up
for i, upsample_block in enumerate(self.up_blocks ):
lowerCamelCase__ : Dict = down_block_res_samples[-1:]
lowerCamelCase__ : Optional[Any] = down_block_res_samples[:-1]
lowerCamelCase__ : Any = upsample_block(UpperCAmelCase , res_hidden_states_tuple=UpperCAmelCase , temb=UpperCAmelCase )
# 5. post-process
if self.out_block:
lowerCamelCase__ : Any = self.out_block(UpperCAmelCase , UpperCAmelCase )
if not return_dict:
return (sample,)
return UNetaDOutput(sample=UpperCAmelCase )
| 50 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 281 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : int = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
"tokenization_deberta": ["DebertaTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : int = ["DebertaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : List[str] = [
"DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"DebertaForMaskedLM",
"DebertaForQuestionAnswering",
"DebertaForSequenceClassification",
"DebertaForTokenClassification",
"DebertaModel",
"DebertaPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Any = [
"TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDebertaForMaskedLM",
"TFDebertaForQuestionAnswering",
"TFDebertaForSequenceClassification",
"TFDebertaForTokenClassification",
"TFDebertaModel",
"TFDebertaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
snake_case_ : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 51 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCAmelCase_ ( _snake_case : List[Any] ) -> List[Any]:
'''simple docstring'''
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ) -> Tuple:
'''simple docstring'''
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : Dict = "mock-s3-bucket"
__magic_name__ : Any = F'''s3://{mock_bucket}'''
__magic_name__ : str = extract_path_from_uri(_snake_case )
assert dataset_path.startswith("s3://" ) is False
__magic_name__ : Tuple = "./local/path"
__magic_name__ : Optional[Any] = extract_path_from_uri(_snake_case )
assert dataset_path == new_dataset_path
def lowerCAmelCase_ ( _snake_case : List[str] ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ : str = is_remote_filesystem(_snake_case )
assert is_remote is True
__magic_name__ : Optional[int] = fsspec.filesystem("file" )
__magic_name__ : int = is_remote_filesystem(_snake_case )
assert is_remote is False
@pytest.mark.parametrize("compression_fs_class" , _snake_case )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : Optional[Any] , _snake_case : int , _snake_case : Tuple , _snake_case : Any , _snake_case : Union[str, Any] , _snake_case : Any ) -> int:
'''simple docstring'''
__magic_name__ : Any = {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
__magic_name__ : str = input_paths[compression_fs_class.protocol]
if input_path is None:
__magic_name__ : Dict = F'''for \'{compression_fs_class.protocol}\' compression protocol, '''
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_snake_case )
__magic_name__ : str = fsspec.filesystem(compression_fs_class.protocol , fo=_snake_case )
assert isinstance(_snake_case , _snake_case )
__magic_name__ : int = os.path.basename(_snake_case )
__magic_name__ : Optional[int] = expected_filename[: expected_filename.rindex("." )]
assert fs.glob("*" ) == [expected_filename]
with fs.open(_snake_case , "r" , encoding="utf-8" ) as f, open(_snake_case , encoding="utf-8" ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize("protocol" , ["zip", "gzip"] )
def lowerCAmelCase_ ( _snake_case : List[Any] , _snake_case : Optional[Any] , _snake_case : Optional[Any] ) -> str:
'''simple docstring'''
__magic_name__ : int = {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
__magic_name__ : int = compressed_file_paths[protocol]
__magic_name__ : Tuple = "dataset.jsonl"
__magic_name__ : List[str] = F'''{protocol}://{member_file_path}::{compressed_file_path}'''
__magic_name__ , *__magic_name__ : Optional[Any] = fsspec.get_fs_token_paths(_snake_case )
assert fs.isfile(_snake_case )
assert not fs.isfile("non_existing_" + member_file_path )
@pytest.mark.integration
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Dict , _snake_case : List[str] , _snake_case : Tuple ) -> str:
'''simple docstring'''
__magic_name__ : int = hf_api.dataset_info(_snake_case , token=_snake_case )
__magic_name__ : Optional[Any] = HfFileSystem(repo_info=_snake_case , token=_snake_case )
assert sorted(hffs.glob("*" ) ) == [".gitattributes", "data"]
assert hffs.isdir("data" )
assert hffs.isfile(".gitattributes" ) and hffs.isfile("data/text_data.txt" )
with open(_snake_case ) as f:
assert hffs.open("data/text_data.txt" , "r" ).read() == f.read()
def lowerCAmelCase_ ( ) -> Optional[int]:
'''simple docstring'''
__magic_name__ : Optional[Any] = "bz2"
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(_snake_case , _snake_case , clobber=_snake_case )
with pytest.warns(_snake_case ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(_snake_case ) == 1
assert (
str(warning_info[0].message )
== F'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
)
| 281 | 0 |
import os
import sys
__lowerCamelCase : Any = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
__lowerCamelCase : Dict = [
"""torch""",
"""numpy""",
"""tokenizers""",
"""filelock""",
"""requests""",
"""tqdm""",
"""regex""",
"""sentencepiece""",
"""sacremoses""",
"""importlib_metadata""",
"""huggingface_hub""",
]
@add_start_docstrings(AutoConfig.__doc__ )
def A_ ( *_lowerCAmelCase , **_lowerCAmelCase ) -> List[str]:
return AutoConfig.from_pretrained(*_lowerCAmelCase , **_lowerCAmelCase )
@add_start_docstrings(AutoTokenizer.__doc__ )
def A_ ( *_lowerCAmelCase , **_lowerCAmelCase ) -> List[Any]:
return AutoTokenizer.from_pretrained(*_lowerCAmelCase , **_lowerCAmelCase )
@add_start_docstrings(AutoModel.__doc__ )
def A_ ( *_lowerCAmelCase , **_lowerCAmelCase ) -> Union[str, Any]:
return AutoModel.from_pretrained(*_lowerCAmelCase , **_lowerCAmelCase )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def A_ ( *_lowerCAmelCase , **_lowerCAmelCase ) -> Tuple:
return AutoModelForCausalLM.from_pretrained(*_lowerCAmelCase , **_lowerCAmelCase )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def A_ ( *_lowerCAmelCase , **_lowerCAmelCase ) -> List[Any]:
return AutoModelForMaskedLM.from_pretrained(*_lowerCAmelCase , **_lowerCAmelCase )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def A_ ( *_lowerCAmelCase , **_lowerCAmelCase ) -> int:
return AutoModelForSequenceClassification.from_pretrained(*_lowerCAmelCase , **_lowerCAmelCase )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def A_ ( *_lowerCAmelCase , **_lowerCAmelCase ) -> int:
return AutoModelForQuestionAnswering.from_pretrained(*_lowerCAmelCase , **_lowerCAmelCase )
| 52 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case : Dict = logging.get_logger(__name__)
snake_case : List[Any] = {
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
"YituTech/conv-bert-medium-small": (
"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
),
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class _snake_case ( snake_case ):
UpperCamelCase__ = 'convbert'
def __init__( self , _a=30_522 , _a=768 , _a=12 , _a=12 , _a=3_072 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=2 , _a=0.02 , _a=1e-12 , _a=1 , _a=0 , _a=2 , _a=768 , _a=2 , _a=9 , _a=1 , _a=None , **_a , ):
super().__init__(
pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a , )
__magic_name__ : Tuple = vocab_size
__magic_name__ : List[Any] = hidden_size
__magic_name__ : Union[str, Any] = num_hidden_layers
__magic_name__ : List[Any] = num_attention_heads
__magic_name__ : str = intermediate_size
__magic_name__ : Any = hidden_act
__magic_name__ : List[Any] = hidden_dropout_prob
__magic_name__ : Optional[int] = attention_probs_dropout_prob
__magic_name__ : Tuple = max_position_embeddings
__magic_name__ : str = type_vocab_size
__magic_name__ : List[str] = initializer_range
__magic_name__ : Tuple = layer_norm_eps
__magic_name__ : List[Any] = embedding_size
__magic_name__ : List[Any] = head_ratio
__magic_name__ : str = conv_kernel_size
__magic_name__ : Dict = num_groups
__magic_name__ : str = classifier_dropout
class _snake_case ( snake_case ):
@property
def SCREAMING_SNAKE_CASE ( self ):
if self.task == "multiple-choice":
__magic_name__ : Dict = {0: "batch", 1: "choice", 2: "sequence"}
else:
__magic_name__ : Dict = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 281 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : List[str] ={
'''configuration_bigbird_pegasus''': [
'''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BigBirdPegasusConfig''',
'''BigBirdPegasusOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Any =[
'''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BigBirdPegasusForCausalLM''',
'''BigBirdPegasusForConditionalGeneration''',
'''BigBirdPegasusForQuestionAnswering''',
'''BigBirdPegasusForSequenceClassification''',
'''BigBirdPegasusModel''',
'''BigBirdPegasusPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
BigBirdPegasusConfig,
BigBirdPegasusOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
BigBirdPegasusForCausalLM,
BigBirdPegasusForConditionalGeneration,
BigBirdPegasusForQuestionAnswering,
BigBirdPegasusForSequenceClassification,
BigBirdPegasusModel,
BigBirdPegasusPreTrainedModel,
)
else:
import sys
a__ : str =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 53 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipaConfig,
BlipaForConditionalGeneration,
BlipaProcessor,
BlipaVisionConfig,
BlipImageProcessor,
OPTConfig,
TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def lowerCAmelCase_ ( ) -> str:
'''simple docstring'''
__magic_name__ : int = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
__magic_name__ : Union[str, Any] = Image.open(requests.get(_snake_case , stream=_snake_case ).raw ).convert("RGB" )
return image
def lowerCAmelCase_ ( _snake_case : str ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : List[str] = []
# fmt: off
# vision encoder
rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding") )
rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding") )
rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight") )
rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias") )
rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight") )
rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias") )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',) )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''') )
# QFormer
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight") )
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias") )
# fmt: on
return rename_keys
def lowerCAmelCase_ ( _snake_case : Any , _snake_case : Union[str, Any] , _snake_case : Optional[Any] ) -> int:
'''simple docstring'''
__magic_name__ : Tuple = dct.pop(_snake_case )
__magic_name__ : int = val
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : Optional[Any] ) -> Dict:
'''simple docstring'''
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
__magic_name__ : List[Any] = state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias''' )
__magic_name__ : Optional[Any] = state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias''' )
# next, set bias in the state dict
__magic_name__ : Optional[int] = torch.cat((q_bias, torch.zeros_like(_snake_case , requires_grad=_snake_case ), v_bias) )
__magic_name__ : Union[str, Any] = qkv_bias
def lowerCAmelCase_ ( _snake_case : Dict , _snake_case : str ) -> int:
'''simple docstring'''
__magic_name__ : List[Any] = 364 if "coco" in model_name else 224
__magic_name__ : Union[str, Any] = BlipaVisionConfig(image_size=_snake_case ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
__magic_name__ : List[str] = OPTConfig.from_pretrained("facebook/opt-2.7b" , eos_token_id=_snake_case ).to_dict()
elif "opt-6.7b" in model_name:
__magic_name__ : Any = OPTConfig.from_pretrained("facebook/opt-6.7b" , eos_token_id=_snake_case ).to_dict()
elif "t5-xl" in model_name:
__magic_name__ : Dict = TaConfig.from_pretrained("google/flan-t5-xl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
__magic_name__ : int = TaConfig.from_pretrained("google/flan-t5-xxl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
__magic_name__ : List[Any] = BlipaConfig(vision_config=_snake_case , text_config=_snake_case )
return config, image_size
@torch.no_grad()
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : str=None , _snake_case : Dict=False ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Optional[int] = (
AutoTokenizer.from_pretrained("facebook/opt-2.7b" )
if "opt" in model_name
else AutoTokenizer.from_pretrained("google/flan-t5-xl" )
)
__magic_name__ : List[Any] = tokenizer("\n" , add_special_tokens=_snake_case ).input_ids[0]
__magic_name__ , __magic_name__ : Tuple = get_blipa_config(_snake_case , eos_token_id=_snake_case )
__magic_name__ : Union[str, Any] = BlipaForConditionalGeneration(_snake_case ).eval()
__magic_name__ : Any = {
"blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
"blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
"blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
"blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
"blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
"blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
"blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
}
__magic_name__ , __magic_name__ : Union[str, Any] = model_name_to_original[model_name]
# load original model
print("Loading original model..." )
__magic_name__ : Union[str, Any] = "cuda" if torch.cuda.is_available() else "cpu"
__magic_name__ , __magic_name__ , __magic_name__ : Optional[Any] = load_model_and_preprocess(
name=_snake_case , model_type=_snake_case , is_eval=_snake_case , device=_snake_case )
original_model.eval()
print("Done!" )
# update state dict keys
__magic_name__ : Dict = original_model.state_dict()
__magic_name__ : str = create_rename_keys(_snake_case )
for src, dest in rename_keys:
rename_key(_snake_case , _snake_case , _snake_case )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
__magic_name__ : Any = state_dict.pop(_snake_case )
if key.startswith("Qformer.bert" ):
__magic_name__ : Optional[int] = key.replace("Qformer.bert" , "qformer" )
if "attention.self" in key:
__magic_name__ : Any = key.replace("self" , "attention" )
if "opt_proj" in key:
__magic_name__ : Union[str, Any] = key.replace("opt_proj" , "language_projection" )
if "t5_proj" in key:
__magic_name__ : Optional[int] = key.replace("t5_proj" , "language_projection" )
if key.startswith("opt" ):
__magic_name__ : List[str] = key.replace("opt" , "language" )
if key.startswith("t5" ):
__magic_name__ : Tuple = key.replace("t5" , "language" )
__magic_name__ : Dict = val
# read in qv biases
read_in_q_v_bias(_snake_case , _snake_case )
__magic_name__ , __magic_name__ : Tuple = hf_model.load_state_dict(_snake_case , strict=_snake_case )
assert len(_snake_case ) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
__magic_name__ : List[Any] = load_demo_image()
__magic_name__ : Tuple = vis_processors["eval"](_snake_case ).unsqueeze(0 ).to(_snake_case )
__magic_name__ : Dict = tokenizer(["\n"] , return_tensors="pt" ).input_ids.to(_snake_case )
# create processor
__magic_name__ : Optional[Any] = BlipImageProcessor(
size={"height": image_size, "width": image_size} , image_mean=_snake_case , image_std=_snake_case )
__magic_name__ : Dict = BlipaProcessor(image_processor=_snake_case , tokenizer=_snake_case )
__magic_name__ : Union[str, Any] = processor(images=_snake_case , return_tensors="pt" ).pixel_values.to(_snake_case )
# make sure processor creates exact same pixel values
assert torch.allclose(_snake_case , _snake_case )
original_model.to(_snake_case )
hf_model.to(_snake_case )
with torch.no_grad():
if "opt" in model_name:
__magic_name__ : List[Any] = original_model({"image": original_pixel_values, "text_input": [""]} ).logits
__magic_name__ : Optional[int] = hf_model(_snake_case , _snake_case ).logits
else:
__magic_name__ : int = original_model(
{"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]} ).logits
__magic_name__ : Tuple = input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -100 )
__magic_name__ : List[str] = hf_model(_snake_case , _snake_case , labels=_snake_case ).logits
assert original_logits.shape == logits.shape
print("First values of original logits:" , original_logits[0, :3, :3] )
print("First values of HF logits:" , logits[0, :3, :3] )
# assert values
if model_name == "blip2-flan-t5-xl":
__magic_name__ : List[str] = torch.tensor(
[[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]] , device=_snake_case )
assert torch.allclose(logits[0, :3, :3] , _snake_case , atol=1E-4 )
elif model_name == "blip2-flan-t5-xl-coco":
__magic_name__ : Tuple = torch.tensor(
[[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]] , device=_snake_case )
else:
# cast to same type
__magic_name__ : str = logits.dtype
assert torch.allclose(original_logits.to(_snake_case ) , _snake_case , atol=1E-2 )
print("Looks ok!" )
print("Generating a caption..." )
__magic_name__ : Optional[int] = ""
__magic_name__ : Dict = tokenizer(_snake_case , return_tensors="pt" ).input_ids.to(_snake_case )
__magic_name__ : int = original_model.generate({"image": original_pixel_values} )
__magic_name__ : Optional[Any] = hf_model.generate(
_snake_case , _snake_case , do_sample=_snake_case , num_beams=5 , max_length=30 , min_length=1 , top_p=0.9 , repetition_penalty=1.0 , length_penalty=1.0 , temperature=1 , )
print("Original generation:" , _snake_case )
__magic_name__ : Tuple = input_ids.shape[1]
__magic_name__ : int = processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=_snake_case )
__magic_name__ : Union[str, Any] = [text.strip() for text in output_text]
print("HF generation:" , _snake_case )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(_snake_case )
hf_model.save_pretrained(_snake_case )
if push_to_hub:
processor.push_to_hub(F'''nielsr/{model_name}''' )
hf_model.push_to_hub(F'''nielsr/{model_name}''' )
if __name__ == "__main__":
snake_case : Any = argparse.ArgumentParser()
snake_case : Union[str, Any] = [
"blip2-opt-2.7b",
"blip2-opt-6.7b",
"blip2-opt-2.7b-coco",
"blip2-opt-6.7b-coco",
"blip2-flan-t5-xl",
"blip2-flan-t5-xl-coco",
"blip2-flan-t5-xxl",
]
parser.add_argument(
"--model_name",
default="blip2-opt-2.7b",
choices=choices,
type=str,
help="Path to hf config.json of model to convert",
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model and processor to the hub after converting",
)
snake_case : int = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 281 | 0 |
"""simple docstring"""
from math import sqrt
def UpperCAmelCase__ (lowerCAmelCase_ = 100_0000 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):
if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
num_cuboids += (
min(lowerCAmelCase_ , sum_shortest_sides // 2 )
- max(1 , sum_shortest_sides - max_cuboid_size )
+ 1
)
return max_cuboid_size
if __name__ == "__main__":
print(F"{solution() = }")
| 54 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case : Dict = logging.get_logger(__name__)
snake_case : Union[str, Any] = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
snake_case : Dict = {
"vocab_file": {
"vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
"vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
},
"merges_file": {
"vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
"vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
},
}
snake_case : Union[str, Any] = {
"vinai/phobert-base": 256,
"vinai/phobert-large": 256,
}
def lowerCAmelCase_ ( _snake_case : str ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : List[str] = set()
__magic_name__ : Any = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__magic_name__ : int = char
__magic_name__ : List[str] = set(_snake_case )
return pairs
class _snake_case ( snake_case ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , _a , _a , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , **_a , ):
super().__init__(
bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , cls_token=_a , pad_token=_a , mask_token=_a , **_a , )
__magic_name__ : Dict = vocab_file
__magic_name__ : Tuple = merges_file
__magic_name__ : List[Any] = {}
__magic_name__ : List[Any] = 0
__magic_name__ : Tuple = 1
__magic_name__ : int = 2
__magic_name__ : Union[str, Any] = 3
self.add_from_file(_a )
__magic_name__ : Optional[int] = {v: k for k, v in self.encoder.items()}
with open(_a , encoding="utf-8" ) as merges_handle:
__magic_name__ : List[str] = merges_handle.read().split("\n" )[:-1]
__magic_name__ : Union[str, Any] = [tuple(merge.split()[:-1] ) for merge in merges]
__magic_name__ : Union[str, Any] = dict(zip(_a , range(len(_a ) ) ) )
__magic_name__ : Optional[int] = {}
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__magic_name__ : Optional[Any] = [self.cls_token_id]
__magic_name__ : Union[str, Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self , _a , _a = None , _a = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is None:
return [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1, 1] + ([0] * len(_a )) + [1]
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
__magic_name__ : Optional[Any] = [self.sep_token_id]
__magic_name__ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def SCREAMING_SNAKE_CASE ( self ):
return len(self.encoder )
def SCREAMING_SNAKE_CASE ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def SCREAMING_SNAKE_CASE ( self , _a ):
if token in self.cache:
return self.cache[token]
__magic_name__ : List[Any] = tuple(_a )
__magic_name__ : List[Any] = tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
__magic_name__ : Any = get_pairs(_a )
if not pairs:
return token
while True:
__magic_name__ : str = min(_a , key=lambda _a : self.bpe_ranks.get(_a , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
__magic_name__ , __magic_name__ : List[str] = bigram
__magic_name__ : List[str] = []
__magic_name__ : List[str] = 0
while i < len(_a ):
try:
__magic_name__ : Any = word.index(_a , _a )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__magic_name__ : Tuple = j
if word[i] == first and i < len(_a ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__magic_name__ : Union[str, Any] = tuple(_a )
__magic_name__ : Optional[int] = new_word
if len(_a ) == 1:
break
else:
__magic_name__ : List[Any] = get_pairs(_a )
__magic_name__ : Optional[int] = "@@ ".join(_a )
__magic_name__ : Tuple = word[:-4]
__magic_name__ : str = word
return word
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Optional[Any] = []
__magic_name__ : Dict = re.findall(r"\S+\n?" , _a )
for token in words:
split_tokens.extend(list(self.bpe(_a ).split(" " ) ) )
return split_tokens
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.encoder.get(_a , self.encoder.get(self.unk_token ) )
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.decoder.get(_a , self.unk_token )
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Tuple = " ".join(_a ).replace("@@ " , "" ).strip()
return out_string
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__magic_name__ : Optional[int] = os.path.join(
_a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
__magic_name__ : Union[str, Any] = os.path.join(
_a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
if os.path.abspath(self.merges_file ) != os.path.abspath(_a ):
copyfile(self.merges_file , _a )
return out_vocab_file, out_merge_file
def SCREAMING_SNAKE_CASE ( self , _a ):
if isinstance(_a , _a ):
try:
with open(_a , "r" , encoding="utf-8" ) as fd:
self.add_from_file(_a )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset''' )
return
__magic_name__ : List[Any] = f.readlines()
for lineTmp in lines:
__magic_name__ : Optional[Any] = lineTmp.strip()
__magic_name__ : Union[str, Any] = line.rfind(" " )
if idx == -1:
raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'" )
__magic_name__ : Optional[int] = line[:idx]
__magic_name__ : Dict = len(self.encoder )
| 281 | 0 |
'''simple docstring'''
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class snake_case ( lowercase , lowercase ):
"""simple docstring"""
@register_to_config
def __init__( self , *,
UpperCamelCase = 4 , UpperCamelCase = 768 , UpperCamelCase , UpperCamelCase , ):
"""simple docstring"""
super().__init__()
lowerCamelCase_ = nn.Parameter(torch.zeros(UpperCamelCase ) )
# parameters for additional clip time embeddings
lowerCamelCase_ = nn.Linear(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = nn.Linear(UpperCamelCase , UpperCamelCase )
# parameters for encoder hidden states
lowerCamelCase_ = clip_extra_context_tokens
lowerCamelCase_ = nn.Linear(
UpperCamelCase , self.clip_extra_context_tokens * cross_attention_dim )
lowerCamelCase_ = nn.Linear(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = nn.LayerNorm(UpperCamelCase )
def snake_case ( self , *, UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
lowerCamelCase_ = image_embeddings.shape[0]
lowerCamelCase_ = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
lowerCamelCase_ = classifier_free_guidance_embeddings.expand(
UpperCamelCase , -1 )
lowerCamelCase_ = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
lowerCamelCase_ = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
lowerCamelCase_ = self.embedding_proj(UpperCamelCase )
lowerCamelCase_ = self.clip_image_embeddings_project_to_time_embeddings(UpperCamelCase )
lowerCamelCase_ = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
lowerCamelCase_ = self.clip_extra_context_tokens_proj(UpperCamelCase )
lowerCamelCase_ = clip_extra_context_tokens.reshape(UpperCamelCase , -1 , self.clip_extra_context_tokens )
lowerCamelCase_ = clip_extra_context_tokens.permute(0 , 2 , 1 )
lowerCamelCase_ = self.encoder_hidden_states_proj(UpperCamelCase )
lowerCamelCase_ = self.text_encoder_hidden_states_norm(UpperCamelCase )
lowerCamelCase_ = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 55 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase_ ( _snake_case : str = "laptop" ) -> DataFrame:
'''simple docstring'''
__magic_name__ : Tuple = F'''https://www.amazon.in/laptop/s?k={product}'''
__magic_name__ : Dict = {
"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
"Accept-Language": "en-US, en;q=0.5",
}
__magic_name__ : Tuple = BeautifulSoup(requests.get(_snake_case , headers=_snake_case ).text )
# Initialize a Pandas dataframe with the column titles
__magic_name__ : int = DataFrame(
columns=[
"Product Title",
"Product Link",
"Current Price of the product",
"Product Rating",
"MRP of the product",
"Discount",
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
"div" , attrs={"class": "s-result-item", "data-component-type": "s-search-result"} , ) , soup.find_all("div" , attrs={"class": "a-row a-size-base a-color-base"} ) , ):
try:
__magic_name__ : Dict = item.ha.text
__magic_name__ : Optional[int] = "https://www.amazon.in/" + item.ha.a["href"]
__magic_name__ : Optional[Any] = item.find("span" , attrs={"class": "a-offscreen"} ).text
try:
__magic_name__ : Union[str, Any] = item.find("span" , attrs={"class": "a-icon-alt"} ).text
except AttributeError:
__magic_name__ : Dict = "Not available"
try:
__magic_name__ : Optional[int] = (
"₹"
+ item.find(
"span" , attrs={"class": "a-price a-text-price"} ).text.split("₹" )[1]
)
except AttributeError:
__magic_name__ : List[str] = ""
try:
__magic_name__ : int = float(
(
(
float(product_mrp.strip("₹" ).replace("," , "" ) )
- float(product_price.strip("₹" ).replace("," , "" ) )
)
/ float(product_mrp.strip("₹" ).replace("," , "" ) )
)
* 100 )
except ValueError:
__magic_name__ : str = float("nan" )
except AttributeError:
pass
__magic_name__ : Optional[int] = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
__magic_name__ : Optional[Any] = " "
__magic_name__ : str = " "
data_frame.index += 1
return data_frame
if __name__ == "__main__":
snake_case : Any = "headphones"
get_amazon_product_data(product).to_csv(F"Amazon Product Data for {product}.csv")
| 281 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_lowerCamelCase )
class a ( _lowerCamelCase ):
snake_case_ = field(default="automatic-speech-recognition" , metadata={"include_in_asdict_even_if_is_default": True} )
snake_case_ = Features({"audio": Audio()} )
snake_case_ = Features({"transcription": Value("string" )} )
snake_case_ = "audio"
snake_case_ = "transcription"
def A_ ( self : Any , lowercase_ : Optional[int] ):
if self.audio_column not in features:
raise ValueError(F"Column {self.audio_column} is not present in features." )
if not isinstance(features[self.audio_column] , lowercase_ ):
raise ValueError(F"Column {self.audio_column} is not an Audio type." )
snake_case_ = copy.deepcopy(self )
snake_case_ = self.input_schema.copy()
snake_case_ = features[self.audio_column]
snake_case_ = input_schema
return task_template
@property
def A_ ( self : Any ):
return {self.audio_column: "audio", self.transcription_column: "transcription"}
| 56 |
from __future__ import annotations
class _snake_case :
def __init__( self , _a ):
__magic_name__ : Optional[Any] = data
__magic_name__ : Node | None = None
__magic_name__ : Node | None = None
def lowerCAmelCase_ ( _snake_case : Node | None ) -> None: # In Order traversal of the tree
'''simple docstring'''
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def lowerCAmelCase_ ( _snake_case : Node | None ) -> int:
'''simple docstring'''
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def lowerCAmelCase_ ( _snake_case : Node ) -> bool:
'''simple docstring'''
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def lowerCAmelCase_ ( ) -> None: # Main function for testing.
'''simple docstring'''
__magic_name__ : int = Node(1 )
__magic_name__ : Union[str, Any] = Node(2 )
__magic_name__ : Tuple = Node(3 )
__magic_name__ : Optional[Any] = Node(4 )
__magic_name__ : Union[str, Any] = Node(5 )
__magic_name__ : Any = Node(6 )
__magic_name__ : int = Node(7 )
__magic_name__ : List[str] = Node(8 )
__magic_name__ : Union[str, Any] = Node(9 )
print(is_full_binary_tree(_snake_case ) )
print(depth_of_tree(_snake_case ) )
print("Tree is: " )
display(_snake_case )
if __name__ == "__main__":
main()
| 281 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=5_12 , __a=16 , __a=2 , __a=0.0_2 , __a=4 , ):
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = seq_length
__lowerCAmelCase = is_training
__lowerCAmelCase = use_attention_mask
__lowerCAmelCase = use_token_type_ids
__lowerCAmelCase = use_labels
__lowerCAmelCase = vocab_size
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = max_position_embeddings
__lowerCAmelCase = type_vocab_size
__lowerCAmelCase = type_sequence_label_size
__lowerCAmelCase = initializer_range
__lowerCAmelCase = num_choices
def snake_case ( self ):
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase = None
if self.use_attention_mask:
__lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , tie_weights_=__a , )
return config, input_ids, attention_mask
def snake_case ( self ):
__lowerCAmelCase = self.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = config_and_inputs
__lowerCAmelCase = {"input_ids": input_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_flax
class _UpperCamelCase ( lowerCAmelCase__ ,unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase : Optional[int] =(
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def snake_case ( self ):
__lowerCAmelCase = FlaxDistilBertModelTester(self )
@slow
def snake_case ( self ):
for model_class_name in self.all_model_classes:
__lowerCAmelCase = model_class_name.from_pretrained("distilbert-base-uncased" )
__lowerCAmelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(__a )
@require_flax
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def snake_case ( self ):
__lowerCAmelCase = FlaxDistilBertModel.from_pretrained("distilbert-base-uncased" )
__lowerCAmelCase = np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
__lowerCAmelCase = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
__lowerCAmelCase = model(__a , attention_mask=__a )[0]
__lowerCAmelCase = (1, 11, 7_68)
self.assertEqual(output.shape , __a )
__lowerCAmelCase = np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , __a , atol=1e-4 ) )
| 57 |
def lowerCAmelCase_ ( _snake_case : str , _snake_case : str ) -> bool:
'''simple docstring'''
__magic_name__ : Union[str, Any] = len(_snake_case ) + 1
__magic_name__ : List[str] = len(_snake_case ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
__magic_name__ : str = [[0 for i in range(_snake_case )] for j in range(_snake_case )]
# since string of zero length match pattern of zero length
__magic_name__ : Optional[int] = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _snake_case ):
__magic_name__ : Optional[int] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _snake_case ):
__magic_name__ : Union[str, Any] = dp[0][j - 2] if pattern[j - 1] == "*" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _snake_case ):
for j in range(1 , _snake_case ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
__magic_name__ : Optional[int] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
__magic_name__ : Optional[Any] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
__magic_name__ : List[Any] = dp[i - 1][j]
else:
__magic_name__ : Union[str, Any] = 0
else:
__magic_name__ : Dict = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
snake_case : Optional[Any] = "aab"
snake_case : List[str] = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F"{input_string} matches the given pattern {pattern}")
else:
print(F"{input_string} does not match with the given pattern {pattern}")
| 281 | 0 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ = logging.get_logger(__name__)
set_seed(770)
lowercase_ = {
"""c_attn""": """att_proj""",
"""c_proj""": """out_proj""",
"""c_fc""": """in_proj""",
"""transformer.""": """""",
"""h.""": """layers.""",
"""ln_1""": """layernorm_1""",
"""ln_2""": """layernorm_2""",
"""ln_f""": """layernorm_final""",
"""wpe""": """position_embeds_layer""",
"""wte""": """input_embeds_layer""",
}
lowercase_ = {
"""text_small""": {
"""repo_id""": """suno/bark""",
"""file_name""": """text.pt""",
},
"""coarse_small""": {
"""repo_id""": """suno/bark""",
"""file_name""": """coarse.pt""",
},
"""fine_small""": {
"""repo_id""": """suno/bark""",
"""file_name""": """fine.pt""",
},
"""text""": {
"""repo_id""": """suno/bark""",
"""file_name""": """text_2.pt""",
},
"""coarse""": {
"""repo_id""": """suno/bark""",
"""file_name""": """coarse_2.pt""",
},
"""fine""": {
"""repo_id""": """suno/bark""",
"""file_name""": """fine_2.pt""",
},
}
lowercase_ = os.path.dirname(os.path.abspath(__file__))
lowercase_ = os.path.join(os.path.expanduser("""~"""), """.cache""")
lowercase_ = os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")
def lowerCamelCase ( __lowerCamelCase : int , __lowerCamelCase : Tuple=False ) ->Any:
_SCREAMING_SNAKE_CASE = model_type
if use_small:
key += "_small"
return os.path.join(__lowerCamelCase , REMOTE_MODEL_PATHS[key]["""file_name"""] )
def lowerCamelCase ( __lowerCamelCase : Any , __lowerCamelCase : int ) ->Any:
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
hf_hub_download(repo_id=__lowerCamelCase , filename=__lowerCamelCase , local_dir=__lowerCamelCase )
def lowerCamelCase ( __lowerCamelCase : Tuple , __lowerCamelCase : List[str] , __lowerCamelCase : Dict=False , __lowerCamelCase : Optional[int]="text" ) ->Optional[int]:
if model_type == "text":
_SCREAMING_SNAKE_CASE = BarkSemanticModel
_SCREAMING_SNAKE_CASE = BarkSemanticConfig
_SCREAMING_SNAKE_CASE = BarkSemanticGenerationConfig
elif model_type == "coarse":
_SCREAMING_SNAKE_CASE = BarkCoarseModel
_SCREAMING_SNAKE_CASE = BarkCoarseConfig
_SCREAMING_SNAKE_CASE = BarkCoarseGenerationConfig
elif model_type == "fine":
_SCREAMING_SNAKE_CASE = BarkFineModel
_SCREAMING_SNAKE_CASE = BarkFineConfig
_SCREAMING_SNAKE_CASE = BarkFineGenerationConfig
else:
raise NotImplementedError()
_SCREAMING_SNAKE_CASE = F'{model_type}_small' if use_small else model_type
_SCREAMING_SNAKE_CASE = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(__lowerCamelCase ):
logger.info(F'{model_type} model not found, downloading into `{CACHE_DIR}`.' )
_download(model_info["""repo_id"""] , model_info["""file_name"""] )
_SCREAMING_SNAKE_CASE = torch.load(__lowerCamelCase , map_location=__lowerCamelCase )
# this is a hack
_SCREAMING_SNAKE_CASE = checkpoint["""model_args"""]
if "input_vocab_size" not in model_args:
_SCREAMING_SNAKE_CASE = model_args["""vocab_size"""]
_SCREAMING_SNAKE_CASE = model_args["""vocab_size"""]
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
_SCREAMING_SNAKE_CASE = model_args.pop("""n_head""" )
_SCREAMING_SNAKE_CASE = model_args.pop("""n_embd""" )
_SCREAMING_SNAKE_CASE = model_args.pop("""n_layer""" )
_SCREAMING_SNAKE_CASE = ConfigClass(**checkpoint["""model_args"""] )
_SCREAMING_SNAKE_CASE = ModelClass(config=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = GenerationConfigClass()
_SCREAMING_SNAKE_CASE = model_generation_config
_SCREAMING_SNAKE_CASE = checkpoint["""model"""]
# fixup checkpoint
_SCREAMING_SNAKE_CASE = """_orig_mod."""
for k, v in list(state_dict.items() ):
if k.startswith(__lowerCamelCase ):
# replace part of the key with corresponding layer name in HF implementation
_SCREAMING_SNAKE_CASE = k[len(__lowerCamelCase ) :]
for old_layer_name in new_layer_name_dict:
_SCREAMING_SNAKE_CASE = new_k.replace(__lowerCamelCase , new_layer_name_dict[old_layer_name] )
_SCREAMING_SNAKE_CASE = state_dict.pop(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = set(state_dict.keys() ) - set(model.state_dict().keys() )
_SCREAMING_SNAKE_CASE = {k for k in extra_keys if not k.endswith(""".attn.bias""" )}
_SCREAMING_SNAKE_CASE = set(model.state_dict().keys() ) - set(state_dict.keys() )
_SCREAMING_SNAKE_CASE = {k for k in missing_keys if not k.endswith(""".attn.bias""" )}
if len(__lowerCamelCase ) != 0:
raise ValueError(F'extra keys found: {extra_keys}' )
if len(__lowerCamelCase ) != 0:
raise ValueError(F'missing keys: {missing_keys}' )
model.load_state_dict(__lowerCamelCase , strict=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = model.num_parameters(exclude_embeddings=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = checkpoint["""best_val_loss"""].item()
logger.info(F'model loaded: {round(n_params/1e6 , 1 )}M params, {round(__lowerCamelCase , 3 )} loss' )
model.eval()
model.to(__lowerCamelCase )
del checkpoint, state_dict
return model
def lowerCamelCase ( __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Any=False , __lowerCamelCase : Union[str, Any]="text" ) ->Tuple:
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
_SCREAMING_SNAKE_CASE = """cpu""" # do conversion on cpu
_SCREAMING_SNAKE_CASE = _get_ckpt_path(__lowerCamelCase , use_small=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = _load_model(__lowerCamelCase , __lowerCamelCase , model_type=__lowerCamelCase , use_small=__lowerCamelCase )
# load bark initial model
_SCREAMING_SNAKE_CASE = _bark_load_model(__lowerCamelCase , """cpu""" , model_type=__lowerCamelCase , use_small=__lowerCamelCase )
if model_type == "text":
_SCREAMING_SNAKE_CASE = bark_model["""model"""]
if model.num_parameters(exclude_embeddings=__lowerCamelCase ) != bark_model.get_num_params():
raise ValueError("""initial and new models don't have the same number of parameters""" )
# check if same output as the bark model
_SCREAMING_SNAKE_CASE = 5
_SCREAMING_SNAKE_CASE = 10
if model_type in ["text", "coarse"]:
_SCREAMING_SNAKE_CASE = torch.randint(256 , (batch_size, sequence_length) , dtype=torch.int )
_SCREAMING_SNAKE_CASE = bark_model(__lowerCamelCase )[0]
_SCREAMING_SNAKE_CASE = model(__lowerCamelCase )
# take last logits
_SCREAMING_SNAKE_CASE = output_new_model_total.logits[:, [-1], :]
else:
_SCREAMING_SNAKE_CASE = 3
_SCREAMING_SNAKE_CASE = 8
_SCREAMING_SNAKE_CASE = torch.randint(256 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int )
_SCREAMING_SNAKE_CASE = model(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE = bark_model(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE = output_new_model_total.logits
# output difference should come from the difference of self-attention implementation design
if output_new_model.shape != output_old_model.shape:
raise ValueError("""initial and new outputs don't have the same shape""" )
if (output_new_model - output_old_model).abs().max().item() > 1e-3:
raise ValueError("""initial and new outputs are not equal""" )
Path(__lowerCamelCase ).mkdir(exist_ok=__lowerCamelCase )
model.save_pretrained(__lowerCamelCase )
def lowerCamelCase ( __lowerCamelCase : List[str] , __lowerCamelCase : Any , __lowerCamelCase : Dict , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Union[str, Any] , ) ->List[str]:
_SCREAMING_SNAKE_CASE = os.path.join(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE = BarkSemanticConfig.from_pretrained(os.path.join(__lowerCamelCase , """config.json""" ) )
_SCREAMING_SNAKE_CASE = BarkCoarseConfig.from_pretrained(os.path.join(__lowerCamelCase , """config.json""" ) )
_SCREAMING_SNAKE_CASE = BarkFineConfig.from_pretrained(os.path.join(__lowerCamelCase , """config.json""" ) )
_SCREAMING_SNAKE_CASE = EncodecConfig.from_pretrained("""facebook/encodec_24khz""" )
_SCREAMING_SNAKE_CASE = BarkSemanticModel.from_pretrained(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = BarkCoarseModel.from_pretrained(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = BarkFineModel.from_pretrained(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = EncodecModel.from_pretrained("""facebook/encodec_24khz""" )
_SCREAMING_SNAKE_CASE = BarkConfig.from_sub_model_configs(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config )
_SCREAMING_SNAKE_CASE = BarkModel(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = semantic
_SCREAMING_SNAKE_CASE = coarseAcoustic
_SCREAMING_SNAKE_CASE = fineAcoustic
_SCREAMING_SNAKE_CASE = codec
_SCREAMING_SNAKE_CASE = bark_generation_config
Path(__lowerCamelCase ).mkdir(exist_ok=__lowerCamelCase )
bark.save_pretrained(__lowerCamelCase , repo_id=__lowerCamelCase , push_to_hub=__lowerCamelCase )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""model_type""", type=str, help="""text, coarse or fine.""")
parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--is_small""", action="""store_true""", help="""convert the small version instead of the large.""")
lowercase_ = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
| 58 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class _snake_case :
@staticmethod
def SCREAMING_SNAKE_CASE ( *_a , **_a ):
pass
def lowerCAmelCase_ ( _snake_case : Image ) -> str:
'''simple docstring'''
__magic_name__ : Optional[int] = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def lowerCAmelCase_ ( _snake_case : Image ) -> Dict:
'''simple docstring'''
__magic_name__ : List[Any] = np.array(_snake_case )
__magic_name__ : Optional[int] = npimg.shape
return {"hash": hashimage(_snake_case ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class _snake_case ( unittest.TestCase ):
UpperCamelCase__ = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
UpperCamelCase__ = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a ):
__magic_name__ : Dict = MaskGenerationPipeline(model=_a , image_processor=_a )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def SCREAMING_SNAKE_CASE ( self , _a , _a ):
pass
@require_tf
@unittest.skip("Image segmentation not implemented in TF" )
def SCREAMING_SNAKE_CASE ( self ):
pass
@slow
@require_torch
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = pipeline("mask-generation" , model="facebook/sam-vit-huge" )
__magic_name__ : str = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg" , points_per_batch=256 )
# Shortening by hashing
__magic_name__ : Dict = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(_a ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
{"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
{"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
{"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
{"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
{"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
{"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
{"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
{"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
{"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
{"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
{"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
{"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
{"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
{"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
{"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
{"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
{"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
{"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
{"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
{"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
{"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
{"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
{"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
{"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
{"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
{"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
{"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
{"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
{"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
] , )
# fmt: on
@require_torch
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : str = "facebook/sam-vit-huge"
__magic_name__ : str = pipeline("mask-generation" , model=_a )
__magic_name__ : Tuple = image_segmenter(
"http://images.cocodataset.org/val2017/000000039769.jpg" , pred_iou_thresh=1 , points_per_batch=256 )
# Shortening by hashing
__magic_name__ : Any = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(_a ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
{"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
{"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
{"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
{"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
] , )
| 281 | 0 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"""tensor(bool)""": np.bool_,
"""tensor(int8)""": np.inta,
"""tensor(uint8)""": np.uinta,
"""tensor(int16)""": np.intaa,
"""tensor(uint16)""": np.uintaa,
"""tensor(int32)""": np.intaa,
"""tensor(uint32)""": np.uintaa,
"""tensor(int64)""": np.intaa,
"""tensor(uint64)""": np.uintaa,
"""tensor(float16)""": np.floataa,
"""tensor(float)""": np.floataa,
"""tensor(double)""": np.floataa,
}
class UpperCAmelCase :
def __init__(self : Optional[Any] , snake_case__ : Optional[Any]=None , **snake_case__ : Optional[Any] ) -> List[str]:
'''simple docstring'''
logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future." )
snake_case : Optional[Any] = model
snake_case : Dict = kwargs.get("model_save_dir" , snake_case__ )
snake_case : int = kwargs.get("latest_model_name" , snake_case__ )
def __call__(self : Tuple , **snake_case__ : str ) -> List[str]:
'''simple docstring'''
snake_case : Union[str, Any] = {k: np.array(snake_case__ ) for k, v in kwargs.items()}
return self.model.run(snake_case__ , snake_case__ )
@staticmethod
def _SCREAMING_SNAKE_CASE (snake_case__ : Union[str, Path] , snake_case__ : Optional[int]=None , snake_case__ : Optional[int]=None ) -> Any:
'''simple docstring'''
if provider is None:
logger.info("No onnxruntime provider specified, using CPUExecutionProvider" )
snake_case : Optional[int] = "CPUExecutionProvider"
return ort.InferenceSession(snake_case__ , providers=[provider] , sess_options=snake_case__ )
def _SCREAMING_SNAKE_CASE (self : List[Any] , snake_case__ : Union[str, Path] , snake_case__ : Optional[str] = None , **snake_case__ : Any ) -> List[Any]:
'''simple docstring'''
snake_case : Tuple = file_name if file_name is not None else ONNX_WEIGHTS_NAME
snake_case : Any = self.model_save_dir.joinpath(self.latest_model_name )
snake_case : str = Path(snake_case__ ).joinpath(snake_case__ )
try:
shutil.copyfile(snake_case__ , snake_case__ )
except shutil.SameFileError:
pass
# copy external weights (for models >2GB)
snake_case : List[str] = self.model_save_dir.joinpath(snake_case__ )
if src_path.exists():
snake_case : Tuple = Path(snake_case__ ).joinpath(snake_case__ )
try:
shutil.copyfile(snake_case__ , snake_case__ )
except shutil.SameFileError:
pass
def _SCREAMING_SNAKE_CASE (self : str , snake_case__ : Union[str, os.PathLike] , **snake_case__ : Optional[int] , ) -> str:
'''simple docstring'''
if os.path.isfile(snake_case__ ):
logger.error(f"""Provided path ({save_directory}) should be a directory, not a file""" )
return
os.makedirs(snake_case__ , exist_ok=snake_case__ )
# saving model weights/files
self._save_pretrained(snake_case__ , **snake_case__ )
@classmethod
def _SCREAMING_SNAKE_CASE (cls : Tuple , snake_case__ : Union[str, Path] , snake_case__ : Optional[Union[bool, str, None]] = None , snake_case__ : Optional[Union[str, None]] = None , snake_case__ : bool = False , snake_case__ : Optional[str] = None , snake_case__ : Optional[str] = None , snake_case__ : Optional[str] = None , snake_case__ : Optional["ort.SessionOptions"] = None , **snake_case__ : Tuple , ) -> Tuple:
'''simple docstring'''
snake_case : List[str] = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(snake_case__ ):
snake_case : Any = OnnxRuntimeModel.load_model(
os.path.join(snake_case__ , snake_case__ ) , provider=snake_case__ , sess_options=snake_case__ )
snake_case : Union[str, Any] = Path(snake_case__ )
# load model from hub
else:
# download model
snake_case : Dict = hf_hub_download(
repo_id=snake_case__ , filename=snake_case__ , use_auth_token=snake_case__ , revision=snake_case__ , cache_dir=snake_case__ , force_download=snake_case__ , )
snake_case : List[Any] = Path(snake_case__ ).parent
snake_case : Union[str, Any] = Path(snake_case__ ).name
snake_case : Dict = OnnxRuntimeModel.load_model(snake_case__ , provider=snake_case__ , sess_options=snake_case__ )
return cls(model=snake_case__ , **snake_case__ )
@classmethod
def _SCREAMING_SNAKE_CASE (cls : Optional[Any] , snake_case__ : Union[str, Path] , snake_case__ : bool = True , snake_case__ : Optional[str] = None , snake_case__ : Optional[str] = None , **snake_case__ : Dict , ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Dict = None
if len(str(snake_case__ ).split("@" ) ) == 2:
snake_case , snake_case : int = model_id.split("@" )
return cls._from_pretrained(
model_id=snake_case__ , revision=snake_case__ , cache_dir=snake_case__ , force_download=snake_case__ , use_auth_token=snake_case__ , **snake_case__ , )
| 59 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
snake_case : List[Any] = "\\n@inproceedings{lin-2004-rouge,\n title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n author = \"Lin, Chin-Yew\",\n booktitle = \"Text Summarization Branches Out\",\n month = jul,\n year = \"2004\",\n address = \"Barcelona, Spain\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W04-1013\",\n pages = \"74--81\",\n}\n"
snake_case : Any = "\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"
snake_case : str = "\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n rouge_types: A list of rouge types to calculate.\n Valid names:\n `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n `\"rougeL\"`: Longest common subsequence based scoring.\n `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n See details in https://github.com/huggingface/datasets/issues/617\n use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n use_aggregator: Return aggregates if this is set to True\nReturns:\n rouge1: rouge_1 (precision, recall, f1),\n rouge2: rouge_2 (precision, recall, f1),\n rougeL: rouge_l (precision, recall, f1),\n rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n >>> rouge = datasets.load_metric('rouge')\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> results = rouge.compute(predictions=predictions, references=references)\n >>> print(list(results.keys()))\n ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n >>> print(results[\"rouge1\"])\n AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n >>> print(results[\"rouge1\"].mid.fmeasure)\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _snake_case ( datasets.Metric ):
def SCREAMING_SNAKE_CASE ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"] , reference_urls=[
"https://en.wikipedia.org/wiki/ROUGE_(metric)",
"https://github.com/google-research/google-research/tree/master/rouge",
] , )
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a=None , _a=True , _a=False ):
if rouge_types is None:
__magic_name__ : str = ["rouge1", "rouge2", "rougeL", "rougeLsum"]
__magic_name__ : List[str] = rouge_scorer.RougeScorer(rouge_types=_a , use_stemmer=_a )
if use_aggregator:
__magic_name__ : Dict = scoring.BootstrapAggregator()
else:
__magic_name__ : str = []
for ref, pred in zip(_a , _a ):
__magic_name__ : Union[str, Any] = scorer.score(_a , _a )
if use_aggregator:
aggregator.add_scores(_a )
else:
scores.append(_a )
if use_aggregator:
__magic_name__ : Any = aggregator.aggregate()
else:
__magic_name__ : List[Any] = {}
for key in scores[0]:
__magic_name__ : str = [score[key] for score in scores]
return result
| 281 | 0 |
"""simple docstring"""
snake_case__ : str = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
snake_case__ : Optional[Any] = [
999,
976,
952,
928,
905,
882,
858,
857,
810,
762,
715,
714,
572,
429,
428,
286,
285,
238,
190,
143,
142,
118,
95,
71,
47,
24,
0,
]
snake_case__ : Any = [
999,
988,
977,
966,
955,
944,
933,
922,
911,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
350,
300,
299,
266,
233,
200,
199,
179,
159,
140,
120,
100,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
snake_case__ : Optional[Any] = [
999,
995,
992,
989,
985,
981,
978,
975,
971,
967,
964,
961,
957,
956,
951,
947,
942,
937,
933,
928,
923,
919,
914,
913,
908,
903,
897,
892,
887,
881,
876,
871,
870,
864,
858,
852,
846,
840,
834,
828,
827,
820,
813,
806,
799,
792,
785,
784,
777,
770,
763,
756,
749,
742,
741,
733,
724,
716,
707,
699,
698,
688,
677,
666,
656,
655,
645,
634,
623,
613,
612,
598,
584,
570,
569,
555,
541,
527,
526,
505,
484,
483,
462,
440,
439,
396,
395,
352,
351,
308,
307,
264,
263,
220,
219,
176,
132,
88,
44,
0,
]
snake_case__ : int = [
999,
997,
995,
992,
990,
988,
986,
984,
981,
979,
977,
975,
972,
970,
968,
966,
964,
961,
959,
957,
956,
954,
951,
949,
946,
944,
941,
939,
936,
934,
931,
929,
926,
924,
921,
919,
916,
914,
913,
910,
907,
905,
902,
899,
896,
893,
891,
888,
885,
882,
879,
877,
874,
871,
870,
867,
864,
861,
858,
855,
852,
849,
846,
843,
840,
837,
834,
831,
828,
827,
824,
821,
817,
814,
811,
808,
804,
801,
798,
795,
791,
788,
785,
784,
780,
777,
774,
770,
766,
763,
760,
756,
752,
749,
746,
742,
741,
737,
733,
730,
726,
722,
718,
714,
710,
707,
703,
699,
698,
694,
690,
685,
681,
677,
673,
669,
664,
660,
656,
655,
650,
646,
641,
636,
632,
627,
622,
618,
613,
612,
607,
602,
596,
591,
586,
580,
575,
570,
569,
563,
557,
551,
545,
539,
533,
527,
526,
519,
512,
505,
498,
491,
484,
483,
474,
466,
457,
449,
440,
439,
428,
418,
407,
396,
395,
381,
366,
352,
351,
330,
308,
307,
286,
264,
263,
242,
220,
219,
176,
175,
132,
131,
88,
44,
0,
]
snake_case__ : Union[str, Any] = [
999,
991,
982,
974,
966,
958,
950,
941,
933,
925,
916,
908,
900,
899,
874,
850,
825,
800,
799,
700,
600,
500,
400,
300,
200,
100,
0,
]
snake_case__ : List[Any] = [
999,
992,
985,
978,
971,
964,
957,
949,
942,
935,
928,
921,
914,
907,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
300,
299,
200,
199,
100,
99,
0,
]
snake_case__ : Optional[int] = [
999,
996,
992,
989,
985,
982,
979,
975,
972,
968,
965,
961,
958,
955,
951,
948,
944,
941,
938,
934,
931,
927,
924,
920,
917,
914,
910,
907,
903,
900,
899,
891,
884,
876,
869,
861,
853,
846,
838,
830,
823,
815,
808,
800,
799,
788,
777,
766,
755,
744,
733,
722,
711,
700,
699,
688,
677,
666,
655,
644,
633,
622,
611,
600,
599,
585,
571,
557,
542,
528,
514,
500,
499,
485,
471,
457,
442,
428,
414,
400,
399,
379,
359,
340,
320,
300,
299,
279,
259,
240,
220,
200,
199,
166,
133,
100,
99,
66,
33,
0,
]
| 60 |
snake_case : Optional[int] = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowerCAmelCase_ ( _snake_case : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(_snake_case , _snake_case ):
__magic_name__ : Tuple = F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
raise TypeError(_snake_case )
__magic_name__ : Optional[int] = "".join(bin(_snake_case )[2:].zfill(8 ) for byte in data )
__magic_name__ : List[Any] = len(_snake_case ) % 6 != 0
if padding_needed:
# The padding that will be added later
__magic_name__ : List[str] = B"=" * ((6 - len(_snake_case ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(_snake_case ) % 6)
else:
__magic_name__ : List[str] = B""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(_snake_case ) , 6 ) ).encode()
+ padding
)
def lowerCAmelCase_ ( _snake_case : str ) -> bytes:
'''simple docstring'''
if not isinstance(_snake_case , _snake_case ) and not isinstance(_snake_case , _snake_case ):
__magic_name__ : List[str] = (
"argument should be a bytes-like object or ASCII string, "
F'''not \'{encoded_data.__class__.__name__}\''''
)
raise TypeError(_snake_case )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(_snake_case , _snake_case ):
try:
__magic_name__ : List[Any] = encoded_data.decode("utf-8" )
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters" )
__magic_name__ : List[str] = encoded_data.count("=" )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(_snake_case ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
__magic_name__ : Optional[int] = encoded_data[:-padding]
__magic_name__ : Dict = "".join(
bin(B64_CHARSET.index(_snake_case ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
__magic_name__ : Union[str, Any] = "".join(
bin(B64_CHARSET.index(_snake_case ) )[2:].zfill(6 ) for char in encoded_data )
__magic_name__ : List[Any] = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(_snake_case ) , 8 )
]
return bytes(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 281 | 0 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class A_ (lowercase__ ,lowercase__ ,unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = StableDiffusionDiffEditPipeline
SCREAMING_SNAKE_CASE__ : Optional[Any] = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
SCREAMING_SNAKE_CASE__ : Optional[int] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
SCREAMING_SNAKE_CASE__ : str = frozenset([] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
UpperCAmelCase_ : str = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=lowercase_ , )
UpperCAmelCase_ : str = DDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule="scaled_linear" , clip_sample=lowercase_ , set_alpha_to_one=lowercase_ , )
UpperCAmelCase_ : str = DDIMInverseScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule="scaled_linear" , clip_sample=lowercase_ , set_alpha_to_zero=lowercase_ , )
torch.manual_seed(0 )
UpperCAmelCase_ : str = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
UpperCAmelCase_ : int = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="gelu" , projection_dim=512 , )
UpperCAmelCase_ : Optional[int] = CLIPTextModel(lowercase_ )
UpperCAmelCase_ : Union[str, Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
UpperCAmelCase_ : List[Any] = {
"unet": unet,
"scheduler": scheduler,
"inverse_scheduler": inverse_scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def UpperCamelCase__ ( self , lowercase_ , lowercase_=0 ):
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = floats_tensor((1, 16, 16) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
UpperCAmelCase_ : int = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
if str(lowercase_ ).startswith("mps" ):
UpperCAmelCase_ : List[Any] = torch.manual_seed(lowercase_ )
else:
UpperCAmelCase_ : Optional[int] = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
UpperCAmelCase_ : Tuple = {
"prompt": "a dog and a newt",
"mask_image": mask,
"image_latents": latents,
"generator": generator,
"num_inference_steps": 2,
"inpaint_strength": 1.0,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def UpperCamelCase__ ( self , lowercase_ , lowercase_=0 ):
"""simple docstring"""
UpperCAmelCase_ : Any = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
UpperCAmelCase_ : List[str] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCAmelCase_ : Optional[int] = Image.fromarray(np.uinta(lowercase_ ) ).convert("RGB" )
if str(lowercase_ ).startswith("mps" ):
UpperCAmelCase_ : int = torch.manual_seed(lowercase_ )
else:
UpperCAmelCase_ : Optional[Any] = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
UpperCAmelCase_ : Tuple = {
"image": image,
"source_prompt": "a cat and a frog",
"target_prompt": "a dog and a newt",
"generator": generator,
"num_inference_steps": 2,
"num_maps_per_mask": 2,
"mask_encode_strength": 1.0,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def UpperCamelCase__ ( self , lowercase_ , lowercase_=0 ):
"""simple docstring"""
UpperCAmelCase_ : int = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
UpperCAmelCase_ : int = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCAmelCase_ : Dict = Image.fromarray(np.uinta(lowercase_ ) ).convert("RGB" )
if str(lowercase_ ).startswith("mps" ):
UpperCAmelCase_ : Union[str, Any] = torch.manual_seed(lowercase_ )
else:
UpperCAmelCase_ : str = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
UpperCAmelCase_ : Union[str, Any] = {
"image": image,
"prompt": "a cat and a frog",
"generator": generator,
"num_inference_steps": 2,
"inpaint_strength": 1.0,
"guidance_scale": 6.0,
"decode_latents": True,
"output_type": "numpy",
}
return inputs
def UpperCamelCase__ ( self ):
"""simple docstring"""
if not hasattr(self.pipeline_class , "_optional_components" ):
return
UpperCAmelCase_ : str = self.get_dummy_components()
UpperCAmelCase_ : List[str] = self.pipeline_class(**lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(lowercase_ , lowercase_ , lowercase_ )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
UpperCAmelCase_ : List[Any] = self.get_dummy_inputs(lowercase_ )
UpperCAmelCase_ : Dict = pipe(**lowercase_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowercase_ )
UpperCAmelCase_ : Optional[int] = self.pipeline_class.from_pretrained(lowercase_ )
pipe_loaded.to(lowercase_ )
pipe_loaded.set_progress_bar_config(disable=lowercase_ )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(lowercase_ , lowercase_ ) is None , F"""`{optional_component}` did not stay set to None after loading.""" , )
UpperCAmelCase_ : Union[str, Any] = self.get_dummy_inputs(lowercase_ )
UpperCAmelCase_ : Optional[Any] = pipe_loaded(**lowercase_ )[0]
UpperCAmelCase_ : Any = np.abs(output - output_loaded ).max()
self.assertLess(lowercase_ , 1E-4 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Dict = "cpu"
UpperCAmelCase_ : List[Any] = self.get_dummy_components()
UpperCAmelCase_ : Any = self.pipeline_class(**lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase_ : Dict = self.get_dummy_mask_inputs(lowercase_ )
UpperCAmelCase_ : str = pipe.generate_mask(**lowercase_ )
UpperCAmelCase_ : Optional[Any] = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
UpperCAmelCase_ : Union[str, Any] = np.array([0] * 9 )
UpperCAmelCase_ : Dict = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase_ , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = "cpu"
UpperCAmelCase_ : str = self.get_dummy_components()
UpperCAmelCase_ : Optional[int] = self.pipeline_class(**lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase_ : Dict = self.get_dummy_inversion_inputs(lowercase_ )
UpperCAmelCase_ : Optional[Any] = pipe.invert(**lowercase_ ).images
UpperCAmelCase_ : Union[str, Any] = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
UpperCAmelCase_ : Optional[int] = np.array(
[0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99] , )
UpperCAmelCase_ : Optional[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase_ , 1E-3 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : int = "cpu"
UpperCAmelCase_ : Optional[int] = self.get_dummy_components()
UpperCAmelCase_ : Optional[Any] = {"beta_start": 0.0_00_85, "beta_end": 0.0_12, "beta_schedule": "scaled_linear"}
UpperCAmelCase_ : Any = DPMSolverMultistepScheduler(**lowercase_ )
UpperCAmelCase_ : Dict = DPMSolverMultistepInverseScheduler(**lowercase_ )
UpperCAmelCase_ : Optional[int] = self.pipeline_class(**lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase_ : str = self.get_dummy_inversion_inputs(lowercase_ )
UpperCAmelCase_ : Any = pipe.invert(**lowercase_ ).images
UpperCAmelCase_ : str = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
UpperCAmelCase_ : Optional[Any] = np.array(
[0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99] , )
UpperCAmelCase_ : Optional[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase_ , 1E-3 )
@require_torch_gpu
@slow
class A_ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def UpperCamelCase__ ( cls ):
"""simple docstring"""
UpperCAmelCase_ : str = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png" )
UpperCAmelCase_ : int = raw_image.convert("RGB" ).resize((768, 768) )
UpperCAmelCase_ : List[Any] = raw_image
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Tuple = torch.manual_seed(0 )
UpperCAmelCase_ : int = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=lowercase_ , torch_dtype=torch.floataa )
UpperCAmelCase_ : List[str] = DDIMScheduler.from_config(pipe.scheduler.config )
UpperCAmelCase_ : Union[str, Any] = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase_ : Optional[Any] = "a bowl of fruit"
UpperCAmelCase_ : Any = "a bowl of pears"
UpperCAmelCase_ : List[str] = pipe.generate_mask(
image=self.raw_image , source_prompt=lowercase_ , target_prompt=lowercase_ , generator=lowercase_ , )
UpperCAmelCase_ : List[Any] = pipe.invert(
prompt=lowercase_ , image=self.raw_image , inpaint_strength=0.7 , generator=lowercase_ ).latents
UpperCAmelCase_ : Tuple = pipe(
prompt=lowercase_ , mask_image=lowercase_ , image_latents=lowercase_ , generator=lowercase_ , negative_prompt=lowercase_ , inpaint_strength=0.7 , output_type="numpy" , ).images[0]
UpperCAmelCase_ : List[Any] = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Tuple = torch.manual_seed(0 )
UpperCAmelCase_ : Optional[Any] = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=lowercase_ , torch_dtype=torch.floataa )
UpperCAmelCase_ : Any = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
UpperCAmelCase_ : List[Any] = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase_ : List[str] = "a bowl of fruit"
UpperCAmelCase_ : Optional[Any] = "a bowl of pears"
UpperCAmelCase_ : List[Any] = pipe.generate_mask(
image=self.raw_image , source_prompt=lowercase_ , target_prompt=lowercase_ , generator=lowercase_ , )
UpperCAmelCase_ : Tuple = pipe.invert(
prompt=lowercase_ , image=self.raw_image , inpaint_strength=0.7 , generator=lowercase_ , num_inference_steps=25 , ).latents
UpperCAmelCase_ : List[str] = pipe(
prompt=lowercase_ , mask_image=lowercase_ , image_latents=lowercase_ , generator=lowercase_ , negative_prompt=lowercase_ , inpaint_strength=0.7 , num_inference_steps=25 , output_type="numpy" , ).images[0]
UpperCAmelCase_ : List[Any] = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 61 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class _snake_case ( unittest.TestCase ):
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ):
__magic_name__ : List[Any] = parent
__magic_name__ : Optional[Any] = batch_size
__magic_name__ : Dict = seq_length
__magic_name__ : Union[str, Any] = is_training
__magic_name__ : Optional[Any] = use_attention_mask
__magic_name__ : Optional[Any] = use_token_type_ids
__magic_name__ : int = use_labels
__magic_name__ : List[Any] = vocab_size
__magic_name__ : Union[str, Any] = hidden_size
__magic_name__ : Optional[Any] = num_hidden_layers
__magic_name__ : int = num_attention_heads
__magic_name__ : Any = intermediate_size
__magic_name__ : List[Any] = hidden_act
__magic_name__ : List[Any] = hidden_dropout_prob
__magic_name__ : Optional[int] = attention_probs_dropout_prob
__magic_name__ : List[Any] = max_position_embeddings
__magic_name__ : Tuple = type_vocab_size
__magic_name__ : List[str] = type_sequence_label_size
__magic_name__ : Dict = initializer_range
__magic_name__ : List[Any] = num_choices
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__magic_name__ : List[Any] = None
if self.use_attention_mask:
__magic_name__ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
__magic_name__ : str = None
if self.use_token_type_ids:
__magic_name__ : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__magic_name__ : List[str] = RobertaPreLayerNormConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : int = self.prepare_config_and_inputs()
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : List[Any] = config_and_inputs
__magic_name__ : List[str] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = self.prepare_config_and_inputs()
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] = config_and_inputs
__magic_name__ : Tuple = True
__magic_name__ : int = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__magic_name__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class _snake_case ( snake_case , unittest.TestCase ):
UpperCamelCase__ = True
UpperCamelCase__ = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[Any] = FlaxRobertaPreLayerNormModelTester(self )
@slow
def SCREAMING_SNAKE_CASE ( self ):
for model_class_name in self.all_model_classes:
__magic_name__ : Optional[Any] = model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_a )
__magic_name__ : Dict = model(np.ones((1, 1) ) )
self.assertIsNotNone(_a )
@require_flax
class _snake_case ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_a )
__magic_name__ : Union[str, Any] = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__magic_name__ : List[str] = model(_a )[0]
__magic_name__ : str = [1, 11, 50_265]
self.assertEqual(list(output.shape ) , _a )
# compare the actual values for a slice.
__magic_name__ : List[str] = np.array(
[[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _a , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_a )
__magic_name__ : Tuple = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__magic_name__ : Tuple = model(_a )[0]
# compare the actual values for a slice.
__magic_name__ : Dict = np.array(
[[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _a , atol=1e-4 ) )
| 281 | 0 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , A_ ) -> Any:
__UpperCamelCase =parent
def _a ( self ) -> Optional[Any]:
return {}
def _UpperCAmelCase ( ):
__UpperCamelCase ='<HTML>\n\n <HEAD>\n <TITLE>sample document</TITLE>\n </HEAD>\n\n <BODY BGCOLOR="FFFFFF">\n <HR>\n <a href="http://google.com">Goog</a>\n <H1>This is one header</H1>\n <H2>This is a another Header</H2>\n <P>Travel from\n <P>\n <B>SFO to JFK</B>\n <BR>\n <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n <HR>\n <div style="color:#0000FF">\n <h3>Traveler <b> name </b> is\n <p> John Doe </p>\n </div>'
__UpperCamelCase ='\n <!DOCTYPE html>\n <html>\n <body>\n\n <h1>My First Heading</h1>\n <p>My first paragraph.</p>\n\n </body>\n </html>\n '
return [html_string_a, html_string_a]
@require_bsa
class UpperCAmelCase__ ( A_ , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : int = MarkupLMFeatureExtractor if is_bsa_available() else None
def _a ( self ) -> str:
__UpperCamelCase =MarkupLMFeatureExtractionTester(self )
@property
def _a ( self ) -> Union[str, Any]:
return self.feature_extract_tester.prepare_feat_extract_dict()
def _a ( self ) -> Tuple:
# Initialize feature_extractor
__UpperCamelCase =self.feature_extraction_class()
# Test not batched input
__UpperCamelCase =get_html_strings()[0]
__UpperCamelCase =feature_extractor(A_ )
# fmt: off
__UpperCamelCase =[['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
__UpperCamelCase =[['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
# fmt: on
self.assertEqual(encoding.nodes , A_ )
self.assertEqual(encoding.xpaths , A_ )
# Test batched
__UpperCamelCase =get_html_strings()
__UpperCamelCase =feature_extractor(A_ )
# fmt: off
__UpperCamelCase =expected_nodes + [['My First Heading', 'My first paragraph.']]
__UpperCamelCase =expected_xpaths + [['/html/body/h1', '/html/body/p']]
self.assertEqual(len(encoding.nodes ) , 2 )
self.assertEqual(len(encoding.xpaths ) , 2 )
self.assertEqual(encoding.nodes , A_ )
self.assertEqual(encoding.xpaths , A_ )
| 62 |
def lowerCAmelCase_ ( _snake_case : list[list[int | float]] ) -> int:
'''simple docstring'''
__magic_name__ : Any = len(_snake_case )
__magic_name__ : Optional[Any] = len(matrix[0] )
__magic_name__ : Union[str, Any] = min(_snake_case , _snake_case )
for row in range(_snake_case ):
# Check if diagonal element is not zero
if matrix[row][row] != 0:
# Eliminate all the elements below the diagonal
for col in range(row + 1 , _snake_case ):
__magic_name__ : Optional[Any] = matrix[col][row] / matrix[row][row]
for i in range(_snake_case , _snake_case ):
matrix[col][i] -= multiplier * matrix[row][i]
else:
# Find a non-zero diagonal element to swap rows
__magic_name__ : str = True
for i in range(row + 1 , _snake_case ):
if matrix[i][row] != 0:
__magic_name__ , __magic_name__ : List[str] = matrix[i], matrix[row]
__magic_name__ : Union[str, Any] = False
break
if reduce:
rank -= 1
for i in range(_snake_case ):
__magic_name__ : Any = matrix[i][rank]
# Reduce the row pointer by one to stay on the same row
row -= 1
return rank
if __name__ == "__main__":
import doctest
doctest.testmod()
| 281 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : Any = logging.get_logger(__name__)
lowerCAmelCase_ : Any = {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
),
'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
'cl-tohoku/bert-base-japanese-whole-word-masking': (
'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
),
'cl-tohoku/bert-base-japanese-char': (
'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
),
'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
),
'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
# See all BERT models at https://huggingface.co/models?filter=bert
}
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
__a ='bert'
def __init__( self : Dict , __a : Dict=3_05_22 , __a : int=7_68 , __a : Any=12 , __a : Tuple=12 , __a : List[str]=30_72 , __a : int="gelu" , __a : List[str]=0.1 , __a : Union[str, Any]=0.1 , __a : str=5_12 , __a : Any=2 , __a : Union[str, Any]=0.02 , __a : int=1e-1_2 , __a : Tuple=0 , __a : Tuple="absolute" , __a : Optional[Any]=True , __a : Optional[Any]=None , **__a : List[Any] , ):
super().__init__(pad_token_id=__a , **__a )
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = hidden_act
_a = intermediate_size
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = initializer_range
_a = layer_norm_eps
_a = position_embedding_type
_a = use_cache
_a = classifier_dropout
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
@property
def UpperCamelCase__ ( self : Dict ):
if self.task == "multiple-choice":
_a = {0: "batch", 1: "choice", 2: "sequence"}
else:
_a = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 63 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
snake_case : Dict = re.compile(R"\b(a|an|the)\b", re.UNICODE)
snake_case : Optional[int] = None
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : Any = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0." )
parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file." )
parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions." )
parser.add_argument(
"--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout)." )
parser.add_argument(
"--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer." )
parser.add_argument(
"--na-prob-thresh" , "-t" , type=_snake_case , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , )
parser.add_argument(
"--out-image-dir" , "-p" , metavar="out_images" , default=_snake_case , help="Save precision-recall curves to directory." )
parser.add_argument("--verbose" , "-v" , action="store_true" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def lowerCAmelCase_ ( _snake_case : Optional[Any] ) -> Tuple:
'''simple docstring'''
__magic_name__ : Optional[int] = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
__magic_name__ : str = bool(qa["answers"]["text"] )
return qid_to_has_ans
def lowerCAmelCase_ ( _snake_case : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
def remove_articles(_snake_case : List[str] ):
return ARTICLES_REGEX.sub(" " , _snake_case )
def white_space_fix(_snake_case : Optional[int] ):
return " ".join(text.split() )
def remove_punc(_snake_case : Optional[int] ):
__magic_name__ : Dict = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_snake_case : str ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_snake_case ) ) ) )
def lowerCAmelCase_ ( _snake_case : Any ) -> Optional[Any]:
'''simple docstring'''
if not s:
return []
return normalize_answer(_snake_case ).split()
def lowerCAmelCase_ ( _snake_case : str , _snake_case : Dict ) -> Tuple:
'''simple docstring'''
return int(normalize_answer(_snake_case ) == normalize_answer(_snake_case ) )
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : int ) -> str:
'''simple docstring'''
__magic_name__ : Any = get_tokens(_snake_case )
__magic_name__ : Optional[int] = get_tokens(_snake_case )
__magic_name__ : Tuple = collections.Counter(_snake_case ) & collections.Counter(_snake_case )
__magic_name__ : Tuple = sum(common.values() )
if len(_snake_case ) == 0 or len(_snake_case ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
__magic_name__ : Dict = 1.0 * num_same / len(_snake_case )
__magic_name__ : Optional[Any] = 1.0 * num_same / len(_snake_case )
__magic_name__ : List[Any] = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( _snake_case : Optional[Any] , _snake_case : List[Any] ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = {}
__magic_name__ : int = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
__magic_name__ : Union[str, Any] = qa["id"]
__magic_name__ : Any = [t for t in qa["answers"]["text"] if normalize_answer(_snake_case )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
__magic_name__ : Tuple = [""]
if qid not in preds:
print(F'''Missing prediction for {qid}''' )
continue
__magic_name__ : Any = preds[qid]
# Take max over all gold answers
__magic_name__ : List[Any] = max(compute_exact(_snake_case , _snake_case ) for a in gold_answers )
__magic_name__ : int = max(compute_fa(_snake_case , _snake_case ) for a in gold_answers )
return exact_scores, fa_scores
def lowerCAmelCase_ ( _snake_case : Optional[Any] , _snake_case : List[Any] , _snake_case : Optional[int] , _snake_case : Dict ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : str = {}
for qid, s in scores.items():
__magic_name__ : Dict = na_probs[qid] > na_prob_thresh
if pred_na:
__magic_name__ : str = float(not qid_to_has_ans[qid] )
else:
__magic_name__ : Optional[int] = s
return new_scores
def lowerCAmelCase_ ( _snake_case : List[Any] , _snake_case : List[str] , _snake_case : Tuple=None ) -> Tuple:
'''simple docstring'''
if not qid_list:
__magic_name__ : Any = len(_snake_case )
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores.values() ) / total),
("f1", 100.0 * sum(fa_scores.values() ) / total),
("total", total),
] )
else:
__magic_name__ : Tuple = len(_snake_case )
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
("f1", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
("total", total),
] )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : str , _snake_case : str ) -> Dict:
'''simple docstring'''
for k in new_eval:
__magic_name__ : int = new_eval[k]
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Dict , _snake_case : Optional[Any] , _snake_case : Union[str, Any] ) -> str:
'''simple docstring'''
plt.step(_snake_case , _snake_case , color="b" , alpha=0.2 , where="post" )
plt.fill_between(_snake_case , _snake_case , step="post" , alpha=0.2 , color="b" )
plt.xlabel("Recall" )
plt.ylabel("Precision" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(_snake_case )
plt.savefig(_snake_case )
plt.clf()
def lowerCAmelCase_ ( _snake_case : Dict , _snake_case : Any , _snake_case : Optional[int] , _snake_case : List[Any] , _snake_case : Optional[int]=None , _snake_case : int=None ) -> str:
'''simple docstring'''
__magic_name__ : Union[str, Any] = sorted(_snake_case , key=lambda _snake_case : na_probs[k] )
__magic_name__ : Optional[int] = 0.0
__magic_name__ : str = 1.0
__magic_name__ : str = 0.0
__magic_name__ : List[str] = [1.0]
__magic_name__ : str = [0.0]
__magic_name__ : Optional[Any] = 0.0
for i, qid in enumerate(_snake_case ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
__magic_name__ : List[str] = true_pos / float(i + 1 )
__magic_name__ : Any = true_pos / float(_snake_case )
if i == len(_snake_case ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(_snake_case )
recalls.append(_snake_case )
if out_image:
plot_pr_curve(_snake_case , _snake_case , _snake_case , _snake_case )
return {"ap": 100.0 * avg_prec}
def lowerCAmelCase_ ( _snake_case : Tuple , _snake_case : Optional[Any] , _snake_case : Optional[int] , _snake_case : Optional[Any] , _snake_case : Any , _snake_case : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
if out_image_dir and not os.path.exists(_snake_case ):
os.makedirs(_snake_case )
__magic_name__ : Any = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
__magic_name__ : str = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_exact.png" ) , title="Precision-Recall curve for Exact Match score" , )
__magic_name__ : Union[str, Any] = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_f1.png" ) , title="Precision-Recall curve for F1 score" , )
__magic_name__ : str = {k: float(_snake_case ) for k, v in qid_to_has_ans.items()}
__magic_name__ : str = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_oracle.png" ) , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , )
merge_eval(_snake_case , _snake_case , "pr_exact" )
merge_eval(_snake_case , _snake_case , "pr_f1" )
merge_eval(_snake_case , _snake_case , "pr_oracle" )
def lowerCAmelCase_ ( _snake_case : int , _snake_case : Optional[Any] , _snake_case : List[str] , _snake_case : Optional[Any] ) -> Dict:
'''simple docstring'''
if not qid_list:
return
__magic_name__ : Dict = [na_probs[k] for k in qid_list]
__magic_name__ : str = np.ones_like(_snake_case ) / float(len(_snake_case ) )
plt.hist(_snake_case , weights=_snake_case , bins=20 , range=(0.0, 1.0) )
plt.xlabel("Model probability of no-answer" )
plt.ylabel("Proportion of dataset" )
plt.title(F'''Histogram of no-answer probability: {name}''' )
plt.savefig(os.path.join(_snake_case , F'''na_prob_hist_{name}.png''' ) )
plt.clf()
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Tuple , _snake_case : List[str] , _snake_case : Dict ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
__magic_name__ : List[str] = num_no_ans
__magic_name__ : Dict = cur_score
__magic_name__ : Dict = 0.0
__magic_name__ : Any = sorted(_snake_case , key=lambda _snake_case : na_probs[k] )
for i, qid in enumerate(_snake_case ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
__magic_name__ : Union[str, Any] = scores[qid]
else:
if preds[qid]:
__magic_name__ : List[Any] = -1
else:
__magic_name__ : Optional[int] = 0
cur_score += diff
if cur_score > best_score:
__magic_name__ : Optional[int] = cur_score
__magic_name__ : List[Any] = na_probs[qid]
return 100.0 * best_score / len(_snake_case ), best_thresh
def lowerCAmelCase_ ( _snake_case : int , _snake_case : str , _snake_case : List[str] , _snake_case : Tuple , _snake_case : List[Any] , _snake_case : Dict ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ , __magic_name__ : List[str] = find_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case )
__magic_name__ , __magic_name__ : int = find_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case )
__magic_name__ : Optional[int] = best_exact
__magic_name__ : List[Any] = exact_thresh
__magic_name__ : Dict = best_fa
__magic_name__ : Any = fa_thresh
def lowerCAmelCase_ ( ) -> int:
'''simple docstring'''
with open(OPTS.data_file ) as f:
__magic_name__ : Optional[Any] = json.load(_snake_case )
__magic_name__ : List[Any] = dataset_json["data"]
with open(OPTS.pred_file ) as f:
__magic_name__ : Optional[Any] = json.load(_snake_case )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
__magic_name__ : Any = json.load(_snake_case )
else:
__magic_name__ : Any = {k: 0.0 for k in preds}
__magic_name__ : str = make_qid_to_has_ans(_snake_case ) # maps qid to True/False
__magic_name__ : Tuple = [k for k, v in qid_to_has_ans.items() if v]
__magic_name__ : Optional[Any] = [k for k, v in qid_to_has_ans.items() if not v]
__magic_name__ , __magic_name__ : Union[str, Any] = get_raw_scores(_snake_case , _snake_case )
__magic_name__ : Optional[Any] = apply_no_ans_threshold(_snake_case , _snake_case , _snake_case , OPTS.na_prob_thresh )
__magic_name__ : Optional[Any] = apply_no_ans_threshold(_snake_case , _snake_case , _snake_case , OPTS.na_prob_thresh )
__magic_name__ : List[Any] = make_eval_dict(_snake_case , _snake_case )
if has_ans_qids:
__magic_name__ : int = make_eval_dict(_snake_case , _snake_case , qid_list=_snake_case )
merge_eval(_snake_case , _snake_case , "HasAns" )
if no_ans_qids:
__magic_name__ : List[Any] = make_eval_dict(_snake_case , _snake_case , qid_list=_snake_case )
merge_eval(_snake_case , _snake_case , "NoAns" )
if OPTS.na_prob_file:
find_all_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , OPTS.out_image_dir )
histogram_na_prob(_snake_case , _snake_case , OPTS.out_image_dir , "hasAns" )
histogram_na_prob(_snake_case , _snake_case , OPTS.out_image_dir , "noAns" )
if OPTS.out_file:
with open(OPTS.out_file , "w" ) as f:
json.dump(_snake_case , _snake_case )
else:
print(json.dumps(_snake_case , indent=2 ) )
if __name__ == "__main__":
snake_case : int = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
main()
| 281 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowercase( __a , unittest.TestCase ):
'''simple docstring'''
lowercase__ = DiTPipeline
lowercase__ = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
lowercase__ = PipelineTesterMixin.required_optional_params - {
"latents",
"num_images_per_prompt",
"callback",
"callback_steps",
}
lowercase__ = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
lowercase__ = False
def UpperCamelCase_ ( self: Any ):
'''simple docstring'''
torch.manual_seed(0 )
_snake_case : Tuple = TransformeraDModel(
sample_size=16, num_layers=2, patch_size=4, attention_head_dim=8, num_attention_heads=2, in_channels=4, out_channels=8, attention_bias=a_, activation_fn="""gelu-approximate""", num_embeds_ada_norm=1_000, norm_type="""ada_norm_zero""", norm_elementwise_affine=a_, )
_snake_case : List[Any] = AutoencoderKL()
_snake_case : Any = DDIMScheduler()
_snake_case : Optional[Any] = {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
return components
def UpperCamelCase_ ( self: Any, a_: Dict, a_: List[Any]=0 ):
'''simple docstring'''
if str(a_ ).startswith("""mps""" ):
_snake_case : str = torch.manual_seed(a_ )
else:
_snake_case : Dict = torch.Generator(device=a_ ).manual_seed(a_ )
_snake_case : Union[str, Any] = {
"""class_labels""": [1],
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def UpperCamelCase_ ( self: Dict ):
'''simple docstring'''
_snake_case : Tuple = """cpu"""
_snake_case : str = self.get_dummy_components()
_snake_case : Union[str, Any] = self.pipeline_class(**a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
_snake_case : int = self.get_dummy_inputs(a_ )
_snake_case : Any = pipe(**a_ ).images
_snake_case : Tuple = image[0, -3:, -3:, -1]
self.assertEqual(image.shape, (1, 16, 16, 3) )
_snake_case : List[str] = np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457] )
_snake_case : Optional[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(a_, 1E-3 )
def UpperCamelCase_ ( self: Dict ):
'''simple docstring'''
self._test_inference_batch_single_identical(relax_max_difference=a_, expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available(), reason="""XFormers attention is only available with CUDA and `xformers` installed""", )
def UpperCamelCase_ ( self: Any ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class lowercase( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self: Optional[int] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self: str ):
'''simple docstring'''
_snake_case : List[Any] = torch.manual_seed(0 )
_snake_case : Dict = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""" )
pipe.to("""cuda""" )
_snake_case : Optional[Any] = ["""vase""", """umbrella""", """white shark""", """white wolf"""]
_snake_case : List[str] = pipe.get_label_ids(a_ )
_snake_case : List[str] = pipe(a_, generator=a_, num_inference_steps=40, output_type="""np""" ).images
for word, image in zip(a_, a_ ):
_snake_case : Dict = load_numpy(
f"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy" )
assert np.abs((expected_image - image).max() ) < 1E-2
def UpperCamelCase_ ( self: Dict ):
'''simple docstring'''
_snake_case : Dict = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""" )
_snake_case : List[Any] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("""cuda""" )
_snake_case : Dict = ["""vase""", """umbrella"""]
_snake_case : List[str] = pipe.get_label_ids(a_ )
_snake_case : Tuple = torch.manual_seed(0 )
_snake_case : Dict = pipe(a_, generator=a_, num_inference_steps=25, output_type="""np""" ).images
for word, image in zip(a_, a_ ):
_snake_case : int = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
f"/dit/{word}_512.npy" )
assert np.abs((expected_image - image).max() ) < 1E-1
| 64 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
snake_case : str = "▁"
snake_case : List[Any] = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class _snake_case ( snake_case , unittest.TestCase ):
UpperCamelCase__ = BigBirdTokenizer
UpperCamelCase__ = BigBirdTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = True
def SCREAMING_SNAKE_CASE ( self ):
super().setUp()
__magic_name__ : Optional[Any] = self.tokenizer_class(_a , keep_accents=_a )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Union[str, Any] = "<s>"
__magic_name__ : Dict = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<unk>" )
self.assertEqual(vocab_keys[1] , "<s>" )
self.assertEqual(vocab_keys[-1] , "[MASK]" )
self.assertEqual(len(_a ) , 1_004 )
def SCREAMING_SNAKE_CASE ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_000 )
def SCREAMING_SNAKE_CASE ( self ):
if not self.test_rust_tokenizer:
return
__magic_name__ : Dict = self.get_tokenizer()
__magic_name__ : str = self.get_rust_tokenizer()
__magic_name__ : Any = "I was born in 92000, and this is falsé."
__magic_name__ : Dict = tokenizer.tokenize(_a )
__magic_name__ : Any = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
__magic_name__ : List[Any] = tokenizer.encode(_a , add_special_tokens=_a )
__magic_name__ : List[str] = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
__magic_name__ : str = self.get_rust_tokenizer()
__magic_name__ : Dict = tokenizer.encode(_a )
__magic_name__ : Optional[int] = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = BigBirdTokenizer(_a , keep_accents=_a )
__magic_name__ : str = tokenizer.tokenize("This is a test" )
self.assertListEqual(_a , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_a ) , [285, 46, 10, 170, 382] , )
__magic_name__ : Dict = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
__magic_name__ : Union[str, Any] = tokenizer.convert_tokens_to_ids(_a )
self.assertListEqual(
_a , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
__magic_name__ : int = tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
@cached_property
def SCREAMING_SNAKE_CASE ( self ):
return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base" )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Any = "Hello World!"
__magic_name__ : Dict = [65, 18_536, 2_260, 101, 66]
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = (
"This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
)
# fmt: off
__magic_name__ : List[str] = [65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66] # noqa: E231
# fmt: on
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@require_torch
@slow
def SCREAMING_SNAKE_CASE ( self ):
import torch
from transformers import BigBirdConfig, BigBirdModel
# Build sequence
__magic_name__ : Optional[Any] = list(self.big_tokenizer.get_vocab().keys() )[:10]
__magic_name__ : List[Any] = " ".join(_a )
__magic_name__ : Any = self.big_tokenizer.encode_plus(_a , return_tensors="pt" , return_token_type_ids=_a )
__magic_name__ : Union[str, Any] = self.big_tokenizer.batch_encode_plus(
[sequence + " " + sequence] , return_tensors="pt" , return_token_type_ids=_a )
__magic_name__ : List[str] = BigBirdConfig(attention_type="original_full" )
__magic_name__ : Optional[int] = BigBirdModel(_a )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_a )
model(**_a )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : int = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base" )
__magic_name__ : int = tokenizer.decode(tokenizer("Paris is the [MASK]." ).input_ids )
self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]" )
@slow
def SCREAMING_SNAKE_CASE ( self ):
# fmt: off
__magic_name__ : Optional[Any] = {"input_ids": [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_a , model_name="google/bigbird-roberta-base" , revision="215c99f1600e06f83acce68422f2035b2b5c3510" , )
| 281 | 0 |
import math
def lowerCAmelCase_ ( __A ) -> int:
'''simple docstring'''
if not isinstance(__A, __A ):
UpperCAmelCase__ = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__A )
if number < 1:
UpperCAmelCase__ = f"""Input value of [number={number}] must be > 0"""
raise ValueError(__A )
elif number == 1:
return 3
elif number == 2:
return 5
else:
UpperCAmelCase__ = int(math.log(number // 3, 2 ) ) + 2
UpperCAmelCase__ = [3, 5]
UpperCAmelCase__ = 2
UpperCAmelCase__ = 3
for block in range(1, __A ):
for _ in range(__A ):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(1_1):
UpperCamelCase__ = 0
try:
UpperCamelCase__ = proth(number)
except ValueError:
print(f'''ValueError: there is no {number}th Proth number''')
continue
print(f'''The {number}th Proth number: {value}''')
| 65 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
snake_case : int = logging.get_logger(__name__)
snake_case : List[str] = {"vocab_file": "spiece.model"}
snake_case : List[str] = {
"vocab_file": {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
}
}
snake_case : Tuple = {
"albert-base-v1": 512,
"albert-large-v1": 512,
"albert-xlarge-v1": 512,
"albert-xxlarge-v1": 512,
"albert-base-v2": 512,
"albert-large-v2": 512,
"albert-xlarge-v2": 512,
"albert-xxlarge-v2": 512,
}
snake_case : List[str] = "▁"
class _snake_case ( snake_case ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , _a , _a=True , _a=True , _a=False , _a="[CLS]" , _a="[SEP]" , _a="<unk>" , _a="[SEP]" , _a="<pad>" , _a="[CLS]" , _a="[MASK]" , _a = None , **_a , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
__magic_name__ : str = (
AddedToken(_a , lstrip=_a , rstrip=_a , normalized=_a )
if isinstance(_a , _a )
else mask_token
)
__magic_name__ : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , )
__magic_name__ : Dict = do_lower_case
__magic_name__ : Tuple = remove_space
__magic_name__ : Union[str, Any] = keep_accents
__magic_name__ : Tuple = vocab_file
__magic_name__ : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_a )
@property
def SCREAMING_SNAKE_CASE ( self ):
return len(self.sp_model )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
__magic_name__ : List[str] = self.__dict__.copy()
__magic_name__ : Any = None
return state
def __setstate__( self , _a ):
__magic_name__ : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
__magic_name__ : str = {}
__magic_name__ : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE ( self , _a ):
if self.remove_space:
__magic_name__ : List[Any] = " ".join(inputs.strip().split() )
else:
__magic_name__ : str = inputs
__magic_name__ : int = outputs.replace("``" , "\"" ).replace("''" , "\"" )
if not self.keep_accents:
__magic_name__ : str = unicodedata.normalize("NFKD" , _a )
__magic_name__ : Tuple = "".join([c for c in outputs if not unicodedata.combining(_a )] )
if self.do_lower_case:
__magic_name__ : int = outputs.lower()
return outputs
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Optional[Any] = self.preprocess_text(_a )
__magic_name__ : Dict = self.sp_model.encode(_a , out_type=_a )
__magic_name__ : Any = []
for piece in pieces:
if len(_a ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
__magic_name__ : Optional[Any] = self.sp_model.EncodeAsPieces(piece[:-1].replace(_a , "" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
__magic_name__ : List[str] = cur_pieces[1:]
else:
__magic_name__ : Optional[int] = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_a )
else:
new_pieces.append(_a )
return new_pieces
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.sp_model.PieceToId(_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.sp_model.IdToPiece(_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Any = []
__magic_name__ : Union[str, Any] = ""
__magic_name__ : int = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_a ) + token
__magic_name__ : List[Any] = True
__magic_name__ : Optional[int] = []
else:
current_sub_tokens.append(_a )
__magic_name__ : Optional[Any] = False
out_string += self.sp_model.decode(_a )
return out_string.strip()
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
__magic_name__ : List[str] = [self.sep_token_id]
__magic_name__ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self , _a , _a = None , _a = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is not None:
return [1] + ([0] * len(_a )) + [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1]
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
__magic_name__ : Optional[int] = [self.sep_token_id]
__magic_name__ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__magic_name__ : List[str] = os.path.join(
_a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _a )
elif not os.path.isfile(self.vocab_file ):
with open(_a , "wb" ) as fi:
__magic_name__ : Union[str, Any] = self.sp_model.serialized_model_proto()
fi.write(_a )
return (out_vocab_file,)
| 281 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowerCamelCase :
'''simple docstring'''
def __init__( self: str , snake_case: Dict , snake_case: List[Any]=3 , snake_case: Any=32 , snake_case: Optional[int]=3 , snake_case: List[Any]=10 , snake_case: List[str]=[10, 20, 30, 40] , snake_case: Dict=[1, 1, 2, 1] , snake_case: Optional[int]=True , snake_case: Dict=True , snake_case: Union[str, Any]="relu" , snake_case: List[Any]=3 , snake_case: Dict=None , ) -> Dict:
snake_case_ :str = parent
snake_case_ :List[Any] = batch_size
snake_case_ :int = image_size
snake_case_ :Dict = num_channels
snake_case_ :Any = embeddings_size
snake_case_ :str = hidden_sizes
snake_case_ :Tuple = depths
snake_case_ :str = is_training
snake_case_ :int = use_labels
snake_case_ :Optional[int] = hidden_act
snake_case_ :Dict = num_labels
snake_case_ :Tuple = scope
snake_case_ :List[Any] = len(snake_case )
def lowerCAmelCase_ ( self: Optional[Any] ) -> Optional[Any]:
snake_case_ :Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ :Any = None
if self.use_labels:
snake_case_ :Any = ids_tensor([self.batch_size] , self.num_labels )
snake_case_ :Union[str, Any] = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase_ ( self: Optional[int] ) -> Union[str, Any]:
return ResNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def lowerCAmelCase_ ( self: int , snake_case: List[str] , snake_case: Any , snake_case: Optional[int] ) -> Tuple:
snake_case_ :Dict = TFResNetModel(config=snake_case )
snake_case_ :Dict = model(snake_case )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowerCAmelCase_ ( self: str , snake_case: List[str] , snake_case: Union[str, Any] , snake_case: Any ) -> int:
snake_case_ :Any = self.num_labels
snake_case_ :Optional[int] = TFResNetForImageClassification(snake_case )
snake_case_ :List[Any] = model(snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase_ ( self: Tuple ) -> Union[str, Any]:
snake_case_ :Optional[int] = self.prepare_config_and_inputs()
snake_case_, snake_case_, snake_case_ :Tuple = config_and_inputs
snake_case_ :Tuple = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
_A : Union[str, Any] = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
_A : List[str] = (
{"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
if is_tf_available()
else {}
)
_A : List[str] = False
_A : Any = False
_A : int = False
_A : List[Any] = False
_A : Any = False
def lowerCAmelCase_ ( self: str ) -> Optional[Any]:
snake_case_ :List[str] = TFResNetModelTester(self )
snake_case_ :List[Any] = ConfigTester(self , config_class=snake_case , has_text_modality=snake_case )
def lowerCAmelCase_ ( self: int ) -> List[Any]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCAmelCase_ ( self: List[str] ) -> List[str]:
return
@unittest.skip(reason="""ResNet does not use inputs_embeds""" )
def lowerCAmelCase_ ( self: Dict ) -> Optional[Any]:
pass
@unittest.skip(reason="""ResNet does not support input and output embeddings""" )
def lowerCAmelCase_ ( self: Dict ) -> Optional[Any]:
pass
def lowerCAmelCase_ ( self: Dict ) -> Optional[int]:
snake_case_, snake_case_ :Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ :Tuple = model_class(snake_case )
snake_case_ :Optional[int] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ :Optional[int] = [*signature.parameters.keys()]
snake_case_ :str = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , snake_case )
def lowerCAmelCase_ ( self: List[Any] ) -> List[str]:
snake_case_ :Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def lowerCAmelCase_ ( self: str ) -> List[Any]:
def check_hidden_states_output(snake_case: List[str] , snake_case: List[str] , snake_case: str ):
snake_case_ :Optional[Any] = model_class(snake_case )
snake_case_ :Optional[int] = model(**self._prepare_for_class(snake_case , snake_case ) )
snake_case_ :List[str] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
snake_case_ :List[str] = self.model_tester.num_stages
self.assertEqual(len(snake_case ) , expected_num_stages + 1 )
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
snake_case_, snake_case_ :Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ :Tuple = ["""basic""", """bottleneck"""]
for model_class in self.all_model_classes:
for layer_type in layers_type:
snake_case_ :Any = layer_type
snake_case_ :List[Any] = True
check_hidden_states_output(snake_case , snake_case , snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ :str = True
check_hidden_states_output(snake_case , snake_case , snake_case )
def lowerCAmelCase_ ( self: List[Any] ) -> Optional[Any]:
snake_case_ :str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
@slow
def lowerCAmelCase_ ( self: Dict ) -> Optional[int]:
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ :int = TFResNetModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def A_ ( ):
'''simple docstring'''
snake_case_ :Dict = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase_ ( self: Optional[Any] ) -> Any:
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def lowerCAmelCase_ ( self: Optional[Any] ) -> List[str]:
snake_case_ :Any = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
snake_case_ :int = self.default_image_processor
snake_case_ :Union[str, Any] = prepare_img()
snake_case_ :Optional[Any] = image_processor(images=snake_case , return_tensors="""tf""" )
# forward pass
snake_case_ :List[str] = model(**snake_case )
# verify the logits
snake_case_ :Optional[Any] = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , snake_case )
snake_case_ :Any = tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , snake_case , atol=1E-4 ) )
| 66 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : Tuple , _snake_case : List[Any] , _snake_case : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if isinstance(_snake_case , _snake_case ):
__magic_name__ : Union[str, Any] = np.full((len(_snake_case ), sequence_length, 2) , _snake_case )
else:
__magic_name__ : List[Any] = np.full((len(_snake_case ), sequence_length) , _snake_case )
for i, tensor in enumerate(_snake_case ):
if padding_side == "right":
if isinstance(_snake_case , _snake_case ):
__magic_name__ : Optional[Any] = tensor[:sequence_length]
else:
__magic_name__ : Union[str, Any] = tensor[:sequence_length]
else:
if isinstance(_snake_case , _snake_case ):
__magic_name__ : List[Any] = tensor[:sequence_length]
else:
__magic_name__ : Optional[Any] = tensor[:sequence_length]
return out_tensor.tolist()
def lowerCAmelCase_ ( _snake_case : Optional[int] ) -> Tuple:
'''simple docstring'''
__magic_name__ : Union[str, Any] = ord(_snake_case )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
__magic_name__ : Any = unicodedata.category(_snake_case )
if cat.startswith("P" ):
return True
return False
@dataclass
class _snake_case ( snake_case ):
UpperCamelCase__ = 42
UpperCamelCase__ = True
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = -100
UpperCamelCase__ = "pt"
def SCREAMING_SNAKE_CASE ( self , _a ):
import torch
__magic_name__ : List[str] = "label" if "label" in features[0].keys() else "labels"
__magic_name__ : Union[str, Any] = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
__magic_name__ : Optional[int] = self.tokenizer.pad(
_a , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" if labels is None else None , )
if labels is None:
return batch
__magic_name__ : Dict = torch.tensor(batch["entity_ids"] ).shape[1]
__magic_name__ : List[Any] = self.tokenizer.padding_side
if padding_side == "right":
__magic_name__ : str = [
list(_a ) + [self.label_pad_token_id] * (sequence_length - len(_a )) for label in labels
]
else:
__magic_name__ : int = [
[self.label_pad_token_id] * (sequence_length - len(_a )) + list(_a ) for label in labels
]
__magic_name__ : Dict = [feature["ner_tags"] for feature in features]
__magic_name__ : List[Any] = padding_tensor(_a , -1 , _a , _a )
__magic_name__ : Any = [feature["original_entity_spans"] for feature in features]
__magic_name__ : Any = padding_tensor(_a , (-1, -1) , _a , _a )
__magic_name__ : List[Any] = {k: torch.tensor(_a , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 281 | 0 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
__UpperCAmelCase =False
try:
__UpperCAmelCase =_is_package_available("google.colab")
except ModuleNotFoundError:
pass
@input.register
class a__ :
def __init__( self : Dict , a : str = None , a : list = [] ):
"""simple docstring"""
__lowerCamelCase = 0
__lowerCamelCase = choices
__lowerCamelCase = prompt
if sys.platform == "win32":
__lowerCamelCase = '''*'''
else:
__lowerCamelCase = '''➔ '''
def SCREAMING_SNAKE_CASE__ ( self : str , a : int , a : str = "" ):
"""simple docstring"""
if sys.platform != "win32":
writeColor(self.choices[index] , 32 , a )
else:
forceWrite(self.choices[index] , a )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , a : int ):
"""simple docstring"""
if index == self.position:
forceWrite(f""" {self.arrow_char} """ )
self.write_choice(a )
else:
forceWrite(f""" {self.choices[index]}""" )
reset_cursor()
def SCREAMING_SNAKE_CASE__ ( self : Any , a : Direction , a : int = 1 ):
"""simple docstring"""
__lowerCamelCase = self.position
if direction == Direction.DOWN:
if self.position + 1 >= len(self.choices ):
return
self.position += num_spaces
else:
if self.position - 1 < 0:
return
self.position -= num_spaces
clear_line()
self.print_choice(a )
move_cursor(a , direction.name )
self.print_choice(self.position )
@input.mark(KEYMAP['''up'''] )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
self.move_direction(Direction.UP )
@input.mark(KEYMAP['''down'''] )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
self.move_direction(Direction.DOWN )
@input.mark(KEYMAP['''newline'''] )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
move_cursor(len(self.choices ) - self.position , '''DOWN''' )
return self.position
@input.mark(KEYMAP['''interrupt'''] )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
move_cursor(len(self.choices ) - self.position , '''DOWN''' )
raise KeyboardInterrupt
@input.mark_multiple(*[KEYMAP[str(a )] for number in range(10 )] )
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
__lowerCamelCase = int(chr(self.current_selection ) )
__lowerCamelCase = index - self.position
if index == self.position:
return
if index < len(self.choices ):
if self.position > index:
self.move_direction(Direction.UP , -movement )
elif self.position < index:
self.move_direction(Direction.DOWN , a )
else:
return
else:
return
def SCREAMING_SNAKE_CASE__ ( self : List[str] , a : int = 0 ):
"""simple docstring"""
if self.prompt:
linebreak()
forceWrite(self.prompt , '''\n''' )
if in_colab:
forceWrite('''Please input a choice index (starting from 0), and press enter''' , '''\n''' )
else:
forceWrite('''Please select a choice using the arrow or number keys, and selecting with enter''' , '''\n''' )
__lowerCamelCase = default_choice
for i in range(len(self.choices ) ):
self.print_choice(a )
forceWrite('''\n''' )
move_cursor(len(self.choices ) - self.position , '''UP''' )
with cursor.hide():
while True:
if in_colab:
try:
__lowerCamelCase = int(builtins.input() )
except ValueError:
__lowerCamelCase = default_choice
else:
__lowerCamelCase = self.handle_input()
if choice is not None:
reset_cursor()
for _ in range(len(self.choices ) + 1 ):
move_cursor(1 , '''UP''' )
clear_line()
self.write_choice(a , '''\n''' )
return choice
| 67 |
import math
def lowerCAmelCase_ ( _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
return math.pow(_snake_case , 2 ) - a
def lowerCAmelCase_ ( _snake_case : float ) -> float:
'''simple docstring'''
return 2 * x
def lowerCAmelCase_ ( _snake_case : float ) -> float:
'''simple docstring'''
__magic_name__ : Optional[int] = 2.0
while start <= a:
__magic_name__ : str = math.pow(_snake_case , 2 )
return start
def lowerCAmelCase_ ( _snake_case : float , _snake_case : int = 9999 , _snake_case : float = 0.00_000_000_000_001 ) -> float:
'''simple docstring'''
if a < 0:
raise ValueError("math domain error" )
__magic_name__ : Optional[int] = get_initial_point(_snake_case )
for _ in range(_snake_case ):
__magic_name__ : int = value
__magic_name__ : str = value - fx(_snake_case , _snake_case ) / fx_derivative(_snake_case )
if abs(prev_value - value ) < tolerance:
return value
return value
if __name__ == "__main__":
from doctest import testmod
testmod()
| 281 | 0 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import sys
import transformers
lowerCAmelCase__ = """3"""
print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)
try:
import torch
print("""Torch version:""", torch.__version__)
print("""Cuda available:""", torch.cuda.is_available())
print("""Cuda version:""", torch.version.cuda)
print("""CuDNN version:""", torch.backends.cudnn.version())
print("""Number of GPUs available:""", torch.cuda.device_count())
print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
print("""Torch version:""", None)
try:
import deepspeed
print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
print("""DeepSpeed version:""", None)
try:
import tensorflow as tf
print("""TensorFlow version:""", tf.__version__)
print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
print("""TensorFlow version:""", None)
| 68 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class _snake_case :
UpperCamelCase__ = LEDConfig
UpperCamelCase__ = {}
UpperCamelCase__ = 'gelu'
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=False , _a=99 , _a=32 , _a=2 , _a=4 , _a=37 , _a=0.1 , _a=0.1 , _a=20 , _a=2 , _a=1 , _a=0 , _a=4 , ):
__magic_name__ : int = parent
__magic_name__ : Optional[int] = batch_size
__magic_name__ : Tuple = seq_length
__magic_name__ : List[Any] = is_training
__magic_name__ : Dict = use_labels
__magic_name__ : Optional[Any] = vocab_size
__magic_name__ : int = hidden_size
__magic_name__ : Optional[int] = num_hidden_layers
__magic_name__ : Optional[int] = num_attention_heads
__magic_name__ : Tuple = intermediate_size
__magic_name__ : Any = hidden_dropout_prob
__magic_name__ : Optional[int] = attention_probs_dropout_prob
__magic_name__ : List[str] = max_position_embeddings
__magic_name__ : Any = eos_token_id
__magic_name__ : str = pad_token_id
__magic_name__ : int = bos_token_id
__magic_name__ : Optional[int] = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
__magic_name__ : Tuple = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
__magic_name__ : Tuple = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__magic_name__ : int = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__magic_name__ : int = tf.concat([input_ids, eos_tensor] , axis=1 )
__magic_name__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__magic_name__ : Dict = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
__magic_name__ : List[str] = prepare_led_inputs_dict(_a , _a , _a )
__magic_name__ : Union[str, Any] = tf.concat(
[tf.zeros_like(_a )[:, :-1], tf.ones_like(_a )[:, -1:]] , axis=-1 , )
__magic_name__ : List[Any] = global_attention_mask
return config, inputs_dict
def SCREAMING_SNAKE_CASE ( self , _a , _a ):
__magic_name__ : Dict = TFLEDModel(config=_a ).get_decoder()
__magic_name__ : Optional[int] = inputs_dict["input_ids"]
__magic_name__ : Union[str, Any] = input_ids[:1, :]
__magic_name__ : str = inputs_dict["attention_mask"][:1, :]
__magic_name__ : int = 1
# first forward pass
__magic_name__ : Tuple = model(_a , attention_mask=_a , use_cache=_a )
__magic_name__ , __magic_name__ : str = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__magic_name__ : Optional[int] = ids_tensor((self.batch_size, 3) , config.vocab_size )
__magic_name__ : Any = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
__magic_name__ : Optional[Any] = tf.concat([input_ids, next_tokens] , axis=-1 )
__magic_name__ : List[Any] = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
__magic_name__ : List[str] = model(_a , attention_mask=_a )[0]
__magic_name__ : Dict = model(_a , attention_mask=_a , past_key_values=_a )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__magic_name__ : List[Any] = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
__magic_name__ : Union[str, Any] = output_from_no_past[:, -3:, random_slice_idx]
__magic_name__ : List[str] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_a , _a , rtol=1e-3 )
def lowerCAmelCase_ ( _snake_case : Any , _snake_case : List[Any] , _snake_case : Any , _snake_case : str=None , _snake_case : List[str]=None , _snake_case : int=None , _snake_case : Any=None , ) -> int:
'''simple docstring'''
if attention_mask is None:
__magic_name__ : str = tf.cast(tf.math.not_equal(_snake_case , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__magic_name__ : List[Any] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__magic_name__ : Dict = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__magic_name__ : Any = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class _snake_case ( snake_case , snake_case , unittest.TestCase ):
UpperCamelCase__ = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
UpperCamelCase__ = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
UpperCamelCase__ = (
{
'conversational': TFLEDForConditionalGeneration,
'feature-extraction': TFLEDModel,
'summarization': TFLEDForConditionalGeneration,
'text2text-generation': TFLEDForConditionalGeneration,
'translation': TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
UpperCamelCase__ = True
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = TFLEDModelTester(self )
__magic_name__ : List[Any] = ConfigTester(self , config_class=_a )
def SCREAMING_SNAKE_CASE ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ , __magic_name__ : int = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ : List[str] = tf.zeros_like(inputs_dict["attention_mask"] )
__magic_name__ : Optional[Any] = 2
__magic_name__ : Tuple = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict["global_attention_mask"] , )
__magic_name__ : Any = True
__magic_name__ : str = self.model_tester.seq_length
__magic_name__ : Dict = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(_a ):
__magic_name__ : str = outputs.decoder_attentions
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(_a ):
__magic_name__ : Any = [t.numpy() for t in outputs.encoder_attentions]
__magic_name__ : Tuple = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
__magic_name__ : Union[str, Any] = True
__magic_name__ : List[str] = False
__magic_name__ : Tuple = False
__magic_name__ : Optional[int] = model_class(_a )
__magic_name__ : str = model(self._prepare_for_class(_a , _a ) )
__magic_name__ : Any = len(_a )
self.assertEqual(config.output_hidden_states , _a )
check_encoder_attentions_output(_a )
if self.is_encoder_decoder:
__magic_name__ : Tuple = model_class(_a )
__magic_name__ : Optional[Any] = model(self._prepare_for_class(_a , _a ) )
self.assertEqual(config.output_hidden_states , _a )
check_decoder_attentions_output(_a )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
__magic_name__ : Dict = True
__magic_name__ : str = model_class(_a )
__magic_name__ : Any = model(self._prepare_for_class(_a , _a ) )
self.assertEqual(config.output_hidden_states , _a )
check_encoder_attentions_output(_a )
# Check attention is always last and order is fine
__magic_name__ : Union[str, Any] = True
__magic_name__ : Union[str, Any] = True
__magic_name__ : List[str] = model_class(_a )
__magic_name__ : Any = model(self._prepare_for_class(_a , _a ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_a ) )
self.assertEqual(model.config.output_hidden_states , _a )
check_encoder_attentions_output(_a )
@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing." )
def SCREAMING_SNAKE_CASE ( self ):
pass
def SCREAMING_SNAKE_CASE ( self ):
# TODO: Head-masking not yet implement
pass
def lowerCAmelCase_ ( _snake_case : int ) -> Optional[int]:
'''simple docstring'''
return tf.constant(_snake_case , dtype=tf.intaa )
snake_case : Optional[int] = 1E-4
@slow
@require_tf
class _snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[Any] = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" ).led
# change to intended input here
__magic_name__ : Optional[int] = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : str = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : Any = prepare_led_inputs_dict(model.config , _a , _a )
__magic_name__ : List[Any] = model(**_a )[0]
__magic_name__ : List[str] = (1, 1_024, 768)
self.assertEqual(output.shape , _a )
# change to expected output here
__magic_name__ : int = tf.convert_to_tensor(
[[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]] , )
tf.debugging.assert_near(output[:, :3, :3] , _a , atol=1e-3 )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Tuple = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" )
# change to intended input here
__magic_name__ : int = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : Tuple = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : Optional[Any] = prepare_led_inputs_dict(model.config , _a , _a )
__magic_name__ : Union[str, Any] = model(**_a )[0]
__magic_name__ : Optional[int] = (1, 1_024, model.config.vocab_size)
self.assertEqual(output.shape , _a )
# change to expected output here
__magic_name__ : str = tf.convert_to_tensor(
[[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]] , )
tf.debugging.assert_near(output[:, :3, :3] , _a , atol=1e-3 , rtol=1e-3 )
| 281 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''huggingface/time-series-transformer-tourism-monthly''': (
'''https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'''
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class UpperCamelCase ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ = "time_series_transformer"
SCREAMING_SNAKE_CASE_ = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
"num_hidden_layers": "encoder_layers",
}
def __init__( self, lowerCAmelCase__ = None, lowerCAmelCase__ = None, lowerCAmelCase__ = "student_t", lowerCAmelCase__ = "nll", lowerCAmelCase__ = 1, lowerCAmelCase__ = [1, 2, 3, 4, 5, 6, 7], lowerCAmelCase__ = "mean", lowerCAmelCase__ = 0, lowerCAmelCase__ = 0, lowerCAmelCase__ = 0, lowerCAmelCase__ = 0, lowerCAmelCase__ = None, lowerCAmelCase__ = None, lowerCAmelCase__ = 32, lowerCAmelCase__ = 32, lowerCAmelCase__ = 2, lowerCAmelCase__ = 2, lowerCAmelCase__ = 2, lowerCAmelCase__ = 2, lowerCAmelCase__ = True, lowerCAmelCase__ = "gelu", lowerCAmelCase__ = 64, lowerCAmelCase__ = 0.1, lowerCAmelCase__ = 0.1, lowerCAmelCase__ = 0.1, lowerCAmelCase__ = 0.1, lowerCAmelCase__ = 0.1, lowerCAmelCase__ = 100, lowerCAmelCase__ = 0.02, lowerCAmelCase__=True, **lowerCAmelCase__, ) -> Optional[Any]:
# time series specific configuration
snake_case_ = prediction_length
snake_case_ = context_length or prediction_length
snake_case_ = distribution_output
snake_case_ = loss
snake_case_ = input_size
snake_case_ = num_time_features
snake_case_ = lags_sequence
snake_case_ = scaling
snake_case_ = num_dynamic_real_features
snake_case_ = num_static_real_features
snake_case_ = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(lowerCAmelCase__) != num_static_categorical_features:
raise ValueError(
'The cardinality should be a list of the same length as `num_static_categorical_features`')
snake_case_ = cardinality
else:
snake_case_ = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(lowerCAmelCase__) != num_static_categorical_features:
raise ValueError(
'The embedding dimension should be a list of the same length as `num_static_categorical_features`')
snake_case_ = embedding_dimension
else:
snake_case_ = [min(50, (cat + 1) // 2) for cat in self.cardinality]
snake_case_ = num_parallel_samples
# Transformer architecture configuration
snake_case_ = input_size * len(lowerCAmelCase__) + self._number_of_features
snake_case_ = d_model
snake_case_ = encoder_attention_heads
snake_case_ = decoder_attention_heads
snake_case_ = encoder_ffn_dim
snake_case_ = decoder_ffn_dim
snake_case_ = encoder_layers
snake_case_ = decoder_layers
snake_case_ = dropout
snake_case_ = attention_dropout
snake_case_ = activation_dropout
snake_case_ = encoder_layerdrop
snake_case_ = decoder_layerdrop
snake_case_ = activation_function
snake_case_ = init_std
snake_case_ = use_cache
super().__init__(is_encoder_decoder=lowerCAmelCase__, **lowerCAmelCase__)
@property
def a_ ( self) -> int:
return (
sum(self.embedding_dimension)
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 69 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
snake_case : Optional[Any] = logging.get_logger(__name__)
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Union[str, Any]=False ) -> List[str]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = []
# fmt: off
# stem:
rename_keys.append(("cls_token", "vit.embeddings.cls_token") )
rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings") )
rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias") )
# backbone
rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias") )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''') )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''') )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
("pre_logits.fc.weight", "pooler.dense.weight"),
("pre_logits.fc.bias", "pooler.dense.bias"),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
__magic_name__ : int = [(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
# fmt: on
return rename_keys
def lowerCAmelCase_ ( _snake_case : Any , _snake_case : Any , _snake_case : Dict=False ) -> int:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
__magic_name__ : int = ""
else:
__magic_name__ : Union[str, Any] = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__magic_name__ : Optional[Any] = state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
__magic_name__ : int = state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
__magic_name__ : Dict = in_proj_weight[
: config.hidden_size, :
]
__magic_name__ : List[str] = in_proj_bias[: config.hidden_size]
__magic_name__ : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__magic_name__ : Optional[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__magic_name__ : Optional[Any] = in_proj_weight[
-config.hidden_size :, :
]
__magic_name__ : int = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase_ ( _snake_case : List[str] ) -> List[str]:
'''simple docstring'''
__magic_name__ : List[str] = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(_snake_case , _snake_case )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : int , _snake_case : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
__magic_name__ : int = dct.pop(_snake_case )
__magic_name__ : List[Any] = val
def lowerCAmelCase_ ( ) -> Dict:
'''simple docstring'''
__magic_name__ : List[str] = "http://images.cocodataset.org/val2017/000000039769.jpg"
__magic_name__ : List[str] = Image.open(requests.get(_snake_case , stream=_snake_case ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : Any , _snake_case : int=False ) -> Dict:
'''simple docstring'''
__magic_name__ : List[str] = BitConfig(
global_padding="same" , layer_type="bottleneck" , depths=(3, 4, 9) , out_features=["stage3"] , embedding_dynamic_padding=_snake_case , )
__magic_name__ : List[str] = ViTHybridConfig(backbone_config=_snake_case , image_size=384 , num_labels=1000 )
__magic_name__ : str = False
# load original model from timm
__magic_name__ : Union[str, Any] = timm.create_model(_snake_case , pretrained=_snake_case )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
__magic_name__ : List[Any] = timm_model.state_dict()
if base_model:
remove_classification_head_(_snake_case )
__magic_name__ : Tuple = create_rename_keys(_snake_case , _snake_case )
for src, dest in rename_keys:
rename_key(_snake_case , _snake_case , _snake_case )
read_in_q_k_v(_snake_case , _snake_case , _snake_case )
__magic_name__ : List[str] = "huggingface/label-files"
__magic_name__ : int = "imagenet-1k-id2label.json"
__magic_name__ : Optional[int] = json.load(open(hf_hub_download(_snake_case , _snake_case , repo_type="dataset" ) , "r" ) )
__magic_name__ : int = {int(_snake_case ): v for k, v in idalabel.items()}
__magic_name__ : List[str] = idalabel
__magic_name__ : List[str] = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
__magic_name__ : List[str] = ViTHybridModel(_snake_case ).eval()
else:
__magic_name__ : str = ViTHybridForImageClassification(_snake_case ).eval()
model.load_state_dict(_snake_case )
# create image processor
__magic_name__ : List[Any] = create_transform(**resolve_data_config({} , model=_snake_case ) )
__magic_name__ : int = transform.transforms
__magic_name__ : List[str] = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
__magic_name__ : int = ViTHybridImageProcessor(
do_resize=_snake_case , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_snake_case , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=_snake_case , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
__magic_name__ : List[Any] = prepare_img()
__magic_name__ : Any = transform(_snake_case ).unsqueeze(0 )
__magic_name__ : Tuple = processor(_snake_case , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(_snake_case , _snake_case )
# verify logits
with torch.no_grad():
__magic_name__ : Optional[int] = model(_snake_case )
__magic_name__ : List[str] = outputs.logits
print("Predicted class:" , logits.argmax(-1 ).item() )
if base_model:
__magic_name__ : List[str] = timm_model.forward_features(_snake_case )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_snake_case , outputs.pooler_output , atol=1E-3 )
else:
__magic_name__ : Any = timm_model(_snake_case )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_snake_case , outputs.logits , atol=1E-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(_snake_case ).mkdir(exist_ok=_snake_case )
print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_snake_case )
print(F'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(_snake_case )
if push_to_hub:
print(F'''Pushing model and processor to the hub {vit_name}''' )
model.push_to_hub(F'''ybelkada/{vit_name}''' )
processor.push_to_hub(F'''ybelkada/{vit_name}''' )
if __name__ == "__main__":
snake_case : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--vit_name",
default="vit_base_r50_s16_384",
type=str,
help="Name of the hybrid ViT timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
)
snake_case : List[Any] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 281 | 0 |
'''simple docstring'''
A__ : Optional[int] ='''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
A__ : int =[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
A__ : str ={
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 70 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
snake_case : List[str] = "facebook/wmt19-en-de"
snake_case : Dict = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
snake_case : List[str] = FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
snake_case : int = FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")
# Test
snake_case : Optional[Any] = tokenizer(["Making tiny model"], return_tensors="pt")
snake_case : List[str] = tiny_model(**batch)
print("test output:", len(outputs.logits[0]))
# Save
snake_case : Dict = "tiny-wmt19-en-de"
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F"Generated {mname_tiny}")
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 281 | 0 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
A_ :List[Any] = get_tests_dir('''fixtures/dummy-config.json''')
class __A ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : str =0
def __lowercase ( self ):
"""simple docstring"""
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec('transformers.models.auto' ) )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Tuple =AutoConfig.from_pretrained('bert-base-uncased' )
self.assertIsInstance(lowerCamelCase__ , lowerCamelCase__ )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] =AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ , lowerCamelCase__ )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Dict =AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ , lowerCamelCase__ )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[str] =AutoConfig.for_model('roberta' )
self.assertIsInstance(lowerCamelCase__ , lowerCamelCase__ )
def __lowercase ( self ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
__UpperCamelCase : Tuple =os.path.join(lowerCamelCase__ , 'fake-roberta' )
os.makedirs(lowerCamelCase__ , exist_ok=lowerCamelCase__ )
with open(os.path.join(lowerCamelCase__ , 'config.json' ) , 'w' ) as f:
f.write(json.dumps({} ) )
__UpperCamelCase : Union[str, Any] =AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertEqual(type(lowerCamelCase__ ) , lowerCamelCase__ )
def __lowercase ( self ):
"""simple docstring"""
try:
AutoConfig.register('custom' , lowerCamelCase__ )
# Wrong model type will raise an error
with self.assertRaises(lowerCamelCase__ ):
AutoConfig.register('model' , lowerCamelCase__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowerCamelCase__ ):
AutoConfig.register('bert' , lowerCamelCase__ )
# Now that the config is registered, it can be used as any other config with the auto-API
__UpperCamelCase : int =CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase__ )
__UpperCamelCase : Dict =AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ , lowerCamelCase__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def __lowercase ( self ):
"""simple docstring"""
with self.assertRaisesRegex(
lowerCamelCase__ , 'bert-base is not a local folder and is not a valid model identifier' ):
__UpperCamelCase : int =AutoConfig.from_pretrained('bert-base' )
def __lowercase ( self ):
"""simple docstring"""
with self.assertRaisesRegex(
lowerCamelCase__ , R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
__UpperCamelCase : Tuple =AutoConfig.from_pretrained(lowerCamelCase__ , revision='aaaaaa' )
def __lowercase ( self ):
"""simple docstring"""
with self.assertRaisesRegex(
lowerCamelCase__ , 'hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.' , ):
__UpperCamelCase : Dict =AutoConfig.from_pretrained('hf-internal-testing/no-config-test-repo' )
def __lowercase ( self ):
"""simple docstring"""
with self.assertRaises(lowerCamelCase__ ):
__UpperCamelCase : Tuple =AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowerCamelCase__ ):
__UpperCamelCase : Tuple =AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' , trust_remote_code=lowerCamelCase__ )
__UpperCamelCase : Tuple =AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' , trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ , 'NewModelConfig' )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase__ )
__UpperCamelCase : List[str] =AutoConfig.from_pretrained(lowerCamelCase__ , trust_remote_code=lowerCamelCase__ )
self.assertEqual(reloaded_config.__class__.__name__ , 'NewModelConfig' )
def __lowercase ( self ):
"""simple docstring"""
class __A ( a ):
"""simple docstring"""
UpperCamelCase__ : Optional[Any] ="""new-model"""
try:
AutoConfig.register('new-model' , lowerCamelCase__ )
# If remote code is not set, the default is to use local
__UpperCamelCase : str =AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' )
self.assertEqual(config.__class__.__name__ , 'NewModelConfigLocal' )
# If remote code is disabled, we load the local one.
__UpperCamelCase : Dict =AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' , trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ , 'NewModelConfigLocal' )
# If remote is enabled, we load from the Hub
__UpperCamelCase : List[str] =AutoConfig.from_pretrained('hf-internal-testing/test_dynamic_model' , trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ , 'NewModelConfig' )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 71 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
snake_case : Optional[int] = logging.getLogger(__name__)
def lowerCAmelCase_ ( _snake_case : Dict , _snake_case : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__magic_name__ : List[str] = np.argmax(_snake_case , axis=1 )
return np.sum(outputs == labels )
def lowerCAmelCase_ ( _snake_case : Optional[Any] ) -> Dict:
'''simple docstring'''
with open(_snake_case , encoding="utf_8" ) as f:
__magic_name__ : List[str] = csv.reader(_snake_case )
__magic_name__ : List[Any] = []
next(_snake_case ) # skip the first line
for line in tqdm(_snake_case ):
output.append((" ".join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def lowerCAmelCase_ ( _snake_case : str , _snake_case : Tuple , _snake_case : Union[str, Any] , _snake_case : List[Any] , _snake_case : Tuple , _snake_case : Optional[int] ) -> int:
'''simple docstring'''
__magic_name__ : Optional[int] = []
for dataset in encoded_datasets:
__magic_name__ : Union[str, Any] = len(_snake_case )
__magic_name__ : Dict = np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
__magic_name__ : List[str] = np.zeros((n_batch, 2) , dtype=np.intaa )
__magic_name__ : Optional[int] = np.full((n_batch, 2, input_len) , fill_value=-100 , dtype=np.intaa )
__magic_name__ : int = np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(_snake_case ):
__magic_name__ : Dict = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__magic_name__ : Dict = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__magic_name__ : str = with_conta
__magic_name__ : Tuple = with_conta
__magic_name__ : Union[str, Any] = len(_snake_case ) - 1
__magic_name__ : int = len(_snake_case ) - 1
__magic_name__ : Optional[Any] = with_conta
__magic_name__ : Optional[Any] = with_conta
__magic_name__ : Optional[int] = mc_label
__magic_name__ : str = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(_snake_case ) for t in all_inputs ) )
return tensor_datasets
def lowerCAmelCase_ ( ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Any = argparse.ArgumentParser()
parser.add_argument("--model_name" , type=_snake_case , default="openai-gpt" , help="pretrained model name" )
parser.add_argument("--do_train" , action="store_true" , help="Whether to run training." )
parser.add_argument("--do_eval" , action="store_true" , help="Whether to run eval on the dev set." )
parser.add_argument(
"--output_dir" , default=_snake_case , type=_snake_case , required=_snake_case , help="The output directory where the model predictions and checkpoints will be written." , )
parser.add_argument("--train_dataset" , type=_snake_case , default="" )
parser.add_argument("--eval_dataset" , type=_snake_case , default="" )
parser.add_argument("--seed" , type=_snake_case , default=42 )
parser.add_argument("--num_train_epochs" , type=_snake_case , default=3 )
parser.add_argument("--train_batch_size" , type=_snake_case , default=8 )
parser.add_argument("--eval_batch_size" , type=_snake_case , default=16 )
parser.add_argument("--adam_epsilon" , default=1E-8 , type=_snake_case , help="Epsilon for Adam optimizer." )
parser.add_argument("--max_grad_norm" , type=_snake_case , default=1 )
parser.add_argument(
"--max_steps" , default=-1 , type=_snake_case , help=(
"If > 0: set total number of training steps to perform. Override num_train_epochs."
) , )
parser.add_argument(
"--gradient_accumulation_steps" , type=_snake_case , default=1 , help="Number of updates steps to accumulate before performing a backward/update pass." , )
parser.add_argument("--learning_rate" , type=_snake_case , default=6.25E-5 )
parser.add_argument("--warmup_steps" , default=0 , type=_snake_case , help="Linear warmup over warmup_steps." )
parser.add_argument("--lr_schedule" , type=_snake_case , default="warmup_linear" )
parser.add_argument("--weight_decay" , type=_snake_case , default=0.01 )
parser.add_argument("--lm_coef" , type=_snake_case , default=0.9 )
parser.add_argument("--n_valid" , type=_snake_case , default=374 )
parser.add_argument("--server_ip" , type=_snake_case , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=_snake_case , default="" , help="Can be used for distant debugging." )
__magic_name__ : List[Any] = parser.parse_args()
print(_snake_case )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=_snake_case )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
__magic_name__ : Dict = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
__magic_name__ : Optional[int] = torch.cuda.device_count()
logger.info("device: {}, n_gpu {}".format(_snake_case , _snake_case ) )
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True." )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
__magic_name__ : List[Any] = ["_start_", "_delimiter_", "_classify_"]
__magic_name__ : Optional[int] = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(_snake_case )
__magic_name__ : Optional[Any] = tokenizer.convert_tokens_to_ids(_snake_case )
__magic_name__ : List[str] = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(_snake_case ) )
model.to(_snake_case )
# Load and encode the datasets
def tokenize_and_encode(_snake_case : str ):
if isinstance(_snake_case , _snake_case ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_snake_case ) )
elif isinstance(_snake_case , _snake_case ):
return obj
return [tokenize_and_encode(_snake_case ) for o in obj]
logger.info("Encoding dataset..." )
__magic_name__ : Optional[int] = load_rocstories_dataset(args.train_dataset )
__magic_name__ : str = load_rocstories_dataset(args.eval_dataset )
__magic_name__ : int = (train_dataset, eval_dataset)
__magic_name__ : List[str] = tokenize_and_encode(_snake_case )
# Compute the max input length for the Transformer
__magic_name__ : Optional[Any] = model.config.n_positions // 2 - 2
__magic_name__ : Optional[int] = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
__magic_name__ : List[str] = min(_snake_case , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
__magic_name__ : List[Any] = pre_process_datasets(_snake_case , _snake_case , _snake_case , *_snake_case )
__magic_name__ , __magic_name__ : Optional[int] = tensor_datasets[0], tensor_datasets[1]
__magic_name__ : Tuple = TensorDataset(*_snake_case )
__magic_name__ : Union[str, Any] = RandomSampler(_snake_case )
__magic_name__ : Dict = DataLoader(_snake_case , sampler=_snake_case , batch_size=args.train_batch_size )
__magic_name__ : Any = TensorDataset(*_snake_case )
__magic_name__ : Optional[Any] = SequentialSampler(_snake_case )
__magic_name__ : int = DataLoader(_snake_case , sampler=_snake_case , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
__magic_name__ : Tuple = args.max_steps
__magic_name__ : List[str] = args.max_steps // (len(_snake_case ) // args.gradient_accumulation_steps) + 1
else:
__magic_name__ : List[str] = len(_snake_case ) // args.gradient_accumulation_steps * args.num_train_epochs
__magic_name__ : str = list(model.named_parameters() )
__magic_name__ : Dict = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
__magic_name__ : str = [
{
"params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
"weight_decay": args.weight_decay,
},
{"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], "weight_decay": 0.0},
]
__magic_name__ : str = AdamW(_snake_case , lr=args.learning_rate , eps=args.adam_epsilon )
__magic_name__ : List[str] = get_linear_schedule_with_warmup(
_snake_case , num_warmup_steps=args.warmup_steps , num_training_steps=_snake_case )
if args.do_train:
__magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc="Epoch" ):
__magic_name__ : List[str] = 0
__magic_name__ : Tuple = 0
__magic_name__ : Dict = tqdm(_snake_case , desc="Training" )
for step, batch in enumerate(_snake_case ):
__magic_name__ : Optional[Any] = tuple(t.to(_snake_case ) for t in batch )
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Dict = batch
__magic_name__ : Optional[Any] = model(_snake_case , mc_token_ids=_snake_case , lm_labels=_snake_case , mc_labels=_snake_case )
__magic_name__ : Optional[Any] = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
__magic_name__ : List[str] = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
__magic_name__ : int = "Training loss: {:.2e} lr: {:.2e}".format(_snake_case , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
__magic_name__ : Dict = model.module if hasattr(_snake_case , "module" ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
__magic_name__ : List[Any] = os.path.join(args.output_dir , _snake_case )
__magic_name__ : Dict = os.path.join(args.output_dir , _snake_case )
torch.save(model_to_save.state_dict() , _snake_case )
model_to_save.config.to_json_file(_snake_case )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
__magic_name__ : Dict = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
__magic_name__ : Optional[int] = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(_snake_case )
if args.do_eval:
model.eval()
__magic_name__ , __magic_name__ : Any = 0, 0
__magic_name__ , __magic_name__ : Union[str, Any] = 0, 0
for batch in tqdm(_snake_case , desc="Evaluating" ):
__magic_name__ : int = tuple(t.to(_snake_case ) for t in batch )
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] = batch
with torch.no_grad():
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Dict = model(
_snake_case , mc_token_ids=_snake_case , lm_labels=_snake_case , mc_labels=_snake_case )
__magic_name__ : Tuple = mc_logits.detach().cpu().numpy()
__magic_name__ : Any = mc_labels.to("cpu" ).numpy()
__magic_name__ : str = accuracy(_snake_case , _snake_case )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
__magic_name__ : Tuple = eval_loss / nb_eval_steps
__magic_name__ : List[Any] = eval_accuracy / nb_eval_examples
__magic_name__ : int = tr_loss / nb_tr_steps if args.do_train else None
__magic_name__ : Any = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}
__magic_name__ : int = os.path.join(args.output_dir , "eval_results.txt" )
with open(_snake_case , "w" ) as writer:
logger.info("***** Eval results *****" )
for key in sorted(result.keys() ):
logger.info(" %s = %s" , _snake_case , str(result[key] ) )
writer.write("%s = %s\n" % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 281 | 0 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def snake_case_ ( A_ : list, A_ : list, A_ : list, A_ : list, A_ : list ):
'''simple docstring'''
_lowerCamelCase : Any = np.array([[1, item, train_mtch[i]] for i, item in enumerate(A_ )] )
_lowerCamelCase : Optional[int] = np.array(A_ )
_lowerCamelCase : List[str] = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose(), A_ ) ), x.transpose() ), A_ )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def snake_case_ ( A_ : list, A_ : list, A_ : list ):
'''simple docstring'''
_lowerCamelCase : List[str] = (1, 2, 1)
_lowerCamelCase : Any = (1, 1, 0, 7)
_lowerCamelCase : int = SARIMAX(
A_, exog=A_, order=A_, seasonal_order=A_ )
_lowerCamelCase : Optional[int] = model.fit(disp=A_, maxiter=6_00, method='''nm''' )
_lowerCamelCase : Any = model_fit.predict(1, len(A_ ), exog=[test_match] )
return result[0]
def snake_case_ ( A_ : list, A_ : list, A_ : list ):
'''simple docstring'''
_lowerCamelCase : Any = SVR(kernel='''rbf''', C=1, gamma=0.1, epsilon=0.1 )
regressor.fit(A_, A_ )
_lowerCamelCase : Optional[Any] = regressor.predict(A_ )
return y_pred[0]
def snake_case_ ( A_ : list ):
'''simple docstring'''
train_user.sort()
_lowerCamelCase : Dict = np.percentile(A_, 25 )
_lowerCamelCase : Optional[int] = np.percentile(A_, 75 )
_lowerCamelCase : Dict = qa - qa
_lowerCamelCase : Tuple = qa - (iqr * 0.1)
return low_lim
def snake_case_ ( A_ : list, A_ : float ):
'''simple docstring'''
_lowerCamelCase : Any = 0
_lowerCamelCase : Dict = 0
for i in list_vote:
if i > actual_result:
_lowerCamelCase : Optional[Any] = not_safe + 1
else:
if abs(abs(A_ ) - abs(A_ ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
lowerCAmelCase__ = [[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
lowerCAmelCase__ = pd.DataFrame(
data_input, columns=['''total_user''', '''total_even''', '''days''']
)
lowerCAmelCase__ = Normalizer().fit_transform(data_input_df.values)
# split data
lowerCAmelCase__ = normalize_df[:, 2].tolist()
lowerCAmelCase__ = normalize_df[:, 0].tolist()
lowerCAmelCase__ = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
lowerCAmelCase__ = normalize_df[:, [1, 2]].tolist()
lowerCAmelCase__ = x[: len(x) - 1]
lowerCAmelCase__ = x[len(x) - 1 :]
# for linear regression & sarimax
lowerCAmelCase__ = total_date[: len(total_date) - 1]
lowerCAmelCase__ = total_user[: len(total_user) - 1]
lowerCAmelCase__ = total_match[: len(total_match) - 1]
lowerCAmelCase__ = total_date[len(total_date) - 1 :]
lowerCAmelCase__ = total_user[len(total_user) - 1 :]
lowerCAmelCase__ = total_match[len(total_match) - 1 :]
# voting system with forecasting
lowerCAmelCase__ = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
lowerCAmelCase__ = '''''' if data_safety_checker(res_vote, tst_user) else '''not '''
print('''Today\'s data is {not_str}safe.''')
| 72 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 281 | 0 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
a =16
a =32
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ = 1_6 ) -> Optional[int]:
__lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained('bert-base-cased' )
__lowerCamelCase : int = load_dataset('glue' , 'mrpc' )
def tokenize_function(lowerCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
__lowerCamelCase : List[Any] = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__lowerCamelCase : int = datasets.map(
lowerCamelCase__ , batched=lowerCamelCase__ , remove_columns=['idx', 'sentence1', 'sentence2'] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowerCamelCase : Dict = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(lowerCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__lowerCamelCase : List[str] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__lowerCamelCase : Optional[int] = 1_6
elif accelerator.mixed_precision != "no":
__lowerCamelCase : List[Any] = 8
else:
__lowerCamelCase : Any = None
return tokenizer.pad(
lowerCamelCase__ , padding='longest' , max_length=lowerCamelCase__ , pad_to_multiple_of=lowerCamelCase__ , return_tensors='pt' , )
# Instantiate dataloaders.
__lowerCamelCase : Any = DataLoader(
tokenized_datasets['train'] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=lowerCamelCase__ )
__lowerCamelCase : str = DataLoader(
tokenized_datasets['validation'] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=lowerCamelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
a =mocked_dataloaders # noqa: F811
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> Any:
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS' , lowerCamelCase__ ) == "1":
__lowerCamelCase : Union[str, Any] = 2
# Initialize accelerator
__lowerCamelCase : List[Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowerCamelCase : Tuple = config['lr']
__lowerCamelCase : List[str] = int(config['num_epochs'] )
__lowerCamelCase : List[Any] = int(config['seed'] )
__lowerCamelCase : int = int(config['batch_size'] )
__lowerCamelCase : List[str] = evaluate.load('glue' , 'mrpc' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=lowerCamelCase__ )
def inner_training_loop(lowerCamelCase__ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(lowerCamelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowerCamelCase : Optional[int] = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=lowerCamelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__lowerCamelCase : Union[str, Any] = model.to(accelerator.device )
# Instantiate optimizer
__lowerCamelCase : Any = AdamW(params=model.parameters() , lr=lowerCamelCase__ )
__lowerCamelCase , __lowerCamelCase : str = get_dataloaders(lowerCamelCase__ , lowerCamelCase__ )
# Instantiate scheduler
__lowerCamelCase : int = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase__ , num_warmup_steps=1_0_0 , num_training_steps=(len(lowerCamelCase__ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase : Any = accelerator.prepare(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Now we train the model
for epoch in range(lowerCamelCase__ ):
model.train()
for step, batch in enumerate(lowerCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__lowerCamelCase : Optional[Any] = model(**lowerCamelCase__ )
__lowerCamelCase : Tuple = outputs.loss
accelerator.backward(lowerCamelCase__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowerCamelCase : List[str] = model(**lowerCamelCase__ )
__lowerCamelCase : str = outputs.logits.argmax(dim=-1 )
__lowerCamelCase , __lowerCamelCase : Optional[int] = accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=lowerCamelCase__ , references=lowerCamelCase__ , )
__lowerCamelCase : Optional[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"epoch {epoch}:" , lowerCamelCase__ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def SCREAMING_SNAKE_CASE__ ( ) -> Union[str, Any]:
__lowerCamelCase : Union[str, Any] = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=lowerCamelCase__ , default=lowerCamelCase__ , choices=['no', 'fp16', 'bf16', 'fp8'] , help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
__lowerCamelCase : Optional[int] = parser.parse_args()
__lowerCamelCase : Optional[int] = {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
training_function(lowerCamelCase__ , lowerCamelCase__ )
if __name__ == "__main__":
main()
| 73 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCAmelCase_ ( _snake_case : List[Any] ) -> List[Any]:
'''simple docstring'''
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ) -> Tuple:
'''simple docstring'''
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : Dict = "mock-s3-bucket"
__magic_name__ : Any = F'''s3://{mock_bucket}'''
__magic_name__ : str = extract_path_from_uri(_snake_case )
assert dataset_path.startswith("s3://" ) is False
__magic_name__ : Tuple = "./local/path"
__magic_name__ : Optional[Any] = extract_path_from_uri(_snake_case )
assert dataset_path == new_dataset_path
def lowerCAmelCase_ ( _snake_case : List[str] ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ : str = is_remote_filesystem(_snake_case )
assert is_remote is True
__magic_name__ : Optional[int] = fsspec.filesystem("file" )
__magic_name__ : int = is_remote_filesystem(_snake_case )
assert is_remote is False
@pytest.mark.parametrize("compression_fs_class" , _snake_case )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : Optional[Any] , _snake_case : int , _snake_case : Tuple , _snake_case : Any , _snake_case : Union[str, Any] , _snake_case : Any ) -> int:
'''simple docstring'''
__magic_name__ : Any = {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
__magic_name__ : str = input_paths[compression_fs_class.protocol]
if input_path is None:
__magic_name__ : Dict = F'''for \'{compression_fs_class.protocol}\' compression protocol, '''
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_snake_case )
__magic_name__ : str = fsspec.filesystem(compression_fs_class.protocol , fo=_snake_case )
assert isinstance(_snake_case , _snake_case )
__magic_name__ : int = os.path.basename(_snake_case )
__magic_name__ : Optional[int] = expected_filename[: expected_filename.rindex("." )]
assert fs.glob("*" ) == [expected_filename]
with fs.open(_snake_case , "r" , encoding="utf-8" ) as f, open(_snake_case , encoding="utf-8" ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize("protocol" , ["zip", "gzip"] )
def lowerCAmelCase_ ( _snake_case : List[Any] , _snake_case : Optional[Any] , _snake_case : Optional[Any] ) -> str:
'''simple docstring'''
__magic_name__ : int = {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
__magic_name__ : int = compressed_file_paths[protocol]
__magic_name__ : Tuple = "dataset.jsonl"
__magic_name__ : List[str] = F'''{protocol}://{member_file_path}::{compressed_file_path}'''
__magic_name__ , *__magic_name__ : Optional[Any] = fsspec.get_fs_token_paths(_snake_case )
assert fs.isfile(_snake_case )
assert not fs.isfile("non_existing_" + member_file_path )
@pytest.mark.integration
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Dict , _snake_case : List[str] , _snake_case : Tuple ) -> str:
'''simple docstring'''
__magic_name__ : int = hf_api.dataset_info(_snake_case , token=_snake_case )
__magic_name__ : Optional[Any] = HfFileSystem(repo_info=_snake_case , token=_snake_case )
assert sorted(hffs.glob("*" ) ) == [".gitattributes", "data"]
assert hffs.isdir("data" )
assert hffs.isfile(".gitattributes" ) and hffs.isfile("data/text_data.txt" )
with open(_snake_case ) as f:
assert hffs.open("data/text_data.txt" , "r" ).read() == f.read()
def lowerCAmelCase_ ( ) -> Optional[int]:
'''simple docstring'''
__magic_name__ : Optional[Any] = "bz2"
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(_snake_case , _snake_case , clobber=_snake_case )
with pytest.warns(_snake_case ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(_snake_case ) == 1
assert (
str(warning_info[0].message )
== F'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
)
| 281 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
)
from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] ,A_ : Optional[Any] ,A_ : Optional[Any]=13 ,A_ : List[Any]=30 ,A_ : Optional[Any]=2 ,A_ : Optional[int]=3 ,A_ : int=True ,A_ : Optional[Any]=True ,A_ : List[str]=32 ,A_ : str=2 ,A_ : str=4 ,A_ : int=37 ,A_ : Tuple="gelu" ,A_ : Any=0.1 ,A_ : int=0.1 ,A_ : str=10 ,A_ : List[str]=0.02 ,A_ : int=3 ,A_ : List[Any]=None ,A_ : int=2 ,) -> Dict:
A = parent
A = batch_size
A = image_size
A = patch_size
A = num_channels
A = is_training
A = use_labels
A = hidden_size
A = num_hidden_layers
A = num_attention_heads
A = intermediate_size
A = hidden_act
A = hidden_dropout_prob
A = attention_probs_dropout_prob
A = type_sequence_label_size
A = initializer_range
A = scope
A = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
A = (image_size // patch_size) ** 2
A = num_patches + 2
def _SCREAMING_SNAKE_CASE ( self : str ) -> Union[str, Any]:
A = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A = None
if self.use_labels:
A = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
A = self.get_config()
return config, pixel_values, labels
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[Any]:
return DeiTConfig(
image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,is_decoder=A_ ,initializer_range=self.initializer_range ,encoder_stride=self.encoder_stride ,)
def _SCREAMING_SNAKE_CASE ( self : Dict ,A_ : Any ,A_ : Any ,A_ : int ) -> int:
A = TFDeiTModel(config=A_ )
A = model(A_ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _SCREAMING_SNAKE_CASE ( self : Tuple ,A_ : List[str] ,A_ : List[str] ,A_ : Optional[Any] ) -> List[Any]:
A = TFDeiTForMaskedImageModeling(config=A_ )
A = model(A_ )
self.parent.assertEqual(
result.reconstruction.shape ,(self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
A = 1
A = TFDeiTForMaskedImageModeling(A_ )
A = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A = model(A_ )
self.parent.assertEqual(result.reconstruction.shape ,(self.batch_size, 1, self.image_size, self.image_size) )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ,A_ : Union[str, Any] ,A_ : str ,A_ : Tuple ) -> List[Any]:
A = self.type_sequence_label_size
A = TFDeiTForImageClassification(A_ )
A = model(A_ ,labels=A_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
A = 1
A = TFDeiTForImageClassification(A_ )
A = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A = model(A_ ,labels=A_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Any:
A = self.prepare_config_and_inputs()
A , A , A = config_and_inputs
A = {'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class lowerCAmelCase_ ( _lowercase , _lowercase , unittest.TestCase ):
'''simple docstring'''
_lowerCamelCase: Tuple = (
(
TFDeiTModel,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
)
if is_tf_available()
else ()
)
_lowerCamelCase: Dict = (
{
'''feature-extraction''': TFDeiTModel,
'''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
}
if is_tf_available()
else {}
)
_lowerCamelCase: Union[str, Any] = False
_lowerCamelCase: Dict = False
_lowerCamelCase: Any = False
_lowerCamelCase: Dict = False
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Tuple:
A = TFDeiTModelTester(self )
A = ConfigTester(self ,config_class=A_ ,has_text_modality=A_ ,hidden_size=37 )
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Optional[int]:
self.config_tester.run_common_tests()
@unittest.skip(reason='DeiT does not use inputs_embeds' )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> str:
pass
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Dict:
A , A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A = model_class(A_ )
self.assertIsInstance(model.get_input_embeddings() ,(tf.keras.layers.Layer) )
A = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A_ ,tf.keras.layers.Dense ) )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[Any]:
A , A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A = model_class(A_ )
A = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A = [*signature.parameters.keys()]
A = ['pixel_values']
self.assertListEqual(arg_names[:1] ,A_ )
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Any:
A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Optional[Any]:
A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*A_ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> int:
A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A_ )
def _SCREAMING_SNAKE_CASE ( self : Dict ,A_ : List[str] ,A_ : Any ,A_ : List[Any]=False ) -> str:
A = super()._prepare_for_class(A_ ,A_ ,return_labels=A_ )
if return_labels:
if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call ).parameters:
del inputs_dict["labels"]
return inputs_dict
@slow
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> List[str]:
for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A = TFDeiTModel.from_pretrained(A_ )
self.assertIsNotNone(A_ )
def _snake_case ( ):
A = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
@require_vision
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> Optional[int]:
return (
DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224' )
if is_vision_available()
else None
)
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Any:
A = TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224' )
A = self.default_image_processor
A = prepare_img()
A = image_processor(images=A_ ,return_tensors='tf' )
# forward pass
A = model(**A_ )
# verify the logits
A = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape ,A_ )
A = tf.constant([-1.02_66, 0.19_12, -1.28_61] )
self.assertTrue(np.allclose(outputs.logits[0, :3] ,A_ ,atol=1e-4 ) ) | 74 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case : Dict = logging.get_logger(__name__)
snake_case : List[Any] = {
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
"YituTech/conv-bert-medium-small": (
"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
),
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class _snake_case ( snake_case ):
UpperCamelCase__ = 'convbert'
def __init__( self , _a=30_522 , _a=768 , _a=12 , _a=12 , _a=3_072 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=2 , _a=0.02 , _a=1e-12 , _a=1 , _a=0 , _a=2 , _a=768 , _a=2 , _a=9 , _a=1 , _a=None , **_a , ):
super().__init__(
pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a , )
__magic_name__ : Tuple = vocab_size
__magic_name__ : List[Any] = hidden_size
__magic_name__ : Union[str, Any] = num_hidden_layers
__magic_name__ : List[Any] = num_attention_heads
__magic_name__ : str = intermediate_size
__magic_name__ : Any = hidden_act
__magic_name__ : List[Any] = hidden_dropout_prob
__magic_name__ : Optional[int] = attention_probs_dropout_prob
__magic_name__ : Tuple = max_position_embeddings
__magic_name__ : str = type_vocab_size
__magic_name__ : List[str] = initializer_range
__magic_name__ : Tuple = layer_norm_eps
__magic_name__ : List[Any] = embedding_size
__magic_name__ : List[Any] = head_ratio
__magic_name__ : str = conv_kernel_size
__magic_name__ : Dict = num_groups
__magic_name__ : str = classifier_dropout
class _snake_case ( snake_case ):
@property
def SCREAMING_SNAKE_CASE ( self ):
if self.task == "multiple-choice":
__magic_name__ : Dict = {0: "batch", 1: "choice", 2: "sequence"}
else:
__magic_name__ : Dict = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 281 | 0 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def a_ ( __snake_case : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a_ ( ) -> Iterator[int]:
"""simple docstring"""
lowerCamelCase_ =2
while True:
if is_prime(__snake_case ):
yield num
num += 1
def a_ ( __snake_case : int = 200_0000 ) -> int:
"""simple docstring"""
return sum(takewhile(lambda __snake_case : x < n , prime_generator() ) )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 75 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipaConfig,
BlipaForConditionalGeneration,
BlipaProcessor,
BlipaVisionConfig,
BlipImageProcessor,
OPTConfig,
TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def lowerCAmelCase_ ( ) -> str:
'''simple docstring'''
__magic_name__ : int = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
__magic_name__ : Union[str, Any] = Image.open(requests.get(_snake_case , stream=_snake_case ).raw ).convert("RGB" )
return image
def lowerCAmelCase_ ( _snake_case : str ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : List[str] = []
# fmt: off
# vision encoder
rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding") )
rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding") )
rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight") )
rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias") )
rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight") )
rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias") )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',) )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''') )
# QFormer
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight") )
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias") )
# fmt: on
return rename_keys
def lowerCAmelCase_ ( _snake_case : Any , _snake_case : Union[str, Any] , _snake_case : Optional[Any] ) -> int:
'''simple docstring'''
__magic_name__ : Tuple = dct.pop(_snake_case )
__magic_name__ : int = val
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : Optional[Any] ) -> Dict:
'''simple docstring'''
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
__magic_name__ : List[Any] = state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias''' )
__magic_name__ : Optional[Any] = state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias''' )
# next, set bias in the state dict
__magic_name__ : Optional[int] = torch.cat((q_bias, torch.zeros_like(_snake_case , requires_grad=_snake_case ), v_bias) )
__magic_name__ : Union[str, Any] = qkv_bias
def lowerCAmelCase_ ( _snake_case : Dict , _snake_case : str ) -> int:
'''simple docstring'''
__magic_name__ : List[Any] = 364 if "coco" in model_name else 224
__magic_name__ : Union[str, Any] = BlipaVisionConfig(image_size=_snake_case ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
__magic_name__ : List[str] = OPTConfig.from_pretrained("facebook/opt-2.7b" , eos_token_id=_snake_case ).to_dict()
elif "opt-6.7b" in model_name:
__magic_name__ : Any = OPTConfig.from_pretrained("facebook/opt-6.7b" , eos_token_id=_snake_case ).to_dict()
elif "t5-xl" in model_name:
__magic_name__ : Dict = TaConfig.from_pretrained("google/flan-t5-xl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
__magic_name__ : int = TaConfig.from_pretrained("google/flan-t5-xxl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
__magic_name__ : List[Any] = BlipaConfig(vision_config=_snake_case , text_config=_snake_case )
return config, image_size
@torch.no_grad()
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : str=None , _snake_case : Dict=False ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Optional[int] = (
AutoTokenizer.from_pretrained("facebook/opt-2.7b" )
if "opt" in model_name
else AutoTokenizer.from_pretrained("google/flan-t5-xl" )
)
__magic_name__ : List[Any] = tokenizer("\n" , add_special_tokens=_snake_case ).input_ids[0]
__magic_name__ , __magic_name__ : Tuple = get_blipa_config(_snake_case , eos_token_id=_snake_case )
__magic_name__ : Union[str, Any] = BlipaForConditionalGeneration(_snake_case ).eval()
__magic_name__ : Any = {
"blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
"blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
"blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
"blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
"blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
"blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
"blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
}
__magic_name__ , __magic_name__ : Union[str, Any] = model_name_to_original[model_name]
# load original model
print("Loading original model..." )
__magic_name__ : Union[str, Any] = "cuda" if torch.cuda.is_available() else "cpu"
__magic_name__ , __magic_name__ , __magic_name__ : Optional[Any] = load_model_and_preprocess(
name=_snake_case , model_type=_snake_case , is_eval=_snake_case , device=_snake_case )
original_model.eval()
print("Done!" )
# update state dict keys
__magic_name__ : Dict = original_model.state_dict()
__magic_name__ : str = create_rename_keys(_snake_case )
for src, dest in rename_keys:
rename_key(_snake_case , _snake_case , _snake_case )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
__magic_name__ : Any = state_dict.pop(_snake_case )
if key.startswith("Qformer.bert" ):
__magic_name__ : Optional[int] = key.replace("Qformer.bert" , "qformer" )
if "attention.self" in key:
__magic_name__ : Any = key.replace("self" , "attention" )
if "opt_proj" in key:
__magic_name__ : Union[str, Any] = key.replace("opt_proj" , "language_projection" )
if "t5_proj" in key:
__magic_name__ : Optional[int] = key.replace("t5_proj" , "language_projection" )
if key.startswith("opt" ):
__magic_name__ : List[str] = key.replace("opt" , "language" )
if key.startswith("t5" ):
__magic_name__ : Tuple = key.replace("t5" , "language" )
__magic_name__ : Dict = val
# read in qv biases
read_in_q_v_bias(_snake_case , _snake_case )
__magic_name__ , __magic_name__ : Tuple = hf_model.load_state_dict(_snake_case , strict=_snake_case )
assert len(_snake_case ) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
__magic_name__ : List[Any] = load_demo_image()
__magic_name__ : Tuple = vis_processors["eval"](_snake_case ).unsqueeze(0 ).to(_snake_case )
__magic_name__ : Dict = tokenizer(["\n"] , return_tensors="pt" ).input_ids.to(_snake_case )
# create processor
__magic_name__ : Optional[Any] = BlipImageProcessor(
size={"height": image_size, "width": image_size} , image_mean=_snake_case , image_std=_snake_case )
__magic_name__ : Dict = BlipaProcessor(image_processor=_snake_case , tokenizer=_snake_case )
__magic_name__ : Union[str, Any] = processor(images=_snake_case , return_tensors="pt" ).pixel_values.to(_snake_case )
# make sure processor creates exact same pixel values
assert torch.allclose(_snake_case , _snake_case )
original_model.to(_snake_case )
hf_model.to(_snake_case )
with torch.no_grad():
if "opt" in model_name:
__magic_name__ : List[Any] = original_model({"image": original_pixel_values, "text_input": [""]} ).logits
__magic_name__ : Optional[int] = hf_model(_snake_case , _snake_case ).logits
else:
__magic_name__ : int = original_model(
{"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]} ).logits
__magic_name__ : Tuple = input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -100 )
__magic_name__ : List[str] = hf_model(_snake_case , _snake_case , labels=_snake_case ).logits
assert original_logits.shape == logits.shape
print("First values of original logits:" , original_logits[0, :3, :3] )
print("First values of HF logits:" , logits[0, :3, :3] )
# assert values
if model_name == "blip2-flan-t5-xl":
__magic_name__ : List[str] = torch.tensor(
[[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]] , device=_snake_case )
assert torch.allclose(logits[0, :3, :3] , _snake_case , atol=1E-4 )
elif model_name == "blip2-flan-t5-xl-coco":
__magic_name__ : Tuple = torch.tensor(
[[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]] , device=_snake_case )
else:
# cast to same type
__magic_name__ : str = logits.dtype
assert torch.allclose(original_logits.to(_snake_case ) , _snake_case , atol=1E-2 )
print("Looks ok!" )
print("Generating a caption..." )
__magic_name__ : Optional[int] = ""
__magic_name__ : Dict = tokenizer(_snake_case , return_tensors="pt" ).input_ids.to(_snake_case )
__magic_name__ : int = original_model.generate({"image": original_pixel_values} )
__magic_name__ : Optional[Any] = hf_model.generate(
_snake_case , _snake_case , do_sample=_snake_case , num_beams=5 , max_length=30 , min_length=1 , top_p=0.9 , repetition_penalty=1.0 , length_penalty=1.0 , temperature=1 , )
print("Original generation:" , _snake_case )
__magic_name__ : Tuple = input_ids.shape[1]
__magic_name__ : int = processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=_snake_case )
__magic_name__ : Union[str, Any] = [text.strip() for text in output_text]
print("HF generation:" , _snake_case )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(_snake_case )
hf_model.save_pretrained(_snake_case )
if push_to_hub:
processor.push_to_hub(F'''nielsr/{model_name}''' )
hf_model.push_to_hub(F'''nielsr/{model_name}''' )
if __name__ == "__main__":
snake_case : Any = argparse.ArgumentParser()
snake_case : Union[str, Any] = [
"blip2-opt-2.7b",
"blip2-opt-6.7b",
"blip2-opt-2.7b-coco",
"blip2-opt-6.7b-coco",
"blip2-flan-t5-xl",
"blip2-flan-t5-xl-coco",
"blip2-flan-t5-xxl",
]
parser.add_argument(
"--model_name",
default="blip2-opt-2.7b",
choices=choices,
type=str,
help="Path to hf config.json of model to convert",
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model and processor to the hub after converting",
)
snake_case : int = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 281 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =42
lowerCamelCase__ =42
def __init__( self : Optional[int] , a : UNetaDModel , a : ScoreSdeVeScheduler ) -> List[Any]:
"""simple docstring"""
super().__init__()
self.register_modules(unet=a , scheduler=a )
@torch.no_grad()
def __call__( self : List[Any] , a : int = 1 , a : int = 2000 , a : Optional[Union[torch.Generator, List[torch.Generator]]] = None , a : Optional[str] = "pil" , a : bool = True , **a : List[Any] , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.unet.config.sample_size
SCREAMING_SNAKE_CASE : List[Any] = (batch_size, 3, img_size, img_size)
SCREAMING_SNAKE_CASE : List[Any] = self.unet
SCREAMING_SNAKE_CASE : List[Any] = randn_tensor(a , generator=a ) * self.scheduler.init_noise_sigma
SCREAMING_SNAKE_CASE : Any = sample.to(self.device )
self.scheduler.set_timesteps(a )
self.scheduler.set_sigmas(a )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
SCREAMING_SNAKE_CASE : Tuple = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
SCREAMING_SNAKE_CASE : int = self.unet(a , a ).sample
SCREAMING_SNAKE_CASE : str = self.scheduler.step_correct(a , a , generator=a ).prev_sample
# prediction step
SCREAMING_SNAKE_CASE : Optional[int] = model(a , a ).sample
SCREAMING_SNAKE_CASE : List[str] = self.scheduler.step_pred(a , a , a , generator=a )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Dict = output.prev_sample, output.prev_sample_mean
SCREAMING_SNAKE_CASE : Union[str, Any] = sample_mean.clamp(0 , 1 )
SCREAMING_SNAKE_CASE : Any = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE : Any = self.numpy_to_pil(a )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=a ) | 76 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case : Dict = logging.get_logger(__name__)
snake_case : Union[str, Any] = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
snake_case : Dict = {
"vocab_file": {
"vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
"vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
},
"merges_file": {
"vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
"vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
},
}
snake_case : Union[str, Any] = {
"vinai/phobert-base": 256,
"vinai/phobert-large": 256,
}
def lowerCAmelCase_ ( _snake_case : str ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : List[str] = set()
__magic_name__ : Any = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__magic_name__ : int = char
__magic_name__ : List[str] = set(_snake_case )
return pairs
class _snake_case ( snake_case ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , _a , _a , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , **_a , ):
super().__init__(
bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , cls_token=_a , pad_token=_a , mask_token=_a , **_a , )
__magic_name__ : Dict = vocab_file
__magic_name__ : Tuple = merges_file
__magic_name__ : List[Any] = {}
__magic_name__ : List[Any] = 0
__magic_name__ : Tuple = 1
__magic_name__ : int = 2
__magic_name__ : Union[str, Any] = 3
self.add_from_file(_a )
__magic_name__ : Optional[int] = {v: k for k, v in self.encoder.items()}
with open(_a , encoding="utf-8" ) as merges_handle:
__magic_name__ : List[str] = merges_handle.read().split("\n" )[:-1]
__magic_name__ : Union[str, Any] = [tuple(merge.split()[:-1] ) for merge in merges]
__magic_name__ : Union[str, Any] = dict(zip(_a , range(len(_a ) ) ) )
__magic_name__ : Optional[int] = {}
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__magic_name__ : Optional[Any] = [self.cls_token_id]
__magic_name__ : Union[str, Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self , _a , _a = None , _a = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is None:
return [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1, 1] + ([0] * len(_a )) + [1]
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
__magic_name__ : Optional[Any] = [self.sep_token_id]
__magic_name__ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def SCREAMING_SNAKE_CASE ( self ):
return len(self.encoder )
def SCREAMING_SNAKE_CASE ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def SCREAMING_SNAKE_CASE ( self , _a ):
if token in self.cache:
return self.cache[token]
__magic_name__ : List[Any] = tuple(_a )
__magic_name__ : List[Any] = tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
__magic_name__ : Any = get_pairs(_a )
if not pairs:
return token
while True:
__magic_name__ : str = min(_a , key=lambda _a : self.bpe_ranks.get(_a , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
__magic_name__ , __magic_name__ : List[str] = bigram
__magic_name__ : List[str] = []
__magic_name__ : List[str] = 0
while i < len(_a ):
try:
__magic_name__ : Any = word.index(_a , _a )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__magic_name__ : Tuple = j
if word[i] == first and i < len(_a ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__magic_name__ : Union[str, Any] = tuple(_a )
__magic_name__ : Optional[int] = new_word
if len(_a ) == 1:
break
else:
__magic_name__ : List[Any] = get_pairs(_a )
__magic_name__ : Optional[int] = "@@ ".join(_a )
__magic_name__ : Tuple = word[:-4]
__magic_name__ : str = word
return word
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Optional[Any] = []
__magic_name__ : Dict = re.findall(r"\S+\n?" , _a )
for token in words:
split_tokens.extend(list(self.bpe(_a ).split(" " ) ) )
return split_tokens
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.encoder.get(_a , self.encoder.get(self.unk_token ) )
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.decoder.get(_a , self.unk_token )
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Tuple = " ".join(_a ).replace("@@ " , "" ).strip()
return out_string
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__magic_name__ : Optional[int] = os.path.join(
_a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
__magic_name__ : Union[str, Any] = os.path.join(
_a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
if os.path.abspath(self.merges_file ) != os.path.abspath(_a ):
copyfile(self.merges_file , _a )
return out_vocab_file, out_merge_file
def SCREAMING_SNAKE_CASE ( self , _a ):
if isinstance(_a , _a ):
try:
with open(_a , "r" , encoding="utf-8" ) as fd:
self.add_from_file(_a )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset''' )
return
__magic_name__ : List[Any] = f.readlines()
for lineTmp in lines:
__magic_name__ : Optional[Any] = lineTmp.strip()
__magic_name__ : Union[str, Any] = line.rfind(" " )
if idx == -1:
raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'" )
__magic_name__ : Optional[int] = line[:idx]
__magic_name__ : Dict = len(self.encoder )
| 281 | 0 |
"""simple docstring"""
import math
import flax.linen as nn
import jax.numpy as jnp
def a_ ( _lowerCAmelCase : jnp.ndarray , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 , _lowerCAmelCase : float = 1 , _lowerCAmelCase : float = 1.0E4 , _lowerCAmelCase : bool = False , _lowerCAmelCase : float = 1.0 , ):
'''simple docstring'''
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, f"""Embedding dimension {embedding_dim} should be even"""
lowercase__ : Optional[Any] = float(embedding_dim // 2 )
lowercase__ : Optional[Any] = math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
lowercase__ : Any = min_timescale * jnp.exp(jnp.arange(_lowerCAmelCase , dtype=jnp.floataa ) * -log_timescale_increment )
lowercase__ : Dict = jnp.expand_dims(_lowerCAmelCase , 1 ) * jnp.expand_dims(_lowerCAmelCase , 0 )
# scale embeddings
lowercase__ : List[str] = scale * emb
if flip_sin_to_cos:
lowercase__ : Dict = jnp.concatenate([jnp.cos(_lowerCAmelCase ), jnp.sin(_lowerCAmelCase )] , axis=1 )
else:
lowercase__ : Optional[int] = jnp.concatenate([jnp.sin(_lowerCAmelCase ), jnp.cos(_lowerCAmelCase )] , axis=1 )
lowercase__ : List[Any] = jnp.reshape(_lowerCAmelCase , [jnp.shape(_lowerCAmelCase )[0], embedding_dim] )
return signal
class UpperCAmelCase_ ( nn.Module):
lowerCamelCase__ : int = 3_2
lowerCamelCase__ : jnp.dtype = jnp.floataa
@nn.compact
def __call__( self , a ) -> Any:
lowercase__ : str = nn.Dense(self.time_embed_dim , dtype=self.dtype , name='linear_1' )(a )
lowercase__ : Union[str, Any] = nn.silu(a )
lowercase__ : Optional[Any] = nn.Dense(self.time_embed_dim , dtype=self.dtype , name='linear_2' )(a )
return temb
class UpperCAmelCase_ ( nn.Module):
lowerCamelCase__ : int = 3_2
lowerCamelCase__ : bool = False
lowerCamelCase__ : float = 1
@nn.compact
def __call__( self , a ) -> str:
return get_sinusoidal_embeddings(
a , embedding_dim=self.dim , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.freq_shift )
| 77 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase_ ( _snake_case : str = "laptop" ) -> DataFrame:
'''simple docstring'''
__magic_name__ : Tuple = F'''https://www.amazon.in/laptop/s?k={product}'''
__magic_name__ : Dict = {
"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
"Accept-Language": "en-US, en;q=0.5",
}
__magic_name__ : Tuple = BeautifulSoup(requests.get(_snake_case , headers=_snake_case ).text )
# Initialize a Pandas dataframe with the column titles
__magic_name__ : int = DataFrame(
columns=[
"Product Title",
"Product Link",
"Current Price of the product",
"Product Rating",
"MRP of the product",
"Discount",
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
"div" , attrs={"class": "s-result-item", "data-component-type": "s-search-result"} , ) , soup.find_all("div" , attrs={"class": "a-row a-size-base a-color-base"} ) , ):
try:
__magic_name__ : Dict = item.ha.text
__magic_name__ : Optional[int] = "https://www.amazon.in/" + item.ha.a["href"]
__magic_name__ : Optional[Any] = item.find("span" , attrs={"class": "a-offscreen"} ).text
try:
__magic_name__ : Union[str, Any] = item.find("span" , attrs={"class": "a-icon-alt"} ).text
except AttributeError:
__magic_name__ : Dict = "Not available"
try:
__magic_name__ : Optional[int] = (
"₹"
+ item.find(
"span" , attrs={"class": "a-price a-text-price"} ).text.split("₹" )[1]
)
except AttributeError:
__magic_name__ : List[str] = ""
try:
__magic_name__ : int = float(
(
(
float(product_mrp.strip("₹" ).replace("," , "" ) )
- float(product_price.strip("₹" ).replace("," , "" ) )
)
/ float(product_mrp.strip("₹" ).replace("," , "" ) )
)
* 100 )
except ValueError:
__magic_name__ : str = float("nan" )
except AttributeError:
pass
__magic_name__ : Optional[int] = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
__magic_name__ : Optional[Any] = " "
__magic_name__ : str = " "
data_frame.index += 1
return data_frame
if __name__ == "__main__":
snake_case : Any = "headphones"
get_amazon_product_data(product).to_csv(F"Amazon Product Data for {product}.csv")
| 281 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
snake_case_ = [
"""openmmlab/upernet-convnext-tiny""",
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
snake_case_ = """UperNetConfig"""
class A_ ( nn.Module ):
"""simple docstring"""
def __init__( self :Tuple , lowercase_ :int , lowercase_ :int , lowercase_ :Union[int, Tuple[int, int]] , lowercase_ :Union[int, Tuple[int, int], str] = 0 , lowercase_ :bool = False , lowercase_ :Union[int, Tuple[int, int]] = 1 , ) -> None:
super().__init__()
UpperCAmelCase = nn.Convad(
in_channels=lowercase_ , out_channels=lowercase_ , kernel_size=lowercase_ , padding=lowercase_ , bias=lowercase_ , dilation=lowercase_ , )
UpperCAmelCase = nn.BatchNormad(lowercase_ )
UpperCAmelCase = nn.ReLU()
def UpperCAmelCase__ ( self :Optional[Any] , lowercase_ :torch.Tensor ) -> torch.Tensor:
UpperCAmelCase = self.conv(lowercase_ )
UpperCAmelCase = self.batch_norm(lowercase_ )
UpperCAmelCase = self.activation(lowercase_ )
return output
class A_ ( nn.Module ):
"""simple docstring"""
def __init__( self :Optional[Any] , lowercase_ :int , lowercase_ :int , lowercase_ :int ) -> None:
super().__init__()
UpperCAmelCase = [
nn.AdaptiveAvgPoolad(lowercase_ ),
UperNetConvModule(lowercase_ , lowercase_ , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(lowercase_ ) , lowercase_ )
def UpperCAmelCase__ ( self :List[str] , lowercase_ :torch.Tensor ) -> torch.Tensor:
UpperCAmelCase = input
for layer in self.layers:
UpperCAmelCase = layer(lowercase_ )
return hidden_state
class A_ ( nn.Module ):
"""simple docstring"""
def __init__( self :Optional[Any] , lowercase_ :Tuple[int, ...] , lowercase_ :int , lowercase_ :int , lowercase_ :bool ) -> None:
super().__init__()
UpperCAmelCase = pool_scales
UpperCAmelCase = align_corners
UpperCAmelCase = in_channels
UpperCAmelCase = channels
UpperCAmelCase = []
for i, pool_scale in enumerate(lowercase_ ):
UpperCAmelCase = UperNetPyramidPoolingBlock(pool_scale=lowercase_ , in_channels=lowercase_ , channels=lowercase_ )
self.blocks.append(lowercase_ )
self.add_module(str(lowercase_ ) , lowercase_ )
def UpperCAmelCase__ ( self :List[Any] , lowercase_ :torch.Tensor ) -> List[torch.Tensor]:
UpperCAmelCase = []
for ppm in self.blocks:
UpperCAmelCase = ppm(lowercase_ )
UpperCAmelCase = nn.functional.interpolate(
lowercase_ , size=x.size()[2:] , mode='bilinear' , align_corners=self.align_corners )
ppm_outs.append(lowercase_ )
return ppm_outs
class A_ ( nn.Module ):
"""simple docstring"""
def __init__( self :Dict , lowercase_ :Optional[Any] , lowercase_ :Optional[int] ) -> Any:
super().__init__()
UpperCAmelCase = config
UpperCAmelCase = config.pool_scales # e.g. (1, 2, 3, 6)
UpperCAmelCase = in_channels
UpperCAmelCase = config.hidden_size
UpperCAmelCase = False
UpperCAmelCase = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
UpperCAmelCase = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
UpperCAmelCase = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
UpperCAmelCase = nn.ModuleList()
UpperCAmelCase = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
UpperCAmelCase = UperNetConvModule(lowercase_ , self.channels , kernel_size=1 )
UpperCAmelCase = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(lowercase_ )
self.fpn_convs.append(lowercase_ )
UpperCAmelCase = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def UpperCAmelCase__ ( self :Optional[Any] ) -> List[Any]:
self.apply(self._init_weights )
def UpperCAmelCase__ ( self :str , lowercase_ :Union[str, Any] ) -> str:
if isinstance(lowercase_ , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase__ ( self :Dict , lowercase_ :int ) -> int:
UpperCAmelCase = inputs[-1]
UpperCAmelCase = [x]
psp_outs.extend(self.psp_modules(lowercase_ ) )
UpperCAmelCase = torch.cat(lowercase_ , dim=1 )
UpperCAmelCase = self.bottleneck(lowercase_ )
return output
def UpperCAmelCase__ ( self :str , lowercase_ :torch.Tensor ) -> torch.Tensor:
# build laterals
UpperCAmelCase = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(lowercase_ ) )
# build top-down path
UpperCAmelCase = len(lowercase_ )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
UpperCAmelCase = laterals[i - 1].shape[2:]
UpperCAmelCase = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=lowercase_ , mode='bilinear' , align_corners=self.align_corners )
# build outputs
UpperCAmelCase = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
UpperCAmelCase = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode='bilinear' , align_corners=self.align_corners )
UpperCAmelCase = torch.cat(lowercase_ , dim=1 )
UpperCAmelCase = self.fpn_bottleneck(lowercase_ )
UpperCAmelCase = self.classifier(lowercase_ )
return output
class A_ ( nn.Module ):
"""simple docstring"""
def __init__( self :Optional[Any] , lowercase_ :Optional[Any] , lowercase_ :int = 2 , lowercase_ :int = 3 , lowercase_ :Union[int, Tuple[int, int]] = 1 ) -> None:
super().__init__()
UpperCAmelCase = config
UpperCAmelCase = config.auxiliary_in_channels
UpperCAmelCase = config.auxiliary_channels
UpperCAmelCase = config.auxiliary_num_convs
UpperCAmelCase = config.auxiliary_concat_input
UpperCAmelCase = in_index
UpperCAmelCase = (kernel_size // 2) * dilation
UpperCAmelCase = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=lowercase_ , padding=lowercase_ , dilation=lowercase_ ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=lowercase_ , padding=lowercase_ , dilation=lowercase_ ) )
if self.num_convs == 0:
UpperCAmelCase = nn.Identity()
else:
UpperCAmelCase = nn.Sequential(*lowercase_ )
if self.concat_input:
UpperCAmelCase = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=lowercase_ , padding=kernel_size // 2 )
UpperCAmelCase = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def UpperCAmelCase__ ( self :List[str] ) -> Dict:
self.apply(self._init_weights )
def UpperCAmelCase__ ( self :int , lowercase_ :Any ) -> List[Any]:
if isinstance(lowercase_ , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase__ ( self :Dict , lowercase_ :torch.Tensor ) -> torch.Tensor:
# just take the relevant feature maps
UpperCAmelCase = encoder_hidden_states[self.in_index]
UpperCAmelCase = self.convs(lowercase_ )
if self.concat_input:
UpperCAmelCase = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
UpperCAmelCase = self.classifier(lowercase_ )
return output
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
__UpperCamelCase = UperNetConfig
__UpperCamelCase = """pixel_values"""
__UpperCamelCase = True
def UpperCAmelCase__ ( self :Tuple , lowercase_ :List[str] ) -> Union[str, Any]:
if isinstance(lowercase_ , lowercase_ ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def UpperCAmelCase__ ( self :int ) -> Tuple:
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def UpperCAmelCase__ ( self :Any , lowercase_ :Dict , lowercase_ :Union[str, Any]=False ) -> Optional[int]:
if isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = value
snake_case_ = R"""
Parameters:
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
snake_case_ = R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
`attentions` under returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under
returned tensors for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"""UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.""" , SCREAMING_SNAKE_CASE_ , )
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self :int , lowercase_ :Tuple ) -> int:
super().__init__(lowercase_ )
UpperCAmelCase = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
UpperCAmelCase = UperNetHead(lowercase_ , in_channels=self.backbone.channels )
UpperCAmelCase = UperNetFCNHead(lowercase_ ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('batch_size, sequence_length' ) )
@replace_return_docstrings(output_type=lowercase_ , config_class=_CONFIG_FOR_DOC )
def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :Optional[torch.Tensor] = None , lowercase_ :Optional[bool] = None , lowercase_ :Optional[bool] = None , lowercase_ :Optional[torch.Tensor] = None , lowercase_ :Optional[bool] = None , ) -> Union[tuple, SemanticSegmenterOutput]:
UpperCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase = output_attentions if output_attentions is not None else self.config.output_attentions
UpperCAmelCase = self.backbone.forward_with_filtered_kwargs(
lowercase_ , output_hidden_states=lowercase_ , output_attentions=lowercase_ )
UpperCAmelCase = outputs.feature_maps
UpperCAmelCase = self.decode_head(lowercase_ )
UpperCAmelCase = nn.functional.interpolate(lowercase_ , size=pixel_values.shape[2:] , mode='bilinear' , align_corners=lowercase_ )
UpperCAmelCase = None
if self.auxiliary_head is not None:
UpperCAmelCase = self.auxiliary_head(lowercase_ )
UpperCAmelCase = nn.functional.interpolate(
lowercase_ , size=pixel_values.shape[2:] , mode='bilinear' , align_corners=lowercase_ )
UpperCAmelCase = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('The number of labels should be greater than one' )
else:
# compute weighted loss
UpperCAmelCase = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
UpperCAmelCase = loss_fct(lowercase_ , lowercase_ )
UpperCAmelCase = loss_fct(lowercase_ , lowercase_ )
UpperCAmelCase = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
UpperCAmelCase = (logits,) + outputs[1:]
else:
UpperCAmelCase = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=lowercase_ , logits=lowercase_ , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 78 |
from __future__ import annotations
class _snake_case :
def __init__( self , _a ):
__magic_name__ : Optional[Any] = data
__magic_name__ : Node | None = None
__magic_name__ : Node | None = None
def lowerCAmelCase_ ( _snake_case : Node | None ) -> None: # In Order traversal of the tree
'''simple docstring'''
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def lowerCAmelCase_ ( _snake_case : Node | None ) -> int:
'''simple docstring'''
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def lowerCAmelCase_ ( _snake_case : Node ) -> bool:
'''simple docstring'''
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def lowerCAmelCase_ ( ) -> None: # Main function for testing.
'''simple docstring'''
__magic_name__ : int = Node(1 )
__magic_name__ : Union[str, Any] = Node(2 )
__magic_name__ : Tuple = Node(3 )
__magic_name__ : Optional[Any] = Node(4 )
__magic_name__ : Union[str, Any] = Node(5 )
__magic_name__ : Any = Node(6 )
__magic_name__ : int = Node(7 )
__magic_name__ : List[str] = Node(8 )
__magic_name__ : Union[str, Any] = Node(9 )
print(is_full_binary_tree(_snake_case ) )
print(depth_of_tree(_snake_case ) )
print("Tree is: " )
display(_snake_case )
if __name__ == "__main__":
main()
| 281 | 0 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {'''vocab_file''': '''spiece.model'''}
lowerCamelCase_ = {
'''vocab_file''': {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
'''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
}
}
lowerCamelCase_ = {
'''xlnet-base-cased''': None,
'''xlnet-large-cased''': None,
}
# Segments (not really needed)
lowerCamelCase_ = 0
lowerCamelCase_ = 1
lowerCamelCase_ = 2
lowerCamelCase_ = 3
lowerCamelCase_ = 4
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
snake_case = VOCAB_FILES_NAMES
snake_case = PRETRAINED_VOCAB_FILES_MAP
snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case = '''left'''
def __init__( self : Any , __UpperCAmelCase : Tuple , __UpperCAmelCase : Tuple=False , __UpperCAmelCase : Optional[Any]=True , __UpperCAmelCase : List[Any]=False , __UpperCAmelCase : List[Any]="<s>" , __UpperCAmelCase : Optional[Any]="</s>" , __UpperCAmelCase : Dict="<unk>" , __UpperCAmelCase : Tuple="<sep>" , __UpperCAmelCase : List[str]="<pad>" , __UpperCAmelCase : int="<cls>" , __UpperCAmelCase : Dict="<mask>" , __UpperCAmelCase : Optional[Any]=["<eop>", "<eod>"] , __UpperCAmelCase : Optional[Dict[str, Any]] = None , **__UpperCAmelCase : List[Any] , ):
'''simple docstring'''
_A = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else mask_token
_A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=__UpperCAmelCase , remove_space=__UpperCAmelCase , keep_accents=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , additional_special_tokens=__UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **__UpperCAmelCase , )
_A = 3
_A = do_lower_case
_A = remove_space
_A = keep_accents
_A = vocab_file
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCAmelCase )
@property
def lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
return len(self.sp_model )
def lowerCAmelCase ( self : int ):
'''simple docstring'''
_A = {self.convert_ids_to_tokens(__UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Union[str, Any] ):
'''simple docstring'''
_A = self.__dict__.copy()
_A = None
return state
def __setstate__( self : Any , __UpperCAmelCase : str ):
'''simple docstring'''
_A = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
_A = {}
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Optional[int] ):
'''simple docstring'''
if self.remove_space:
_A = " ".join(inputs.strip().split() )
else:
_A = inputs
_A = outputs.replace("``" , "\"" ).replace("''" , "\"" )
if not self.keep_accents:
_A = unicodedata.normalize("NFKD" , __UpperCAmelCase )
_A = "".join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase )] )
if self.do_lower_case:
_A = outputs.lower()
return outputs
def lowerCAmelCase ( self : str , __UpperCAmelCase : str ):
'''simple docstring'''
_A = self.preprocess_text(__UpperCAmelCase )
_A = self.sp_model.encode(__UpperCAmelCase , out_type=__UpperCAmelCase )
_A = []
for piece in pieces:
if len(__UpperCAmelCase ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
_A = self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCAmelCase , "" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
_A = cur_pieces[1:]
else:
_A = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(__UpperCAmelCase )
else:
new_pieces.append(__UpperCAmelCase )
return new_pieces
def lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Optional[int] ):
'''simple docstring'''
return self.sp_model.PieceToId(__UpperCAmelCase )
def lowerCAmelCase ( self : List[str] , __UpperCAmelCase : List[Any] ):
'''simple docstring'''
return self.sp_model.IdToPiece(__UpperCAmelCase )
def lowerCAmelCase ( self : str , __UpperCAmelCase : Dict ):
'''simple docstring'''
_A = "".join(__UpperCAmelCase ).replace(__UpperCAmelCase , " " ).strip()
return out_string
def lowerCAmelCase ( self : int , __UpperCAmelCase : List[int] , __UpperCAmelCase : bool = False , __UpperCAmelCase : bool = None , __UpperCAmelCase : bool = True , **__UpperCAmelCase : List[Any] , ):
'''simple docstring'''
_A = kwargs.pop("use_source_tokenizer" , __UpperCAmelCase )
_A = self.convert_ids_to_tokens(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
_A = []
_A = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(__UpperCAmelCase ) )
_A = []
sub_texts.append(__UpperCAmelCase )
else:
current_sub_text.append(__UpperCAmelCase )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(__UpperCAmelCase ) )
# Mimic the behavior of the Rust tokenizer:
# By default, there are no spaces between special tokens
_A = "".join(__UpperCAmelCase )
_A = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
_A = self.clean_up_tokenization(__UpperCAmelCase )
return clean_text
else:
return text
def lowerCAmelCase ( self : Dict , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ):
'''simple docstring'''
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def lowerCAmelCase ( self : Any , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None , __UpperCAmelCase : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCAmelCase , token_ids_a=__UpperCAmelCase , already_has_special_tokens=__UpperCAmelCase )
if token_ids_a is not None:
return ([0] * len(__UpperCAmelCase )) + [1] + ([0] * len(__UpperCAmelCase )) + [1, 1]
return ([0] * len(__UpperCAmelCase )) + [1, 1]
def lowerCAmelCase ( self : Any , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ):
'''simple docstring'''
_A = [self.sep_token_id]
_A = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(__UpperCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A = os.path.join(
__UpperCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __UpperCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__UpperCAmelCase , "wb" ) as fi:
_A = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (out_vocab_file,)
| 79 |
def lowerCAmelCase_ ( _snake_case : str , _snake_case : str ) -> bool:
'''simple docstring'''
__magic_name__ : Union[str, Any] = len(_snake_case ) + 1
__magic_name__ : List[str] = len(_snake_case ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
__magic_name__ : str = [[0 for i in range(_snake_case )] for j in range(_snake_case )]
# since string of zero length match pattern of zero length
__magic_name__ : Optional[int] = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _snake_case ):
__magic_name__ : Optional[int] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _snake_case ):
__magic_name__ : Union[str, Any] = dp[0][j - 2] if pattern[j - 1] == "*" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _snake_case ):
for j in range(1 , _snake_case ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
__magic_name__ : Optional[int] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
__magic_name__ : Optional[Any] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
__magic_name__ : List[Any] = dp[i - 1][j]
else:
__magic_name__ : Union[str, Any] = 0
else:
__magic_name__ : Dict = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
snake_case : Optional[Any] = "aab"
snake_case : List[str] = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F"{input_string} matches the given pattern {pattern}")
else:
print(F"{input_string} does not match with the given pattern {pattern}")
| 281 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a__ : Dict = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
a__ : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 80 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class _snake_case :
@staticmethod
def SCREAMING_SNAKE_CASE ( *_a , **_a ):
pass
def lowerCAmelCase_ ( _snake_case : Image ) -> str:
'''simple docstring'''
__magic_name__ : Optional[int] = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def lowerCAmelCase_ ( _snake_case : Image ) -> Dict:
'''simple docstring'''
__magic_name__ : List[Any] = np.array(_snake_case )
__magic_name__ : Optional[int] = npimg.shape
return {"hash": hashimage(_snake_case ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class _snake_case ( unittest.TestCase ):
UpperCamelCase__ = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
UpperCamelCase__ = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a ):
__magic_name__ : Dict = MaskGenerationPipeline(model=_a , image_processor=_a )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def SCREAMING_SNAKE_CASE ( self , _a , _a ):
pass
@require_tf
@unittest.skip("Image segmentation not implemented in TF" )
def SCREAMING_SNAKE_CASE ( self ):
pass
@slow
@require_torch
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = pipeline("mask-generation" , model="facebook/sam-vit-huge" )
__magic_name__ : str = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg" , points_per_batch=256 )
# Shortening by hashing
__magic_name__ : Dict = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(_a ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
{"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
{"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
{"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
{"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
{"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
{"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
{"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
{"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
{"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
{"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
{"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
{"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
{"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
{"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
{"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
{"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
{"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
{"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
{"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
{"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
{"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
{"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
{"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
{"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
{"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
{"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
{"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
{"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
{"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
] , )
# fmt: on
@require_torch
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : str = "facebook/sam-vit-huge"
__magic_name__ : str = pipeline("mask-generation" , model=_a )
__magic_name__ : Tuple = image_segmenter(
"http://images.cocodataset.org/val2017/000000039769.jpg" , pred_iou_thresh=1 , points_per_batch=256 )
# Shortening by hashing
__magic_name__ : Any = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(_a ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
{"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
{"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
{"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
{"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
] , )
| 281 | 0 |
"""simple docstring"""
def _A ( lowercase ):
"""simple docstring"""
if not head:
return True
# split the list to two parts
a , a =head.next, head
while fast and fast.next:
a =fast.next.next
a =slow.next
a =slow.next
a =None # Don't forget here! But forget still works!
# reverse the second part
a =None
while second:
a =second.next
a =node
a =second
a =nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
a =node.next
a =head.next
return True
def _A ( lowercase ):
"""simple docstring"""
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
a =a =a =head
while fast and fast.next:
a , a =fast.next.next, slow.next
# 2. Push the second half into the stack
a =[slow.val]
while slow.next:
a =slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
a =cur.next
return True
def _A ( lowercase ):
"""simple docstring"""
if not head or not head.next:
return True
a ={}
a =0
while head:
if head.val in d:
d[head.val].append(lowercase )
else:
a =[pos]
a =head.next
pos += 1
a =pos - 1
a =0
for v in d.values():
if len(lowercase ) % 2 != 0:
middle += 1
else:
a =0
for i in range(0 , len(lowercase ) ):
if v[i] + v[len(lowercase ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True | 81 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
snake_case : List[Any] = "\\n@inproceedings{lin-2004-rouge,\n title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n author = \"Lin, Chin-Yew\",\n booktitle = \"Text Summarization Branches Out\",\n month = jul,\n year = \"2004\",\n address = \"Barcelona, Spain\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W04-1013\",\n pages = \"74--81\",\n}\n"
snake_case : Any = "\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"
snake_case : str = "\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n rouge_types: A list of rouge types to calculate.\n Valid names:\n `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n `\"rougeL\"`: Longest common subsequence based scoring.\n `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n See details in https://github.com/huggingface/datasets/issues/617\n use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n use_aggregator: Return aggregates if this is set to True\nReturns:\n rouge1: rouge_1 (precision, recall, f1),\n rouge2: rouge_2 (precision, recall, f1),\n rougeL: rouge_l (precision, recall, f1),\n rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n >>> rouge = datasets.load_metric('rouge')\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> results = rouge.compute(predictions=predictions, references=references)\n >>> print(list(results.keys()))\n ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n >>> print(results[\"rouge1\"])\n AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n >>> print(results[\"rouge1\"].mid.fmeasure)\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _snake_case ( datasets.Metric ):
def SCREAMING_SNAKE_CASE ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"] , reference_urls=[
"https://en.wikipedia.org/wiki/ROUGE_(metric)",
"https://github.com/google-research/google-research/tree/master/rouge",
] , )
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a=None , _a=True , _a=False ):
if rouge_types is None:
__magic_name__ : str = ["rouge1", "rouge2", "rougeL", "rougeLsum"]
__magic_name__ : List[str] = rouge_scorer.RougeScorer(rouge_types=_a , use_stemmer=_a )
if use_aggregator:
__magic_name__ : Dict = scoring.BootstrapAggregator()
else:
__magic_name__ : str = []
for ref, pred in zip(_a , _a ):
__magic_name__ : Union[str, Any] = scorer.score(_a , _a )
if use_aggregator:
aggregator.add_scores(_a )
else:
scores.append(_a )
if use_aggregator:
__magic_name__ : Any = aggregator.aggregate()
else:
__magic_name__ : List[Any] = {}
for key in scores[0]:
__magic_name__ : str = [score[key] for score in scores]
return result
| 281 | 0 |
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor
if is_flax_available():
import jax
from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self , _snake_case , _snake_case=100 , _snake_case=13 , _snake_case=30 , _snake_case=2 , _snake_case=3 , _snake_case=True , _snake_case=True , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=10 , _snake_case=0.02 , _snake_case=3 , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = vocab_size
_lowerCAmelCase = batch_size
_lowerCAmelCase = image_size
_lowerCAmelCase = patch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = is_training
_lowerCAmelCase = use_labels
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = type_sequence_label_size
_lowerCAmelCase = initializer_range
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
_lowerCAmelCase = (image_size // patch_size) ** 2
_lowerCAmelCase = num_patches + 1
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase = BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_snake_case , initializer_range=self.initializer_range , )
return config, pixel_values, labels
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = FlaxBeitModel(config=_snake_case )
_lowerCAmelCase = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = FlaxBeitForMaskedImageModeling(config=_snake_case )
_lowerCAmelCase = model(_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def snake_case ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = self.type_sequence_label_size
_lowerCAmelCase = FlaxBeitForImageClassification(config=_snake_case )
_lowerCAmelCase = model(_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_lowerCAmelCase = 1
_lowerCAmelCase = FlaxBeitForImageClassification(_snake_case )
_lowerCAmelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_lowerCAmelCase = model(_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = config_and_inputs
_lowerCAmelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_flax
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = (
(FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
)
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = FlaxBeitModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=_snake_case , has_text_modality=_snake_case , hidden_size=37 )
def snake_case ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(_snake_case )
_lowerCAmelCase = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCAmelCase = [*signature.parameters.keys()]
_lowerCAmelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_lowerCAmelCase = self._prepare_for_class(_snake_case , _snake_case )
_lowerCAmelCase = model_class(_snake_case )
@jax.jit
def model_jitted(_snake_case , **_snake_case ):
return model(pixel_values=_snake_case , **_snake_case )
with self.subTest("""JIT Enabled""" ):
_lowerCAmelCase = model_jitted(**_snake_case ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
_lowerCAmelCase = model_jitted(**_snake_case ).to_tuple()
self.assertEqual(len(_snake_case ) , len(_snake_case ) )
for jitted_output, output in zip(_snake_case , _snake_case ):
self.assertEqual(jitted_output.shape , output.shape )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_snake_case )
@slow
def snake_case ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
_lowerCAmelCase = model_class_name.from_pretrained("""microsoft/beit-base-patch16-224""" )
_lowerCAmelCase = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(_snake_case )
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def snake_case ( self ):
"""simple docstring"""
return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224""" ) if is_vision_available() else None
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k""" )
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=_snake_case , return_tensors="""np""" ).pixel_values
# prepare bool_masked_pos
_lowerCAmelCase = np.ones((1, 196) , dtype=_snake_case )
# forward pass
_lowerCAmelCase = model(pixel_values=_snake_case , bool_masked_pos=_snake_case )
_lowerCAmelCase = outputs.logits
# verify the logits
_lowerCAmelCase = (1, 196, 8192)
self.assertEqual(logits.shape , _snake_case )
_lowerCAmelCase = np.array(
[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] )
self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] , _snake_case , atol=1e-2 ) )
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224""" )
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=_snake_case , return_tensors="""np""" )
# forward pass
_lowerCAmelCase = model(**_snake_case )
_lowerCAmelCase = outputs.logits
# verify the logits
_lowerCAmelCase = (1, 1000)
self.assertEqual(logits.shape , _snake_case )
_lowerCAmelCase = np.array([-1.2385, -1.0987, -1.0108] )
self.assertTrue(np.allclose(logits[0, :3] , _snake_case , atol=1e-4 ) )
_lowerCAmelCase = 281
self.assertEqual(logits.argmax(-1 ).item() , _snake_case )
@slow
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k""" )
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=_snake_case , return_tensors="""np""" )
# forward pass
_lowerCAmelCase = model(**_snake_case )
_lowerCAmelCase = outputs.logits
# verify the logits
_lowerCAmelCase = (1, 21841)
self.assertEqual(logits.shape , _snake_case )
_lowerCAmelCase = np.array([1.6881, -0.2787, 0.5901] )
self.assertTrue(np.allclose(logits[0, :3] , _snake_case , atol=1e-4 ) )
_lowerCAmelCase = 2396
self.assertEqual(logits.argmax(-1 ).item() , _snake_case )
| 82 |
snake_case : Optional[int] = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowerCAmelCase_ ( _snake_case : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(_snake_case , _snake_case ):
__magic_name__ : Tuple = F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
raise TypeError(_snake_case )
__magic_name__ : Optional[int] = "".join(bin(_snake_case )[2:].zfill(8 ) for byte in data )
__magic_name__ : List[Any] = len(_snake_case ) % 6 != 0
if padding_needed:
# The padding that will be added later
__magic_name__ : List[str] = B"=" * ((6 - len(_snake_case ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(_snake_case ) % 6)
else:
__magic_name__ : List[str] = B""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(_snake_case ) , 6 ) ).encode()
+ padding
)
def lowerCAmelCase_ ( _snake_case : str ) -> bytes:
'''simple docstring'''
if not isinstance(_snake_case , _snake_case ) and not isinstance(_snake_case , _snake_case ):
__magic_name__ : List[str] = (
"argument should be a bytes-like object or ASCII string, "
F'''not \'{encoded_data.__class__.__name__}\''''
)
raise TypeError(_snake_case )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(_snake_case , _snake_case ):
try:
__magic_name__ : List[Any] = encoded_data.decode("utf-8" )
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters" )
__magic_name__ : List[str] = encoded_data.count("=" )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(_snake_case ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
__magic_name__ : Optional[int] = encoded_data[:-padding]
__magic_name__ : Dict = "".join(
bin(B64_CHARSET.index(_snake_case ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
__magic_name__ : Union[str, Any] = "".join(
bin(B64_CHARSET.index(_snake_case ) )[2:].zfill(6 ) for char in encoded_data )
__magic_name__ : List[Any] = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(_snake_case ) , 8 )
]
return bytes(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 281 | 0 |
'''simple docstring'''
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
DataProcessor,
InputExample,
InputFeatures,
SingleSentenceClassificationProcessor,
SquadExample,
SquadFeatures,
SquadVaProcessor,
SquadVaProcessor,
glue_convert_examples_to_features,
glue_output_modes,
glue_processors,
glue_tasks_num_labels,
squad_convert_examples_to_features,
xnli_output_modes,
xnli_processors,
xnli_tasks_num_labels,
)
| 83 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class _snake_case ( unittest.TestCase ):
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ):
__magic_name__ : List[Any] = parent
__magic_name__ : Optional[Any] = batch_size
__magic_name__ : Dict = seq_length
__magic_name__ : Union[str, Any] = is_training
__magic_name__ : Optional[Any] = use_attention_mask
__magic_name__ : Optional[Any] = use_token_type_ids
__magic_name__ : int = use_labels
__magic_name__ : List[Any] = vocab_size
__magic_name__ : Union[str, Any] = hidden_size
__magic_name__ : Optional[Any] = num_hidden_layers
__magic_name__ : int = num_attention_heads
__magic_name__ : Any = intermediate_size
__magic_name__ : List[Any] = hidden_act
__magic_name__ : List[Any] = hidden_dropout_prob
__magic_name__ : Optional[int] = attention_probs_dropout_prob
__magic_name__ : List[Any] = max_position_embeddings
__magic_name__ : Tuple = type_vocab_size
__magic_name__ : List[str] = type_sequence_label_size
__magic_name__ : Dict = initializer_range
__magic_name__ : List[Any] = num_choices
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__magic_name__ : List[Any] = None
if self.use_attention_mask:
__magic_name__ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
__magic_name__ : str = None
if self.use_token_type_ids:
__magic_name__ : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__magic_name__ : List[str] = RobertaPreLayerNormConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : int = self.prepare_config_and_inputs()
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : List[Any] = config_and_inputs
__magic_name__ : List[str] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = self.prepare_config_and_inputs()
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] = config_and_inputs
__magic_name__ : Tuple = True
__magic_name__ : int = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__magic_name__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class _snake_case ( snake_case , unittest.TestCase ):
UpperCamelCase__ = True
UpperCamelCase__ = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[Any] = FlaxRobertaPreLayerNormModelTester(self )
@slow
def SCREAMING_SNAKE_CASE ( self ):
for model_class_name in self.all_model_classes:
__magic_name__ : Optional[Any] = model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_a )
__magic_name__ : Dict = model(np.ones((1, 1) ) )
self.assertIsNotNone(_a )
@require_flax
class _snake_case ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_a )
__magic_name__ : Union[str, Any] = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__magic_name__ : List[str] = model(_a )[0]
__magic_name__ : str = [1, 11, 50_265]
self.assertEqual(list(output.shape ) , _a )
# compare the actual values for a slice.
__magic_name__ : List[str] = np.array(
[[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _a , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_a )
__magic_name__ : Tuple = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__magic_name__ : Tuple = model(_a )[0]
# compare the actual values for a slice.
__magic_name__ : Dict = np.array(
[[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _a , atol=1e-4 ) )
| 281 | 0 |
"""simple docstring"""
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :Union[str, Any] = 1_0
lowerCAmelCase_ :Optional[int] = datasets.Features(
{
"""tokens""": datasets.Sequence(datasets.Value("""string""" ) ),
"""labels""": datasets.Sequence(datasets.ClassLabel(names=["""negative""", """positive"""] ) ),
"""answers""": datasets.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
"""id""": datasets.Value("""int64""" ),
} )
lowerCAmelCase_ :int = datasets.Dataset.from_dict(
{
"""tokens""": [["""foo"""] * 5] * n,
"""labels""": [[1] * 5] * n,
"""answers""": [{"""answer_start""": [9_7], """text""": ["""1976"""]}] * 1_0,
"""id""": list(range(lowercase__ ) ),
} , features=lowercase__ , )
return dataset
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple , lowercase__ : int ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """file.arrow""" )
dataset.map(cache_file_name=lowercase__ )
return filename
# FILE_CONTENT + files
__UpperCAmelCase = '\\n Text data.\n Second line of data.'
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt"""
lowerCAmelCase_ :List[Any] = FILE_CONTENT
with open(lowercase__ , """w""" ) as f:
f.write(lowercase__ )
return filename
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[Any] ) -> Tuple:
'''simple docstring'''
import bza
lowerCAmelCase_ :Optional[int] = tmp_path_factory.mktemp("""data""" ) / """file.txt.bz2"""
lowerCAmelCase_ :Tuple = bytes(lowercase__ , """utf-8""" )
with bza.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[Any] ) -> Dict:
'''simple docstring'''
import gzip
lowerCAmelCase_ :int = str(tmp_path_factory.mktemp("""data""" ) / """file.txt.gz""" )
lowerCAmelCase_ :Tuple = bytes(lowercase__ , """utf-8""" )
with gzip.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict ) -> Optional[int]:
'''simple docstring'''
if datasets.config.LZ4_AVAILABLE:
import lza.frame
lowerCAmelCase_ :List[Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt.lz4"""
lowerCAmelCase_ :int = bytes(lowercase__ , """utf-8""" )
with lza.frame.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict , lowercase__ : Optional[int] ) -> Any:
'''simple docstring'''
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
lowerCAmelCase_ :Dict = tmp_path_factory.mktemp("""data""" ) / """file.txt.7z"""
with pyazr.SevenZipFile(lowercase__ , """w""" ) as archive:
archive.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
import tarfile
lowerCAmelCase_ :Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.tar"""
with tarfile.TarFile(lowercase__ , """w""" ) as f:
f.add(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> str:
'''simple docstring'''
import lzma
lowerCAmelCase_ :Optional[Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt.xz"""
lowerCAmelCase_ :Optional[Any] = bytes(lowercase__ , """utf-8""" )
with lzma.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : List[Any] ) -> Any:
'''simple docstring'''
import zipfile
lowerCAmelCase_ :Dict = tmp_path_factory.mktemp("""data""" ) / """file.txt.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int ) -> Tuple:
'''simple docstring'''
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
lowerCAmelCase_ :Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt.zst"""
lowerCAmelCase_ :Any = bytes(lowercase__ , """utf-8""" )
with zstd.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] ) -> str:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """file.xml"""
lowerCAmelCase_ :Any = textwrap.dedent(
"""\
<?xml version=\"1.0\" encoding=\"UTF-8\" ?>
<tmx version=\"1.4\">
<header segtype=\"sentence\" srclang=\"ca\" />
<body>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>
</tu>
</body>
</tmx>""" )
with open(lowercase__ , """w""" ) as f:
f.write(lowercase__ )
return filename
__UpperCAmelCase = [
{'col_1': '0', 'col_2': 0, 'col_3': 0.0},
{'col_1': '1', 'col_2': 1, 'col_3': 1.0},
{'col_1': '2', 'col_2': 2, 'col_3': 2.0},
{'col_1': '3', 'col_2': 3, 'col_3': 3.0},
]
__UpperCAmelCase = [
{'col_1': '4', 'col_2': 4, 'col_3': 4.0},
{'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
__UpperCAmelCase = {
'col_1': ['0', '1', '2', '3'],
'col_2': [0, 1, 2, 3],
'col_3': [0.0, 1.0, 2.0, 3.0],
}
__UpperCAmelCase = [
{'col_3': 0.0, 'col_1': '0', 'col_2': 0},
{'col_3': 1.0, 'col_1': '1', 'col_2': 1},
]
__UpperCAmelCase = [
{'col_1': 's0', 'col_2': 0, 'col_3': 0.0},
{'col_1': 's1', 'col_2': 1, 'col_3': 1.0},
{'col_1': 's2', 'col_2': 2, 'col_3': 2.0},
{'col_1': 's3', 'col_2': 3, 'col_3': 3.0},
]
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> Union[str, Any]:
'''simple docstring'''
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Tuple = datasets.Dataset.from_dict(lowercase__ )
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.arrow""" )
dataset.map(cache_file_name=lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int ) -> str:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.sqlite""" )
with contextlib.closing(sqlitea.connect(lowercase__ ) ) as con:
lowerCAmelCase_ :Union[str, Any] = con.cursor()
cur.execute("""CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)""" )
for item in DATA:
cur.execute("""INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)""" , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> int:
'''simple docstring'''
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.csv""" )
with open(lowercase__ , """w""" , newline="""""" ) as f:
lowerCAmelCase_ :Optional[int] = csv.DictWriter(lowercase__ , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :str = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.csv""" )
with open(lowercase__ , """w""" , newline="""""" ) as f:
lowerCAmelCase_ :Dict = csv.DictWriter(lowercase__ , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str , lowercase__ : Dict ) -> Union[str, Any]:
'''simple docstring'''
import bza
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.bz2"""
with open(lowercase__ , """rb""" ) as f:
lowerCAmelCase_ :Union[str, Any] = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str , lowercase__ : Optional[Any] , lowercase__ : Any ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : List[str] , lowercase__ : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(csv_path.replace(""".csv""" , """.CSV""" ) ) )
f.write(lowercase__ , arcname=os.path.basename(csva_path.replace(""".csv""" , """.CSV""" ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[int] , lowercase__ : Tuple , lowercase__ : str ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.csv.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.parquet""" )
lowerCAmelCase_ :Optional[Any] = pa.schema(
{
"""col_1""": pa.string(),
"""col_2""": pa.intaa(),
"""col_3""": pa.floataa(),
} )
with open(lowercase__ , """wb""" ) as f:
lowerCAmelCase_ :Optional[int] = pq.ParquetWriter(lowercase__ , schema=lowercase__ )
lowerCAmelCase_ :List[str] = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(lowercase__ ) )] for k in DATA[0]} , schema=lowercase__ )
writer.write_table(lowercase__ )
writer.close()
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Dict = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
lowerCAmelCase_ :Union[str, Any] = {"""data""": DATA}
with open(lowercase__ , """w""" ) as f:
json.dump(lowercase__ , lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
lowerCAmelCase_ :Optional[Any] = {"""data""": DATA_DICT_OF_LISTS}
with open(lowercase__ , """w""" ) as f:
json.dump(lowercase__ , lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :str = str(tmp_path_factory.mktemp("""data""" ) / """dataset_312.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA_312:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = str(tmp_path_factory.mktemp("""data""" ) / """dataset-str.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA_STR:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int , lowercase__ : Dict ) -> Optional[int]:
'''simple docstring'''
import gzip
lowerCAmelCase_ :Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt.gz""" )
with open(lowercase__ , """rb""" ) as orig_file:
with gzip.open(lowercase__ , """wb""" ) as zipped_file:
zipped_file.writelines(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : List[Any] ) -> Any:
'''simple docstring'''
import gzip
lowerCAmelCase_ :Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.gz""" )
with open(lowercase__ , """rb""" ) as orig_file:
with gzip.open(lowercase__ , """wb""" ) as zipped_file:
zipped_file.writelines(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : Optional[int] , lowercase__ : List[Any] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : str , lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""nested""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : List[Any] , lowercase__ : List[str] ) -> int:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.jsonl.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : str , lowercase__ : List[str] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Any = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.tar"""
with tarfile.TarFile(lowercase__ , """w""" ) as f:
f.add(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.add(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict , lowercase__ : str , lowercase__ : List[str] , lowercase__ : int ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.tar"""
with tarfile.TarFile(lowercase__ , """w""" ) as f:
f.add(lowercase__ , arcname=os.path.join("""nested""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ :str = ["""0""", """1""", """2""", """3"""]
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt""" )
with open(lowercase__ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :int = ["""0""", """1""", """2""", """3"""]
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.txt""" )
with open(lowercase__ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :Dict = ["""0""", """1""", """2""", """3"""]
lowerCAmelCase_ :Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.abc"""
with open(lowercase__ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : str , lowercase__ : int ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Any = tmp_path_factory.mktemp("""data""" ) / """dataset.text.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple , lowercase__ : Tuple , lowercase__ : List[str] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.text.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[int] , lowercase__ : Any , lowercase__ : Tuple ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.ext.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename("""unsupported.ext""" ) )
f.write(lowercase__ , arcname=os.path.basename("""unsupported_2.ext""" ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = """\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""] )
lowerCAmelCase_ :str = str(tmp_path_factory.mktemp("""data""" ) / """dataset_with_unicode_new_lines.txt""" )
with open(lowercase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> int:
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_image_rgb.jpg""" )
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> Tuple:
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_audio_44100.wav""" )
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : Tuple ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.img.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ).replace(""".jpg""" , """2.jpg""" ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data_dir""" )
(data_dir / "subdir").mkdir()
with open(data_dir / """subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden file
with open(data_dir / """subdir""" / """.test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / """.subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """.subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
return data_dir
| 84 |
def lowerCAmelCase_ ( _snake_case : list[list[int | float]] ) -> int:
'''simple docstring'''
__magic_name__ : Any = len(_snake_case )
__magic_name__ : Optional[Any] = len(matrix[0] )
__magic_name__ : Union[str, Any] = min(_snake_case , _snake_case )
for row in range(_snake_case ):
# Check if diagonal element is not zero
if matrix[row][row] != 0:
# Eliminate all the elements below the diagonal
for col in range(row + 1 , _snake_case ):
__magic_name__ : Optional[Any] = matrix[col][row] / matrix[row][row]
for i in range(_snake_case , _snake_case ):
matrix[col][i] -= multiplier * matrix[row][i]
else:
# Find a non-zero diagonal element to swap rows
__magic_name__ : str = True
for i in range(row + 1 , _snake_case ):
if matrix[i][row] != 0:
__magic_name__ , __magic_name__ : List[str] = matrix[i], matrix[row]
__magic_name__ : Union[str, Any] = False
break
if reduce:
rank -= 1
for i in range(_snake_case ):
__magic_name__ : Any = matrix[i][rank]
# Reduce the row pointer by one to stay on the same row
row -= 1
return rank
if __name__ == "__main__":
import doctest
doctest.testmod()
| 281 | 0 |
'''simple docstring'''
from statistics import mean, stdev
def UpperCamelCase_( snake_case : list , snake_case : int = 3 ):
'''simple docstring'''
snake_case_ = min(snake_case )
snake_case_ = max(snake_case )
# normalize data
return [round((x - x_min) / (x_max - x_min) , snake_case ) for x in data]
def UpperCamelCase_( snake_case : list , snake_case : int = 3 ):
'''simple docstring'''
snake_case_ = mean(snake_case )
snake_case_ = stdev(snake_case )
# standardize data
return [round((x - mu) / (sigma) , snake_case ) for x in data]
| 85 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
snake_case : Dict = re.compile(R"\b(a|an|the)\b", re.UNICODE)
snake_case : Optional[int] = None
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : Any = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0." )
parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file." )
parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions." )
parser.add_argument(
"--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout)." )
parser.add_argument(
"--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer." )
parser.add_argument(
"--na-prob-thresh" , "-t" , type=_snake_case , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , )
parser.add_argument(
"--out-image-dir" , "-p" , metavar="out_images" , default=_snake_case , help="Save precision-recall curves to directory." )
parser.add_argument("--verbose" , "-v" , action="store_true" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def lowerCAmelCase_ ( _snake_case : Optional[Any] ) -> Tuple:
'''simple docstring'''
__magic_name__ : Optional[int] = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
__magic_name__ : str = bool(qa["answers"]["text"] )
return qid_to_has_ans
def lowerCAmelCase_ ( _snake_case : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
def remove_articles(_snake_case : List[str] ):
return ARTICLES_REGEX.sub(" " , _snake_case )
def white_space_fix(_snake_case : Optional[int] ):
return " ".join(text.split() )
def remove_punc(_snake_case : Optional[int] ):
__magic_name__ : Dict = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_snake_case : str ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_snake_case ) ) ) )
def lowerCAmelCase_ ( _snake_case : Any ) -> Optional[Any]:
'''simple docstring'''
if not s:
return []
return normalize_answer(_snake_case ).split()
def lowerCAmelCase_ ( _snake_case : str , _snake_case : Dict ) -> Tuple:
'''simple docstring'''
return int(normalize_answer(_snake_case ) == normalize_answer(_snake_case ) )
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : int ) -> str:
'''simple docstring'''
__magic_name__ : Any = get_tokens(_snake_case )
__magic_name__ : Optional[int] = get_tokens(_snake_case )
__magic_name__ : Tuple = collections.Counter(_snake_case ) & collections.Counter(_snake_case )
__magic_name__ : Tuple = sum(common.values() )
if len(_snake_case ) == 0 or len(_snake_case ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
__magic_name__ : Dict = 1.0 * num_same / len(_snake_case )
__magic_name__ : Optional[Any] = 1.0 * num_same / len(_snake_case )
__magic_name__ : List[Any] = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( _snake_case : Optional[Any] , _snake_case : List[Any] ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = {}
__magic_name__ : int = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
__magic_name__ : Union[str, Any] = qa["id"]
__magic_name__ : Any = [t for t in qa["answers"]["text"] if normalize_answer(_snake_case )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
__magic_name__ : Tuple = [""]
if qid not in preds:
print(F'''Missing prediction for {qid}''' )
continue
__magic_name__ : Any = preds[qid]
# Take max over all gold answers
__magic_name__ : List[Any] = max(compute_exact(_snake_case , _snake_case ) for a in gold_answers )
__magic_name__ : int = max(compute_fa(_snake_case , _snake_case ) for a in gold_answers )
return exact_scores, fa_scores
def lowerCAmelCase_ ( _snake_case : Optional[Any] , _snake_case : List[Any] , _snake_case : Optional[int] , _snake_case : Dict ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : str = {}
for qid, s in scores.items():
__magic_name__ : Dict = na_probs[qid] > na_prob_thresh
if pred_na:
__magic_name__ : str = float(not qid_to_has_ans[qid] )
else:
__magic_name__ : Optional[int] = s
return new_scores
def lowerCAmelCase_ ( _snake_case : List[Any] , _snake_case : List[str] , _snake_case : Tuple=None ) -> Tuple:
'''simple docstring'''
if not qid_list:
__magic_name__ : Any = len(_snake_case )
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores.values() ) / total),
("f1", 100.0 * sum(fa_scores.values() ) / total),
("total", total),
] )
else:
__magic_name__ : Tuple = len(_snake_case )
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
("f1", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
("total", total),
] )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : str , _snake_case : str ) -> Dict:
'''simple docstring'''
for k in new_eval:
__magic_name__ : int = new_eval[k]
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Dict , _snake_case : Optional[Any] , _snake_case : Union[str, Any] ) -> str:
'''simple docstring'''
plt.step(_snake_case , _snake_case , color="b" , alpha=0.2 , where="post" )
plt.fill_between(_snake_case , _snake_case , step="post" , alpha=0.2 , color="b" )
plt.xlabel("Recall" )
plt.ylabel("Precision" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(_snake_case )
plt.savefig(_snake_case )
plt.clf()
def lowerCAmelCase_ ( _snake_case : Dict , _snake_case : Any , _snake_case : Optional[int] , _snake_case : List[Any] , _snake_case : Optional[int]=None , _snake_case : int=None ) -> str:
'''simple docstring'''
__magic_name__ : Union[str, Any] = sorted(_snake_case , key=lambda _snake_case : na_probs[k] )
__magic_name__ : Optional[int] = 0.0
__magic_name__ : str = 1.0
__magic_name__ : str = 0.0
__magic_name__ : List[str] = [1.0]
__magic_name__ : str = [0.0]
__magic_name__ : Optional[Any] = 0.0
for i, qid in enumerate(_snake_case ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
__magic_name__ : List[str] = true_pos / float(i + 1 )
__magic_name__ : Any = true_pos / float(_snake_case )
if i == len(_snake_case ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(_snake_case )
recalls.append(_snake_case )
if out_image:
plot_pr_curve(_snake_case , _snake_case , _snake_case , _snake_case )
return {"ap": 100.0 * avg_prec}
def lowerCAmelCase_ ( _snake_case : Tuple , _snake_case : Optional[Any] , _snake_case : Optional[int] , _snake_case : Optional[Any] , _snake_case : Any , _snake_case : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
if out_image_dir and not os.path.exists(_snake_case ):
os.makedirs(_snake_case )
__magic_name__ : Any = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
__magic_name__ : str = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_exact.png" ) , title="Precision-Recall curve for Exact Match score" , )
__magic_name__ : Union[str, Any] = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_f1.png" ) , title="Precision-Recall curve for F1 score" , )
__magic_name__ : str = {k: float(_snake_case ) for k, v in qid_to_has_ans.items()}
__magic_name__ : str = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_oracle.png" ) , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , )
merge_eval(_snake_case , _snake_case , "pr_exact" )
merge_eval(_snake_case , _snake_case , "pr_f1" )
merge_eval(_snake_case , _snake_case , "pr_oracle" )
def lowerCAmelCase_ ( _snake_case : int , _snake_case : Optional[Any] , _snake_case : List[str] , _snake_case : Optional[Any] ) -> Dict:
'''simple docstring'''
if not qid_list:
return
__magic_name__ : Dict = [na_probs[k] for k in qid_list]
__magic_name__ : str = np.ones_like(_snake_case ) / float(len(_snake_case ) )
plt.hist(_snake_case , weights=_snake_case , bins=20 , range=(0.0, 1.0) )
plt.xlabel("Model probability of no-answer" )
plt.ylabel("Proportion of dataset" )
plt.title(F'''Histogram of no-answer probability: {name}''' )
plt.savefig(os.path.join(_snake_case , F'''na_prob_hist_{name}.png''' ) )
plt.clf()
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Tuple , _snake_case : List[str] , _snake_case : Dict ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
__magic_name__ : List[str] = num_no_ans
__magic_name__ : Dict = cur_score
__magic_name__ : Dict = 0.0
__magic_name__ : Any = sorted(_snake_case , key=lambda _snake_case : na_probs[k] )
for i, qid in enumerate(_snake_case ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
__magic_name__ : Union[str, Any] = scores[qid]
else:
if preds[qid]:
__magic_name__ : List[Any] = -1
else:
__magic_name__ : Optional[int] = 0
cur_score += diff
if cur_score > best_score:
__magic_name__ : Optional[int] = cur_score
__magic_name__ : List[Any] = na_probs[qid]
return 100.0 * best_score / len(_snake_case ), best_thresh
def lowerCAmelCase_ ( _snake_case : int , _snake_case : str , _snake_case : List[str] , _snake_case : Tuple , _snake_case : List[Any] , _snake_case : Dict ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ , __magic_name__ : List[str] = find_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case )
__magic_name__ , __magic_name__ : int = find_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case )
__magic_name__ : Optional[int] = best_exact
__magic_name__ : List[Any] = exact_thresh
__magic_name__ : Dict = best_fa
__magic_name__ : Any = fa_thresh
def lowerCAmelCase_ ( ) -> int:
'''simple docstring'''
with open(OPTS.data_file ) as f:
__magic_name__ : Optional[Any] = json.load(_snake_case )
__magic_name__ : List[Any] = dataset_json["data"]
with open(OPTS.pred_file ) as f:
__magic_name__ : Optional[Any] = json.load(_snake_case )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
__magic_name__ : Any = json.load(_snake_case )
else:
__magic_name__ : Any = {k: 0.0 for k in preds}
__magic_name__ : str = make_qid_to_has_ans(_snake_case ) # maps qid to True/False
__magic_name__ : Tuple = [k for k, v in qid_to_has_ans.items() if v]
__magic_name__ : Optional[Any] = [k for k, v in qid_to_has_ans.items() if not v]
__magic_name__ , __magic_name__ : Union[str, Any] = get_raw_scores(_snake_case , _snake_case )
__magic_name__ : Optional[Any] = apply_no_ans_threshold(_snake_case , _snake_case , _snake_case , OPTS.na_prob_thresh )
__magic_name__ : Optional[Any] = apply_no_ans_threshold(_snake_case , _snake_case , _snake_case , OPTS.na_prob_thresh )
__magic_name__ : List[Any] = make_eval_dict(_snake_case , _snake_case )
if has_ans_qids:
__magic_name__ : int = make_eval_dict(_snake_case , _snake_case , qid_list=_snake_case )
merge_eval(_snake_case , _snake_case , "HasAns" )
if no_ans_qids:
__magic_name__ : List[Any] = make_eval_dict(_snake_case , _snake_case , qid_list=_snake_case )
merge_eval(_snake_case , _snake_case , "NoAns" )
if OPTS.na_prob_file:
find_all_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , OPTS.out_image_dir )
histogram_na_prob(_snake_case , _snake_case , OPTS.out_image_dir , "hasAns" )
histogram_na_prob(_snake_case , _snake_case , OPTS.out_image_dir , "noAns" )
if OPTS.out_file:
with open(OPTS.out_file , "w" ) as f:
json.dump(_snake_case , _snake_case )
else:
print(json.dumps(_snake_case , indent=2 ) )
if __name__ == "__main__":
snake_case : int = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
main()
| 281 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class A__ ( _lowerCamelCase , unittest.TestCase):
A_ : Any = KandinskyVaaInpaintPipeline
A_ : Tuple = ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
A_ : List[str] = [
'image_embeds',
'negative_image_embeds',
'image',
'mask_image',
]
A_ : List[str] = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
A_ : Tuple = False
@property
def __lowerCamelCase ( self ):
return 32
@property
def __lowerCamelCase ( self ):
return 32
@property
def __lowerCamelCase ( self ):
return self.time_input_dim
@property
def __lowerCamelCase ( self ):
return self.time_input_dim * 4
@property
def __lowerCamelCase ( self ):
return 1_00
@property
def __lowerCamelCase ( self ):
torch.manual_seed(0 )
__lowerCAmelCase : Optional[Any] = {
'in_channels': 9,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'image',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
__lowerCAmelCase : Optional[Any] = UNetaDConditionModel(**_SCREAMING_SNAKE_CASE )
return model
@property
def __lowerCamelCase ( self ):
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __lowerCamelCase ( self ):
torch.manual_seed(0 )
__lowerCAmelCase : str = VQModel(**self.dummy_movq_kwargs )
return model
def __lowerCamelCase ( self ):
__lowerCAmelCase : Dict = self.dummy_unet
__lowerCAmelCase : List[str] = self.dummy_movq
__lowerCAmelCase : Union[str, Any] = DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule='linear' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=_SCREAMING_SNAKE_CASE , set_alpha_to_one=_SCREAMING_SNAKE_CASE , steps_offset=1 , prediction_type='epsilon' , thresholding=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : str = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=0 ):
__lowerCAmelCase : List[Any] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
_SCREAMING_SNAKE_CASE )
# create init_image
__lowerCAmelCase : Tuple = floats_tensor((1, 3, 64, 64) , rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : str = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowerCAmelCase : List[Any] = Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE ) ).convert('RGB' ).resize((2_56, 2_56) )
# create mask
__lowerCAmelCase : Optional[Any] = np.ones((64, 64) , dtype=np.floataa )
__lowerCAmelCase : Dict = 0
if str(_SCREAMING_SNAKE_CASE ).startswith('mps' ):
__lowerCAmelCase : Dict = torch.manual_seed(_SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase : Optional[int] = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : int = {
'image': init_image,
'mask_image': mask,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 2,
'guidance_scale': 4.0,
'output_type': 'np',
}
return inputs
def __lowerCamelCase ( self ):
__lowerCAmelCase : Dict = 'cpu'
__lowerCAmelCase : Union[str, Any] = self.get_dummy_components()
__lowerCAmelCase : List[Any] = self.pipeline_class(**_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : str = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[str] = pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase : Union[str, Any] = output.images
__lowerCAmelCase : Optional[Any] = pipe(
**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE ) , return_dict=_SCREAMING_SNAKE_CASE , )[0]
__lowerCAmelCase : str = image[0, -3:, -3:, -1]
__lowerCAmelCase : int = image_from_tuple[0, -3:, -3:, -1]
print(f"image.shape {image.shape}" )
assert image.shape == (1, 64, 64, 3)
__lowerCAmelCase : List[Any] = np.array(
[0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
def __lowerCamelCase ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class A__ ( unittest.TestCase):
def __lowerCamelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCamelCase ( self ):
__lowerCAmelCase : Optional[int] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy' )
__lowerCAmelCase : Optional[int] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
__lowerCAmelCase : Union[str, Any] = np.ones((7_68, 7_68) , dtype=np.floataa )
__lowerCAmelCase : List[str] = 0
__lowerCAmelCase : Dict = 'a hat'
__lowerCAmelCase : Optional[int] = KandinskyVaaPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior' , torch_dtype=torch.floataa )
pipe_prior.to(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : str = KandinskyVaaInpaintPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-decoder-inpaint' , torch_dtype=torch.floataa )
__lowerCAmelCase : str = pipeline.to(_SCREAMING_SNAKE_CASE )
pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : int = torch.Generator(device='cpu' ).manual_seed(0 )
__lowerCAmelCase , __lowerCAmelCase : Union[str, Any] = pipe_prior(
_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
__lowerCAmelCase : int = pipeline(
image=_SCREAMING_SNAKE_CASE , mask_image=_SCREAMING_SNAKE_CASE , image_embeds=_SCREAMING_SNAKE_CASE , negative_image_embeds=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type='np' , )
__lowerCAmelCase : Optional[int] = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) | 86 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
snake_case : str = "▁"
snake_case : List[Any] = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class _snake_case ( snake_case , unittest.TestCase ):
UpperCamelCase__ = BigBirdTokenizer
UpperCamelCase__ = BigBirdTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = True
def SCREAMING_SNAKE_CASE ( self ):
super().setUp()
__magic_name__ : Optional[Any] = self.tokenizer_class(_a , keep_accents=_a )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Union[str, Any] = "<s>"
__magic_name__ : Dict = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<unk>" )
self.assertEqual(vocab_keys[1] , "<s>" )
self.assertEqual(vocab_keys[-1] , "[MASK]" )
self.assertEqual(len(_a ) , 1_004 )
def SCREAMING_SNAKE_CASE ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_000 )
def SCREAMING_SNAKE_CASE ( self ):
if not self.test_rust_tokenizer:
return
__magic_name__ : Dict = self.get_tokenizer()
__magic_name__ : str = self.get_rust_tokenizer()
__magic_name__ : Any = "I was born in 92000, and this is falsé."
__magic_name__ : Dict = tokenizer.tokenize(_a )
__magic_name__ : Any = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
__magic_name__ : List[Any] = tokenizer.encode(_a , add_special_tokens=_a )
__magic_name__ : List[str] = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
__magic_name__ : str = self.get_rust_tokenizer()
__magic_name__ : Dict = tokenizer.encode(_a )
__magic_name__ : Optional[int] = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = BigBirdTokenizer(_a , keep_accents=_a )
__magic_name__ : str = tokenizer.tokenize("This is a test" )
self.assertListEqual(_a , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_a ) , [285, 46, 10, 170, 382] , )
__magic_name__ : Dict = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
__magic_name__ : Union[str, Any] = tokenizer.convert_tokens_to_ids(_a )
self.assertListEqual(
_a , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
__magic_name__ : int = tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
@cached_property
def SCREAMING_SNAKE_CASE ( self ):
return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base" )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Any = "Hello World!"
__magic_name__ : Dict = [65, 18_536, 2_260, 101, 66]
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = (
"This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
)
# fmt: off
__magic_name__ : List[str] = [65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66] # noqa: E231
# fmt: on
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@require_torch
@slow
def SCREAMING_SNAKE_CASE ( self ):
import torch
from transformers import BigBirdConfig, BigBirdModel
# Build sequence
__magic_name__ : Optional[Any] = list(self.big_tokenizer.get_vocab().keys() )[:10]
__magic_name__ : List[Any] = " ".join(_a )
__magic_name__ : Any = self.big_tokenizer.encode_plus(_a , return_tensors="pt" , return_token_type_ids=_a )
__magic_name__ : Union[str, Any] = self.big_tokenizer.batch_encode_plus(
[sequence + " " + sequence] , return_tensors="pt" , return_token_type_ids=_a )
__magic_name__ : List[str] = BigBirdConfig(attention_type="original_full" )
__magic_name__ : Optional[int] = BigBirdModel(_a )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_a )
model(**_a )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : int = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base" )
__magic_name__ : int = tokenizer.decode(tokenizer("Paris is the [MASK]." ).input_ids )
self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]" )
@slow
def SCREAMING_SNAKE_CASE ( self ):
# fmt: off
__magic_name__ : Optional[Any] = {"input_ids": [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_a , model_name="google/bigbird-roberta-base" , revision="215c99f1600e06f83acce68422f2035b2b5c3510" , )
| 281 | 0 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
UpperCamelCase = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
UpperCamelCase = None
def lowercase_ ( ):
lowercase__ : List[Any] = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0.")
parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file.")
parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions.")
parser.add_argument(
"--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout).")
parser.add_argument(
"--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer.")
parser.add_argument(
"--na-prob-thresh" , "-t" , type=_lowerCamelCase , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , )
parser.add_argument(
"--out-image-dir" , "-p" , metavar="out_images" , default=_lowerCamelCase , help="Save precision-recall curves to directory.")
parser.add_argument("--verbose" , "-v" , action="store_true")
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
return parser.parse_args()
def lowercase_ ( _lowerCamelCase : List[Any]):
lowercase__ : Union[str, Any] = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
lowercase__ : Optional[int] = bool(qa["answers"]["text"])
return qid_to_has_ans
def lowercase_ ( _lowerCamelCase : Union[str, Any]):
def remove_articles(_lowerCamelCase : List[str]):
return ARTICLES_REGEX.sub(" " , _lowerCamelCase)
def white_space_fix(_lowerCamelCase : int):
return " ".join(text.split())
def remove_punc(_lowerCamelCase : Tuple):
lowercase__ : Any = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(_lowerCamelCase : Any):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase))))
def lowercase_ ( _lowerCamelCase : str):
if not s:
return []
return normalize_answer(_lowerCamelCase).split()
def lowercase_ ( _lowerCamelCase : Optional[int] , _lowerCamelCase : Any):
return int(normalize_answer(_lowerCamelCase) == normalize_answer(_lowerCamelCase))
def lowercase_ ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Optional[Any]):
lowercase__ : str = get_tokens(_lowerCamelCase)
lowercase__ : int = get_tokens(_lowerCamelCase)
lowercase__ : List[Any] = collections.Counter(_lowerCamelCase) & collections.Counter(_lowerCamelCase)
lowercase__ : List[Any] = sum(common.values())
if len(_lowerCamelCase) == 0 or len(_lowerCamelCase) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks)
if num_same == 0:
return 0
lowercase__ : Union[str, Any] = 1.0 * num_same / len(_lowerCamelCase)
lowercase__ : Optional[Any] = 1.0 * num_same / len(_lowerCamelCase)
lowercase__ : Dict = (2 * precision * recall) / (precision + recall)
return fa
def lowercase_ ( _lowerCamelCase : Tuple , _lowerCamelCase : Any):
lowercase__ : Union[str, Any] = {}
lowercase__ : List[str] = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
lowercase__ : List[Any] = qa["id"]
lowercase__ : List[Any] = [t for t in qa["answers"]["text"] if normalize_answer(_lowerCamelCase)]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
lowercase__ : List[str] = [""]
if qid not in preds:
print(f'''Missing prediction for {qid}''')
continue
lowercase__ : Any = preds[qid]
# Take max over all gold answers
lowercase__ : Tuple = max(compute_exact(_lowerCamelCase , _lowerCamelCase) for a in gold_answers)
lowercase__ : Any = max(compute_fa(_lowerCamelCase , _lowerCamelCase) for a in gold_answers)
return exact_scores, fa_scores
def lowercase_ ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Dict , _lowerCamelCase : Any , _lowerCamelCase : Any):
lowercase__ : Union[str, Any] = {}
for qid, s in scores.items():
lowercase__ : Optional[int] = na_probs[qid] > na_prob_thresh
if pred_na:
lowercase__ : Dict = float(not qid_to_has_ans[qid])
else:
lowercase__ : List[Any] = s
return new_scores
def lowercase_ ( _lowerCamelCase : Dict , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Union[str, Any]=None):
if not qid_list:
lowercase__ : List[str] = len(_lowerCamelCase)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores.values()) / total),
("f1", 100.0 * sum(fa_scores.values()) / total),
("total", total),
])
else:
lowercase__ : Union[str, Any] = len(_lowerCamelCase)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores[k] for k in qid_list) / total),
("f1", 100.0 * sum(fa_scores[k] for k in qid_list) / total),
("total", total),
])
def lowercase_ ( _lowerCamelCase : Dict , _lowerCamelCase : Any , _lowerCamelCase : Any):
for k in new_eval:
lowercase__ : Union[str, Any] = new_eval[k]
def lowercase_ ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[int] , _lowerCamelCase : Tuple , _lowerCamelCase : List[Any]):
plt.step(_lowerCamelCase , _lowerCamelCase , color="b" , alpha=0.2 , where="post")
plt.fill_between(_lowerCamelCase , _lowerCamelCase , step="post" , alpha=0.2 , color="b")
plt.xlabel("Recall")
plt.ylabel("Precision")
plt.xlim([0.0, 1.05])
plt.ylim([0.0, 1.05])
plt.title(_lowerCamelCase)
plt.savefig(_lowerCamelCase)
plt.clf()
def lowercase_ ( _lowerCamelCase : Any , _lowerCamelCase : List[Any] , _lowerCamelCase : int , _lowerCamelCase : List[Any] , _lowerCamelCase : Dict=None , _lowerCamelCase : Optional[int]=None):
lowercase__ : Optional[Any] = sorted(_lowerCamelCase , key=lambda _lowerCamelCase: na_probs[k])
lowercase__ : Optional[int] = 0.0
lowercase__ : str = 1.0
lowercase__ : List[str] = 0.0
lowercase__ : List[str] = [1.0]
lowercase__ : Union[str, Any] = [0.0]
lowercase__ : Dict = 0.0
for i, qid in enumerate(_lowerCamelCase):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
lowercase__ : str = true_pos / float(i + 1)
lowercase__ : List[str] = true_pos / float(_lowerCamelCase)
if i == len(_lowerCamelCase) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(_lowerCamelCase)
recalls.append(_lowerCamelCase)
if out_image:
plot_pr_curve(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
return {"ap": 100.0 * avg_prec}
def lowercase_ ( _lowerCamelCase : str , _lowerCamelCase : Optional[Any] , _lowerCamelCase : int , _lowerCamelCase : Tuple , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Optional[Any]):
if out_image_dir and not os.path.exists(_lowerCamelCase):
os.makedirs(_lowerCamelCase)
lowercase__ : Union[str, Any] = sum(1 for v in qid_to_has_ans.values() if v)
if num_true_pos == 0:
return
lowercase__ : Tuple = make_precision_recall_eval(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , out_image=os.path.join(_lowerCamelCase , "pr_exact.png") , title="Precision-Recall curve for Exact Match score" , )
lowercase__ : Union[str, Any] = make_precision_recall_eval(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , out_image=os.path.join(_lowerCamelCase , "pr_f1.png") , title="Precision-Recall curve for F1 score" , )
lowercase__ : Union[str, Any] = {k: float(_lowerCamelCase) for k, v in qid_to_has_ans.items()}
lowercase__ : Optional[int] = make_precision_recall_eval(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , out_image=os.path.join(_lowerCamelCase , "pr_oracle.png") , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , )
merge_eval(_lowerCamelCase , _lowerCamelCase , "pr_exact")
merge_eval(_lowerCamelCase , _lowerCamelCase , "pr_f1")
merge_eval(_lowerCamelCase , _lowerCamelCase , "pr_oracle")
def lowercase_ ( _lowerCamelCase : Tuple , _lowerCamelCase : List[Any] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : List[Any]):
if not qid_list:
return
lowercase__ : List[str] = [na_probs[k] for k in qid_list]
lowercase__ : Tuple = np.ones_like(_lowerCamelCase) / float(len(_lowerCamelCase))
plt.hist(_lowerCamelCase , weights=_lowerCamelCase , bins=20 , range=(0.0, 1.0))
plt.xlabel("Model probability of no-answer")
plt.ylabel("Proportion of dataset")
plt.title(f'''Histogram of no-answer probability: {name}''')
plt.savefig(os.path.join(_lowerCamelCase , f'''na_prob_hist_{name}.png'''))
plt.clf()
def lowercase_ ( _lowerCamelCase : List[str] , _lowerCamelCase : Any , _lowerCamelCase : Any , _lowerCamelCase : Optional[int]):
lowercase__ : Dict = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
lowercase__ : Union[str, Any] = num_no_ans
lowercase__ : Optional[int] = cur_score
lowercase__ : List[str] = 0.0
lowercase__ : List[Any] = sorted(_lowerCamelCase , key=lambda _lowerCamelCase: na_probs[k])
for i, qid in enumerate(_lowerCamelCase):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
lowercase__ : List[Any] = scores[qid]
else:
if preds[qid]:
lowercase__ : str = -1
else:
lowercase__ : List[Any] = 0
cur_score += diff
if cur_score > best_score:
lowercase__ : Any = cur_score
lowercase__ : Dict = na_probs[qid]
return 100.0 * best_score / len(_lowerCamelCase), best_thresh
def lowercase_ ( _lowerCamelCase : Optional[int] , _lowerCamelCase : str , _lowerCamelCase : List[str] , _lowerCamelCase : int , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[Any]):
lowercase__ , lowercase__ : int = find_best_thresh(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
lowercase__ , lowercase__ : List[str] = find_best_thresh(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
lowercase__ : Optional[Any] = best_exact
lowercase__ : int = exact_thresh
lowercase__ : Tuple = best_fa
lowercase__ : Optional[Any] = fa_thresh
def lowercase_ ( ):
with open(OPTS.data_file) as f:
lowercase__ : List[Any] = json.load(_lowerCamelCase)
lowercase__ : Union[str, Any] = dataset_json["data"]
with open(OPTS.pred_file) as f:
lowercase__ : Optional[int] = json.load(_lowerCamelCase)
if OPTS.na_prob_file:
with open(OPTS.na_prob_file) as f:
lowercase__ : Union[str, Any] = json.load(_lowerCamelCase)
else:
lowercase__ : List[str] = {k: 0.0 for k in preds}
lowercase__ : Tuple = make_qid_to_has_ans(_lowerCamelCase) # maps qid to True/False
lowercase__ : int = [k for k, v in qid_to_has_ans.items() if v]
lowercase__ : str = [k for k, v in qid_to_has_ans.items() if not v]
lowercase__ , lowercase__ : Any = get_raw_scores(_lowerCamelCase , _lowerCamelCase)
lowercase__ : int = apply_no_ans_threshold(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , OPTS.na_prob_thresh)
lowercase__ : int = apply_no_ans_threshold(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , OPTS.na_prob_thresh)
lowercase__ : Dict = make_eval_dict(_lowerCamelCase , _lowerCamelCase)
if has_ans_qids:
lowercase__ : Dict = make_eval_dict(_lowerCamelCase , _lowerCamelCase , qid_list=_lowerCamelCase)
merge_eval(_lowerCamelCase , _lowerCamelCase , "HasAns")
if no_ans_qids:
lowercase__ : str = make_eval_dict(_lowerCamelCase , _lowerCamelCase , qid_list=_lowerCamelCase)
merge_eval(_lowerCamelCase , _lowerCamelCase , "NoAns")
if OPTS.na_prob_file:
find_all_best_thresh(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , OPTS.out_image_dir)
histogram_na_prob(_lowerCamelCase , _lowerCamelCase , OPTS.out_image_dir , "hasAns")
histogram_na_prob(_lowerCamelCase , _lowerCamelCase , OPTS.out_image_dir , "noAns")
if OPTS.out_file:
with open(OPTS.out_file , "w") as f:
json.dump(_lowerCamelCase , _lowerCamelCase)
else:
print(json.dumps(_lowerCamelCase , indent=2))
if __name__ == "__main__":
UpperCamelCase = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('''Agg''')
import matplotlib.pyplot as plt
main()
| 87 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
snake_case : int = logging.get_logger(__name__)
snake_case : List[str] = {"vocab_file": "spiece.model"}
snake_case : List[str] = {
"vocab_file": {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
}
}
snake_case : Tuple = {
"albert-base-v1": 512,
"albert-large-v1": 512,
"albert-xlarge-v1": 512,
"albert-xxlarge-v1": 512,
"albert-base-v2": 512,
"albert-large-v2": 512,
"albert-xlarge-v2": 512,
"albert-xxlarge-v2": 512,
}
snake_case : List[str] = "▁"
class _snake_case ( snake_case ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , _a , _a=True , _a=True , _a=False , _a="[CLS]" , _a="[SEP]" , _a="<unk>" , _a="[SEP]" , _a="<pad>" , _a="[CLS]" , _a="[MASK]" , _a = None , **_a , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
__magic_name__ : str = (
AddedToken(_a , lstrip=_a , rstrip=_a , normalized=_a )
if isinstance(_a , _a )
else mask_token
)
__magic_name__ : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , )
__magic_name__ : Dict = do_lower_case
__magic_name__ : Tuple = remove_space
__magic_name__ : Union[str, Any] = keep_accents
__magic_name__ : Tuple = vocab_file
__magic_name__ : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_a )
@property
def SCREAMING_SNAKE_CASE ( self ):
return len(self.sp_model )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
__magic_name__ : List[str] = self.__dict__.copy()
__magic_name__ : Any = None
return state
def __setstate__( self , _a ):
__magic_name__ : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
__magic_name__ : str = {}
__magic_name__ : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE ( self , _a ):
if self.remove_space:
__magic_name__ : List[Any] = " ".join(inputs.strip().split() )
else:
__magic_name__ : str = inputs
__magic_name__ : int = outputs.replace("``" , "\"" ).replace("''" , "\"" )
if not self.keep_accents:
__magic_name__ : str = unicodedata.normalize("NFKD" , _a )
__magic_name__ : Tuple = "".join([c for c in outputs if not unicodedata.combining(_a )] )
if self.do_lower_case:
__magic_name__ : int = outputs.lower()
return outputs
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Optional[Any] = self.preprocess_text(_a )
__magic_name__ : Dict = self.sp_model.encode(_a , out_type=_a )
__magic_name__ : Any = []
for piece in pieces:
if len(_a ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
__magic_name__ : Optional[Any] = self.sp_model.EncodeAsPieces(piece[:-1].replace(_a , "" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
__magic_name__ : List[str] = cur_pieces[1:]
else:
__magic_name__ : Optional[int] = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_a )
else:
new_pieces.append(_a )
return new_pieces
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.sp_model.PieceToId(_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.sp_model.IdToPiece(_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Any = []
__magic_name__ : Union[str, Any] = ""
__magic_name__ : int = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_a ) + token
__magic_name__ : List[Any] = True
__magic_name__ : Optional[int] = []
else:
current_sub_tokens.append(_a )
__magic_name__ : Optional[Any] = False
out_string += self.sp_model.decode(_a )
return out_string.strip()
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
__magic_name__ : List[str] = [self.sep_token_id]
__magic_name__ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self , _a , _a = None , _a = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is not None:
return [1] + ([0] * len(_a )) + [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1]
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
__magic_name__ : Optional[int] = [self.sep_token_id]
__magic_name__ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__magic_name__ : List[str] = os.path.join(
_a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _a )
elif not os.path.isfile(self.vocab_file ):
with open(_a , "wb" ) as fi:
__magic_name__ : Union[str, Any] = self.sp_model.serialized_model_proto()
fi.write(_a )
return (out_vocab_file,)
| 281 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : List[Any] = logging.get_logger(__name__)
__lowerCAmelCase : int = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class UpperCAmelCase_ ( _A ):
'''simple docstring'''
a__ = """vit_msn"""
def __init__( self : Optional[int] , UpperCamelCase__ : Optional[int]=768 , UpperCamelCase__ : Any=12 , UpperCamelCase__ : Optional[int]=12 , UpperCamelCase__ : int=3072 , UpperCamelCase__ : List[Any]="gelu" , UpperCamelCase__ : Tuple=0.0 , UpperCamelCase__ : Optional[Any]=0.0 , UpperCamelCase__ : Dict=0.02 , UpperCamelCase__ : Any=1E-06 , UpperCamelCase__ : Any=224 , UpperCamelCase__ : Any=16 , UpperCamelCase__ : Optional[Any]=3 , UpperCamelCase__ : List[str]=True , **UpperCamelCase__ : List[Any] , ) -> int:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
__magic_name__ = hidden_size
__magic_name__ = num_hidden_layers
__magic_name__ = num_attention_heads
__magic_name__ = intermediate_size
__magic_name__ = hidden_act
__magic_name__ = hidden_dropout_prob
__magic_name__ = attention_probs_dropout_prob
__magic_name__ = initializer_range
__magic_name__ = layer_norm_eps
__magic_name__ = image_size
__magic_name__ = patch_size
__magic_name__ = num_channels
__magic_name__ = qkv_bias
| 88 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : Tuple , _snake_case : List[Any] , _snake_case : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if isinstance(_snake_case , _snake_case ):
__magic_name__ : Union[str, Any] = np.full((len(_snake_case ), sequence_length, 2) , _snake_case )
else:
__magic_name__ : List[Any] = np.full((len(_snake_case ), sequence_length) , _snake_case )
for i, tensor in enumerate(_snake_case ):
if padding_side == "right":
if isinstance(_snake_case , _snake_case ):
__magic_name__ : Optional[Any] = tensor[:sequence_length]
else:
__magic_name__ : Union[str, Any] = tensor[:sequence_length]
else:
if isinstance(_snake_case , _snake_case ):
__magic_name__ : List[Any] = tensor[:sequence_length]
else:
__magic_name__ : Optional[Any] = tensor[:sequence_length]
return out_tensor.tolist()
def lowerCAmelCase_ ( _snake_case : Optional[int] ) -> Tuple:
'''simple docstring'''
__magic_name__ : Union[str, Any] = ord(_snake_case )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
__magic_name__ : Any = unicodedata.category(_snake_case )
if cat.startswith("P" ):
return True
return False
@dataclass
class _snake_case ( snake_case ):
UpperCamelCase__ = 42
UpperCamelCase__ = True
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = -100
UpperCamelCase__ = "pt"
def SCREAMING_SNAKE_CASE ( self , _a ):
import torch
__magic_name__ : List[str] = "label" if "label" in features[0].keys() else "labels"
__magic_name__ : Union[str, Any] = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
__magic_name__ : Optional[int] = self.tokenizer.pad(
_a , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" if labels is None else None , )
if labels is None:
return batch
__magic_name__ : Dict = torch.tensor(batch["entity_ids"] ).shape[1]
__magic_name__ : List[Any] = self.tokenizer.padding_side
if padding_side == "right":
__magic_name__ : str = [
list(_a ) + [self.label_pad_token_id] * (sequence_length - len(_a )) for label in labels
]
else:
__magic_name__ : int = [
[self.label_pad_token_id] * (sequence_length - len(_a )) + list(_a ) for label in labels
]
__magic_name__ : Dict = [feature["ner_tags"] for feature in features]
__magic_name__ : List[Any] = padding_tensor(_a , -1 , _a , _a )
__magic_name__ : Any = [feature["original_entity_spans"] for feature in features]
__magic_name__ : Any = padding_tensor(_a , (-1, -1) , _a , _a )
__magic_name__ : List[Any] = {k: torch.tensor(_a , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 281 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __magic_name__ ( _UpperCamelCase , unittest.TestCase ):
lowerCAmelCase : Tuple = KandinskyVaaPipeline
lowerCAmelCase : List[str] = [
'image_embeds',
'negative_image_embeds',
]
lowerCAmelCase : Union[str, Any] = ['image_embeds', 'negative_image_embeds']
lowerCAmelCase : Tuple = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
lowerCAmelCase : str = False
@property
def __lowercase ( self : Tuple ):
return 32
@property
def __lowercase ( self : int ):
return 32
@property
def __lowercase ( self : int ):
return self.time_input_dim
@property
def __lowercase ( self : List[Any] ):
return self.time_input_dim * 4
@property
def __lowercase ( self : Union[str, Any] ):
return 100
@property
def __lowercase ( self : Dict ):
torch.manual_seed(0 )
_a : Any = {
'in_channels': 4,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'image',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
_a : List[Any] = UNetaDConditionModel(**_UpperCAmelCase )
return model
@property
def __lowercase ( self : Optional[Any] ):
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __lowercase ( self : Dict ):
torch.manual_seed(0 )
_a : int = VQModel(**self.dummy_movq_kwargs )
return model
def __lowercase ( self : Optional[Any] ):
_a : int = self.dummy_unet
_a : List[Any] = self.dummy_movq
_a : Tuple = DDIMScheduler(
num_train_timesteps=1000 ,beta_schedule='linear' ,beta_start=0.0_00_85 ,beta_end=0.0_12 ,clip_sample=_UpperCAmelCase ,set_alpha_to_one=_UpperCAmelCase ,steps_offset=1 ,prediction_type='epsilon' ,thresholding=_UpperCAmelCase ,)
_a : Tuple = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def __lowercase ( self : List[str] ,_UpperCAmelCase : str ,_UpperCAmelCase : Dict=0 ):
_a : Tuple = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
_a : str = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(seed + 1 ) ).to(
_UpperCAmelCase )
if str(_UpperCAmelCase ).startswith('mps' ):
_a : str = torch.manual_seed(_UpperCAmelCase )
else:
_a : Tuple = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
_a : Optional[int] = {
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'guidance_scale': 4.0,
'num_inference_steps': 2,
'output_type': 'np',
}
return inputs
def __lowercase ( self : List[str] ):
_a : Optional[Any] = 'cpu'
_a : str = self.get_dummy_components()
_a : List[Any] = self.pipeline_class(**_UpperCAmelCase )
_a : Optional[int] = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
_a : Union[str, Any] = pipe(**self.get_dummy_inputs(_UpperCAmelCase ) )
_a : int = output.images
_a : str = pipe(
**self.get_dummy_inputs(_UpperCAmelCase ) ,return_dict=_UpperCAmelCase ,)[0]
_a : str = image[0, -3:, -3:, -1]
_a : str = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_a : Union[str, Any] = np.array(
[0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class __magic_name__ ( unittest.TestCase ):
def __lowercase ( self : Dict ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self : int ):
_a : List[str] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy' )
_a : Optional[int] = KandinskyVaaPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior' ,torch_dtype=torch.floataa )
pipe_prior.to(_UpperCAmelCase )
_a : List[str] = KandinskyVaaPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-decoder' ,torch_dtype=torch.floataa )
_a : Dict = pipeline.to(_UpperCAmelCase )
pipeline.set_progress_bar_config(disable=_UpperCAmelCase )
_a : Tuple = 'red cat, 4k photo'
_a : List[str] = torch.Generator(device='cuda' ).manual_seed(0 )
_a , _a : Optional[Any] = pipe_prior(
_UpperCAmelCase ,generator=_UpperCAmelCase ,num_inference_steps=5 ,negative_prompt='' ,).to_tuple()
_a : List[str] = torch.Generator(device='cuda' ).manual_seed(0 )
_a : str = pipeline(
image_embeds=_UpperCAmelCase ,negative_image_embeds=_UpperCAmelCase ,generator=_UpperCAmelCase ,num_inference_steps=100 ,output_type='np' ,)
_a : Optional[int] = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(_UpperCAmelCase ,_UpperCAmelCase )
| 89 |
import math
def lowerCAmelCase_ ( _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
return math.pow(_snake_case , 2 ) - a
def lowerCAmelCase_ ( _snake_case : float ) -> float:
'''simple docstring'''
return 2 * x
def lowerCAmelCase_ ( _snake_case : float ) -> float:
'''simple docstring'''
__magic_name__ : Optional[int] = 2.0
while start <= a:
__magic_name__ : str = math.pow(_snake_case , 2 )
return start
def lowerCAmelCase_ ( _snake_case : float , _snake_case : int = 9999 , _snake_case : float = 0.00_000_000_000_001 ) -> float:
'''simple docstring'''
if a < 0:
raise ValueError("math domain error" )
__magic_name__ : Optional[int] = get_initial_point(_snake_case )
for _ in range(_snake_case ):
__magic_name__ : int = value
__magic_name__ : str = value - fx(_snake_case , _snake_case ) / fx_derivative(_snake_case )
if abs(prev_value - value ) < tolerance:
return value
return value
if __name__ == "__main__":
from doctest import testmod
testmod()
| 281 | 0 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str ) -> List[str]:
"""simple docstring"""
__lowerCamelCase = 1.5
__lowerCamelCase = int(factor * num_class_images )
__lowerCamelCase = ClipClient(
url='https://knn.laion.ai/knn-service' , indice_name='laion_400m' , num_images=UpperCamelCase__ , aesthetic_weight=0.1 )
os.makedirs(F"""{class_data_dir}/images""" , exist_ok=UpperCamelCase__ )
if len(list(Path(F"""{class_data_dir}/images""" ).iterdir() ) ) >= num_class_images:
return
while True:
__lowerCamelCase = client.query(text=UpperCamelCase__ )
if len(UpperCamelCase__ ) >= factor * num_class_images or num_images > 1E4:
break
else:
__lowerCamelCase = int(factor * num_images )
__lowerCamelCase = ClipClient(
url='https://knn.laion.ai/knn-service' , indice_name='laion_400m' , num_images=UpperCamelCase__ , aesthetic_weight=0.1 , )
__lowerCamelCase = 0
__lowerCamelCase = 0
__lowerCamelCase = tqdm(desc='downloading real regularization images' , total=UpperCamelCase__ )
with open(F"""{class_data_dir}/caption.txt""" , 'w' ) as fa, open(F"""{class_data_dir}/urls.txt""" , 'w' ) as fa, open(
F"""{class_data_dir}/images.txt""" , 'w' ) as fa:
while total < num_class_images:
__lowerCamelCase = class_images[count]
count += 1
try:
__lowerCamelCase = requests.get(images['url'] )
if img.status_code == 200:
__lowerCamelCase = Image.open(BytesIO(img.content ) )
with open(F"""{class_data_dir}/images/{total}.jpg""" , 'wb' ) as f:
f.write(img.content )
fa.write(images['caption'] + '\n' )
fa.write(images['url'] + '\n' )
fa.write(F"""{class_data_dir}/images/{total}.jpg""" + '\n' )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
__lowerCamelCase = argparse.ArgumentParser('' , add_help=UpperCamelCase__ )
parser.add_argument('--class_prompt' , help='text prompt to retrieve images' , required=UpperCamelCase__ , type=UpperCamelCase__ )
parser.add_argument('--class_data_dir' , help='path to save images' , required=UpperCamelCase__ , type=UpperCamelCase__ )
parser.add_argument('--num_class_images' , help='number of images to download' , default=200 , type=UpperCamelCase__ )
return parser.parse_args()
if __name__ == "__main__":
__A = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 90 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class _snake_case :
UpperCamelCase__ = LEDConfig
UpperCamelCase__ = {}
UpperCamelCase__ = 'gelu'
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=False , _a=99 , _a=32 , _a=2 , _a=4 , _a=37 , _a=0.1 , _a=0.1 , _a=20 , _a=2 , _a=1 , _a=0 , _a=4 , ):
__magic_name__ : int = parent
__magic_name__ : Optional[int] = batch_size
__magic_name__ : Tuple = seq_length
__magic_name__ : List[Any] = is_training
__magic_name__ : Dict = use_labels
__magic_name__ : Optional[Any] = vocab_size
__magic_name__ : int = hidden_size
__magic_name__ : Optional[int] = num_hidden_layers
__magic_name__ : Optional[int] = num_attention_heads
__magic_name__ : Tuple = intermediate_size
__magic_name__ : Any = hidden_dropout_prob
__magic_name__ : Optional[int] = attention_probs_dropout_prob
__magic_name__ : List[str] = max_position_embeddings
__magic_name__ : Any = eos_token_id
__magic_name__ : str = pad_token_id
__magic_name__ : int = bos_token_id
__magic_name__ : Optional[int] = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
__magic_name__ : Tuple = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
__magic_name__ : Tuple = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__magic_name__ : int = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__magic_name__ : int = tf.concat([input_ids, eos_tensor] , axis=1 )
__magic_name__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__magic_name__ : Dict = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
__magic_name__ : List[str] = prepare_led_inputs_dict(_a , _a , _a )
__magic_name__ : Union[str, Any] = tf.concat(
[tf.zeros_like(_a )[:, :-1], tf.ones_like(_a )[:, -1:]] , axis=-1 , )
__magic_name__ : List[Any] = global_attention_mask
return config, inputs_dict
def SCREAMING_SNAKE_CASE ( self , _a , _a ):
__magic_name__ : Dict = TFLEDModel(config=_a ).get_decoder()
__magic_name__ : Optional[int] = inputs_dict["input_ids"]
__magic_name__ : Union[str, Any] = input_ids[:1, :]
__magic_name__ : str = inputs_dict["attention_mask"][:1, :]
__magic_name__ : int = 1
# first forward pass
__magic_name__ : Tuple = model(_a , attention_mask=_a , use_cache=_a )
__magic_name__ , __magic_name__ : str = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__magic_name__ : Optional[int] = ids_tensor((self.batch_size, 3) , config.vocab_size )
__magic_name__ : Any = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
__magic_name__ : Optional[Any] = tf.concat([input_ids, next_tokens] , axis=-1 )
__magic_name__ : List[Any] = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
__magic_name__ : List[str] = model(_a , attention_mask=_a )[0]
__magic_name__ : Dict = model(_a , attention_mask=_a , past_key_values=_a )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__magic_name__ : List[Any] = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
__magic_name__ : Union[str, Any] = output_from_no_past[:, -3:, random_slice_idx]
__magic_name__ : List[str] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_a , _a , rtol=1e-3 )
def lowerCAmelCase_ ( _snake_case : Any , _snake_case : List[Any] , _snake_case : Any , _snake_case : str=None , _snake_case : List[str]=None , _snake_case : int=None , _snake_case : Any=None , ) -> int:
'''simple docstring'''
if attention_mask is None:
__magic_name__ : str = tf.cast(tf.math.not_equal(_snake_case , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__magic_name__ : List[Any] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__magic_name__ : Dict = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__magic_name__ : Any = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class _snake_case ( snake_case , snake_case , unittest.TestCase ):
UpperCamelCase__ = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
UpperCamelCase__ = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
UpperCamelCase__ = (
{
'conversational': TFLEDForConditionalGeneration,
'feature-extraction': TFLEDModel,
'summarization': TFLEDForConditionalGeneration,
'text2text-generation': TFLEDForConditionalGeneration,
'translation': TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
UpperCamelCase__ = True
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = TFLEDModelTester(self )
__magic_name__ : List[Any] = ConfigTester(self , config_class=_a )
def SCREAMING_SNAKE_CASE ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ , __magic_name__ : int = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ : List[str] = tf.zeros_like(inputs_dict["attention_mask"] )
__magic_name__ : Optional[Any] = 2
__magic_name__ : Tuple = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict["global_attention_mask"] , )
__magic_name__ : Any = True
__magic_name__ : str = self.model_tester.seq_length
__magic_name__ : Dict = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(_a ):
__magic_name__ : str = outputs.decoder_attentions
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(_a ):
__magic_name__ : Any = [t.numpy() for t in outputs.encoder_attentions]
__magic_name__ : Tuple = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
__magic_name__ : Union[str, Any] = True
__magic_name__ : List[str] = False
__magic_name__ : Tuple = False
__magic_name__ : Optional[int] = model_class(_a )
__magic_name__ : str = model(self._prepare_for_class(_a , _a ) )
__magic_name__ : Any = len(_a )
self.assertEqual(config.output_hidden_states , _a )
check_encoder_attentions_output(_a )
if self.is_encoder_decoder:
__magic_name__ : Tuple = model_class(_a )
__magic_name__ : Optional[Any] = model(self._prepare_for_class(_a , _a ) )
self.assertEqual(config.output_hidden_states , _a )
check_decoder_attentions_output(_a )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
__magic_name__ : Dict = True
__magic_name__ : str = model_class(_a )
__magic_name__ : Any = model(self._prepare_for_class(_a , _a ) )
self.assertEqual(config.output_hidden_states , _a )
check_encoder_attentions_output(_a )
# Check attention is always last and order is fine
__magic_name__ : Union[str, Any] = True
__magic_name__ : Union[str, Any] = True
__magic_name__ : List[str] = model_class(_a )
__magic_name__ : Any = model(self._prepare_for_class(_a , _a ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_a ) )
self.assertEqual(model.config.output_hidden_states , _a )
check_encoder_attentions_output(_a )
@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing." )
def SCREAMING_SNAKE_CASE ( self ):
pass
def SCREAMING_SNAKE_CASE ( self ):
# TODO: Head-masking not yet implement
pass
def lowerCAmelCase_ ( _snake_case : int ) -> Optional[int]:
'''simple docstring'''
return tf.constant(_snake_case , dtype=tf.intaa )
snake_case : Optional[int] = 1E-4
@slow
@require_tf
class _snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[Any] = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" ).led
# change to intended input here
__magic_name__ : Optional[int] = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : str = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : Any = prepare_led_inputs_dict(model.config , _a , _a )
__magic_name__ : List[Any] = model(**_a )[0]
__magic_name__ : List[str] = (1, 1_024, 768)
self.assertEqual(output.shape , _a )
# change to expected output here
__magic_name__ : int = tf.convert_to_tensor(
[[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]] , )
tf.debugging.assert_near(output[:, :3, :3] , _a , atol=1e-3 )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Tuple = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" )
# change to intended input here
__magic_name__ : int = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : Tuple = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : Optional[Any] = prepare_led_inputs_dict(model.config , _a , _a )
__magic_name__ : Union[str, Any] = model(**_a )[0]
__magic_name__ : Optional[int] = (1, 1_024, model.config.vocab_size)
self.assertEqual(output.shape , _a )
# change to expected output here
__magic_name__ : str = tf.convert_to_tensor(
[[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]] , )
tf.debugging.assert_near(output[:, :3, :3] , _a , atol=1e-3 , rtol=1e-3 )
| 281 | 0 |
"""simple docstring"""
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
def _A (__a=None , __a=None ) -> int:
"""simple docstring"""
return field(default_factory=lambda: default , metadata=__a )
@dataclass
class lowerCAmelCase__ :
'''simple docstring'''
__UpperCamelCase = list_field(
default=[] , metadata={
"help": (
"Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
" of all available models"
)
} , )
__UpperCamelCase = list_field(
default=[8] , metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )
__UpperCamelCase = list_field(
default=[8, 3_2, 1_2_8, 5_1_2] , metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"} , )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."} , )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."} , )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
__UpperCamelCase = field(default=UpperCAmelCase__ , metadata={"help": "Use FP16 to accelerate inference."} )
__UpperCamelCase = field(default=UpperCAmelCase__ , metadata={"help": "Benchmark training of model"} )
__UpperCamelCase = field(default=UpperCAmelCase__ , metadata={"help": "Verbose memory tracing"} )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."} , )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
} , )
__UpperCamelCase = field(default=UpperCAmelCase__ , metadata={"help": "Trace memory line by line"} )
__UpperCamelCase = field(default=UpperCAmelCase__ , metadata={"help": "Save result to a CSV file"} )
__UpperCamelCase = field(default=UpperCAmelCase__ , metadata={"help": "Save all print statements in a log file"} )
__UpperCamelCase = field(default=UpperCAmelCase__ , metadata={"help": "Whether to print environment information"} )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={
"help": (
"Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
" for debugging / testing and on TPU."
)
} , )
__UpperCamelCase = field(
default=f'''inference_time_{round(time() )}.csv''' , metadata={"help": "CSV filename used if saving time results to csv."} , )
__UpperCamelCase = field(
default=f'''inference_memory_{round(time() )}.csv''' , metadata={"help": "CSV filename used if saving memory results to csv."} , )
__UpperCamelCase = field(
default=f'''train_time_{round(time() )}.csv''' , metadata={"help": "CSV filename used if saving time results to csv for training."} , )
__UpperCamelCase = field(
default=f'''train_memory_{round(time() )}.csv''' , metadata={"help": "CSV filename used if saving memory results to csv for training."} , )
__UpperCamelCase = field(
default=f'''env_info_{round(time() )}.csv''' , metadata={"help": "CSV filename used if saving environment information."} , )
__UpperCamelCase = field(
default=f'''log_{round(time() )}.csv''' , metadata={"help": "Log filename used if print statements are saved in log."} , )
__UpperCamelCase = field(default=3 , metadata={"help": "Times an experiment will be run."} )
__UpperCamelCase = field(
default=UpperCAmelCase__ , metadata={
"help": (
"Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
" model weights."
)
} , )
def _SCREAMING_SNAKE_CASE ( self : Tuple):
'''simple docstring'''
warnings.warn(
F'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
''' are deprecated in general and it is advised to use external Benchmarking libraries '''
''' to benchmark Transformer models.''' , lowercase_ , )
def _SCREAMING_SNAKE_CASE ( self : Dict):
'''simple docstring'''
return json.dumps(dataclasses.asdict(self) , indent=2)
@property
def _SCREAMING_SNAKE_CASE ( self : str):
'''simple docstring'''
if len(self.models) <= 0:
raise ValueError(
'''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
''' bert-base-cased` or `args.models = [\'bert-base-cased\'].''')
return self.models
@property
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
'''simple docstring'''
if not self.multi_process:
return False
elif self.is_tpu:
logger.info('''Multiprocessing is currently not possible on TPU.''')
return False
else:
return True
| 91 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
snake_case : Optional[Any] = logging.get_logger(__name__)
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Union[str, Any]=False ) -> List[str]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = []
# fmt: off
# stem:
rename_keys.append(("cls_token", "vit.embeddings.cls_token") )
rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings") )
rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias") )
# backbone
rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias") )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''') )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''') )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
("pre_logits.fc.weight", "pooler.dense.weight"),
("pre_logits.fc.bias", "pooler.dense.bias"),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
__magic_name__ : int = [(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
# fmt: on
return rename_keys
def lowerCAmelCase_ ( _snake_case : Any , _snake_case : Any , _snake_case : Dict=False ) -> int:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
__magic_name__ : int = ""
else:
__magic_name__ : Union[str, Any] = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__magic_name__ : Optional[Any] = state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
__magic_name__ : int = state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
__magic_name__ : Dict = in_proj_weight[
: config.hidden_size, :
]
__magic_name__ : List[str] = in_proj_bias[: config.hidden_size]
__magic_name__ : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__magic_name__ : Optional[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__magic_name__ : Optional[Any] = in_proj_weight[
-config.hidden_size :, :
]
__magic_name__ : int = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase_ ( _snake_case : List[str] ) -> List[str]:
'''simple docstring'''
__magic_name__ : List[str] = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(_snake_case , _snake_case )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : int , _snake_case : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
__magic_name__ : int = dct.pop(_snake_case )
__magic_name__ : List[Any] = val
def lowerCAmelCase_ ( ) -> Dict:
'''simple docstring'''
__magic_name__ : List[str] = "http://images.cocodataset.org/val2017/000000039769.jpg"
__magic_name__ : List[str] = Image.open(requests.get(_snake_case , stream=_snake_case ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : Any , _snake_case : int=False ) -> Dict:
'''simple docstring'''
__magic_name__ : List[str] = BitConfig(
global_padding="same" , layer_type="bottleneck" , depths=(3, 4, 9) , out_features=["stage3"] , embedding_dynamic_padding=_snake_case , )
__magic_name__ : List[str] = ViTHybridConfig(backbone_config=_snake_case , image_size=384 , num_labels=1000 )
__magic_name__ : str = False
# load original model from timm
__magic_name__ : Union[str, Any] = timm.create_model(_snake_case , pretrained=_snake_case )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
__magic_name__ : List[Any] = timm_model.state_dict()
if base_model:
remove_classification_head_(_snake_case )
__magic_name__ : Tuple = create_rename_keys(_snake_case , _snake_case )
for src, dest in rename_keys:
rename_key(_snake_case , _snake_case , _snake_case )
read_in_q_k_v(_snake_case , _snake_case , _snake_case )
__magic_name__ : List[str] = "huggingface/label-files"
__magic_name__ : int = "imagenet-1k-id2label.json"
__magic_name__ : Optional[int] = json.load(open(hf_hub_download(_snake_case , _snake_case , repo_type="dataset" ) , "r" ) )
__magic_name__ : int = {int(_snake_case ): v for k, v in idalabel.items()}
__magic_name__ : List[str] = idalabel
__magic_name__ : List[str] = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
__magic_name__ : List[str] = ViTHybridModel(_snake_case ).eval()
else:
__magic_name__ : str = ViTHybridForImageClassification(_snake_case ).eval()
model.load_state_dict(_snake_case )
# create image processor
__magic_name__ : List[Any] = create_transform(**resolve_data_config({} , model=_snake_case ) )
__magic_name__ : int = transform.transforms
__magic_name__ : List[str] = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
__magic_name__ : int = ViTHybridImageProcessor(
do_resize=_snake_case , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_snake_case , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=_snake_case , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
__magic_name__ : List[Any] = prepare_img()
__magic_name__ : Any = transform(_snake_case ).unsqueeze(0 )
__magic_name__ : Tuple = processor(_snake_case , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(_snake_case , _snake_case )
# verify logits
with torch.no_grad():
__magic_name__ : Optional[int] = model(_snake_case )
__magic_name__ : List[str] = outputs.logits
print("Predicted class:" , logits.argmax(-1 ).item() )
if base_model:
__magic_name__ : List[str] = timm_model.forward_features(_snake_case )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_snake_case , outputs.pooler_output , atol=1E-3 )
else:
__magic_name__ : Any = timm_model(_snake_case )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_snake_case , outputs.logits , atol=1E-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(_snake_case ).mkdir(exist_ok=_snake_case )
print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_snake_case )
print(F'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(_snake_case )
if push_to_hub:
print(F'''Pushing model and processor to the hub {vit_name}''' )
model.push_to_hub(F'''ybelkada/{vit_name}''' )
processor.push_to_hub(F'''ybelkada/{vit_name}''' )
if __name__ == "__main__":
snake_case : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--vit_name",
default="vit_base_r50_s16_384",
type=str,
help="Name of the hybrid ViT timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
)
snake_case : List[Any] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 281 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase__ = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
snake_case : List[str] = "facebook/wmt19-en-de"
snake_case : Dict = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
snake_case : List[str] = FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
snake_case : int = FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")
# Test
snake_case : Optional[Any] = tokenizer(["Making tiny model"], return_tensors="pt")
snake_case : List[str] = tiny_model(**batch)
print("test output:", len(outputs.logits[0]))
# Save
snake_case : Dict = "tiny-wmt19-en-de"
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F"Generated {mname_tiny}")
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 281 | 0 |
'''simple docstring'''
import warnings
from .generation import TFGenerationMixin
class lowerCAmelCase__ ( lowerCamelCase_ ):
# warning at import time
warnings.warn(
'''Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '''
'''be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.''' , lowerCamelCase_ , )
| 93 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
snake_case : Optional[int] = logging.getLogger(__name__)
def lowerCAmelCase_ ( _snake_case : Dict , _snake_case : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__magic_name__ : List[str] = np.argmax(_snake_case , axis=1 )
return np.sum(outputs == labels )
def lowerCAmelCase_ ( _snake_case : Optional[Any] ) -> Dict:
'''simple docstring'''
with open(_snake_case , encoding="utf_8" ) as f:
__magic_name__ : List[str] = csv.reader(_snake_case )
__magic_name__ : List[Any] = []
next(_snake_case ) # skip the first line
for line in tqdm(_snake_case ):
output.append((" ".join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def lowerCAmelCase_ ( _snake_case : str , _snake_case : Tuple , _snake_case : Union[str, Any] , _snake_case : List[Any] , _snake_case : Tuple , _snake_case : Optional[int] ) -> int:
'''simple docstring'''
__magic_name__ : Optional[int] = []
for dataset in encoded_datasets:
__magic_name__ : Union[str, Any] = len(_snake_case )
__magic_name__ : Dict = np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
__magic_name__ : List[str] = np.zeros((n_batch, 2) , dtype=np.intaa )
__magic_name__ : Optional[int] = np.full((n_batch, 2, input_len) , fill_value=-100 , dtype=np.intaa )
__magic_name__ : int = np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(_snake_case ):
__magic_name__ : Dict = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__magic_name__ : Dict = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__magic_name__ : str = with_conta
__magic_name__ : Tuple = with_conta
__magic_name__ : Union[str, Any] = len(_snake_case ) - 1
__magic_name__ : int = len(_snake_case ) - 1
__magic_name__ : Optional[Any] = with_conta
__magic_name__ : Optional[Any] = with_conta
__magic_name__ : Optional[int] = mc_label
__magic_name__ : str = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(_snake_case ) for t in all_inputs ) )
return tensor_datasets
def lowerCAmelCase_ ( ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Any = argparse.ArgumentParser()
parser.add_argument("--model_name" , type=_snake_case , default="openai-gpt" , help="pretrained model name" )
parser.add_argument("--do_train" , action="store_true" , help="Whether to run training." )
parser.add_argument("--do_eval" , action="store_true" , help="Whether to run eval on the dev set." )
parser.add_argument(
"--output_dir" , default=_snake_case , type=_snake_case , required=_snake_case , help="The output directory where the model predictions and checkpoints will be written." , )
parser.add_argument("--train_dataset" , type=_snake_case , default="" )
parser.add_argument("--eval_dataset" , type=_snake_case , default="" )
parser.add_argument("--seed" , type=_snake_case , default=42 )
parser.add_argument("--num_train_epochs" , type=_snake_case , default=3 )
parser.add_argument("--train_batch_size" , type=_snake_case , default=8 )
parser.add_argument("--eval_batch_size" , type=_snake_case , default=16 )
parser.add_argument("--adam_epsilon" , default=1E-8 , type=_snake_case , help="Epsilon for Adam optimizer." )
parser.add_argument("--max_grad_norm" , type=_snake_case , default=1 )
parser.add_argument(
"--max_steps" , default=-1 , type=_snake_case , help=(
"If > 0: set total number of training steps to perform. Override num_train_epochs."
) , )
parser.add_argument(
"--gradient_accumulation_steps" , type=_snake_case , default=1 , help="Number of updates steps to accumulate before performing a backward/update pass." , )
parser.add_argument("--learning_rate" , type=_snake_case , default=6.25E-5 )
parser.add_argument("--warmup_steps" , default=0 , type=_snake_case , help="Linear warmup over warmup_steps." )
parser.add_argument("--lr_schedule" , type=_snake_case , default="warmup_linear" )
parser.add_argument("--weight_decay" , type=_snake_case , default=0.01 )
parser.add_argument("--lm_coef" , type=_snake_case , default=0.9 )
parser.add_argument("--n_valid" , type=_snake_case , default=374 )
parser.add_argument("--server_ip" , type=_snake_case , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=_snake_case , default="" , help="Can be used for distant debugging." )
__magic_name__ : List[Any] = parser.parse_args()
print(_snake_case )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=_snake_case )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
__magic_name__ : Dict = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
__magic_name__ : Optional[int] = torch.cuda.device_count()
logger.info("device: {}, n_gpu {}".format(_snake_case , _snake_case ) )
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True." )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
__magic_name__ : List[Any] = ["_start_", "_delimiter_", "_classify_"]
__magic_name__ : Optional[int] = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(_snake_case )
__magic_name__ : Optional[Any] = tokenizer.convert_tokens_to_ids(_snake_case )
__magic_name__ : List[str] = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(_snake_case ) )
model.to(_snake_case )
# Load and encode the datasets
def tokenize_and_encode(_snake_case : str ):
if isinstance(_snake_case , _snake_case ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_snake_case ) )
elif isinstance(_snake_case , _snake_case ):
return obj
return [tokenize_and_encode(_snake_case ) for o in obj]
logger.info("Encoding dataset..." )
__magic_name__ : Optional[int] = load_rocstories_dataset(args.train_dataset )
__magic_name__ : str = load_rocstories_dataset(args.eval_dataset )
__magic_name__ : int = (train_dataset, eval_dataset)
__magic_name__ : List[str] = tokenize_and_encode(_snake_case )
# Compute the max input length for the Transformer
__magic_name__ : Optional[Any] = model.config.n_positions // 2 - 2
__magic_name__ : Optional[int] = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
__magic_name__ : List[str] = min(_snake_case , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
__magic_name__ : List[Any] = pre_process_datasets(_snake_case , _snake_case , _snake_case , *_snake_case )
__magic_name__ , __magic_name__ : Optional[int] = tensor_datasets[0], tensor_datasets[1]
__magic_name__ : Tuple = TensorDataset(*_snake_case )
__magic_name__ : Union[str, Any] = RandomSampler(_snake_case )
__magic_name__ : Dict = DataLoader(_snake_case , sampler=_snake_case , batch_size=args.train_batch_size )
__magic_name__ : Any = TensorDataset(*_snake_case )
__magic_name__ : Optional[Any] = SequentialSampler(_snake_case )
__magic_name__ : int = DataLoader(_snake_case , sampler=_snake_case , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
__magic_name__ : Tuple = args.max_steps
__magic_name__ : List[str] = args.max_steps // (len(_snake_case ) // args.gradient_accumulation_steps) + 1
else:
__magic_name__ : List[str] = len(_snake_case ) // args.gradient_accumulation_steps * args.num_train_epochs
__magic_name__ : str = list(model.named_parameters() )
__magic_name__ : Dict = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
__magic_name__ : str = [
{
"params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
"weight_decay": args.weight_decay,
},
{"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], "weight_decay": 0.0},
]
__magic_name__ : str = AdamW(_snake_case , lr=args.learning_rate , eps=args.adam_epsilon )
__magic_name__ : List[str] = get_linear_schedule_with_warmup(
_snake_case , num_warmup_steps=args.warmup_steps , num_training_steps=_snake_case )
if args.do_train:
__magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc="Epoch" ):
__magic_name__ : List[str] = 0
__magic_name__ : Tuple = 0
__magic_name__ : Dict = tqdm(_snake_case , desc="Training" )
for step, batch in enumerate(_snake_case ):
__magic_name__ : Optional[Any] = tuple(t.to(_snake_case ) for t in batch )
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Dict = batch
__magic_name__ : Optional[Any] = model(_snake_case , mc_token_ids=_snake_case , lm_labels=_snake_case , mc_labels=_snake_case )
__magic_name__ : Optional[Any] = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
__magic_name__ : List[str] = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
__magic_name__ : int = "Training loss: {:.2e} lr: {:.2e}".format(_snake_case , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
__magic_name__ : Dict = model.module if hasattr(_snake_case , "module" ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
__magic_name__ : List[Any] = os.path.join(args.output_dir , _snake_case )
__magic_name__ : Dict = os.path.join(args.output_dir , _snake_case )
torch.save(model_to_save.state_dict() , _snake_case )
model_to_save.config.to_json_file(_snake_case )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
__magic_name__ : Dict = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
__magic_name__ : Optional[int] = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(_snake_case )
if args.do_eval:
model.eval()
__magic_name__ , __magic_name__ : Any = 0, 0
__magic_name__ , __magic_name__ : Union[str, Any] = 0, 0
for batch in tqdm(_snake_case , desc="Evaluating" ):
__magic_name__ : int = tuple(t.to(_snake_case ) for t in batch )
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] = batch
with torch.no_grad():
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Dict = model(
_snake_case , mc_token_ids=_snake_case , lm_labels=_snake_case , mc_labels=_snake_case )
__magic_name__ : Tuple = mc_logits.detach().cpu().numpy()
__magic_name__ : Any = mc_labels.to("cpu" ).numpy()
__magic_name__ : str = accuracy(_snake_case , _snake_case )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
__magic_name__ : Tuple = eval_loss / nb_eval_steps
__magic_name__ : List[Any] = eval_accuracy / nb_eval_examples
__magic_name__ : int = tr_loss / nb_tr_steps if args.do_train else None
__magic_name__ : Any = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}
__magic_name__ : int = os.path.join(args.output_dir , "eval_results.txt" )
with open(_snake_case , "w" ) as writer:
logger.info("***** Eval results *****" )
for key in sorted(result.keys() ):
logger.info(" %s = %s" , _snake_case , str(result[key] ) )
writer.write("%s = %s\n" % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 281 | 0 |
def __lowerCamelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __lowerCamelCase ( ):
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 94 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 281 | 0 |
UpperCAmelCase : Any = 8.3_1_4_4_5_9_8
def _A ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or equal to 0 kg/mol" )
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# example
UpperCAmelCase : Tuple = 300
UpperCAmelCase : Optional[int] = 28
UpperCAmelCase : Tuple = rms_speed_of_molecule(temperature, molar_mass)
print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")
| 95 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCAmelCase_ ( _snake_case : List[Any] ) -> List[Any]:
'''simple docstring'''
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ) -> Tuple:
'''simple docstring'''
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : Dict = "mock-s3-bucket"
__magic_name__ : Any = F'''s3://{mock_bucket}'''
__magic_name__ : str = extract_path_from_uri(_snake_case )
assert dataset_path.startswith("s3://" ) is False
__magic_name__ : Tuple = "./local/path"
__magic_name__ : Optional[Any] = extract_path_from_uri(_snake_case )
assert dataset_path == new_dataset_path
def lowerCAmelCase_ ( _snake_case : List[str] ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ : str = is_remote_filesystem(_snake_case )
assert is_remote is True
__magic_name__ : Optional[int] = fsspec.filesystem("file" )
__magic_name__ : int = is_remote_filesystem(_snake_case )
assert is_remote is False
@pytest.mark.parametrize("compression_fs_class" , _snake_case )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : Optional[Any] , _snake_case : int , _snake_case : Tuple , _snake_case : Any , _snake_case : Union[str, Any] , _snake_case : Any ) -> int:
'''simple docstring'''
__magic_name__ : Any = {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
__magic_name__ : str = input_paths[compression_fs_class.protocol]
if input_path is None:
__magic_name__ : Dict = F'''for \'{compression_fs_class.protocol}\' compression protocol, '''
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_snake_case )
__magic_name__ : str = fsspec.filesystem(compression_fs_class.protocol , fo=_snake_case )
assert isinstance(_snake_case , _snake_case )
__magic_name__ : int = os.path.basename(_snake_case )
__magic_name__ : Optional[int] = expected_filename[: expected_filename.rindex("." )]
assert fs.glob("*" ) == [expected_filename]
with fs.open(_snake_case , "r" , encoding="utf-8" ) as f, open(_snake_case , encoding="utf-8" ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize("protocol" , ["zip", "gzip"] )
def lowerCAmelCase_ ( _snake_case : List[Any] , _snake_case : Optional[Any] , _snake_case : Optional[Any] ) -> str:
'''simple docstring'''
__magic_name__ : int = {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
__magic_name__ : int = compressed_file_paths[protocol]
__magic_name__ : Tuple = "dataset.jsonl"
__magic_name__ : List[str] = F'''{protocol}://{member_file_path}::{compressed_file_path}'''
__magic_name__ , *__magic_name__ : Optional[Any] = fsspec.get_fs_token_paths(_snake_case )
assert fs.isfile(_snake_case )
assert not fs.isfile("non_existing_" + member_file_path )
@pytest.mark.integration
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Dict , _snake_case : List[str] , _snake_case : Tuple ) -> str:
'''simple docstring'''
__magic_name__ : int = hf_api.dataset_info(_snake_case , token=_snake_case )
__magic_name__ : Optional[Any] = HfFileSystem(repo_info=_snake_case , token=_snake_case )
assert sorted(hffs.glob("*" ) ) == [".gitattributes", "data"]
assert hffs.isdir("data" )
assert hffs.isfile(".gitattributes" ) and hffs.isfile("data/text_data.txt" )
with open(_snake_case ) as f:
assert hffs.open("data/text_data.txt" , "r" ).read() == f.read()
def lowerCAmelCase_ ( ) -> Optional[int]:
'''simple docstring'''
__magic_name__ : Optional[Any] = "bz2"
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(_snake_case , _snake_case , clobber=_snake_case )
with pytest.warns(_snake_case ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(_snake_case ) == 1
assert (
str(warning_info[0].message )
== F'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
)
| 281 | 0 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class lowerCAmelCase__ :
'''simple docstring'''
@staticmethod
def A_ ( *lowercase , **lowercase ):
pass
def _snake_case ( lowercase__ ):
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
lowercase__ = (
"""https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)
@is_pipeline_test
@require_torch
@require_vision
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def A_ ( self , lowercase , lowercase , lowercase ):
_lowerCamelCase : Optional[Any] = pipeline(
'document-question-answering' , model=lowercase , tokenizer=lowercase , image_processor=lowercase )
_lowerCamelCase : Dict = INVOICE_URL
_lowerCamelCase : List[str] = list(zip(*apply_tesseract(load_image(lowercase ) , lowercase , '' ) ) )
_lowerCamelCase : Optional[Any] = 'What is the placebo?'
_lowerCamelCase : List[Any] = [
{
'image': load_image(lowercase ),
'question': question,
},
{
'image': image,
'question': question,
},
{
'image': image,
'question': question,
'word_boxes': word_boxes,
},
]
return dqa_pipeline, examples
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : str = dqa_pipeline(lowercase , top_k=2 )
self.assertEqual(
lowercase , [
[
{'score': ANY(lowercase ), 'answer': ANY(lowercase ), 'start': ANY(lowercase ), 'end': ANY(lowercase )},
{'score': ANY(lowercase ), 'answer': ANY(lowercase ), 'start': ANY(lowercase ), 'end': ANY(lowercase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def A_ ( self ):
_lowerCamelCase : List[Any] = pipeline('document-question-answering' , model='hf-internal-testing/tiny-random-layoutlmv2' )
_lowerCamelCase : Optional[Any] = INVOICE_URL
_lowerCamelCase : List[Any] = 'How many cats are there?'
_lowerCamelCase : Optional[int] = [
{'score': 0.00_01, 'answer': 'oy 2312/2019', 'start': 38, 'end': 39},
{'score': 0.00_01, 'answer': 'oy 2312/2019 DUE', 'start': 38, 'end': 40},
]
_lowerCamelCase : str = dqa_pipeline(image=lowercase , question=lowercase , top_k=2 )
self.assertEqual(nested_simplify(lowercase , decimals=4 ) , lowercase )
_lowerCamelCase : int = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(nested_simplify(lowercase , decimals=4 ) , lowercase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
_lowerCamelCase : List[str] = './tests/fixtures/tests_samples/COCO/000000039769.png'
_lowerCamelCase : List[str] = dqa_pipeline(image=lowercase , question=lowercase , top_k=2 )
self.assertEqual(lowercase , [] )
# We can optionnally pass directly the words and bounding boxes
_lowerCamelCase : Optional[Any] = './tests/fixtures/tests_samples/COCO/000000039769.png'
_lowerCamelCase : Dict = []
_lowerCamelCase : int = []
_lowerCamelCase : Any = dqa_pipeline(image=lowercase , question=lowercase , words=lowercase , boxes=lowercase , top_k=2 )
self.assertEqual(lowercase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def A_ ( self ):
_lowerCamelCase : Optional[int] = pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , )
_lowerCamelCase : Union[str, Any] = INVOICE_URL
_lowerCamelCase : str = 'What is the invoice number?'
_lowerCamelCase : Dict = dqa_pipeline(image=lowercase , question=lowercase , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
{'score': 0.99_44, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.00_09, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
_lowerCamelCase : Optional[Any] = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
{'score': 0.99_44, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.00_09, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
_lowerCamelCase : Optional[int] = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
[
{'score': 0.99_44, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.00_09, 'answer': 'us-001', 'start': 16, 'end': 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def A_ ( self ):
_lowerCamelCase : Optional[Any] = pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , max_seq_len=50 , )
_lowerCamelCase : Optional[int] = INVOICE_URL
_lowerCamelCase : List[Any] = 'What is the invoice number?'
_lowerCamelCase : Tuple = dqa_pipeline(image=lowercase , question=lowercase , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
{'score': 0.99_74, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.99_48, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
_lowerCamelCase : Dict = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
{'score': 0.99_74, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.99_48, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
_lowerCamelCase : List[Any] = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
[
{'score': 0.99_74, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.99_48, 'answer': 'us-001', 'start': 16, 'end': 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def A_ ( self ):
_lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=lowercase )
_lowerCamelCase : Tuple = pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=lowercase , revision='3dc6de3' , )
_lowerCamelCase : Optional[Any] = INVOICE_URL
_lowerCamelCase : Tuple = 'What is the invoice number?'
_lowerCamelCase : List[Any] = dqa_pipeline(image=lowercase , question=lowercase , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
{'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
] , )
_lowerCamelCase : Optional[int] = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
{'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
] , )
_lowerCamelCase : int = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
[
{'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
]
]
* 2 , )
_lowerCamelCase : List[Any] = list(zip(*apply_tesseract(load_image(lowercase ) , lowercase , '' ) ) )
# This model should also work if `image` is set to None
_lowerCamelCase : Optional[Any] = dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
{'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def A_ ( self ):
_lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=lowercase )
_lowerCamelCase : Optional[Any] = pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=lowercase , revision='3dc6de3' , max_seq_len=50 , )
_lowerCamelCase : Any = INVOICE_URL
_lowerCamelCase : Tuple = 'What is the invoice number?'
_lowerCamelCase : Any = dqa_pipeline(image=lowercase , question=lowercase , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
{'score': 0.99_99, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.99_98, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
_lowerCamelCase : Any = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
[
{'score': 0.99_99, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.99_98, 'answer': 'us-001', 'start': 16, 'end': 16},
]
]
* 2 , )
_lowerCamelCase : Optional[int] = list(zip(*apply_tesseract(load_image(lowercase ) , lowercase , '' ) ) )
# This model should also work if `image` is set to None
_lowerCamelCase : Union[str, Any] = dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
{'score': 0.99_99, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.99_98, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
@slow
@require_torch
def A_ ( self ):
_lowerCamelCase : Optional[Any] = pipeline(
'document-question-answering' , model='naver-clova-ix/donut-base-finetuned-docvqa' , tokenizer=AutoTokenizer.from_pretrained('naver-clova-ix/donut-base-finetuned-docvqa' ) , feature_extractor='naver-clova-ix/donut-base-finetuned-docvqa' , )
_lowerCamelCase : List[Any] = INVOICE_URL
_lowerCamelCase : Any = 'What is the invoice number?'
_lowerCamelCase : Dict = dqa_pipeline(image=lowercase , question=lowercase , top_k=2 )
self.assertEqual(nested_simplify(lowercase , decimals=4 ) , [{'answer': 'us-001'}] )
@require_tf
@unittest.skip('Document question answering not implemented in TF' )
def A_ ( self ):
pass | 96 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case : Dict = logging.get_logger(__name__)
snake_case : List[Any] = {
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
"YituTech/conv-bert-medium-small": (
"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
),
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class _snake_case ( snake_case ):
UpperCamelCase__ = 'convbert'
def __init__( self , _a=30_522 , _a=768 , _a=12 , _a=12 , _a=3_072 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=2 , _a=0.02 , _a=1e-12 , _a=1 , _a=0 , _a=2 , _a=768 , _a=2 , _a=9 , _a=1 , _a=None , **_a , ):
super().__init__(
pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a , )
__magic_name__ : Tuple = vocab_size
__magic_name__ : List[Any] = hidden_size
__magic_name__ : Union[str, Any] = num_hidden_layers
__magic_name__ : List[Any] = num_attention_heads
__magic_name__ : str = intermediate_size
__magic_name__ : Any = hidden_act
__magic_name__ : List[Any] = hidden_dropout_prob
__magic_name__ : Optional[int] = attention_probs_dropout_prob
__magic_name__ : Tuple = max_position_embeddings
__magic_name__ : str = type_vocab_size
__magic_name__ : List[str] = initializer_range
__magic_name__ : Tuple = layer_norm_eps
__magic_name__ : List[Any] = embedding_size
__magic_name__ : List[Any] = head_ratio
__magic_name__ : str = conv_kernel_size
__magic_name__ : Dict = num_groups
__magic_name__ : str = classifier_dropout
class _snake_case ( snake_case ):
@property
def SCREAMING_SNAKE_CASE ( self ):
if self.task == "multiple-choice":
__magic_name__ : Dict = {0: "batch", 1: "choice", 2: "sequence"}
else:
__magic_name__ : Dict = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 281 | 0 |
'''simple docstring'''
def a ( __a , __a , __a , __a ) -> str:
'''simple docstring'''
if height >= 1:
move_tower(height - 1 , __a , __a , __a )
move_disk(__a , __a )
move_tower(height - 1 , __a , __a , __a )
def a ( __a , __a ) -> str:
'''simple docstring'''
print('''moving disk from''' , __a , '''to''' , __a )
def a ( ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ :Optional[int] = int(input('''Height of hanoi: ''' ).strip() )
move_tower(__a , '''A''' , '''B''' , '''C''' )
if __name__ == "__main__":
main() | 97 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipaConfig,
BlipaForConditionalGeneration,
BlipaProcessor,
BlipaVisionConfig,
BlipImageProcessor,
OPTConfig,
TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def lowerCAmelCase_ ( ) -> str:
'''simple docstring'''
__magic_name__ : int = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
__magic_name__ : Union[str, Any] = Image.open(requests.get(_snake_case , stream=_snake_case ).raw ).convert("RGB" )
return image
def lowerCAmelCase_ ( _snake_case : str ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : List[str] = []
# fmt: off
# vision encoder
rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding") )
rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding") )
rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight") )
rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias") )
rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight") )
rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias") )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',) )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''') )
# QFormer
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight") )
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias") )
# fmt: on
return rename_keys
def lowerCAmelCase_ ( _snake_case : Any , _snake_case : Union[str, Any] , _snake_case : Optional[Any] ) -> int:
'''simple docstring'''
__magic_name__ : Tuple = dct.pop(_snake_case )
__magic_name__ : int = val
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : Optional[Any] ) -> Dict:
'''simple docstring'''
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
__magic_name__ : List[Any] = state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias''' )
__magic_name__ : Optional[Any] = state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias''' )
# next, set bias in the state dict
__magic_name__ : Optional[int] = torch.cat((q_bias, torch.zeros_like(_snake_case , requires_grad=_snake_case ), v_bias) )
__magic_name__ : Union[str, Any] = qkv_bias
def lowerCAmelCase_ ( _snake_case : Dict , _snake_case : str ) -> int:
'''simple docstring'''
__magic_name__ : List[Any] = 364 if "coco" in model_name else 224
__magic_name__ : Union[str, Any] = BlipaVisionConfig(image_size=_snake_case ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
__magic_name__ : List[str] = OPTConfig.from_pretrained("facebook/opt-2.7b" , eos_token_id=_snake_case ).to_dict()
elif "opt-6.7b" in model_name:
__magic_name__ : Any = OPTConfig.from_pretrained("facebook/opt-6.7b" , eos_token_id=_snake_case ).to_dict()
elif "t5-xl" in model_name:
__magic_name__ : Dict = TaConfig.from_pretrained("google/flan-t5-xl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
__magic_name__ : int = TaConfig.from_pretrained("google/flan-t5-xxl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
__magic_name__ : List[Any] = BlipaConfig(vision_config=_snake_case , text_config=_snake_case )
return config, image_size
@torch.no_grad()
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : str=None , _snake_case : Dict=False ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Optional[int] = (
AutoTokenizer.from_pretrained("facebook/opt-2.7b" )
if "opt" in model_name
else AutoTokenizer.from_pretrained("google/flan-t5-xl" )
)
__magic_name__ : List[Any] = tokenizer("\n" , add_special_tokens=_snake_case ).input_ids[0]
__magic_name__ , __magic_name__ : Tuple = get_blipa_config(_snake_case , eos_token_id=_snake_case )
__magic_name__ : Union[str, Any] = BlipaForConditionalGeneration(_snake_case ).eval()
__magic_name__ : Any = {
"blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
"blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
"blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
"blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
"blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
"blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
"blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
}
__magic_name__ , __magic_name__ : Union[str, Any] = model_name_to_original[model_name]
# load original model
print("Loading original model..." )
__magic_name__ : Union[str, Any] = "cuda" if torch.cuda.is_available() else "cpu"
__magic_name__ , __magic_name__ , __magic_name__ : Optional[Any] = load_model_and_preprocess(
name=_snake_case , model_type=_snake_case , is_eval=_snake_case , device=_snake_case )
original_model.eval()
print("Done!" )
# update state dict keys
__magic_name__ : Dict = original_model.state_dict()
__magic_name__ : str = create_rename_keys(_snake_case )
for src, dest in rename_keys:
rename_key(_snake_case , _snake_case , _snake_case )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
__magic_name__ : Any = state_dict.pop(_snake_case )
if key.startswith("Qformer.bert" ):
__magic_name__ : Optional[int] = key.replace("Qformer.bert" , "qformer" )
if "attention.self" in key:
__magic_name__ : Any = key.replace("self" , "attention" )
if "opt_proj" in key:
__magic_name__ : Union[str, Any] = key.replace("opt_proj" , "language_projection" )
if "t5_proj" in key:
__magic_name__ : Optional[int] = key.replace("t5_proj" , "language_projection" )
if key.startswith("opt" ):
__magic_name__ : List[str] = key.replace("opt" , "language" )
if key.startswith("t5" ):
__magic_name__ : Tuple = key.replace("t5" , "language" )
__magic_name__ : Dict = val
# read in qv biases
read_in_q_v_bias(_snake_case , _snake_case )
__magic_name__ , __magic_name__ : Tuple = hf_model.load_state_dict(_snake_case , strict=_snake_case )
assert len(_snake_case ) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
__magic_name__ : List[Any] = load_demo_image()
__magic_name__ : Tuple = vis_processors["eval"](_snake_case ).unsqueeze(0 ).to(_snake_case )
__magic_name__ : Dict = tokenizer(["\n"] , return_tensors="pt" ).input_ids.to(_snake_case )
# create processor
__magic_name__ : Optional[Any] = BlipImageProcessor(
size={"height": image_size, "width": image_size} , image_mean=_snake_case , image_std=_snake_case )
__magic_name__ : Dict = BlipaProcessor(image_processor=_snake_case , tokenizer=_snake_case )
__magic_name__ : Union[str, Any] = processor(images=_snake_case , return_tensors="pt" ).pixel_values.to(_snake_case )
# make sure processor creates exact same pixel values
assert torch.allclose(_snake_case , _snake_case )
original_model.to(_snake_case )
hf_model.to(_snake_case )
with torch.no_grad():
if "opt" in model_name:
__magic_name__ : List[Any] = original_model({"image": original_pixel_values, "text_input": [""]} ).logits
__magic_name__ : Optional[int] = hf_model(_snake_case , _snake_case ).logits
else:
__magic_name__ : int = original_model(
{"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]} ).logits
__magic_name__ : Tuple = input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -100 )
__magic_name__ : List[str] = hf_model(_snake_case , _snake_case , labels=_snake_case ).logits
assert original_logits.shape == logits.shape
print("First values of original logits:" , original_logits[0, :3, :3] )
print("First values of HF logits:" , logits[0, :3, :3] )
# assert values
if model_name == "blip2-flan-t5-xl":
__magic_name__ : List[str] = torch.tensor(
[[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]] , device=_snake_case )
assert torch.allclose(logits[0, :3, :3] , _snake_case , atol=1E-4 )
elif model_name == "blip2-flan-t5-xl-coco":
__magic_name__ : Tuple = torch.tensor(
[[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]] , device=_snake_case )
else:
# cast to same type
__magic_name__ : str = logits.dtype
assert torch.allclose(original_logits.to(_snake_case ) , _snake_case , atol=1E-2 )
print("Looks ok!" )
print("Generating a caption..." )
__magic_name__ : Optional[int] = ""
__magic_name__ : Dict = tokenizer(_snake_case , return_tensors="pt" ).input_ids.to(_snake_case )
__magic_name__ : int = original_model.generate({"image": original_pixel_values} )
__magic_name__ : Optional[Any] = hf_model.generate(
_snake_case , _snake_case , do_sample=_snake_case , num_beams=5 , max_length=30 , min_length=1 , top_p=0.9 , repetition_penalty=1.0 , length_penalty=1.0 , temperature=1 , )
print("Original generation:" , _snake_case )
__magic_name__ : Tuple = input_ids.shape[1]
__magic_name__ : int = processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=_snake_case )
__magic_name__ : Union[str, Any] = [text.strip() for text in output_text]
print("HF generation:" , _snake_case )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(_snake_case )
hf_model.save_pretrained(_snake_case )
if push_to_hub:
processor.push_to_hub(F'''nielsr/{model_name}''' )
hf_model.push_to_hub(F'''nielsr/{model_name}''' )
if __name__ == "__main__":
snake_case : Any = argparse.ArgumentParser()
snake_case : Union[str, Any] = [
"blip2-opt-2.7b",
"blip2-opt-6.7b",
"blip2-opt-2.7b-coco",
"blip2-opt-6.7b-coco",
"blip2-flan-t5-xl",
"blip2-flan-t5-xl-coco",
"blip2-flan-t5-xxl",
]
parser.add_argument(
"--model_name",
default="blip2-opt-2.7b",
choices=choices,
type=str,
help="Path to hf config.json of model to convert",
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model and processor to the hub after converting",
)
snake_case : int = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 281 | 0 |
"""simple docstring"""
import os
def a_ ( ):
UpperCAmelCase__ = os.path.dirname(os.path.realpath(lowerCamelCase ) )
UpperCAmelCase__ = os.path.join(lowerCamelCase , 'triangle.txt' )
with open(lowerCamelCase ) as f:
UpperCAmelCase__ = f.readlines()
UpperCAmelCase__ = []
for line in triangle:
UpperCAmelCase__ = []
for number in line.strip().split(' ' ):
numbers_from_line.append(int(lowerCamelCase ) )
a.append(lowerCamelCase )
for i in range(1 , len(lowerCamelCase ) ):
for j in range(len(a[i] ) ):
UpperCAmelCase__ = a[i - 1][j] if j != len(a[i - 1] ) else 0
UpperCAmelCase__ = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(lowerCamelCase , lowerCamelCase )
return max(a[-1] )
if __name__ == "__main__":
print(solution())
| 98 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case : Dict = logging.get_logger(__name__)
snake_case : Union[str, Any] = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
snake_case : Dict = {
"vocab_file": {
"vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
"vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
},
"merges_file": {
"vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
"vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
},
}
snake_case : Union[str, Any] = {
"vinai/phobert-base": 256,
"vinai/phobert-large": 256,
}
def lowerCAmelCase_ ( _snake_case : str ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : List[str] = set()
__magic_name__ : Any = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__magic_name__ : int = char
__magic_name__ : List[str] = set(_snake_case )
return pairs
class _snake_case ( snake_case ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , _a , _a , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , **_a , ):
super().__init__(
bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , cls_token=_a , pad_token=_a , mask_token=_a , **_a , )
__magic_name__ : Dict = vocab_file
__magic_name__ : Tuple = merges_file
__magic_name__ : List[Any] = {}
__magic_name__ : List[Any] = 0
__magic_name__ : Tuple = 1
__magic_name__ : int = 2
__magic_name__ : Union[str, Any] = 3
self.add_from_file(_a )
__magic_name__ : Optional[int] = {v: k for k, v in self.encoder.items()}
with open(_a , encoding="utf-8" ) as merges_handle:
__magic_name__ : List[str] = merges_handle.read().split("\n" )[:-1]
__magic_name__ : Union[str, Any] = [tuple(merge.split()[:-1] ) for merge in merges]
__magic_name__ : Union[str, Any] = dict(zip(_a , range(len(_a ) ) ) )
__magic_name__ : Optional[int] = {}
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__magic_name__ : Optional[Any] = [self.cls_token_id]
__magic_name__ : Union[str, Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self , _a , _a = None , _a = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is None:
return [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1, 1] + ([0] * len(_a )) + [1]
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
__magic_name__ : Optional[Any] = [self.sep_token_id]
__magic_name__ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def SCREAMING_SNAKE_CASE ( self ):
return len(self.encoder )
def SCREAMING_SNAKE_CASE ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def SCREAMING_SNAKE_CASE ( self , _a ):
if token in self.cache:
return self.cache[token]
__magic_name__ : List[Any] = tuple(_a )
__magic_name__ : List[Any] = tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
__magic_name__ : Any = get_pairs(_a )
if not pairs:
return token
while True:
__magic_name__ : str = min(_a , key=lambda _a : self.bpe_ranks.get(_a , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
__magic_name__ , __magic_name__ : List[str] = bigram
__magic_name__ : List[str] = []
__magic_name__ : List[str] = 0
while i < len(_a ):
try:
__magic_name__ : Any = word.index(_a , _a )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__magic_name__ : Tuple = j
if word[i] == first and i < len(_a ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__magic_name__ : Union[str, Any] = tuple(_a )
__magic_name__ : Optional[int] = new_word
if len(_a ) == 1:
break
else:
__magic_name__ : List[Any] = get_pairs(_a )
__magic_name__ : Optional[int] = "@@ ".join(_a )
__magic_name__ : Tuple = word[:-4]
__magic_name__ : str = word
return word
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Optional[Any] = []
__magic_name__ : Dict = re.findall(r"\S+\n?" , _a )
for token in words:
split_tokens.extend(list(self.bpe(_a ).split(" " ) ) )
return split_tokens
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.encoder.get(_a , self.encoder.get(self.unk_token ) )
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.decoder.get(_a , self.unk_token )
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Tuple = " ".join(_a ).replace("@@ " , "" ).strip()
return out_string
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__magic_name__ : Optional[int] = os.path.join(
_a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
__magic_name__ : Union[str, Any] = os.path.join(
_a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
if os.path.abspath(self.merges_file ) != os.path.abspath(_a ):
copyfile(self.merges_file , _a )
return out_vocab_file, out_merge_file
def SCREAMING_SNAKE_CASE ( self , _a ):
if isinstance(_a , _a ):
try:
with open(_a , "r" , encoding="utf-8" ) as fd:
self.add_from_file(_a )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset''' )
return
__magic_name__ : List[Any] = f.readlines()
for lineTmp in lines:
__magic_name__ : Optional[Any] = lineTmp.strip()
__magic_name__ : Union[str, Any] = line.rfind(" " )
if idx == -1:
raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'" )
__magic_name__ : Optional[int] = line[:idx]
__magic_name__ : Dict = len(self.encoder )
| 281 | 0 |
from collections import defaultdict
class A__ :
"""simple docstring"""
def __init__( self , lowercase , lowercase) -> Dict:
'''simple docstring'''
a__ : Optional[int] = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
a__ : List[str] = [
[-1 for i in range(total + 1)] for j in range(2 ** len(lowercase))
]
a__ : Optional[int] = defaultdict(lowercase) # stores the list of persons for each task
# final_mask is used to check if all persons are included by setting all bits
# to 1
a__ : Optional[int] = (1 << len(lowercase)) - 1
def __lowercase ( self , lowercase , lowercase) -> str:
'''simple docstring'''
if mask == self.final_mask:
return 1
# if not everyone gets the task and no more tasks are available, return 0
if task_no > self.total_tasks:
return 0
# if case already considered
if self.dp[mask][task_no] != -1:
return self.dp[mask][task_no]
# Number of ways when we don't this task in the arrangement
a__ : str = self.count_ways_until(lowercase , task_no + 1)
# now assign the tasks one by one to all possible persons and recursively
# assign for the remaining tasks.
if task_no in self.task:
for p in self.task[task_no]:
# if p is already given a task
if mask & (1 << p):
continue
# assign this task to p and change the mask value. And recursively
# assign tasks with the new mask value.
total_ways_util += self.count_ways_until(mask | (1 << p) , task_no + 1)
# save the value.
a__ : int = total_ways_util
return self.dp[mask][task_no]
def __lowercase ( self , lowercase) -> Optional[int]:
'''simple docstring'''
for i in range(len(lowercase)):
for j in task_performed[i]:
self.task[j].append(lowercase)
# call the function to fill the DP table, final answer is stored in dp[0][1]
return self.count_ways_until(0 , 1)
if __name__ == "__main__":
lowercase : Union[str, Any] = 5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
lowercase : Tuple = [[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
)
| 99 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase_ ( _snake_case : str = "laptop" ) -> DataFrame:
'''simple docstring'''
__magic_name__ : Tuple = F'''https://www.amazon.in/laptop/s?k={product}'''
__magic_name__ : Dict = {
"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
"Accept-Language": "en-US, en;q=0.5",
}
__magic_name__ : Tuple = BeautifulSoup(requests.get(_snake_case , headers=_snake_case ).text )
# Initialize a Pandas dataframe with the column titles
__magic_name__ : int = DataFrame(
columns=[
"Product Title",
"Product Link",
"Current Price of the product",
"Product Rating",
"MRP of the product",
"Discount",
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
"div" , attrs={"class": "s-result-item", "data-component-type": "s-search-result"} , ) , soup.find_all("div" , attrs={"class": "a-row a-size-base a-color-base"} ) , ):
try:
__magic_name__ : Dict = item.ha.text
__magic_name__ : Optional[int] = "https://www.amazon.in/" + item.ha.a["href"]
__magic_name__ : Optional[Any] = item.find("span" , attrs={"class": "a-offscreen"} ).text
try:
__magic_name__ : Union[str, Any] = item.find("span" , attrs={"class": "a-icon-alt"} ).text
except AttributeError:
__magic_name__ : Dict = "Not available"
try:
__magic_name__ : Optional[int] = (
"₹"
+ item.find(
"span" , attrs={"class": "a-price a-text-price"} ).text.split("₹" )[1]
)
except AttributeError:
__magic_name__ : List[str] = ""
try:
__magic_name__ : int = float(
(
(
float(product_mrp.strip("₹" ).replace("," , "" ) )
- float(product_price.strip("₹" ).replace("," , "" ) )
)
/ float(product_mrp.strip("₹" ).replace("," , "" ) )
)
* 100 )
except ValueError:
__magic_name__ : str = float("nan" )
except AttributeError:
pass
__magic_name__ : Optional[int] = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
__magic_name__ : Optional[Any] = " "
__magic_name__ : str = " "
data_frame.index += 1
return data_frame
if __name__ == "__main__":
snake_case : Any = "headphones"
get_amazon_product_data(product).to_csv(F"Amazon Product Data for {product}.csv")
| 281 | 0 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 100 |
from __future__ import annotations
class _snake_case :
def __init__( self , _a ):
__magic_name__ : Optional[Any] = data
__magic_name__ : Node | None = None
__magic_name__ : Node | None = None
def lowerCAmelCase_ ( _snake_case : Node | None ) -> None: # In Order traversal of the tree
'''simple docstring'''
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def lowerCAmelCase_ ( _snake_case : Node | None ) -> int:
'''simple docstring'''
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def lowerCAmelCase_ ( _snake_case : Node ) -> bool:
'''simple docstring'''
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def lowerCAmelCase_ ( ) -> None: # Main function for testing.
'''simple docstring'''
__magic_name__ : int = Node(1 )
__magic_name__ : Union[str, Any] = Node(2 )
__magic_name__ : Tuple = Node(3 )
__magic_name__ : Optional[Any] = Node(4 )
__magic_name__ : Union[str, Any] = Node(5 )
__magic_name__ : Any = Node(6 )
__magic_name__ : int = Node(7 )
__magic_name__ : List[str] = Node(8 )
__magic_name__ : Union[str, Any] = Node(9 )
print(is_full_binary_tree(_snake_case ) )
print(depth_of_tree(_snake_case ) )
print("Tree is: " )
display(_snake_case )
if __name__ == "__main__":
main()
| 281 | 0 |
from __future__ import annotations
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = sorted(numsa + numsa )
lowercase , lowercase = divmod(len(lowerCAmelCase__ ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase__ :List[Any] = [float(x) for x in input("Enter the elements of first array: ").split()]
lowercase__ :List[str] = [float(x) for x in input("Enter the elements of second array: ").split()]
print(F'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')
| 101 |
def lowerCAmelCase_ ( _snake_case : str , _snake_case : str ) -> bool:
'''simple docstring'''
__magic_name__ : Union[str, Any] = len(_snake_case ) + 1
__magic_name__ : List[str] = len(_snake_case ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
__magic_name__ : str = [[0 for i in range(_snake_case )] for j in range(_snake_case )]
# since string of zero length match pattern of zero length
__magic_name__ : Optional[int] = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _snake_case ):
__magic_name__ : Optional[int] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _snake_case ):
__magic_name__ : Union[str, Any] = dp[0][j - 2] if pattern[j - 1] == "*" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _snake_case ):
for j in range(1 , _snake_case ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
__magic_name__ : Optional[int] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
__magic_name__ : Optional[Any] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
__magic_name__ : List[Any] = dp[i - 1][j]
else:
__magic_name__ : Union[str, Any] = 0
else:
__magic_name__ : Dict = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
snake_case : Optional[Any] = "aab"
snake_case : List[str] = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F"{input_string} matches the given pattern {pattern}")
else:
print(F"{input_string} does not match with the given pattern {pattern}")
| 281 | 0 |
"""simple docstring"""
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[Any] = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
SCREAMING_SNAKE_CASE : List[Any] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _UpperCAmelCase :
'''simple docstring'''
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'Model type selected in the list: ' + ', '.join(__snake_case )} )
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'} )
lowerCamelCase__ =field(
default=128, metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
}, )
lowerCamelCase__ =field(
default=128, metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'}, )
lowerCamelCase__ =field(
default=64, metadata={
'help': (
'The maximum number of tokens for the question. Questions longer than this will '
'be truncated to this length.'
)
}, )
lowerCamelCase__ =field(
default=30, metadata={
'help': (
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
)
}, )
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'Overwrite the cached training and evaluation sets'} )
lowerCamelCase__ =field(
default=__snake_case, metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'} )
lowerCamelCase__ =field(
default=0.0, metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
lowerCamelCase__ =field(
default=20, metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
lowerCamelCase__ =field(
default=0, metadata={
'help': (
'language id of input for language-specific xlm models (see'
' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
)
}, )
lowerCamelCase__ =field(default=1, metadata={'help': 'multiple threads for converting example to features'} )
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ ='train'
lowerCamelCase__ ='dev'
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ =42
lowerCamelCase__ =42
lowerCamelCase__ =42
lowerCamelCase__ =42
def __init__(self , a_ , a_ , a_ = None , a_ = Split.train , a_ = False , a_ = None , a_ = "pt" , ):
'''simple docstring'''
__snake_case : Optional[Any] = args
__snake_case : Optional[Any] = is_language_sensitive
__snake_case : Dict = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(a_ , a_ ):
try:
__snake_case : Any = Split[mode]
except KeyError:
raise KeyError('''mode is not a valid split name''' )
__snake_case : Optional[int] = mode
# Load data features from cache or dataset file
__snake_case : Tuple = '''v2''' if args.version_2_with_negative else '''v1'''
__snake_case : Any = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}""" , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__snake_case : Any = cached_features_file + '''.lock'''
with FileLock(a_ ):
if os.path.exists(a_ ) and not args.overwrite_cache:
__snake_case : int = time.time()
__snake_case : Optional[Any] = torch.load(a_ )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
__snake_case : Tuple = self.old_features['''features''']
__snake_case : Union[str, Any] = self.old_features.get('''dataset''' , a_ )
__snake_case : Optional[Any] = self.old_features.get('''examples''' , a_ )
logger.info(
f"""Loading features from cached file {cached_features_file} [took %.3f s]""" , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f"""Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"""
''' future run''' )
else:
if mode == Split.dev:
__snake_case : str = self.processor.get_dev_examples(args.data_dir )
else:
__snake_case : int = self.processor.get_train_examples(args.data_dir )
__snake_case , __snake_case : int = squad_convert_examples_to_features(
examples=self.examples , tokenizer=a_ , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=a_ , )
__snake_case : Union[str, Any] = time.time()
torch.save(
{'''features''': self.features, '''dataset''': self.dataset, '''examples''': self.examples} , a_ , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]""" )
def __len__(self ):
'''simple docstring'''
return len(self.features )
def __getitem__(self , a_ ):
'''simple docstring'''
__snake_case : Tuple = self.features[i]
__snake_case : Any = torch.tensor(feature.input_ids , dtype=torch.long )
__snake_case : Optional[Any] = torch.tensor(feature.attention_mask , dtype=torch.long )
__snake_case : str = torch.tensor(feature.token_type_ids , dtype=torch.long )
__snake_case : Optional[Any] = torch.tensor(feature.cls_index , dtype=torch.long )
__snake_case : Optional[Any] = torch.tensor(feature.p_mask , dtype=torch.float )
__snake_case : str = torch.tensor(feature.is_impossible , dtype=torch.float )
__snake_case : List[str] = {
'''input_ids''': input_ids,
'''attention_mask''': attention_mask,
'''token_type_ids''': token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({'''cls_index''': cls_index, '''p_mask''': p_mask} )
if self.args.version_2_with_negative:
inputs.update({'''is_impossible''': is_impossible} )
if self.is_language_sensitive:
inputs.update({'''langs''': (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
__snake_case : Dict = torch.tensor(feature.start_position , dtype=torch.long )
__snake_case : str = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({'''start_positions''': start_positions, '''end_positions''': end_positions} )
return inputs
| 102 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class _snake_case :
@staticmethod
def SCREAMING_SNAKE_CASE ( *_a , **_a ):
pass
def lowerCAmelCase_ ( _snake_case : Image ) -> str:
'''simple docstring'''
__magic_name__ : Optional[int] = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def lowerCAmelCase_ ( _snake_case : Image ) -> Dict:
'''simple docstring'''
__magic_name__ : List[Any] = np.array(_snake_case )
__magic_name__ : Optional[int] = npimg.shape
return {"hash": hashimage(_snake_case ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class _snake_case ( unittest.TestCase ):
UpperCamelCase__ = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
UpperCamelCase__ = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a ):
__magic_name__ : Dict = MaskGenerationPipeline(model=_a , image_processor=_a )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def SCREAMING_SNAKE_CASE ( self , _a , _a ):
pass
@require_tf
@unittest.skip("Image segmentation not implemented in TF" )
def SCREAMING_SNAKE_CASE ( self ):
pass
@slow
@require_torch
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = pipeline("mask-generation" , model="facebook/sam-vit-huge" )
__magic_name__ : str = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg" , points_per_batch=256 )
# Shortening by hashing
__magic_name__ : Dict = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(_a ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
{"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
{"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
{"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
{"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
{"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
{"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
{"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
{"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
{"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
{"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
{"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
{"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
{"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
{"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
{"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
{"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
{"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
{"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
{"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
{"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
{"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
{"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
{"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
{"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
{"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
{"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
{"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
{"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
{"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
] , )
# fmt: on
@require_torch
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : str = "facebook/sam-vit-huge"
__magic_name__ : str = pipeline("mask-generation" , model=_a )
__magic_name__ : Tuple = image_segmenter(
"http://images.cocodataset.org/val2017/000000039769.jpg" , pred_iou_thresh=1 , points_per_batch=256 )
# Shortening by hashing
__magic_name__ : Any = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(_a ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
{"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
{"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
{"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
{"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
] , )
| 281 | 0 |
from maths.prime_check import is_prime
def UpperCamelCase( __UpperCamelCase : int ):
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
lowerCAmelCase_ : Union[str, Any] = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__UpperCamelCase )
if is_prime(__UpperCamelCase ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 103 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
snake_case : List[Any] = "\\n@inproceedings{lin-2004-rouge,\n title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n author = \"Lin, Chin-Yew\",\n booktitle = \"Text Summarization Branches Out\",\n month = jul,\n year = \"2004\",\n address = \"Barcelona, Spain\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W04-1013\",\n pages = \"74--81\",\n}\n"
snake_case : Any = "\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"
snake_case : str = "\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n rouge_types: A list of rouge types to calculate.\n Valid names:\n `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n `\"rougeL\"`: Longest common subsequence based scoring.\n `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n See details in https://github.com/huggingface/datasets/issues/617\n use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n use_aggregator: Return aggregates if this is set to True\nReturns:\n rouge1: rouge_1 (precision, recall, f1),\n rouge2: rouge_2 (precision, recall, f1),\n rougeL: rouge_l (precision, recall, f1),\n rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n >>> rouge = datasets.load_metric('rouge')\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> results = rouge.compute(predictions=predictions, references=references)\n >>> print(list(results.keys()))\n ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n >>> print(results[\"rouge1\"])\n AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n >>> print(results[\"rouge1\"].mid.fmeasure)\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _snake_case ( datasets.Metric ):
def SCREAMING_SNAKE_CASE ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"] , reference_urls=[
"https://en.wikipedia.org/wiki/ROUGE_(metric)",
"https://github.com/google-research/google-research/tree/master/rouge",
] , )
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a=None , _a=True , _a=False ):
if rouge_types is None:
__magic_name__ : str = ["rouge1", "rouge2", "rougeL", "rougeLsum"]
__magic_name__ : List[str] = rouge_scorer.RougeScorer(rouge_types=_a , use_stemmer=_a )
if use_aggregator:
__magic_name__ : Dict = scoring.BootstrapAggregator()
else:
__magic_name__ : str = []
for ref, pred in zip(_a , _a ):
__magic_name__ : Union[str, Any] = scorer.score(_a , _a )
if use_aggregator:
aggregator.add_scores(_a )
else:
scores.append(_a )
if use_aggregator:
__magic_name__ : Any = aggregator.aggregate()
else:
__magic_name__ : List[Any] = {}
for key in scores[0]:
__magic_name__ : str = [score[key] for score in scores]
return result
| 281 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
lowerCAmelCase__ = logging.getLogger(__name__)
@dataclass
class lowercase_ :
"""simple docstring"""
SCREAMING_SNAKE_CASE : str
SCREAMING_SNAKE_CASE : List[str]
SCREAMING_SNAKE_CASE : Optional[List[str]]
@dataclass
class lowercase_ :
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[int]
SCREAMING_SNAKE_CASE : List[int]
SCREAMING_SNAKE_CASE : Optional[List[int]] = None
SCREAMING_SNAKE_CASE : Optional[List[int]] = None
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = 'train'
SCREAMING_SNAKE_CASE : int = 'dev'
SCREAMING_SNAKE_CASE : List[str] = 'test'
class lowercase_ :
"""simple docstring"""
@staticmethod
def SCREAMING_SNAKE_CASE ( lowercase__ : Optional[Any] ,lowercase__ : Union[Split, str] ):
raise NotImplementedError
@staticmethod
def SCREAMING_SNAKE_CASE ( lowercase__ : str ):
raise NotImplementedError
@staticmethod
def SCREAMING_SNAKE_CASE ( lowercase__ : List[InputExample] ,lowercase__ : List[str] ,lowercase__ : int ,lowercase__ : PreTrainedTokenizer ,lowercase__ : int=False ,lowercase__ : List[Any]="[CLS]" ,lowercase__ : int=1 ,lowercase__ : Optional[int]="[SEP]" ,lowercase__ : int=False ,lowercase__ : Any=False ,lowercase__ : Optional[Any]=0 ,lowercase__ : Any=0 ,lowercase__ : Optional[int]=-1_0_0 ,lowercase__ : str=0 ,lowercase__ : Optional[int]=True ,):
__lowercase = {label: i for i, label in enumerate(lowercase__ )}
__lowercase = []
for ex_index, example in enumerate(lowercase__ ):
if ex_index % 1_0_0_0_0 == 0:
logger.info('''Writing example %d of %d''' ,lowercase__ ,len(lowercase__ ) )
__lowercase = []
__lowercase = []
for word, label in zip(example.words ,example.labels ):
__lowercase = tokenizer.tokenize(lowercase__ )
# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
if len(lowercase__ ) > 0:
tokens.extend(lowercase__ )
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(lowercase__ ) - 1) )
# Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
__lowercase = tokenizer.num_special_tokens_to_add()
if len(lowercase__ ) > max_seq_length - special_tokens_count:
__lowercase = tokens[: (max_seq_length - special_tokens_count)]
__lowercase = label_ids[: (max_seq_length - special_tokens_count)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambiguously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens += [sep_token]
label_ids += [pad_token_label_id]
if sep_token_extra:
# roberta uses an extra separator b/w pairs of sentences
tokens += [sep_token]
label_ids += [pad_token_label_id]
__lowercase = [sequence_a_segment_id] * len(lowercase__ )
if cls_token_at_end:
tokens += [cls_token]
label_ids += [pad_token_label_id]
segment_ids += [cls_token_segment_id]
else:
__lowercase = [cls_token] + tokens
__lowercase = [pad_token_label_id] + label_ids
__lowercase = [cls_token_segment_id] + segment_ids
__lowercase = tokenizer.convert_tokens_to_ids(lowercase__ )
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
__lowercase = [1 if mask_padding_with_zero else 0] * len(lowercase__ )
# Zero-pad up to the sequence length.
__lowercase = max_seq_length - len(lowercase__ )
if pad_on_left:
__lowercase = ([pad_token] * padding_length) + input_ids
__lowercase = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
__lowercase = ([pad_token_segment_id] * padding_length) + segment_ids
__lowercase = ([pad_token_label_id] * padding_length) + label_ids
else:
input_ids += [pad_token] * padding_length
input_mask += [0 if mask_padding_with_zero else 1] * padding_length
segment_ids += [pad_token_segment_id] * padding_length
label_ids += [pad_token_label_id] * padding_length
assert len(lowercase__ ) == max_seq_length
assert len(lowercase__ ) == max_seq_length
assert len(lowercase__ ) == max_seq_length
assert len(lowercase__ ) == max_seq_length
if ex_index < 5:
logger.info('''*** Example ***''' )
logger.info('''guid: %s''' ,example.guid )
logger.info('''tokens: %s''' ,''' '''.join([str(lowercase__ ) for x in tokens] ) )
logger.info('''input_ids: %s''' ,''' '''.join([str(lowercase__ ) for x in input_ids] ) )
logger.info('''input_mask: %s''' ,''' '''.join([str(lowercase__ ) for x in input_mask] ) )
logger.info('''segment_ids: %s''' ,''' '''.join([str(lowercase__ ) for x in segment_ids] ) )
logger.info('''label_ids: %s''' ,''' '''.join([str(lowercase__ ) for x in label_ids] ) )
if "token_type_ids" not in tokenizer.model_input_names:
__lowercase = None
features.append(
InputFeatures(
input_ids=lowercase__ ,attention_mask=lowercase__ ,token_type_ids=lowercase__ ,label_ids=lowercase__ ) )
return features
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[InputFeatures]
SCREAMING_SNAKE_CASE : int = nn.CrossEntropyLoss().ignore_index
def __init__( self : List[str] ,lowercase__ : TokenClassificationTask ,lowercase__ : str ,lowercase__ : PreTrainedTokenizer ,lowercase__ : List[str] ,lowercase__ : str ,lowercase__ : Optional[int] = None ,lowercase__ : Any=False ,lowercase__ : Split = Split.train ,):
# Load data features from cache or dataset file
__lowercase = os.path.join(
lowercase__ ,'''cached_{}_{}_{}'''.format(mode.value ,tokenizer.__class__.__name__ ,str(lowercase__ ) ) ,)
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowercase = cached_features_file + '''.lock'''
with FileLock(lowercase__ ):
if os.path.exists(lowercase__ ) and not overwrite_cache:
logger.info(F"Loading features from cached file {cached_features_file}" )
__lowercase = torch.load(lowercase__ )
else:
logger.info(F"Creating features from dataset file at {data_dir}" )
__lowercase = token_classification_task.read_examples_from_file(lowercase__ ,lowercase__ )
# TODO clean up all this to leverage built-in features of tokenizers
__lowercase = token_classification_task.convert_examples_to_features(
lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ ,cls_token_at_end=bool(model_type in ['''xlnet'''] ) ,cls_token=tokenizer.cls_token ,cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0 ,sep_token=tokenizer.sep_token ,sep_token_extra=lowercase__ ,pad_on_left=bool(tokenizer.padding_side == '''left''' ) ,pad_token=tokenizer.pad_token_id ,pad_token_segment_id=tokenizer.pad_token_type_id ,pad_token_label_id=self.pad_token_label_id ,)
logger.info(F"Saving features into cached file {cached_features_file}" )
torch.save(self.features ,lowercase__ )
def __len__( self : List[str] ):
return len(self.features )
def __getitem__( self : List[Any] ,lowercase__ : Optional[Any] ):
return self.features[i]
if is_tf_available():
import tensorflow as tf
class lowercase_ :
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[InputFeatures]
SCREAMING_SNAKE_CASE : int = -1_0_0
def __init__( self : str ,lowercase__ : TokenClassificationTask ,lowercase__ : str ,lowercase__ : PreTrainedTokenizer ,lowercase__ : List[str] ,lowercase__ : str ,lowercase__ : Optional[int] = None ,lowercase__ : int=False ,lowercase__ : Split = Split.train ,):
__lowercase = token_classification_task.read_examples_from_file(lowercase__ ,lowercase__ )
# TODO clean up all this to leverage built-in features of tokenizers
__lowercase = token_classification_task.convert_examples_to_features(
lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ ,cls_token_at_end=bool(model_type in ['''xlnet'''] ) ,cls_token=tokenizer.cls_token ,cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0 ,sep_token=tokenizer.sep_token ,sep_token_extra=lowercase__ ,pad_on_left=bool(tokenizer.padding_side == '''left''' ) ,pad_token=tokenizer.pad_token_id ,pad_token_segment_id=tokenizer.pad_token_type_id ,pad_token_label_id=self.pad_token_label_id ,)
def gen():
for ex in self.features:
if ex.token_type_ids is None:
yield (
{"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
ex.label_ids,
)
else:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label_ids,
)
if "token_type_ids" not in tokenizer.model_input_names:
__lowercase = tf.data.Dataset.from_generator(
lowercase__ ,({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa) ,(
{'''input_ids''': tf.TensorShape([None] ), '''attention_mask''': tf.TensorShape([None] )},
tf.TensorShape([None] ),
) ,)
else:
__lowercase = tf.data.Dataset.from_generator(
lowercase__ ,({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa) ,(
{
'''input_ids''': tf.TensorShape([None] ),
'''attention_mask''': tf.TensorShape([None] ),
'''token_type_ids''': tf.TensorShape([None] ),
},
tf.TensorShape([None] ),
) ,)
def SCREAMING_SNAKE_CASE ( self : Dict ):
__lowercase = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )
return self.dataset
def __len__( self : Any ):
return len(self.features )
def __getitem__( self : Union[str, Any] ,lowercase__ : str ):
return self.features[i]
| 104 |
snake_case : Optional[int] = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowerCAmelCase_ ( _snake_case : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(_snake_case , _snake_case ):
__magic_name__ : Tuple = F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
raise TypeError(_snake_case )
__magic_name__ : Optional[int] = "".join(bin(_snake_case )[2:].zfill(8 ) for byte in data )
__magic_name__ : List[Any] = len(_snake_case ) % 6 != 0
if padding_needed:
# The padding that will be added later
__magic_name__ : List[str] = B"=" * ((6 - len(_snake_case ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(_snake_case ) % 6)
else:
__magic_name__ : List[str] = B""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(_snake_case ) , 6 ) ).encode()
+ padding
)
def lowerCAmelCase_ ( _snake_case : str ) -> bytes:
'''simple docstring'''
if not isinstance(_snake_case , _snake_case ) and not isinstance(_snake_case , _snake_case ):
__magic_name__ : List[str] = (
"argument should be a bytes-like object or ASCII string, "
F'''not \'{encoded_data.__class__.__name__}\''''
)
raise TypeError(_snake_case )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(_snake_case , _snake_case ):
try:
__magic_name__ : List[Any] = encoded_data.decode("utf-8" )
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters" )
__magic_name__ : List[str] = encoded_data.count("=" )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(_snake_case ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
__magic_name__ : Optional[int] = encoded_data[:-padding]
__magic_name__ : Dict = "".join(
bin(B64_CHARSET.index(_snake_case ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
__magic_name__ : Union[str, Any] = "".join(
bin(B64_CHARSET.index(_snake_case ) )[2:].zfill(6 ) for char in encoded_data )
__magic_name__ : List[Any] = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(_snake_case ) , 8 )
]
return bytes(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 281 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : Tuple = {
'''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/resolve/main/config.json''',
}
class __UpperCamelCase ( a__ ):
lowerCamelCase : Any ="""git_vision_model"""
def __init__( self , lowerCAmelCase__=768 , lowerCAmelCase__=3072 , lowerCAmelCase__=12 , lowerCAmelCase__=12 , lowerCAmelCase__=3 , lowerCAmelCase__=224 , lowerCAmelCase__=16 , lowerCAmelCase__="quick_gelu" , lowerCAmelCase__=1E-5 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.02 , **lowerCAmelCase__ , ) -> Any:
super().__init__(**lowerCAmelCase__ )
a : str = hidden_size
a : Optional[int] = intermediate_size
a : List[str] = num_hidden_layers
a : Any = num_attention_heads
a : List[str] = num_channels
a : int = patch_size
a : Tuple = image_size
a : Dict = initializer_range
a : Optional[int] = attention_dropout
a : Dict = layer_norm_eps
a : Optional[int] = hidden_act
@classmethod
def __a ( cls , lowerCAmelCase__ , **lowerCAmelCase__ ) -> "PretrainedConfig":
cls._set_token_in_kwargs(lowerCAmelCase__ )
a, a : Any = cls.get_config_dict(lowerCAmelCase__ , **lowerCAmelCase__ )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("model_type" ) == "git":
a : str = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(lowerCAmelCase__ , **lowerCAmelCase__ )
class __UpperCamelCase ( a__ ):
lowerCamelCase : int ="""git"""
def __init__( self , lowerCAmelCase__=None , lowerCAmelCase__=3_0522 , lowerCAmelCase__=768 , lowerCAmelCase__=6 , lowerCAmelCase__=12 , lowerCAmelCase__=3072 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=1024 , lowerCAmelCase__=0.02 , lowerCAmelCase__=1E-12 , lowerCAmelCase__=0 , lowerCAmelCase__="absolute" , lowerCAmelCase__=True , lowerCAmelCase__=False , lowerCAmelCase__=101 , lowerCAmelCase__=102 , lowerCAmelCase__=None , **lowerCAmelCase__ , ) -> Optional[Any]:
super().__init__(bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , pad_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
if vision_config is None:
a : List[Any] = {}
logger.info("vision_config is None. initializing the GitVisionConfig with default values." )
a : List[str] = GitVisionConfig(**lowerCAmelCase__ )
a : int = vocab_size
a : Optional[Any] = hidden_size
a : Tuple = num_hidden_layers
a : Union[str, Any] = num_attention_heads
a : Union[str, Any] = hidden_act
a : List[Any] = intermediate_size
a : Tuple = hidden_dropout_prob
a : List[Any] = attention_probs_dropout_prob
a : Union[str, Any] = max_position_embeddings
a : Tuple = initializer_range
a : int = layer_norm_eps
a : List[str] = position_embedding_type
a : int = use_cache
a : List[Any] = tie_word_embeddings
a : str = num_image_with_embedding
a : List[Any] = bos_token_id
a : int = eos_token_id
def __a ( self ) -> int:
a : Union[str, Any] = copy.deepcopy(self.__dict__ )
a : Optional[int] = self.vision_config.to_dict()
a : List[str] = self.__class__.model_type
return output
| 105 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class _snake_case ( unittest.TestCase ):
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ):
__magic_name__ : List[Any] = parent
__magic_name__ : Optional[Any] = batch_size
__magic_name__ : Dict = seq_length
__magic_name__ : Union[str, Any] = is_training
__magic_name__ : Optional[Any] = use_attention_mask
__magic_name__ : Optional[Any] = use_token_type_ids
__magic_name__ : int = use_labels
__magic_name__ : List[Any] = vocab_size
__magic_name__ : Union[str, Any] = hidden_size
__magic_name__ : Optional[Any] = num_hidden_layers
__magic_name__ : int = num_attention_heads
__magic_name__ : Any = intermediate_size
__magic_name__ : List[Any] = hidden_act
__magic_name__ : List[Any] = hidden_dropout_prob
__magic_name__ : Optional[int] = attention_probs_dropout_prob
__magic_name__ : List[Any] = max_position_embeddings
__magic_name__ : Tuple = type_vocab_size
__magic_name__ : List[str] = type_sequence_label_size
__magic_name__ : Dict = initializer_range
__magic_name__ : List[Any] = num_choices
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__magic_name__ : List[Any] = None
if self.use_attention_mask:
__magic_name__ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
__magic_name__ : str = None
if self.use_token_type_ids:
__magic_name__ : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__magic_name__ : List[str] = RobertaPreLayerNormConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : int = self.prepare_config_and_inputs()
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : List[Any] = config_and_inputs
__magic_name__ : List[str] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = self.prepare_config_and_inputs()
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] = config_and_inputs
__magic_name__ : Tuple = True
__magic_name__ : int = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__magic_name__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class _snake_case ( snake_case , unittest.TestCase ):
UpperCamelCase__ = True
UpperCamelCase__ = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[Any] = FlaxRobertaPreLayerNormModelTester(self )
@slow
def SCREAMING_SNAKE_CASE ( self ):
for model_class_name in self.all_model_classes:
__magic_name__ : Optional[Any] = model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_a )
__magic_name__ : Dict = model(np.ones((1, 1) ) )
self.assertIsNotNone(_a )
@require_flax
class _snake_case ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_a )
__magic_name__ : Union[str, Any] = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__magic_name__ : List[str] = model(_a )[0]
__magic_name__ : str = [1, 11, 50_265]
self.assertEqual(list(output.shape ) , _a )
# compare the actual values for a slice.
__magic_name__ : List[str] = np.array(
[[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _a , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_a )
__magic_name__ : Tuple = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__magic_name__ : Tuple = model(_a )[0]
# compare the actual values for a slice.
__magic_name__ : Dict = np.array(
[[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _a , atol=1e-4 ) )
| 281 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__UpperCamelCase : Dict = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ ):
lowerCAmelCase__ : Any = WavaVecaForSequenceClassification.from_pretrained(A_ , config=A_ )
lowerCAmelCase__ : List[Any] = downstream_dict['''projector.weight''']
lowerCAmelCase__ : Tuple = downstream_dict['''projector.bias''']
lowerCAmelCase__ : Dict = downstream_dict['''model.post_net.linear.weight''']
lowerCAmelCase__ : Optional[int] = downstream_dict['''model.post_net.linear.bias''']
return model
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ ):
lowerCAmelCase__ : List[str] = WavaVecaForAudioFrameClassification.from_pretrained(A_ , config=A_ )
lowerCAmelCase__ : Optional[int] = downstream_dict['''model.linear.weight''']
lowerCAmelCase__ : Tuple = downstream_dict['''model.linear.bias''']
return model
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ ):
lowerCAmelCase__ : List[Any] = WavaVecaForXVector.from_pretrained(A_ , config=A_ )
lowerCAmelCase__ : int = downstream_dict['''connector.weight''']
lowerCAmelCase__ : str = downstream_dict['''connector.bias''']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
lowerCAmelCase__ : List[str] = downstream_dict[
f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
]
lowerCAmelCase__ : List[str] = downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']
lowerCAmelCase__ : str = downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
lowerCAmelCase__ : List[str] = downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
lowerCAmelCase__ : Any = downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
lowerCAmelCase__ : str = downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
lowerCAmelCase__ : str = downstream_dict['''objective.W''']
return model
@torch.no_grad()
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ , A_ ):
lowerCAmelCase__ : str = torch.load(A_ , map_location='''cpu''' )
lowerCAmelCase__ : int = checkpoint['''Downstream''']
lowerCAmelCase__ : Dict = WavaVecaConfig.from_pretrained(A_ )
lowerCAmelCase__ : int = WavaVecaFeatureExtractor.from_pretrained(
A_ , return_attention_mask=A_ , do_normalize=A_ )
lowerCAmelCase__ : Tuple = hf_config.architectures[0]
if arch.endswith('''ForSequenceClassification''' ):
lowerCAmelCase__ : List[Any] = convert_classification(A_ , A_ , A_ )
elif arch.endswith('''ForAudioFrameClassification''' ):
lowerCAmelCase__ : Any = convert_diarization(A_ , A_ , A_ )
elif arch.endswith('''ForXVector''' ):
lowerCAmelCase__ : Optional[Any] = convert_xvector(A_ , A_ , A_ )
else:
raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}' )
if hf_config.use_weighted_layer_sum:
lowerCAmelCase__ : Optional[Any] = checkpoint['''Featurizer''']['''weights''']
hf_feature_extractor.save_pretrained(A_ )
hf_model.save_pretrained(A_ )
if __name__ == "__main__":
__UpperCamelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
'''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
)
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
__UpperCamelCase : List[str] = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 106 |
def lowerCAmelCase_ ( _snake_case : list[list[int | float]] ) -> int:
'''simple docstring'''
__magic_name__ : Any = len(_snake_case )
__magic_name__ : Optional[Any] = len(matrix[0] )
__magic_name__ : Union[str, Any] = min(_snake_case , _snake_case )
for row in range(_snake_case ):
# Check if diagonal element is not zero
if matrix[row][row] != 0:
# Eliminate all the elements below the diagonal
for col in range(row + 1 , _snake_case ):
__magic_name__ : Optional[Any] = matrix[col][row] / matrix[row][row]
for i in range(_snake_case , _snake_case ):
matrix[col][i] -= multiplier * matrix[row][i]
else:
# Find a non-zero diagonal element to swap rows
__magic_name__ : str = True
for i in range(row + 1 , _snake_case ):
if matrix[i][row] != 0:
__magic_name__ , __magic_name__ : List[str] = matrix[i], matrix[row]
__magic_name__ : Union[str, Any] = False
break
if reduce:
rank -= 1
for i in range(_snake_case ):
__magic_name__ : Any = matrix[i][rank]
# Reduce the row pointer by one to stay on the same row
row -= 1
return rank
if __name__ == "__main__":
import doctest
doctest.testmod()
| 281 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : Dict = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : List[str] = [
'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegatronBertForCausalLM',
'MegatronBertForMaskedLM',
'MegatronBertForMultipleChoice',
'MegatronBertForNextSentencePrediction',
'MegatronBertForPreTraining',
'MegatronBertForQuestionAnswering',
'MegatronBertForSequenceClassification',
'MegatronBertForTokenClassification',
'MegatronBertModel',
'MegatronBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
__lowerCAmelCase : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 107 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
snake_case : Dict = re.compile(R"\b(a|an|the)\b", re.UNICODE)
snake_case : Optional[int] = None
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : Any = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0." )
parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file." )
parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions." )
parser.add_argument(
"--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout)." )
parser.add_argument(
"--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer." )
parser.add_argument(
"--na-prob-thresh" , "-t" , type=_snake_case , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , )
parser.add_argument(
"--out-image-dir" , "-p" , metavar="out_images" , default=_snake_case , help="Save precision-recall curves to directory." )
parser.add_argument("--verbose" , "-v" , action="store_true" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def lowerCAmelCase_ ( _snake_case : Optional[Any] ) -> Tuple:
'''simple docstring'''
__magic_name__ : Optional[int] = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
__magic_name__ : str = bool(qa["answers"]["text"] )
return qid_to_has_ans
def lowerCAmelCase_ ( _snake_case : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
def remove_articles(_snake_case : List[str] ):
return ARTICLES_REGEX.sub(" " , _snake_case )
def white_space_fix(_snake_case : Optional[int] ):
return " ".join(text.split() )
def remove_punc(_snake_case : Optional[int] ):
__magic_name__ : Dict = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_snake_case : str ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_snake_case ) ) ) )
def lowerCAmelCase_ ( _snake_case : Any ) -> Optional[Any]:
'''simple docstring'''
if not s:
return []
return normalize_answer(_snake_case ).split()
def lowerCAmelCase_ ( _snake_case : str , _snake_case : Dict ) -> Tuple:
'''simple docstring'''
return int(normalize_answer(_snake_case ) == normalize_answer(_snake_case ) )
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : int ) -> str:
'''simple docstring'''
__magic_name__ : Any = get_tokens(_snake_case )
__magic_name__ : Optional[int] = get_tokens(_snake_case )
__magic_name__ : Tuple = collections.Counter(_snake_case ) & collections.Counter(_snake_case )
__magic_name__ : Tuple = sum(common.values() )
if len(_snake_case ) == 0 or len(_snake_case ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
__magic_name__ : Dict = 1.0 * num_same / len(_snake_case )
__magic_name__ : Optional[Any] = 1.0 * num_same / len(_snake_case )
__magic_name__ : List[Any] = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( _snake_case : Optional[Any] , _snake_case : List[Any] ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = {}
__magic_name__ : int = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
__magic_name__ : Union[str, Any] = qa["id"]
__magic_name__ : Any = [t for t in qa["answers"]["text"] if normalize_answer(_snake_case )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
__magic_name__ : Tuple = [""]
if qid not in preds:
print(F'''Missing prediction for {qid}''' )
continue
__magic_name__ : Any = preds[qid]
# Take max over all gold answers
__magic_name__ : List[Any] = max(compute_exact(_snake_case , _snake_case ) for a in gold_answers )
__magic_name__ : int = max(compute_fa(_snake_case , _snake_case ) for a in gold_answers )
return exact_scores, fa_scores
def lowerCAmelCase_ ( _snake_case : Optional[Any] , _snake_case : List[Any] , _snake_case : Optional[int] , _snake_case : Dict ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : str = {}
for qid, s in scores.items():
__magic_name__ : Dict = na_probs[qid] > na_prob_thresh
if pred_na:
__magic_name__ : str = float(not qid_to_has_ans[qid] )
else:
__magic_name__ : Optional[int] = s
return new_scores
def lowerCAmelCase_ ( _snake_case : List[Any] , _snake_case : List[str] , _snake_case : Tuple=None ) -> Tuple:
'''simple docstring'''
if not qid_list:
__magic_name__ : Any = len(_snake_case )
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores.values() ) / total),
("f1", 100.0 * sum(fa_scores.values() ) / total),
("total", total),
] )
else:
__magic_name__ : Tuple = len(_snake_case )
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
("f1", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
("total", total),
] )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : str , _snake_case : str ) -> Dict:
'''simple docstring'''
for k in new_eval:
__magic_name__ : int = new_eval[k]
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Dict , _snake_case : Optional[Any] , _snake_case : Union[str, Any] ) -> str:
'''simple docstring'''
plt.step(_snake_case , _snake_case , color="b" , alpha=0.2 , where="post" )
plt.fill_between(_snake_case , _snake_case , step="post" , alpha=0.2 , color="b" )
plt.xlabel("Recall" )
plt.ylabel("Precision" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(_snake_case )
plt.savefig(_snake_case )
plt.clf()
def lowerCAmelCase_ ( _snake_case : Dict , _snake_case : Any , _snake_case : Optional[int] , _snake_case : List[Any] , _snake_case : Optional[int]=None , _snake_case : int=None ) -> str:
'''simple docstring'''
__magic_name__ : Union[str, Any] = sorted(_snake_case , key=lambda _snake_case : na_probs[k] )
__magic_name__ : Optional[int] = 0.0
__magic_name__ : str = 1.0
__magic_name__ : str = 0.0
__magic_name__ : List[str] = [1.0]
__magic_name__ : str = [0.0]
__magic_name__ : Optional[Any] = 0.0
for i, qid in enumerate(_snake_case ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
__magic_name__ : List[str] = true_pos / float(i + 1 )
__magic_name__ : Any = true_pos / float(_snake_case )
if i == len(_snake_case ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(_snake_case )
recalls.append(_snake_case )
if out_image:
plot_pr_curve(_snake_case , _snake_case , _snake_case , _snake_case )
return {"ap": 100.0 * avg_prec}
def lowerCAmelCase_ ( _snake_case : Tuple , _snake_case : Optional[Any] , _snake_case : Optional[int] , _snake_case : Optional[Any] , _snake_case : Any , _snake_case : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
if out_image_dir and not os.path.exists(_snake_case ):
os.makedirs(_snake_case )
__magic_name__ : Any = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
__magic_name__ : str = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_exact.png" ) , title="Precision-Recall curve for Exact Match score" , )
__magic_name__ : Union[str, Any] = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_f1.png" ) , title="Precision-Recall curve for F1 score" , )
__magic_name__ : str = {k: float(_snake_case ) for k, v in qid_to_has_ans.items()}
__magic_name__ : str = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_oracle.png" ) , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , )
merge_eval(_snake_case , _snake_case , "pr_exact" )
merge_eval(_snake_case , _snake_case , "pr_f1" )
merge_eval(_snake_case , _snake_case , "pr_oracle" )
def lowerCAmelCase_ ( _snake_case : int , _snake_case : Optional[Any] , _snake_case : List[str] , _snake_case : Optional[Any] ) -> Dict:
'''simple docstring'''
if not qid_list:
return
__magic_name__ : Dict = [na_probs[k] for k in qid_list]
__magic_name__ : str = np.ones_like(_snake_case ) / float(len(_snake_case ) )
plt.hist(_snake_case , weights=_snake_case , bins=20 , range=(0.0, 1.0) )
plt.xlabel("Model probability of no-answer" )
plt.ylabel("Proportion of dataset" )
plt.title(F'''Histogram of no-answer probability: {name}''' )
plt.savefig(os.path.join(_snake_case , F'''na_prob_hist_{name}.png''' ) )
plt.clf()
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Tuple , _snake_case : List[str] , _snake_case : Dict ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
__magic_name__ : List[str] = num_no_ans
__magic_name__ : Dict = cur_score
__magic_name__ : Dict = 0.0
__magic_name__ : Any = sorted(_snake_case , key=lambda _snake_case : na_probs[k] )
for i, qid in enumerate(_snake_case ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
__magic_name__ : Union[str, Any] = scores[qid]
else:
if preds[qid]:
__magic_name__ : List[Any] = -1
else:
__magic_name__ : Optional[int] = 0
cur_score += diff
if cur_score > best_score:
__magic_name__ : Optional[int] = cur_score
__magic_name__ : List[Any] = na_probs[qid]
return 100.0 * best_score / len(_snake_case ), best_thresh
def lowerCAmelCase_ ( _snake_case : int , _snake_case : str , _snake_case : List[str] , _snake_case : Tuple , _snake_case : List[Any] , _snake_case : Dict ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ , __magic_name__ : List[str] = find_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case )
__magic_name__ , __magic_name__ : int = find_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case )
__magic_name__ : Optional[int] = best_exact
__magic_name__ : List[Any] = exact_thresh
__magic_name__ : Dict = best_fa
__magic_name__ : Any = fa_thresh
def lowerCAmelCase_ ( ) -> int:
'''simple docstring'''
with open(OPTS.data_file ) as f:
__magic_name__ : Optional[Any] = json.load(_snake_case )
__magic_name__ : List[Any] = dataset_json["data"]
with open(OPTS.pred_file ) as f:
__magic_name__ : Optional[Any] = json.load(_snake_case )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
__magic_name__ : Any = json.load(_snake_case )
else:
__magic_name__ : Any = {k: 0.0 for k in preds}
__magic_name__ : str = make_qid_to_has_ans(_snake_case ) # maps qid to True/False
__magic_name__ : Tuple = [k for k, v in qid_to_has_ans.items() if v]
__magic_name__ : Optional[Any] = [k for k, v in qid_to_has_ans.items() if not v]
__magic_name__ , __magic_name__ : Union[str, Any] = get_raw_scores(_snake_case , _snake_case )
__magic_name__ : Optional[Any] = apply_no_ans_threshold(_snake_case , _snake_case , _snake_case , OPTS.na_prob_thresh )
__magic_name__ : Optional[Any] = apply_no_ans_threshold(_snake_case , _snake_case , _snake_case , OPTS.na_prob_thresh )
__magic_name__ : List[Any] = make_eval_dict(_snake_case , _snake_case )
if has_ans_qids:
__magic_name__ : int = make_eval_dict(_snake_case , _snake_case , qid_list=_snake_case )
merge_eval(_snake_case , _snake_case , "HasAns" )
if no_ans_qids:
__magic_name__ : List[Any] = make_eval_dict(_snake_case , _snake_case , qid_list=_snake_case )
merge_eval(_snake_case , _snake_case , "NoAns" )
if OPTS.na_prob_file:
find_all_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , OPTS.out_image_dir )
histogram_na_prob(_snake_case , _snake_case , OPTS.out_image_dir , "hasAns" )
histogram_na_prob(_snake_case , _snake_case , OPTS.out_image_dir , "noAns" )
if OPTS.out_file:
with open(OPTS.out_file , "w" ) as f:
json.dump(_snake_case , _snake_case )
else:
print(json.dumps(_snake_case , indent=2 ) )
if __name__ == "__main__":
snake_case : int = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
main()
| 281 | 0 |
"""simple docstring"""
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE__ ( lowercase , unittest.TestCase ):
"""simple docstring"""
a : List[Any] =PriorTransformer
a : Optional[Any] ="hidden_states"
@property
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = 4
lowerCAmelCase : str = 8
lowerCAmelCase : Optional[Any] = 7
lowerCAmelCase : Tuple = floats_tensor((batch_size, embedding_dim) ).to(snake_case__ )
lowerCAmelCase : Tuple = floats_tensor((batch_size, embedding_dim) ).to(snake_case__ )
lowerCAmelCase : Tuple = floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def lowercase__ ( self , snake_case__=0 ):
"""simple docstring"""
torch.manual_seed(snake_case__ )
lowerCAmelCase : Union[str, Any] = 4
lowerCAmelCase : List[Any] = 8
lowerCAmelCase : Tuple = 7
lowerCAmelCase : Optional[Any] = torch.randn((batch_size, embedding_dim) ).to(snake_case__ )
lowerCAmelCase : List[Any] = torch.randn((batch_size, embedding_dim) ).to(snake_case__ )
lowerCAmelCase : Tuple = torch.randn((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
@property
def lowercase__ ( self ):
"""simple docstring"""
return (4, 8)
@property
def lowercase__ ( self ):
"""simple docstring"""
return (4, 8)
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : int = {
"num_attention_heads": 2,
"attention_head_dim": 4,
"num_layers": 2,
"embedding_dim": 8,
"num_embeddings": 7,
"additional_embeddings": 4,
}
lowerCAmelCase : Optional[Any] = self.dummy_input
return init_dict, inputs_dict
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase , lowerCAmelCase : Dict = PriorTransformer.from_pretrained(
"hf-internal-testing/prior-dummy" , output_loading_info=snake_case__ )
self.assertIsNotNone(snake_case__ )
self.assertEqual(len(loading_info["missing_keys"] ) , 0 )
model.to(snake_case__ )
lowerCAmelCase : Optional[int] = model(**self.dummy_input )[0]
assert hidden_states is not None, "Make sure output is not None"
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase , lowerCAmelCase : Union[str, Any] = self.prepare_init_args_and_inputs_for_common()
lowerCAmelCase : int = self.model_class(**snake_case__ )
lowerCAmelCase : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase : List[str] = [*signature.parameters.keys()]
lowerCAmelCase : List[str] = ["hidden_states", "timestep"]
self.assertListEqual(arg_names[:2] , snake_case__ )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Optional[Any] = PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy" )
lowerCAmelCase : int = model.to(snake_case__ )
if hasattr(snake_case__ , "set_default_attn_processor" ):
model.set_default_attn_processor()
lowerCAmelCase : Union[str, Any] = self.get_dummy_seed_input()
with torch.no_grad():
lowerCAmelCase : List[Any] = model(**snake_case__ )[0]
lowerCAmelCase : Optional[Any] = output[0, :5].flatten().cpu()
print(snake_case__ )
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
lowerCAmelCase : List[str] = torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239] )
self.assertTrue(torch_all_close(snake_case__ , snake_case__ , rtol=1e-2 ) )
@slow
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
def lowercase__ ( self , snake_case__=1 , snake_case__=768 , snake_case__=77 , snake_case__=0 ):
"""simple docstring"""
torch.manual_seed(snake_case__ )
lowerCAmelCase : Union[str, Any] = batch_size
lowerCAmelCase : Optional[Any] = embedding_dim
lowerCAmelCase : int = num_embeddings
lowerCAmelCase : Union[str, Any] = torch.randn((batch_size, embedding_dim) ).to(snake_case__ )
lowerCAmelCase : Dict = torch.randn((batch_size, embedding_dim) ).to(snake_case__ )
lowerCAmelCase : Optional[Any] = torch.randn((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def lowercase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@parameterized.expand(
[
# fmt: off
[13, [-0.5861, 0.1283, -0.0931, 0.0882, 0.4476, 0.1329, -0.0498, 0.0640]],
[37, [-0.4913, 0.0110, -0.0483, 0.0541, 0.4954, -0.0170, 0.0354, 0.1651]],
# fmt: on
] )
def lowercase__ ( self , snake_case__ , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : str = PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior" , subfolder="prior" )
model.to(snake_case__ )
lowerCAmelCase : List[Any] = self.get_dummy_seed_input(seed=snake_case__ )
with torch.no_grad():
lowerCAmelCase : Tuple = model(**snake_case__ )[0]
assert list(sample.shape ) == [1, 768]
lowerCAmelCase : Optional[int] = sample[0, :8].flatten().cpu()
print(snake_case__ )
lowerCAmelCase : List[Any] = torch.tensor(snake_case__ )
assert torch_all_close(snake_case__ , snake_case__ , atol=1e-3 )
| 108 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
snake_case : str = "▁"
snake_case : List[Any] = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class _snake_case ( snake_case , unittest.TestCase ):
UpperCamelCase__ = BigBirdTokenizer
UpperCamelCase__ = BigBirdTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = True
def SCREAMING_SNAKE_CASE ( self ):
super().setUp()
__magic_name__ : Optional[Any] = self.tokenizer_class(_a , keep_accents=_a )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Union[str, Any] = "<s>"
__magic_name__ : Dict = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<unk>" )
self.assertEqual(vocab_keys[1] , "<s>" )
self.assertEqual(vocab_keys[-1] , "[MASK]" )
self.assertEqual(len(_a ) , 1_004 )
def SCREAMING_SNAKE_CASE ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_000 )
def SCREAMING_SNAKE_CASE ( self ):
if not self.test_rust_tokenizer:
return
__magic_name__ : Dict = self.get_tokenizer()
__magic_name__ : str = self.get_rust_tokenizer()
__magic_name__ : Any = "I was born in 92000, and this is falsé."
__magic_name__ : Dict = tokenizer.tokenize(_a )
__magic_name__ : Any = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
__magic_name__ : List[Any] = tokenizer.encode(_a , add_special_tokens=_a )
__magic_name__ : List[str] = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
__magic_name__ : str = self.get_rust_tokenizer()
__magic_name__ : Dict = tokenizer.encode(_a )
__magic_name__ : Optional[int] = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = BigBirdTokenizer(_a , keep_accents=_a )
__magic_name__ : str = tokenizer.tokenize("This is a test" )
self.assertListEqual(_a , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_a ) , [285, 46, 10, 170, 382] , )
__magic_name__ : Dict = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
__magic_name__ : Union[str, Any] = tokenizer.convert_tokens_to_ids(_a )
self.assertListEqual(
_a , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
__magic_name__ : int = tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
@cached_property
def SCREAMING_SNAKE_CASE ( self ):
return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base" )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Any = "Hello World!"
__magic_name__ : Dict = [65, 18_536, 2_260, 101, 66]
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = (
"This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
)
# fmt: off
__magic_name__ : List[str] = [65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66] # noqa: E231
# fmt: on
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@require_torch
@slow
def SCREAMING_SNAKE_CASE ( self ):
import torch
from transformers import BigBirdConfig, BigBirdModel
# Build sequence
__magic_name__ : Optional[Any] = list(self.big_tokenizer.get_vocab().keys() )[:10]
__magic_name__ : List[Any] = " ".join(_a )
__magic_name__ : Any = self.big_tokenizer.encode_plus(_a , return_tensors="pt" , return_token_type_ids=_a )
__magic_name__ : Union[str, Any] = self.big_tokenizer.batch_encode_plus(
[sequence + " " + sequence] , return_tensors="pt" , return_token_type_ids=_a )
__magic_name__ : List[str] = BigBirdConfig(attention_type="original_full" )
__magic_name__ : Optional[int] = BigBirdModel(_a )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_a )
model(**_a )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : int = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base" )
__magic_name__ : int = tokenizer.decode(tokenizer("Paris is the [MASK]." ).input_ids )
self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]" )
@slow
def SCREAMING_SNAKE_CASE ( self ):
# fmt: off
__magic_name__ : Optional[Any] = {"input_ids": [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_a , model_name="google/bigbird-roberta-base" , revision="215c99f1600e06f83acce68422f2035b2b5c3510" , )
| 281 | 0 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A: List[str] = logging.get_logger(__name__)
A: Dict = {
"microsoft/conditional-detr-resnet-50": (
"https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
),
}
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
__lowerCAmelCase : Any = 'conditional_detr'
__lowerCAmelCase : Union[str, Any] = ['past_key_values']
__lowerCAmelCase : int = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=300 , _SCREAMING_SNAKE_CASE=6 , _SCREAMING_SNAKE_CASE=2048 , _SCREAMING_SNAKE_CASE=8 , _SCREAMING_SNAKE_CASE=6 , _SCREAMING_SNAKE_CASE=2048 , _SCREAMING_SNAKE_CASE=8 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE="relu" , _SCREAMING_SNAKE_CASE=256 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE="sine" , _SCREAMING_SNAKE_CASE="resnet50" , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.25 , **_SCREAMING_SNAKE_CASE , ) -> Tuple:
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
UpperCAmelCase : Tuple = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCAmelCase : str = backbone_config.get("""model_type""" )
UpperCAmelCase : int = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase : Union[str, Any] = config_class.from_dict(_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Union[str, Any] = use_timm_backbone
UpperCAmelCase : Optional[int] = backbone_config
UpperCAmelCase : List[str] = num_channels
UpperCAmelCase : Any = num_queries
UpperCAmelCase : Union[str, Any] = d_model
UpperCAmelCase : List[str] = encoder_ffn_dim
UpperCAmelCase : Optional[int] = encoder_layers
UpperCAmelCase : Union[str, Any] = encoder_attention_heads
UpperCAmelCase : Optional[Any] = decoder_ffn_dim
UpperCAmelCase : Any = decoder_layers
UpperCAmelCase : Optional[int] = decoder_attention_heads
UpperCAmelCase : Optional[int] = dropout
UpperCAmelCase : Dict = attention_dropout
UpperCAmelCase : Dict = activation_dropout
UpperCAmelCase : Any = activation_function
UpperCAmelCase : Any = init_std
UpperCAmelCase : Tuple = init_xavier_std
UpperCAmelCase : Optional[int] = encoder_layerdrop
UpperCAmelCase : Any = decoder_layerdrop
UpperCAmelCase : Any = encoder_layers
UpperCAmelCase : Optional[Any] = auxiliary_loss
UpperCAmelCase : List[Any] = position_embedding_type
UpperCAmelCase : Union[str, Any] = backbone
UpperCAmelCase : List[Any] = use_pretrained_backbone
UpperCAmelCase : Dict = dilation
# Hungarian matcher
UpperCAmelCase : Optional[int] = class_cost
UpperCAmelCase : List[str] = bbox_cost
UpperCAmelCase : List[str] = giou_cost
# Loss coefficients
UpperCAmelCase : List[Any] = mask_loss_coefficient
UpperCAmelCase : List[str] = dice_loss_coefficient
UpperCAmelCase : Optional[int] = cls_loss_coefficient
UpperCAmelCase : Union[str, Any] = bbox_loss_coefficient
UpperCAmelCase : Union[str, Any] = giou_loss_coefficient
UpperCAmelCase : int = focal_alpha
super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@property
def SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
return self.d_model
def SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : List[Any] = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
UpperCAmelCase : Union[str, Any] = self.backbone_config.to_dict()
UpperCAmelCase : Dict = self.__class__.model_type
return output
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
__lowerCAmelCase : Any = version.parse('1.11' )
@property
def SCREAMING_SNAKE_CASE ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def SCREAMING_SNAKE_CASE ( self ) -> float:
'''simple docstring'''
return 1E-5
@property
def SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
return 12
| 109 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
snake_case : int = logging.get_logger(__name__)
snake_case : List[str] = {"vocab_file": "spiece.model"}
snake_case : List[str] = {
"vocab_file": {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
}
}
snake_case : Tuple = {
"albert-base-v1": 512,
"albert-large-v1": 512,
"albert-xlarge-v1": 512,
"albert-xxlarge-v1": 512,
"albert-base-v2": 512,
"albert-large-v2": 512,
"albert-xlarge-v2": 512,
"albert-xxlarge-v2": 512,
}
snake_case : List[str] = "▁"
class _snake_case ( snake_case ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , _a , _a=True , _a=True , _a=False , _a="[CLS]" , _a="[SEP]" , _a="<unk>" , _a="[SEP]" , _a="<pad>" , _a="[CLS]" , _a="[MASK]" , _a = None , **_a , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
__magic_name__ : str = (
AddedToken(_a , lstrip=_a , rstrip=_a , normalized=_a )
if isinstance(_a , _a )
else mask_token
)
__magic_name__ : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , )
__magic_name__ : Dict = do_lower_case
__magic_name__ : Tuple = remove_space
__magic_name__ : Union[str, Any] = keep_accents
__magic_name__ : Tuple = vocab_file
__magic_name__ : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_a )
@property
def SCREAMING_SNAKE_CASE ( self ):
return len(self.sp_model )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
__magic_name__ : List[str] = self.__dict__.copy()
__magic_name__ : Any = None
return state
def __setstate__( self , _a ):
__magic_name__ : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
__magic_name__ : str = {}
__magic_name__ : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE ( self , _a ):
if self.remove_space:
__magic_name__ : List[Any] = " ".join(inputs.strip().split() )
else:
__magic_name__ : str = inputs
__magic_name__ : int = outputs.replace("``" , "\"" ).replace("''" , "\"" )
if not self.keep_accents:
__magic_name__ : str = unicodedata.normalize("NFKD" , _a )
__magic_name__ : Tuple = "".join([c for c in outputs if not unicodedata.combining(_a )] )
if self.do_lower_case:
__magic_name__ : int = outputs.lower()
return outputs
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Optional[Any] = self.preprocess_text(_a )
__magic_name__ : Dict = self.sp_model.encode(_a , out_type=_a )
__magic_name__ : Any = []
for piece in pieces:
if len(_a ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
__magic_name__ : Optional[Any] = self.sp_model.EncodeAsPieces(piece[:-1].replace(_a , "" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
__magic_name__ : List[str] = cur_pieces[1:]
else:
__magic_name__ : Optional[int] = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_a )
else:
new_pieces.append(_a )
return new_pieces
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.sp_model.PieceToId(_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.sp_model.IdToPiece(_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Any = []
__magic_name__ : Union[str, Any] = ""
__magic_name__ : int = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_a ) + token
__magic_name__ : List[Any] = True
__magic_name__ : Optional[int] = []
else:
current_sub_tokens.append(_a )
__magic_name__ : Optional[Any] = False
out_string += self.sp_model.decode(_a )
return out_string.strip()
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
__magic_name__ : List[str] = [self.sep_token_id]
__magic_name__ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self , _a , _a = None , _a = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is not None:
return [1] + ([0] * len(_a )) + [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1]
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
__magic_name__ : Optional[int] = [self.sep_token_id]
__magic_name__ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__magic_name__ : List[str] = os.path.join(
_a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _a )
elif not os.path.isfile(self.vocab_file ):
with open(_a , "wb" ) as fi:
__magic_name__ : Union[str, Any] = self.sp_model.serialized_model_proto()
fi.write(_a )
return (out_vocab_file,)
| 281 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A__ ( unittest.TestCase):
@property
def __lowerCamelCase ( self ):
torch.manual_seed(0 )
__lowerCAmelCase : Optional[Any] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
@property
def __lowerCamelCase ( self ):
torch.manual_seed(0 )
__lowerCAmelCase : Optional[Any] = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=3 , )
return model
@property
def __lowerCamelCase ( self ):
torch.manual_seed(0 )
__lowerCAmelCase : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModel(_a )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Optional[Any] = self.dummy_uncond_unet
__lowerCAmelCase : Tuple = DDIMScheduler()
__lowerCAmelCase : List[Any] = self.dummy_vq_model
__lowerCAmelCase : Union[str, Any] = LDMPipeline(unet=_a , vqvae=_a , scheduler=_a )
ldm.to(_a )
ldm.set_progress_bar_config(disable=_a )
__lowerCAmelCase : str = torch.manual_seed(0 )
__lowerCAmelCase : str = ldm(generator=_a , num_inference_steps=2 , output_type='numpy' ).images
__lowerCAmelCase : int = torch.manual_seed(0 )
__lowerCAmelCase : List[Any] = ldm(generator=_a , num_inference_steps=2 , output_type='numpy' , return_dict=_a )[0]
__lowerCAmelCase : List[str] = image[0, -3:, -3:, -1]
__lowerCAmelCase : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__lowerCAmelCase : Any = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
__lowerCAmelCase : Tuple = 1E-2 if torch_device != "mps" else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class A__ ( unittest.TestCase):
def __lowerCamelCase ( self ):
__lowerCAmelCase : Union[str, Any] = LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
ldm.to(_a )
ldm.set_progress_bar_config(disable=_a )
__lowerCAmelCase : Optional[int] = torch.manual_seed(0 )
__lowerCAmelCase : Union[str, Any] = ldm(generator=_a , num_inference_steps=5 , output_type='numpy' ).images
__lowerCAmelCase : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
__lowerCAmelCase : List[Any] = np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
__lowerCAmelCase : int = 1E-2 if torch_device != "mps" else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance | 86 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : Tuple , _snake_case : List[Any] , _snake_case : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if isinstance(_snake_case , _snake_case ):
__magic_name__ : Union[str, Any] = np.full((len(_snake_case ), sequence_length, 2) , _snake_case )
else:
__magic_name__ : List[Any] = np.full((len(_snake_case ), sequence_length) , _snake_case )
for i, tensor in enumerate(_snake_case ):
if padding_side == "right":
if isinstance(_snake_case , _snake_case ):
__magic_name__ : Optional[Any] = tensor[:sequence_length]
else:
__magic_name__ : Union[str, Any] = tensor[:sequence_length]
else:
if isinstance(_snake_case , _snake_case ):
__magic_name__ : List[Any] = tensor[:sequence_length]
else:
__magic_name__ : Optional[Any] = tensor[:sequence_length]
return out_tensor.tolist()
def lowerCAmelCase_ ( _snake_case : Optional[int] ) -> Tuple:
'''simple docstring'''
__magic_name__ : Union[str, Any] = ord(_snake_case )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
__magic_name__ : Any = unicodedata.category(_snake_case )
if cat.startswith("P" ):
return True
return False
@dataclass
class _snake_case ( snake_case ):
UpperCamelCase__ = 42
UpperCamelCase__ = True
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = -100
UpperCamelCase__ = "pt"
def SCREAMING_SNAKE_CASE ( self , _a ):
import torch
__magic_name__ : List[str] = "label" if "label" in features[0].keys() else "labels"
__magic_name__ : Union[str, Any] = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
__magic_name__ : Optional[int] = self.tokenizer.pad(
_a , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" if labels is None else None , )
if labels is None:
return batch
__magic_name__ : Dict = torch.tensor(batch["entity_ids"] ).shape[1]
__magic_name__ : List[Any] = self.tokenizer.padding_side
if padding_side == "right":
__magic_name__ : str = [
list(_a ) + [self.label_pad_token_id] * (sequence_length - len(_a )) for label in labels
]
else:
__magic_name__ : int = [
[self.label_pad_token_id] * (sequence_length - len(_a )) + list(_a ) for label in labels
]
__magic_name__ : Dict = [feature["ner_tags"] for feature in features]
__magic_name__ : List[Any] = padding_tensor(_a , -1 , _a , _a )
__magic_name__ : Any = [feature["original_entity_spans"] for feature in features]
__magic_name__ : Any = padding_tensor(_a , (-1, -1) , _a , _a )
__magic_name__ : List[Any] = {k: torch.tensor(_a , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 281 | 0 |
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int = 1_00_00_00 ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , _snake_case ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 219 |
import math
def lowerCAmelCase_ ( _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
return math.pow(_snake_case , 2 ) - a
def lowerCAmelCase_ ( _snake_case : float ) -> float:
'''simple docstring'''
return 2 * x
def lowerCAmelCase_ ( _snake_case : float ) -> float:
'''simple docstring'''
__magic_name__ : Optional[int] = 2.0
while start <= a:
__magic_name__ : str = math.pow(_snake_case , 2 )
return start
def lowerCAmelCase_ ( _snake_case : float , _snake_case : int = 9999 , _snake_case : float = 0.00_000_000_000_001 ) -> float:
'''simple docstring'''
if a < 0:
raise ValueError("math domain error" )
__magic_name__ : Optional[int] = get_initial_point(_snake_case )
for _ in range(_snake_case ):
__magic_name__ : int = value
__magic_name__ : str = value - fx(_snake_case , _snake_case ) / fx_derivative(_snake_case )
if abs(prev_value - value ) < tolerance:
return value
return value
if __name__ == "__main__":
from doctest import testmod
testmod()
| 281 | 0 |
'''simple docstring'''
def a ( __a ) -> list:
'''simple docstring'''
def merge(__a , __a ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(_snake_case ) <= 1:
return collection
UpperCamelCase__ :Any = len(_snake_case ) // 2
return merge(merge_sort(collection[:mid] ) , merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
__snake_case = input('''Enter numbers separated by a comma:\n''').strip()
__snake_case = [int(item) for item in user_input.split(''',''')]
print(*merge_sort(unsorted), sep=''',''') | 97 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class _snake_case :
UpperCamelCase__ = LEDConfig
UpperCamelCase__ = {}
UpperCamelCase__ = 'gelu'
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=False , _a=99 , _a=32 , _a=2 , _a=4 , _a=37 , _a=0.1 , _a=0.1 , _a=20 , _a=2 , _a=1 , _a=0 , _a=4 , ):
__magic_name__ : int = parent
__magic_name__ : Optional[int] = batch_size
__magic_name__ : Tuple = seq_length
__magic_name__ : List[Any] = is_training
__magic_name__ : Dict = use_labels
__magic_name__ : Optional[Any] = vocab_size
__magic_name__ : int = hidden_size
__magic_name__ : Optional[int] = num_hidden_layers
__magic_name__ : Optional[int] = num_attention_heads
__magic_name__ : Tuple = intermediate_size
__magic_name__ : Any = hidden_dropout_prob
__magic_name__ : Optional[int] = attention_probs_dropout_prob
__magic_name__ : List[str] = max_position_embeddings
__magic_name__ : Any = eos_token_id
__magic_name__ : str = pad_token_id
__magic_name__ : int = bos_token_id
__magic_name__ : Optional[int] = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
__magic_name__ : Tuple = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
__magic_name__ : Tuple = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__magic_name__ : int = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__magic_name__ : int = tf.concat([input_ids, eos_tensor] , axis=1 )
__magic_name__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__magic_name__ : Dict = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
__magic_name__ : List[str] = prepare_led_inputs_dict(_a , _a , _a )
__magic_name__ : Union[str, Any] = tf.concat(
[tf.zeros_like(_a )[:, :-1], tf.ones_like(_a )[:, -1:]] , axis=-1 , )
__magic_name__ : List[Any] = global_attention_mask
return config, inputs_dict
def SCREAMING_SNAKE_CASE ( self , _a , _a ):
__magic_name__ : Dict = TFLEDModel(config=_a ).get_decoder()
__magic_name__ : Optional[int] = inputs_dict["input_ids"]
__magic_name__ : Union[str, Any] = input_ids[:1, :]
__magic_name__ : str = inputs_dict["attention_mask"][:1, :]
__magic_name__ : int = 1
# first forward pass
__magic_name__ : Tuple = model(_a , attention_mask=_a , use_cache=_a )
__magic_name__ , __magic_name__ : str = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__magic_name__ : Optional[int] = ids_tensor((self.batch_size, 3) , config.vocab_size )
__magic_name__ : Any = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
__magic_name__ : Optional[Any] = tf.concat([input_ids, next_tokens] , axis=-1 )
__magic_name__ : List[Any] = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
__magic_name__ : List[str] = model(_a , attention_mask=_a )[0]
__magic_name__ : Dict = model(_a , attention_mask=_a , past_key_values=_a )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__magic_name__ : List[Any] = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
__magic_name__ : Union[str, Any] = output_from_no_past[:, -3:, random_slice_idx]
__magic_name__ : List[str] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_a , _a , rtol=1e-3 )
def lowerCAmelCase_ ( _snake_case : Any , _snake_case : List[Any] , _snake_case : Any , _snake_case : str=None , _snake_case : List[str]=None , _snake_case : int=None , _snake_case : Any=None , ) -> int:
'''simple docstring'''
if attention_mask is None:
__magic_name__ : str = tf.cast(tf.math.not_equal(_snake_case , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__magic_name__ : List[Any] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__magic_name__ : Dict = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__magic_name__ : Any = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class _snake_case ( snake_case , snake_case , unittest.TestCase ):
UpperCamelCase__ = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
UpperCamelCase__ = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
UpperCamelCase__ = (
{
'conversational': TFLEDForConditionalGeneration,
'feature-extraction': TFLEDModel,
'summarization': TFLEDForConditionalGeneration,
'text2text-generation': TFLEDForConditionalGeneration,
'translation': TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
UpperCamelCase__ = True
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = TFLEDModelTester(self )
__magic_name__ : List[Any] = ConfigTester(self , config_class=_a )
def SCREAMING_SNAKE_CASE ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ , __magic_name__ : int = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ : List[str] = tf.zeros_like(inputs_dict["attention_mask"] )
__magic_name__ : Optional[Any] = 2
__magic_name__ : Tuple = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict["global_attention_mask"] , )
__magic_name__ : Any = True
__magic_name__ : str = self.model_tester.seq_length
__magic_name__ : Dict = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(_a ):
__magic_name__ : str = outputs.decoder_attentions
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(_a ):
__magic_name__ : Any = [t.numpy() for t in outputs.encoder_attentions]
__magic_name__ : Tuple = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
__magic_name__ : Union[str, Any] = True
__magic_name__ : List[str] = False
__magic_name__ : Tuple = False
__magic_name__ : Optional[int] = model_class(_a )
__magic_name__ : str = model(self._prepare_for_class(_a , _a ) )
__magic_name__ : Any = len(_a )
self.assertEqual(config.output_hidden_states , _a )
check_encoder_attentions_output(_a )
if self.is_encoder_decoder:
__magic_name__ : Tuple = model_class(_a )
__magic_name__ : Optional[Any] = model(self._prepare_for_class(_a , _a ) )
self.assertEqual(config.output_hidden_states , _a )
check_decoder_attentions_output(_a )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
__magic_name__ : Dict = True
__magic_name__ : str = model_class(_a )
__magic_name__ : Any = model(self._prepare_for_class(_a , _a ) )
self.assertEqual(config.output_hidden_states , _a )
check_encoder_attentions_output(_a )
# Check attention is always last and order is fine
__magic_name__ : Union[str, Any] = True
__magic_name__ : Union[str, Any] = True
__magic_name__ : List[str] = model_class(_a )
__magic_name__ : Any = model(self._prepare_for_class(_a , _a ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_a ) )
self.assertEqual(model.config.output_hidden_states , _a )
check_encoder_attentions_output(_a )
@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing." )
def SCREAMING_SNAKE_CASE ( self ):
pass
def SCREAMING_SNAKE_CASE ( self ):
# TODO: Head-masking not yet implement
pass
def lowerCAmelCase_ ( _snake_case : int ) -> Optional[int]:
'''simple docstring'''
return tf.constant(_snake_case , dtype=tf.intaa )
snake_case : Optional[int] = 1E-4
@slow
@require_tf
class _snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[Any] = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" ).led
# change to intended input here
__magic_name__ : Optional[int] = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : str = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : Any = prepare_led_inputs_dict(model.config , _a , _a )
__magic_name__ : List[Any] = model(**_a )[0]
__magic_name__ : List[str] = (1, 1_024, 768)
self.assertEqual(output.shape , _a )
# change to expected output here
__magic_name__ : int = tf.convert_to_tensor(
[[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]] , )
tf.debugging.assert_near(output[:, :3, :3] , _a , atol=1e-3 )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Tuple = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" )
# change to intended input here
__magic_name__ : int = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : Tuple = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : Optional[Any] = prepare_led_inputs_dict(model.config , _a , _a )
__magic_name__ : Union[str, Any] = model(**_a )[0]
__magic_name__ : Optional[int] = (1, 1_024, model.config.vocab_size)
self.assertEqual(output.shape , _a )
# change to expected output here
__magic_name__ : str = tf.convert_to_tensor(
[[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]] , )
tf.debugging.assert_near(output[:, :3, :3] , _a , atol=1e-3 , rtol=1e-3 )
| 281 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipImageProcessor,
InstructBlipConfig,
InstructBlipForConditionalGeneration,
InstructBlipProcessor,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
LlamaConfig,
LlamaTokenizerFast,
TaConfig,
TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def _snake_case ( ):
UpperCAmelCase : List[Any] = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
UpperCAmelCase : Optional[int] = Image.open(requests.get(_snake_case , stream=_snake_case ).raw ).convert("""RGB""" )
return image
def _snake_case ( UpperCamelCase : List[Any] ):
UpperCAmelCase : str = []
# fmt: off
# vision encoder
rename_keys.append(("""visual_encoder.cls_token""", """vision_model.embeddings.class_embedding""") )
rename_keys.append(("""visual_encoder.pos_embed""", """vision_model.embeddings.position_embedding""") )
rename_keys.append(("""visual_encoder.patch_embed.proj.weight""", """vision_model.embeddings.patch_embedding.weight""") )
rename_keys.append(("""visual_encoder.patch_embed.proj.bias""", """vision_model.embeddings.patch_embedding.bias""") )
rename_keys.append(("""ln_vision.weight""", """vision_model.post_layernorm.weight""") )
rename_keys.append(("""ln_vision.bias""", """vision_model.post_layernorm.bias""") )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F"visual_encoder.blocks.{i}.norm1.weight", F"vision_model.encoder.layers.{i}.layer_norm1.weight") )
rename_keys.append((F"visual_encoder.blocks.{i}.norm1.bias", F"vision_model.encoder.layers.{i}.layer_norm1.bias") )
rename_keys.append((F"visual_encoder.blocks.{i}.norm2.weight", F"vision_model.encoder.layers.{i}.layer_norm2.weight") )
rename_keys.append((F"visual_encoder.blocks.{i}.norm2.bias", F"vision_model.encoder.layers.{i}.layer_norm2.bias") )
rename_keys.append((F"visual_encoder.blocks.{i}.attn.qkv.weight", F"vision_model.encoder.layers.{i}.self_attn.qkv.weight") )
rename_keys.append((F"visual_encoder.blocks.{i}.attn.proj.weight", F"vision_model.encoder.layers.{i}.self_attn.projection.weight",) )
rename_keys.append((F"visual_encoder.blocks.{i}.attn.proj.bias", F"vision_model.encoder.layers.{i}.self_attn.projection.bias") )
rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc1.weight", F"vision_model.encoder.layers.{i}.mlp.fc1.weight") )
rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc1.bias", F"vision_model.encoder.layers.{i}.mlp.fc1.bias") )
rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc2.weight", F"vision_model.encoder.layers.{i}.mlp.fc2.weight") )
rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc2.bias", F"vision_model.encoder.layers.{i}.mlp.fc2.bias") )
# QFormer
rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.weight""", """qformer.embeddings.layernorm.weight""") )
rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.embeddings.layernorm.bias""") )
# fmt: on
return rename_keys
def _snake_case ( UpperCamelCase : str , UpperCamelCase : int , UpperCamelCase : str ):
UpperCAmelCase : str = dct.pop(_snake_case )
UpperCAmelCase : Any = val
def _snake_case ( UpperCamelCase : List[Any] , UpperCamelCase : int ):
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
UpperCAmelCase : Union[str, Any] = state_dict.pop(F"visual_encoder.blocks.{i}.attn.q_bias" )
UpperCAmelCase : Dict = state_dict.pop(F"visual_encoder.blocks.{i}.attn.v_bias" )
# next, set bias in the state dict
UpperCAmelCase : List[Any] = torch.cat((q_bias, torch.zeros_like(_snake_case , requires_grad=_snake_case ), v_bias) )
UpperCAmelCase : Any = qkv_bias
def _snake_case ( UpperCamelCase : str ):
UpperCAmelCase : int = 364 if "coco" in model_name else 224
UpperCAmelCase : Dict = InstructBlipVisionConfig(image_size=_snake_case ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "t5-xl" in model_name:
UpperCAmelCase : Any = TaConfig.from_pretrained("""google/flan-t5-xl""" , dense_act_fn="""gelu""" , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
UpperCAmelCase : str = TaConfig.from_pretrained("""google/flan-t5-xxl""" , dense_act_fn="""gelu""" , bos_token_id=1 ).to_dict()
elif "vicuna-7b" in model_name:
UpperCAmelCase : Union[str, Any] = LlamaConfig.from_pretrained("""decapoda-research/llama-7b-hf""" , vocab_size=32001 ).to_dict()
elif "vicuna-13b" in model_name:
UpperCAmelCase : List[Any] = LlamaConfig.from_pretrained("""decapoda-research/llama-13b-hf""" , vocab_size=32001 ).to_dict()
else:
raise ValueError("""Model name not supported""" )
# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
UpperCAmelCase : List[Any] = InstructBlipQFormerConfig(vocab_size=30523 ).to_dict()
UpperCAmelCase : str = InstructBlipConfig(vision_config=_snake_case , text_config=_snake_case , qformer_config=_snake_case )
return config, image_size
@torch.no_grad()
def _snake_case ( UpperCamelCase : Any , UpperCamelCase : Dict=None , UpperCamelCase : Tuple=False ):
UpperCAmelCase : Any = AutoTokenizer.from_pretrained("""bert-base-uncased""" , truncation_side="""left""" )
qformer_tokenizer.add_special_tokens({"""bos_token""": """[DEC]"""} )
if "t5" in model_name:
UpperCAmelCase : Optional[Any] = TaTokenizerFast.from_pretrained("""google/flan-t5-xl""" , truncation_side="""left""" )
elif "vicuna" in model_name:
# the following was used in the original implementation:
# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
# tokenizer.add_special_tokens({"bos_token": "</s>"})
# tokenizer.add_special_tokens({"eos_token": "</s>"})
# tokenizer.add_special_tokens({"unk_token": "</s>"})
UpperCAmelCase : List[Any] = LlamaTokenizerFast.from_pretrained(
"""huggyllama/llama-7b""" , truncation_side="""left""" , bos_token="""</s>""" , unk_token="""</s>""" )
tokenizer.add_special_tokens({"""pad_token""": """[PAD]"""} )
UpperCAmelCase : Dict = get_blipa_config(_snake_case )
UpperCAmelCase : int = InstructBlipForConditionalGeneration(_snake_case ).eval()
UpperCAmelCase : Optional[int] = {
"instructblip-vicuna-7b": ("blip2_vicuna_instruct", "vicuna7b"),
"instructblip-vicuna-13b": ("blip2_vicuna_instruct", "vicuna13b"),
"instructblip-flan-t5-xl": ("blip2_t5_instruct", "flant5xl"),
"instructblip-flan-t5-xxl": ("blip2_t5_instruct", "flant5xxl"),
}
UpperCAmelCase : List[Any] = model_name_to_original[model_name]
# load original model
print("""Loading original model...""" )
UpperCAmelCase : str = "cuda:1" if torch.cuda.is_available() else "cpu"
UpperCAmelCase : Optional[int] = "cuda:2" if torch.cuda.is_available() else "cpu"
UpperCAmelCase : List[str] = load_model_and_preprocess(
name=_snake_case , model_type=_snake_case , is_eval=_snake_case , device=_snake_case )
original_model.eval()
print("""Done!""" )
# update state dict keys
UpperCAmelCase : str = original_model.state_dict()
UpperCAmelCase : Optional[int] = create_rename_keys(_snake_case )
for src, dest in rename_keys:
rename_key(_snake_case , _snake_case , _snake_case )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
UpperCAmelCase : int = state_dict.pop(_snake_case )
if key.startswith("""Qformer.bert""" ):
UpperCAmelCase : Dict = key.replace("""Qformer.bert""" , """qformer""" )
if "attention.self" in key:
UpperCAmelCase : str = key.replace("""self""" , """attention""" )
if "llm_proj" in key:
UpperCAmelCase : int = key.replace("""llm_proj""" , """language_projection""" )
if "t5_proj" in key:
UpperCAmelCase : Dict = key.replace("""t5_proj""" , """language_projection""" )
if key.startswith("""llm_model""" ):
UpperCAmelCase : Optional[int] = key.replace("""llm_model""" , """language_model""" )
if key.startswith("""t5""" ):
UpperCAmelCase : Optional[int] = key.replace("""t5""" , """language""" )
UpperCAmelCase : Union[str, Any] = val
# read in qv biases
read_in_q_v_bias(_snake_case , _snake_case )
# note: weights get loaded in torch.float32 by default
hf_model.load_state_dict(_snake_case , strict=_snake_case )
UpperCAmelCase : Union[str, Any] = load_demo_image()
UpperCAmelCase : Tuple = "What is unusual about this image?"
# create processor
UpperCAmelCase : str = BlipImageProcessor(
size={"""height""": image_size, """width""": image_size} , image_mean=_snake_case , image_std=_snake_case )
UpperCAmelCase : Tuple = InstructBlipProcessor(
image_processor=_snake_case , tokenizer=_snake_case , qformer_tokenizer=_snake_case , )
UpperCAmelCase : List[str] = processor(images=_snake_case , text=_snake_case , return_tensors="""pt""" ).to(_snake_case )
# make sure processor creates exact same pixel values
UpperCAmelCase : List[Any] = vis_processors["eval"](_snake_case ).unsqueeze(0 ).to(_snake_case )
UpperCAmelCase : Optional[Any] = inputs.pixel_values
assert torch.allclose(original_pixel_values.to(pixel_values.device ) , _snake_case )
original_model.to(_snake_case )
hf_model.to(_snake_case )
with torch.no_grad():
if "vicuna" in model_name:
UpperCAmelCase : Tuple = original_model({"""image""": original_pixel_values, """text_input""": [prompt]} ).logits
UpperCAmelCase : List[Any] = hf_model(**_snake_case ).logits
else:
UpperCAmelCase : int = original_model(
{"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]} ).logits
UpperCAmelCase : Dict = tokenizer("""\n""" , return_tensors="""pt""" ).input_ids.to(_snake_case )
UpperCAmelCase : Optional[Any] = label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id , -100 )
UpperCAmelCase : Optional[Any] = hf_model(**_snake_case , labels=_snake_case ).logits
print("""First values of original logits:""" , original_logits[0, :3, :3] )
print("""First values of HF logits:""" , logits[0, :3, :3] )
# assert values
assert original_logits.shape == logits.shape
UpperCAmelCase : List[str] = 1e-4 if "vicuna" in model_name else 1e-5
assert torch.allclose(original_logits.to(logits.device ) , _snake_case , atol=_snake_case )
print("""Looks ok!""" )
print("""Generating with original model...""" )
UpperCAmelCase : Dict = original_model.generate({"""image""": original_pixel_values, """prompt""": prompt} , num_beams=5 )
# important: we need to cast the weights of the HF model to the appropriate type
print("""Generating with HF model...""" )
UpperCAmelCase : Optional[int] = hf_model.generate(
**_snake_case , do_sample=_snake_case , num_beams=5 , max_length=256 , min_length=1 , top_p=0.9 , repetition_penalty=1.5 , length_penalty=1.0 , temperature=1 , )
if "vicuna" in model_name:
# convert output id 0 to 2 (eos_token_id)
# TODO add this in the generate method?
UpperCAmelCase : Tuple = 2
print("""Original generation:""" , _snake_case )
UpperCAmelCase : Dict = processor.batch_decode(_snake_case , skip_special_tokens=_snake_case )
UpperCAmelCase : List[str] = [text.strip() for text in output_text]
print("""HF generation:""" , _snake_case )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(_snake_case )
hf_model.save_pretrained(_snake_case )
if push_to_hub:
processor.push_to_hub(F"Salesforce/{model_name}" )
hf_model.push_to_hub(F"Salesforce/{model_name}" )
if __name__ == "__main__":
A: Union[str, Any] = argparse.ArgumentParser()
A: Tuple = [
"instructblip-vicuna-7b",
"instructblip-vicuna-13b",
"instructblip-flan-t5-xl",
"instructblip-flan-t5-xxl",
]
parser.add_argument(
"--model_name",
default="instructblip-flan-t5-xl",
choices=choices,
type=str,
help="Path to hf config.json of model to convert",
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model and processor to the hub after converting",
)
A: Optional[int] = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 109 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
snake_case : Optional[Any] = logging.get_logger(__name__)
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Union[str, Any]=False ) -> List[str]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = []
# fmt: off
# stem:
rename_keys.append(("cls_token", "vit.embeddings.cls_token") )
rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings") )
rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias") )
# backbone
rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias") )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''') )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''') )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
("pre_logits.fc.weight", "pooler.dense.weight"),
("pre_logits.fc.bias", "pooler.dense.bias"),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
__magic_name__ : int = [(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
# fmt: on
return rename_keys
def lowerCAmelCase_ ( _snake_case : Any , _snake_case : Any , _snake_case : Dict=False ) -> int:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
__magic_name__ : int = ""
else:
__magic_name__ : Union[str, Any] = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__magic_name__ : Optional[Any] = state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
__magic_name__ : int = state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
__magic_name__ : Dict = in_proj_weight[
: config.hidden_size, :
]
__magic_name__ : List[str] = in_proj_bias[: config.hidden_size]
__magic_name__ : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__magic_name__ : Optional[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__magic_name__ : Optional[Any] = in_proj_weight[
-config.hidden_size :, :
]
__magic_name__ : int = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase_ ( _snake_case : List[str] ) -> List[str]:
'''simple docstring'''
__magic_name__ : List[str] = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(_snake_case , _snake_case )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : int , _snake_case : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
__magic_name__ : int = dct.pop(_snake_case )
__magic_name__ : List[Any] = val
def lowerCAmelCase_ ( ) -> Dict:
'''simple docstring'''
__magic_name__ : List[str] = "http://images.cocodataset.org/val2017/000000039769.jpg"
__magic_name__ : List[str] = Image.open(requests.get(_snake_case , stream=_snake_case ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : Any , _snake_case : int=False ) -> Dict:
'''simple docstring'''
__magic_name__ : List[str] = BitConfig(
global_padding="same" , layer_type="bottleneck" , depths=(3, 4, 9) , out_features=["stage3"] , embedding_dynamic_padding=_snake_case , )
__magic_name__ : List[str] = ViTHybridConfig(backbone_config=_snake_case , image_size=384 , num_labels=1000 )
__magic_name__ : str = False
# load original model from timm
__magic_name__ : Union[str, Any] = timm.create_model(_snake_case , pretrained=_snake_case )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
__magic_name__ : List[Any] = timm_model.state_dict()
if base_model:
remove_classification_head_(_snake_case )
__magic_name__ : Tuple = create_rename_keys(_snake_case , _snake_case )
for src, dest in rename_keys:
rename_key(_snake_case , _snake_case , _snake_case )
read_in_q_k_v(_snake_case , _snake_case , _snake_case )
__magic_name__ : List[str] = "huggingface/label-files"
__magic_name__ : int = "imagenet-1k-id2label.json"
__magic_name__ : Optional[int] = json.load(open(hf_hub_download(_snake_case , _snake_case , repo_type="dataset" ) , "r" ) )
__magic_name__ : int = {int(_snake_case ): v for k, v in idalabel.items()}
__magic_name__ : List[str] = idalabel
__magic_name__ : List[str] = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
__magic_name__ : List[str] = ViTHybridModel(_snake_case ).eval()
else:
__magic_name__ : str = ViTHybridForImageClassification(_snake_case ).eval()
model.load_state_dict(_snake_case )
# create image processor
__magic_name__ : List[Any] = create_transform(**resolve_data_config({} , model=_snake_case ) )
__magic_name__ : int = transform.transforms
__magic_name__ : List[str] = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
__magic_name__ : int = ViTHybridImageProcessor(
do_resize=_snake_case , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_snake_case , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=_snake_case , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
__magic_name__ : List[Any] = prepare_img()
__magic_name__ : Any = transform(_snake_case ).unsqueeze(0 )
__magic_name__ : Tuple = processor(_snake_case , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(_snake_case , _snake_case )
# verify logits
with torch.no_grad():
__magic_name__ : Optional[int] = model(_snake_case )
__magic_name__ : List[str] = outputs.logits
print("Predicted class:" , logits.argmax(-1 ).item() )
if base_model:
__magic_name__ : List[str] = timm_model.forward_features(_snake_case )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_snake_case , outputs.pooler_output , atol=1E-3 )
else:
__magic_name__ : Any = timm_model(_snake_case )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_snake_case , outputs.logits , atol=1E-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(_snake_case ).mkdir(exist_ok=_snake_case )
print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_snake_case )
print(F'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(_snake_case )
if push_to_hub:
print(F'''Pushing model and processor to the hub {vit_name}''' )
model.push_to_hub(F'''ybelkada/{vit_name}''' )
processor.push_to_hub(F'''ybelkada/{vit_name}''' )
if __name__ == "__main__":
snake_case : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--vit_name",
default="vit_base_r50_s16_384",
type=str,
help="Name of the hybrid ViT timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
)
snake_case : List[Any] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 281 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {"vocab_file": "spiece.model"}
_A = {
"vocab_file": {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
}
}
_A = {
"albert-base-v1": 512,
"albert-large-v1": 512,
"albert-xlarge-v1": 512,
"albert-xxlarge-v1": 512,
"albert-base-v2": 512,
"albert-large-v2": 512,
"albert-xlarge-v2": 512,
"albert-xxlarge-v2": 512,
}
_A = "▁"
class A ( __UpperCAmelCase ):
__snake_case = VOCAB_FILES_NAMES
__snake_case = PRETRAINED_VOCAB_FILES_MAP
__snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self, UpperCamelCase__, UpperCamelCase__=True, UpperCamelCase__=True, UpperCamelCase__=False, UpperCamelCase__="[CLS]", UpperCamelCase__="[SEP]", UpperCamelCase__="<unk>", UpperCamelCase__="[SEP]", UpperCamelCase__="<pad>", UpperCamelCase__="[CLS]", UpperCamelCase__="[MASK]", UpperCamelCase__ = None, **UpperCamelCase__, ):
"""simple docstring"""
lowerCAmelCase_ = (
AddedToken(_a, lstrip=_a, rstrip=_a, normalized=_a )
if isinstance(_a, _a )
else mask_token
)
lowerCAmelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_a, remove_space=_a, keep_accents=_a, bos_token=_a, eos_token=_a, unk_token=_a, sep_token=_a, pad_token=_a, cls_token=_a, mask_token=_a, sp_model_kwargs=self.sp_model_kwargs, **_a, )
lowerCAmelCase_ = do_lower_case
lowerCAmelCase_ = remove_space
lowerCAmelCase_ = keep_accents
lowerCAmelCase_ = vocab_file
lowerCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_a )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
return len(self.sp_model )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
"""simple docstring"""
lowerCAmelCase_ = self.__dict__.copy()
lowerCAmelCase_ = None
return state
def __setstate__( self, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = d
# for backward compatibility
if not hasattr(self, '''sp_model_kwargs''' ):
lowerCAmelCase_ = {}
lowerCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
if self.remove_space:
lowerCAmelCase_ = " ".join(inputs.strip().split() )
else:
lowerCAmelCase_ = inputs
lowerCAmelCase_ = outputs.replace('''``''', '''\"''' ).replace('''\'\'''', '''\"''' )
if not self.keep_accents:
lowerCAmelCase_ = unicodedata.normalize('''NFKD''', _a )
lowerCAmelCase_ = "".join([c for c in outputs if not unicodedata.combining(_a )] )
if self.do_lower_case:
lowerCAmelCase_ = outputs.lower()
return outputs
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = self.preprocess_text(_a )
lowerCAmelCase_ = self.sp_model.encode(_a, out_type=_a )
lowerCAmelCase_ = []
for piece in pieces:
if len(_a ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
lowerCAmelCase_ = self.sp_model.EncodeAsPieces(piece[:-1].replace(_a, '''''' ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
lowerCAmelCase_ = cur_pieces[1:]
else:
lowerCAmelCase_ = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_a )
else:
new_pieces.append(_a )
return new_pieces
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
return self.sp_model.PieceToId(_a )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
return self.sp_model.IdToPiece(_a )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = []
lowerCAmelCase_ = ""
lowerCAmelCase_ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_a ) + token
lowerCAmelCase_ = True
lowerCAmelCase_ = []
else:
current_sub_tokens.append(_a )
lowerCAmelCase_ = False
out_string += self.sp_model.decode(_a )
return out_string.strip()
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__ = None ):
"""simple docstring"""
lowerCAmelCase_ = [self.sep_token_id]
lowerCAmelCase_ = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__ = None, UpperCamelCase__ = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a, token_ids_a=_a, already_has_special_tokens=_a )
if token_ids_a is not None:
return [1] + ([0] * len(_a )) + [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1]
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__ = None ):
"""simple docstring"""
lowerCAmelCase_ = [self.sep_token_id]
lowerCAmelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__ = None ):
"""simple docstring"""
if not os.path.isdir(_a ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
lowerCAmelCase_ = os.path.join(
_a, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file, _a )
elif not os.path.isfile(self.vocab_file ):
with open(_a, '''wb''' ) as fi:
lowerCAmelCase_ = self.sp_model.serialized_model_proto()
fi.write(_a )
return (out_vocab_file,)
| 278 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
snake_case : List[str] = "facebook/wmt19-en-de"
snake_case : Dict = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
snake_case : List[str] = FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
snake_case : int = FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")
# Test
snake_case : Optional[Any] = tokenizer(["Making tiny model"], return_tensors="pt")
snake_case : List[str] = tiny_model(**batch)
print("test output:", len(outputs.logits[0]))
# Save
snake_case : Dict = "tiny-wmt19-en-de"
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F"Generated {mname_tiny}")
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 281 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See all SEW models at https://huggingface.co/models?filter=sew
}
class lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : int = """sew"""
def __init__( self , lowerCAmelCase__=32 , lowerCAmelCase__=768 , lowerCAmelCase__=12 , lowerCAmelCase__=12 , lowerCAmelCase__=3_072 , lowerCAmelCase__=2 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.02 , lowerCAmelCase__=1e-5 , lowerCAmelCase__="group" , lowerCAmelCase__="gelu" , lowerCAmelCase__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , lowerCAmelCase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , lowerCAmelCase__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , lowerCAmelCase__=False , lowerCAmelCase__=128 , lowerCAmelCase__=16 , lowerCAmelCase__=True , lowerCAmelCase__=0.05 , lowerCAmelCase__=10 , lowerCAmelCase__=2 , lowerCAmelCase__=0.0 , lowerCAmelCase__=10 , lowerCAmelCase__=0 , lowerCAmelCase__="mean" , lowerCAmelCase__=False , lowerCAmelCase__=False , lowerCAmelCase__=256 , lowerCAmelCase__=0 , lowerCAmelCase__=1 , lowerCAmelCase__=2 , **lowerCAmelCase__ , ) -> int:
super().__init__(**_a , pad_token_id=_a , bos_token_id=_a , eos_token_id=_a )
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = feat_extract_norm
SCREAMING_SNAKE_CASE = feat_extract_activation
SCREAMING_SNAKE_CASE = list(_a )
SCREAMING_SNAKE_CASE = list(_a )
SCREAMING_SNAKE_CASE = list(_a )
SCREAMING_SNAKE_CASE = conv_bias
SCREAMING_SNAKE_CASE = num_conv_pos_embeddings
SCREAMING_SNAKE_CASE = num_conv_pos_embedding_groups
SCREAMING_SNAKE_CASE = len(self.conv_dim )
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = squeeze_factor
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_dropout
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = activation_dropout
SCREAMING_SNAKE_CASE = feat_proj_dropout
SCREAMING_SNAKE_CASE = final_dropout
SCREAMING_SNAKE_CASE = layerdrop
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect.'
'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
F'but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'
F'= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
SCREAMING_SNAKE_CASE = apply_spec_augment
SCREAMING_SNAKE_CASE = mask_time_prob
SCREAMING_SNAKE_CASE = mask_time_length
SCREAMING_SNAKE_CASE = mask_time_min_masks
SCREAMING_SNAKE_CASE = mask_feature_prob
SCREAMING_SNAKE_CASE = mask_feature_length
SCREAMING_SNAKE_CASE = mask_feature_min_masks
# ctc loss
SCREAMING_SNAKE_CASE = ctc_loss_reduction
SCREAMING_SNAKE_CASE = ctc_zero_infinity
# sequence classification
SCREAMING_SNAKE_CASE = use_weighted_layer_sum
SCREAMING_SNAKE_CASE = classifier_proj_size
@property
def __A ( self ) -> Union[str, Any]:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 113 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
snake_case : Optional[int] = logging.getLogger(__name__)
def lowerCAmelCase_ ( _snake_case : Dict , _snake_case : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__magic_name__ : List[str] = np.argmax(_snake_case , axis=1 )
return np.sum(outputs == labels )
def lowerCAmelCase_ ( _snake_case : Optional[Any] ) -> Dict:
'''simple docstring'''
with open(_snake_case , encoding="utf_8" ) as f:
__magic_name__ : List[str] = csv.reader(_snake_case )
__magic_name__ : List[Any] = []
next(_snake_case ) # skip the first line
for line in tqdm(_snake_case ):
output.append((" ".join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def lowerCAmelCase_ ( _snake_case : str , _snake_case : Tuple , _snake_case : Union[str, Any] , _snake_case : List[Any] , _snake_case : Tuple , _snake_case : Optional[int] ) -> int:
'''simple docstring'''
__magic_name__ : Optional[int] = []
for dataset in encoded_datasets:
__magic_name__ : Union[str, Any] = len(_snake_case )
__magic_name__ : Dict = np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
__magic_name__ : List[str] = np.zeros((n_batch, 2) , dtype=np.intaa )
__magic_name__ : Optional[int] = np.full((n_batch, 2, input_len) , fill_value=-100 , dtype=np.intaa )
__magic_name__ : int = np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(_snake_case ):
__magic_name__ : Dict = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__magic_name__ : Dict = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__magic_name__ : str = with_conta
__magic_name__ : Tuple = with_conta
__magic_name__ : Union[str, Any] = len(_snake_case ) - 1
__magic_name__ : int = len(_snake_case ) - 1
__magic_name__ : Optional[Any] = with_conta
__magic_name__ : Optional[Any] = with_conta
__magic_name__ : Optional[int] = mc_label
__magic_name__ : str = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(_snake_case ) for t in all_inputs ) )
return tensor_datasets
def lowerCAmelCase_ ( ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Any = argparse.ArgumentParser()
parser.add_argument("--model_name" , type=_snake_case , default="openai-gpt" , help="pretrained model name" )
parser.add_argument("--do_train" , action="store_true" , help="Whether to run training." )
parser.add_argument("--do_eval" , action="store_true" , help="Whether to run eval on the dev set." )
parser.add_argument(
"--output_dir" , default=_snake_case , type=_snake_case , required=_snake_case , help="The output directory where the model predictions and checkpoints will be written." , )
parser.add_argument("--train_dataset" , type=_snake_case , default="" )
parser.add_argument("--eval_dataset" , type=_snake_case , default="" )
parser.add_argument("--seed" , type=_snake_case , default=42 )
parser.add_argument("--num_train_epochs" , type=_snake_case , default=3 )
parser.add_argument("--train_batch_size" , type=_snake_case , default=8 )
parser.add_argument("--eval_batch_size" , type=_snake_case , default=16 )
parser.add_argument("--adam_epsilon" , default=1E-8 , type=_snake_case , help="Epsilon for Adam optimizer." )
parser.add_argument("--max_grad_norm" , type=_snake_case , default=1 )
parser.add_argument(
"--max_steps" , default=-1 , type=_snake_case , help=(
"If > 0: set total number of training steps to perform. Override num_train_epochs."
) , )
parser.add_argument(
"--gradient_accumulation_steps" , type=_snake_case , default=1 , help="Number of updates steps to accumulate before performing a backward/update pass." , )
parser.add_argument("--learning_rate" , type=_snake_case , default=6.25E-5 )
parser.add_argument("--warmup_steps" , default=0 , type=_snake_case , help="Linear warmup over warmup_steps." )
parser.add_argument("--lr_schedule" , type=_snake_case , default="warmup_linear" )
parser.add_argument("--weight_decay" , type=_snake_case , default=0.01 )
parser.add_argument("--lm_coef" , type=_snake_case , default=0.9 )
parser.add_argument("--n_valid" , type=_snake_case , default=374 )
parser.add_argument("--server_ip" , type=_snake_case , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=_snake_case , default="" , help="Can be used for distant debugging." )
__magic_name__ : List[Any] = parser.parse_args()
print(_snake_case )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=_snake_case )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
__magic_name__ : Dict = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
__magic_name__ : Optional[int] = torch.cuda.device_count()
logger.info("device: {}, n_gpu {}".format(_snake_case , _snake_case ) )
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True." )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
__magic_name__ : List[Any] = ["_start_", "_delimiter_", "_classify_"]
__magic_name__ : Optional[int] = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(_snake_case )
__magic_name__ : Optional[Any] = tokenizer.convert_tokens_to_ids(_snake_case )
__magic_name__ : List[str] = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(_snake_case ) )
model.to(_snake_case )
# Load and encode the datasets
def tokenize_and_encode(_snake_case : str ):
if isinstance(_snake_case , _snake_case ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_snake_case ) )
elif isinstance(_snake_case , _snake_case ):
return obj
return [tokenize_and_encode(_snake_case ) for o in obj]
logger.info("Encoding dataset..." )
__magic_name__ : Optional[int] = load_rocstories_dataset(args.train_dataset )
__magic_name__ : str = load_rocstories_dataset(args.eval_dataset )
__magic_name__ : int = (train_dataset, eval_dataset)
__magic_name__ : List[str] = tokenize_and_encode(_snake_case )
# Compute the max input length for the Transformer
__magic_name__ : Optional[Any] = model.config.n_positions // 2 - 2
__magic_name__ : Optional[int] = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
__magic_name__ : List[str] = min(_snake_case , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
__magic_name__ : List[Any] = pre_process_datasets(_snake_case , _snake_case , _snake_case , *_snake_case )
__magic_name__ , __magic_name__ : Optional[int] = tensor_datasets[0], tensor_datasets[1]
__magic_name__ : Tuple = TensorDataset(*_snake_case )
__magic_name__ : Union[str, Any] = RandomSampler(_snake_case )
__magic_name__ : Dict = DataLoader(_snake_case , sampler=_snake_case , batch_size=args.train_batch_size )
__magic_name__ : Any = TensorDataset(*_snake_case )
__magic_name__ : Optional[Any] = SequentialSampler(_snake_case )
__magic_name__ : int = DataLoader(_snake_case , sampler=_snake_case , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
__magic_name__ : Tuple = args.max_steps
__magic_name__ : List[str] = args.max_steps // (len(_snake_case ) // args.gradient_accumulation_steps) + 1
else:
__magic_name__ : List[str] = len(_snake_case ) // args.gradient_accumulation_steps * args.num_train_epochs
__magic_name__ : str = list(model.named_parameters() )
__magic_name__ : Dict = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
__magic_name__ : str = [
{
"params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
"weight_decay": args.weight_decay,
},
{"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], "weight_decay": 0.0},
]
__magic_name__ : str = AdamW(_snake_case , lr=args.learning_rate , eps=args.adam_epsilon )
__magic_name__ : List[str] = get_linear_schedule_with_warmup(
_snake_case , num_warmup_steps=args.warmup_steps , num_training_steps=_snake_case )
if args.do_train:
__magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc="Epoch" ):
__magic_name__ : List[str] = 0
__magic_name__ : Tuple = 0
__magic_name__ : Dict = tqdm(_snake_case , desc="Training" )
for step, batch in enumerate(_snake_case ):
__magic_name__ : Optional[Any] = tuple(t.to(_snake_case ) for t in batch )
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Dict = batch
__magic_name__ : Optional[Any] = model(_snake_case , mc_token_ids=_snake_case , lm_labels=_snake_case , mc_labels=_snake_case )
__magic_name__ : Optional[Any] = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
__magic_name__ : List[str] = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
__magic_name__ : int = "Training loss: {:.2e} lr: {:.2e}".format(_snake_case , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
__magic_name__ : Dict = model.module if hasattr(_snake_case , "module" ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
__magic_name__ : List[Any] = os.path.join(args.output_dir , _snake_case )
__magic_name__ : Dict = os.path.join(args.output_dir , _snake_case )
torch.save(model_to_save.state_dict() , _snake_case )
model_to_save.config.to_json_file(_snake_case )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
__magic_name__ : Dict = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
__magic_name__ : Optional[int] = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(_snake_case )
if args.do_eval:
model.eval()
__magic_name__ , __magic_name__ : Any = 0, 0
__magic_name__ , __magic_name__ : Union[str, Any] = 0, 0
for batch in tqdm(_snake_case , desc="Evaluating" ):
__magic_name__ : int = tuple(t.to(_snake_case ) for t in batch )
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] = batch
with torch.no_grad():
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Dict = model(
_snake_case , mc_token_ids=_snake_case , lm_labels=_snake_case , mc_labels=_snake_case )
__magic_name__ : Tuple = mc_logits.detach().cpu().numpy()
__magic_name__ : Any = mc_labels.to("cpu" ).numpy()
__magic_name__ : str = accuracy(_snake_case , _snake_case )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
__magic_name__ : Tuple = eval_loss / nb_eval_steps
__magic_name__ : List[Any] = eval_accuracy / nb_eval_examples
__magic_name__ : int = tr_loss / nb_tr_steps if args.do_train else None
__magic_name__ : Any = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}
__magic_name__ : int = os.path.join(args.output_dir , "eval_results.txt" )
with open(_snake_case , "w" ) as writer:
logger.info("***** Eval results *****" )
for key in sorted(result.keys() ):
logger.info(" %s = %s" , _snake_case , str(result[key] ) )
writer.write("%s = %s\n" % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 281 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCAmelCase__ ( metaclass=lowercase ):
'''simple docstring'''
lowerCamelCase__ = ["""torch""", """torchsde"""]
def __init__( self , *lowercase , **lowercase ):
requires_backends(self , ['torch', 'torchsde'] )
@classmethod
def A_ ( cls , *lowercase , **lowercase ):
requires_backends(cls , ['torch', 'torchsde'] )
@classmethod
def A_ ( cls , *lowercase , **lowercase ):
requires_backends(cls , ['torch', 'torchsde'] ) | 96 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 281 | 0 |
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}
class lowerCAmelCase_ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase : Dict = """align_text_model"""
def __init__( self , lowerCAmelCase=3_05_22 , lowerCAmelCase=7_68 , lowerCAmelCase=12 , lowerCAmelCase=12 , lowerCAmelCase=30_72 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=5_12 , lowerCAmelCase=2 , lowerCAmelCase=0.02 , lowerCAmelCase=1E-12 , lowerCAmelCase=0 , lowerCAmelCase="absolute" , lowerCAmelCase=True , **lowerCAmelCase , ):
"""simple docstring"""
super().__init__(**_a )
snake_case = vocab_size
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = hidden_act
snake_case = intermediate_size
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = max_position_embeddings
snake_case = type_vocab_size
snake_case = initializer_range
snake_case = layer_norm_eps
snake_case = position_embedding_type
snake_case = use_cache
snake_case = pad_token_id
@classmethod
def snake_case ( cls , lowerCAmelCase , **lowerCAmelCase ):
"""simple docstring"""
cls._set_token_in_kwargs(_a )
snake_case = cls.get_config_dict(_a , **_a )
# get the text config dict if we are loading from AlignConfig
if config_dict.get('model_type' ) == "align":
snake_case = config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(_a , **_a )
class lowerCAmelCase_ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase : List[Any] = """align_vision_model"""
def __init__( self , lowerCAmelCase = 3 , lowerCAmelCase = 6_00 , lowerCAmelCase = 2.0 , lowerCAmelCase = 3.1 , lowerCAmelCase = 8 , lowerCAmelCase = [3, 3, 5, 3, 5, 5, 3] , lowerCAmelCase = [32, 16, 24, 40, 80, 1_12, 1_92] , lowerCAmelCase = [16, 24, 40, 80, 1_12, 1_92, 3_20] , lowerCAmelCase = [] , lowerCAmelCase = [1, 2, 2, 2, 1, 2, 1] , lowerCAmelCase = [1, 2, 2, 3, 3, 4, 1] , lowerCAmelCase = [1, 6, 6, 6, 6, 6, 6] , lowerCAmelCase = 0.25 , lowerCAmelCase = "swish" , lowerCAmelCase = 25_60 , lowerCAmelCase = "mean" , lowerCAmelCase = 0.02 , lowerCAmelCase = 0.0_01 , lowerCAmelCase = 0.99 , lowerCAmelCase = 0.2 , **lowerCAmelCase , ):
"""simple docstring"""
super().__init__(**_a )
snake_case = num_channels
snake_case = image_size
snake_case = width_coefficient
snake_case = depth_coefficient
snake_case = depth_divisor
snake_case = kernel_sizes
snake_case = in_channels
snake_case = out_channels
snake_case = depthwise_padding
snake_case = strides
snake_case = num_block_repeats
snake_case = expand_ratios
snake_case = squeeze_expansion_ratio
snake_case = hidden_act
snake_case = hidden_dim
snake_case = pooling_type
snake_case = initializer_range
snake_case = batch_norm_eps
snake_case = batch_norm_momentum
snake_case = drop_connect_rate
snake_case = sum(_a ) * 4
@classmethod
def snake_case ( cls , lowerCAmelCase , **lowerCAmelCase ):
"""simple docstring"""
cls._set_token_in_kwargs(_a )
snake_case = cls.get_config_dict(_a , **_a )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get('model_type' ) == "align":
snake_case = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(_a , **_a )
class lowerCAmelCase_ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase : Any = """align"""
_lowerCAmelCase : List[str] = True
def __init__( self , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=6_40 , lowerCAmelCase=1.0 , lowerCAmelCase=0.02 , **lowerCAmelCase , ):
"""simple docstring"""
super().__init__(**_a )
if text_config is None:
snake_case = {}
logger.info('text_config is None. Initializing the AlignTextConfig with default values.' )
if vision_config is None:
snake_case = {}
logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.' )
snake_case = AlignTextConfig(**_a )
snake_case = AlignVisionConfig(**_a )
snake_case = projection_dim
snake_case = temperature_init_value
snake_case = initializer_range
@classmethod
def snake_case ( cls , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase ):
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **_a )
def snake_case ( self ):
"""simple docstring"""
snake_case = copy.deepcopy(self.__dict__ )
snake_case = self.text_config.to_dict()
snake_case = self.vision_config.to_dict()
snake_case = self.__class__.model_type
return output
| 150 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCAmelCase_ ( _snake_case : List[Any] ) -> List[Any]:
'''simple docstring'''
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ) -> Tuple:
'''simple docstring'''
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : Dict = "mock-s3-bucket"
__magic_name__ : Any = F'''s3://{mock_bucket}'''
__magic_name__ : str = extract_path_from_uri(_snake_case )
assert dataset_path.startswith("s3://" ) is False
__magic_name__ : Tuple = "./local/path"
__magic_name__ : Optional[Any] = extract_path_from_uri(_snake_case )
assert dataset_path == new_dataset_path
def lowerCAmelCase_ ( _snake_case : List[str] ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ : str = is_remote_filesystem(_snake_case )
assert is_remote is True
__magic_name__ : Optional[int] = fsspec.filesystem("file" )
__magic_name__ : int = is_remote_filesystem(_snake_case )
assert is_remote is False
@pytest.mark.parametrize("compression_fs_class" , _snake_case )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : Optional[Any] , _snake_case : int , _snake_case : Tuple , _snake_case : Any , _snake_case : Union[str, Any] , _snake_case : Any ) -> int:
'''simple docstring'''
__magic_name__ : Any = {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
__magic_name__ : str = input_paths[compression_fs_class.protocol]
if input_path is None:
__magic_name__ : Dict = F'''for \'{compression_fs_class.protocol}\' compression protocol, '''
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_snake_case )
__magic_name__ : str = fsspec.filesystem(compression_fs_class.protocol , fo=_snake_case )
assert isinstance(_snake_case , _snake_case )
__magic_name__ : int = os.path.basename(_snake_case )
__magic_name__ : Optional[int] = expected_filename[: expected_filename.rindex("." )]
assert fs.glob("*" ) == [expected_filename]
with fs.open(_snake_case , "r" , encoding="utf-8" ) as f, open(_snake_case , encoding="utf-8" ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize("protocol" , ["zip", "gzip"] )
def lowerCAmelCase_ ( _snake_case : List[Any] , _snake_case : Optional[Any] , _snake_case : Optional[Any] ) -> str:
'''simple docstring'''
__magic_name__ : int = {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
__magic_name__ : int = compressed_file_paths[protocol]
__magic_name__ : Tuple = "dataset.jsonl"
__magic_name__ : List[str] = F'''{protocol}://{member_file_path}::{compressed_file_path}'''
__magic_name__ , *__magic_name__ : Optional[Any] = fsspec.get_fs_token_paths(_snake_case )
assert fs.isfile(_snake_case )
assert not fs.isfile("non_existing_" + member_file_path )
@pytest.mark.integration
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Dict , _snake_case : List[str] , _snake_case : Tuple ) -> str:
'''simple docstring'''
__magic_name__ : int = hf_api.dataset_info(_snake_case , token=_snake_case )
__magic_name__ : Optional[Any] = HfFileSystem(repo_info=_snake_case , token=_snake_case )
assert sorted(hffs.glob("*" ) ) == [".gitattributes", "data"]
assert hffs.isdir("data" )
assert hffs.isfile(".gitattributes" ) and hffs.isfile("data/text_data.txt" )
with open(_snake_case ) as f:
assert hffs.open("data/text_data.txt" , "r" ).read() == f.read()
def lowerCAmelCase_ ( ) -> Optional[int]:
'''simple docstring'''
__magic_name__ : Optional[Any] = "bz2"
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(_snake_case , _snake_case , clobber=_snake_case )
with pytest.warns(_snake_case ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(_snake_case ) == 1
assert (
str(warning_info[0].message )
== F'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
)
| 281 | 0 |
"""simple docstring"""
import os
UpperCAmelCase : List[Any] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def lowerCamelCase ( _UpperCamelCase : str ) -> int:
'''simple docstring'''
__UpperCAmelCase : Tuple = 0
__UpperCAmelCase : Union[str, Any] = 0
while index < len(_snake_case ) - 1:
__UpperCAmelCase : Any = SYMBOLS[numerals[index]]
__UpperCAmelCase : str = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def lowerCamelCase ( _UpperCamelCase : int ) -> str:
'''simple docstring'''
__UpperCAmelCase : str = ""
__UpperCAmelCase : List[Any] = num // 1_0_0_0
numerals += m_count * "M"
num %= 1_0_0_0
__UpperCAmelCase : Optional[int] = num // 1_0_0
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 1_0_0
__UpperCAmelCase : Optional[int] = num // 1_0
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 1_0
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def lowerCamelCase ( _UpperCamelCase : str = "/p089_roman.txt" ) -> int:
'''simple docstring'''
__UpperCAmelCase : int = 0
with open(os.path.dirname(_snake_case ) + roman_numerals_filename ) as filea:
__UpperCAmelCase : List[Any] = filea.readlines()
for line in lines:
__UpperCAmelCase : Tuple = line.strip()
__UpperCAmelCase : List[Any] = parse_roman_numerals(_snake_case )
__UpperCAmelCase : List[str] = generate_roman_numerals(_snake_case )
savings += len(_snake_case ) - len(_snake_case )
return savings
if __name__ == "__main__":
print(F"{solution() = }")
| 115 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case : Dict = logging.get_logger(__name__)
snake_case : List[Any] = {
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
"YituTech/conv-bert-medium-small": (
"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
),
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class _snake_case ( snake_case ):
UpperCamelCase__ = 'convbert'
def __init__( self , _a=30_522 , _a=768 , _a=12 , _a=12 , _a=3_072 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=2 , _a=0.02 , _a=1e-12 , _a=1 , _a=0 , _a=2 , _a=768 , _a=2 , _a=9 , _a=1 , _a=None , **_a , ):
super().__init__(
pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a , )
__magic_name__ : Tuple = vocab_size
__magic_name__ : List[Any] = hidden_size
__magic_name__ : Union[str, Any] = num_hidden_layers
__magic_name__ : List[Any] = num_attention_heads
__magic_name__ : str = intermediate_size
__magic_name__ : Any = hidden_act
__magic_name__ : List[Any] = hidden_dropout_prob
__magic_name__ : Optional[int] = attention_probs_dropout_prob
__magic_name__ : Tuple = max_position_embeddings
__magic_name__ : str = type_vocab_size
__magic_name__ : List[str] = initializer_range
__magic_name__ : Tuple = layer_norm_eps
__magic_name__ : List[Any] = embedding_size
__magic_name__ : List[Any] = head_ratio
__magic_name__ : str = conv_kernel_size
__magic_name__ : Dict = num_groups
__magic_name__ : str = classifier_dropout
class _snake_case ( snake_case ):
@property
def SCREAMING_SNAKE_CASE ( self ):
if self.task == "multiple-choice":
__magic_name__ : Dict = {0: "batch", 1: "choice", 2: "sequence"}
else:
__magic_name__ : Dict = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 281 | 0 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _a ( *SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Optional[Union[Dict, Any]] = None , SCREAMING_SNAKE_CASE : Dict=True , SCREAMING_SNAKE_CASE : Tuple=2 ) -> Any:
"""simple docstring"""
from .. import __version__
__lowerCAmelCase: List[str] = take_from
__lowerCAmelCase: int = ()
if not isinstance(args[0] , _snake_case ):
__lowerCAmelCase: List[Any] = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(_snake_case ).base_version ) >= version.parse(_snake_case ):
raise ValueError(
f'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
f''' version {__version__} is >= {version_name}''' )
__lowerCAmelCase: Tuple = None
if isinstance(_snake_case , _snake_case ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(_snake_case ),)
__lowerCAmelCase: Union[str, Any] = f'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
elif hasattr(_snake_case , _snake_case ):
values += (getattr(_snake_case , _snake_case ),)
__lowerCAmelCase: int = f'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
elif deprecated_kwargs is None:
__lowerCAmelCase: List[Any] = f'''`{attribute}` is deprecated and will be removed in version {version_name}.'''
if warning is not None:
__lowerCAmelCase: Dict = warning + " " if standard_warn else ""
warnings.warn(warning + message , _snake_case , stacklevel=_snake_case )
if isinstance(_snake_case , _snake_case ) and len(_snake_case ) > 0:
__lowerCAmelCase: str = inspect.getouterframes(inspect.currentframe() )[1]
__lowerCAmelCase: Tuple = call_frame.filename
__lowerCAmelCase: int = call_frame.lineno
__lowerCAmelCase: int = call_frame.function
__lowerCAmelCase: List[Any] = next(iter(deprecated_kwargs.items() ) )
raise TypeError(f'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`''' )
if len(_snake_case ) == 0:
return
elif len(_snake_case ) == 1:
return values[0]
return values
| 322 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipaConfig,
BlipaForConditionalGeneration,
BlipaProcessor,
BlipaVisionConfig,
BlipImageProcessor,
OPTConfig,
TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def lowerCAmelCase_ ( ) -> str:
'''simple docstring'''
__magic_name__ : int = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
__magic_name__ : Union[str, Any] = Image.open(requests.get(_snake_case , stream=_snake_case ).raw ).convert("RGB" )
return image
def lowerCAmelCase_ ( _snake_case : str ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : List[str] = []
# fmt: off
# vision encoder
rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding") )
rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding") )
rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight") )
rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias") )
rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight") )
rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias") )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',) )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''') )
# QFormer
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight") )
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias") )
# fmt: on
return rename_keys
def lowerCAmelCase_ ( _snake_case : Any , _snake_case : Union[str, Any] , _snake_case : Optional[Any] ) -> int:
'''simple docstring'''
__magic_name__ : Tuple = dct.pop(_snake_case )
__magic_name__ : int = val
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : Optional[Any] ) -> Dict:
'''simple docstring'''
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
__magic_name__ : List[Any] = state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias''' )
__magic_name__ : Optional[Any] = state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias''' )
# next, set bias in the state dict
__magic_name__ : Optional[int] = torch.cat((q_bias, torch.zeros_like(_snake_case , requires_grad=_snake_case ), v_bias) )
__magic_name__ : Union[str, Any] = qkv_bias
def lowerCAmelCase_ ( _snake_case : Dict , _snake_case : str ) -> int:
'''simple docstring'''
__magic_name__ : List[Any] = 364 if "coco" in model_name else 224
__magic_name__ : Union[str, Any] = BlipaVisionConfig(image_size=_snake_case ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
__magic_name__ : List[str] = OPTConfig.from_pretrained("facebook/opt-2.7b" , eos_token_id=_snake_case ).to_dict()
elif "opt-6.7b" in model_name:
__magic_name__ : Any = OPTConfig.from_pretrained("facebook/opt-6.7b" , eos_token_id=_snake_case ).to_dict()
elif "t5-xl" in model_name:
__magic_name__ : Dict = TaConfig.from_pretrained("google/flan-t5-xl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
__magic_name__ : int = TaConfig.from_pretrained("google/flan-t5-xxl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
__magic_name__ : List[Any] = BlipaConfig(vision_config=_snake_case , text_config=_snake_case )
return config, image_size
@torch.no_grad()
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : str=None , _snake_case : Dict=False ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Optional[int] = (
AutoTokenizer.from_pretrained("facebook/opt-2.7b" )
if "opt" in model_name
else AutoTokenizer.from_pretrained("google/flan-t5-xl" )
)
__magic_name__ : List[Any] = tokenizer("\n" , add_special_tokens=_snake_case ).input_ids[0]
__magic_name__ , __magic_name__ : Tuple = get_blipa_config(_snake_case , eos_token_id=_snake_case )
__magic_name__ : Union[str, Any] = BlipaForConditionalGeneration(_snake_case ).eval()
__magic_name__ : Any = {
"blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
"blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
"blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
"blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
"blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
"blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
"blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
}
__magic_name__ , __magic_name__ : Union[str, Any] = model_name_to_original[model_name]
# load original model
print("Loading original model..." )
__magic_name__ : Union[str, Any] = "cuda" if torch.cuda.is_available() else "cpu"
__magic_name__ , __magic_name__ , __magic_name__ : Optional[Any] = load_model_and_preprocess(
name=_snake_case , model_type=_snake_case , is_eval=_snake_case , device=_snake_case )
original_model.eval()
print("Done!" )
# update state dict keys
__magic_name__ : Dict = original_model.state_dict()
__magic_name__ : str = create_rename_keys(_snake_case )
for src, dest in rename_keys:
rename_key(_snake_case , _snake_case , _snake_case )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
__magic_name__ : Any = state_dict.pop(_snake_case )
if key.startswith("Qformer.bert" ):
__magic_name__ : Optional[int] = key.replace("Qformer.bert" , "qformer" )
if "attention.self" in key:
__magic_name__ : Any = key.replace("self" , "attention" )
if "opt_proj" in key:
__magic_name__ : Union[str, Any] = key.replace("opt_proj" , "language_projection" )
if "t5_proj" in key:
__magic_name__ : Optional[int] = key.replace("t5_proj" , "language_projection" )
if key.startswith("opt" ):
__magic_name__ : List[str] = key.replace("opt" , "language" )
if key.startswith("t5" ):
__magic_name__ : Tuple = key.replace("t5" , "language" )
__magic_name__ : Dict = val
# read in qv biases
read_in_q_v_bias(_snake_case , _snake_case )
__magic_name__ , __magic_name__ : Tuple = hf_model.load_state_dict(_snake_case , strict=_snake_case )
assert len(_snake_case ) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
__magic_name__ : List[Any] = load_demo_image()
__magic_name__ : Tuple = vis_processors["eval"](_snake_case ).unsqueeze(0 ).to(_snake_case )
__magic_name__ : Dict = tokenizer(["\n"] , return_tensors="pt" ).input_ids.to(_snake_case )
# create processor
__magic_name__ : Optional[Any] = BlipImageProcessor(
size={"height": image_size, "width": image_size} , image_mean=_snake_case , image_std=_snake_case )
__magic_name__ : Dict = BlipaProcessor(image_processor=_snake_case , tokenizer=_snake_case )
__magic_name__ : Union[str, Any] = processor(images=_snake_case , return_tensors="pt" ).pixel_values.to(_snake_case )
# make sure processor creates exact same pixel values
assert torch.allclose(_snake_case , _snake_case )
original_model.to(_snake_case )
hf_model.to(_snake_case )
with torch.no_grad():
if "opt" in model_name:
__magic_name__ : List[Any] = original_model({"image": original_pixel_values, "text_input": [""]} ).logits
__magic_name__ : Optional[int] = hf_model(_snake_case , _snake_case ).logits
else:
__magic_name__ : int = original_model(
{"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]} ).logits
__magic_name__ : Tuple = input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -100 )
__magic_name__ : List[str] = hf_model(_snake_case , _snake_case , labels=_snake_case ).logits
assert original_logits.shape == logits.shape
print("First values of original logits:" , original_logits[0, :3, :3] )
print("First values of HF logits:" , logits[0, :3, :3] )
# assert values
if model_name == "blip2-flan-t5-xl":
__magic_name__ : List[str] = torch.tensor(
[[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]] , device=_snake_case )
assert torch.allclose(logits[0, :3, :3] , _snake_case , atol=1E-4 )
elif model_name == "blip2-flan-t5-xl-coco":
__magic_name__ : Tuple = torch.tensor(
[[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]] , device=_snake_case )
else:
# cast to same type
__magic_name__ : str = logits.dtype
assert torch.allclose(original_logits.to(_snake_case ) , _snake_case , atol=1E-2 )
print("Looks ok!" )
print("Generating a caption..." )
__magic_name__ : Optional[int] = ""
__magic_name__ : Dict = tokenizer(_snake_case , return_tensors="pt" ).input_ids.to(_snake_case )
__magic_name__ : int = original_model.generate({"image": original_pixel_values} )
__magic_name__ : Optional[Any] = hf_model.generate(
_snake_case , _snake_case , do_sample=_snake_case , num_beams=5 , max_length=30 , min_length=1 , top_p=0.9 , repetition_penalty=1.0 , length_penalty=1.0 , temperature=1 , )
print("Original generation:" , _snake_case )
__magic_name__ : Tuple = input_ids.shape[1]
__magic_name__ : int = processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=_snake_case )
__magic_name__ : Union[str, Any] = [text.strip() for text in output_text]
print("HF generation:" , _snake_case )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(_snake_case )
hf_model.save_pretrained(_snake_case )
if push_to_hub:
processor.push_to_hub(F'''nielsr/{model_name}''' )
hf_model.push_to_hub(F'''nielsr/{model_name}''' )
if __name__ == "__main__":
snake_case : Any = argparse.ArgumentParser()
snake_case : Union[str, Any] = [
"blip2-opt-2.7b",
"blip2-opt-6.7b",
"blip2-opt-2.7b-coco",
"blip2-opt-6.7b-coco",
"blip2-flan-t5-xl",
"blip2-flan-t5-xl-coco",
"blip2-flan-t5-xxl",
]
parser.add_argument(
"--model_name",
default="blip2-opt-2.7b",
choices=choices,
type=str,
help="Path to hf config.json of model to convert",
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model and processor to the hub after converting",
)
snake_case : int = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 281 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class _snake_case ( unittest.TestCase ):
def _lowerCamelCase ( self: str ) -> Any:
__UpperCAmelCase : Tuple = tempfile.mkdtemp()
# fmt: off
__UpperCAmelCase : Optional[int] = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
# fmt: on
__UpperCAmelCase : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
__UpperCAmelCase : Dict = {
"do_resize": True,
"size": {"height": 18, "width": 18},
"do_normalize": True,
"image_mean": [0.5, 0.5, 0.5],
"image_std": [0.5, 0.5, 0.5],
}
__UpperCAmelCase : List[str] = os.path.join(self.tmpdirname , _a )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(_a , _a )
def _lowerCamelCase ( self: str , **__lowerCamelCase: str ) -> List[str]:
return BertTokenizer.from_pretrained(self.tmpdirname , **_a )
def _lowerCamelCase ( self: Dict , **__lowerCamelCase: Any ) -> List[str]:
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_a )
def _lowerCamelCase ( self: Union[str, Any] ) -> int:
shutil.rmtree(self.tmpdirname )
def _lowerCamelCase ( self: str ) -> List[Any]:
__UpperCAmelCase : Tuple = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
__UpperCAmelCase : Tuple = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _lowerCamelCase ( self: Optional[Any] ) -> int:
__UpperCAmelCase : Union[str, Any] = self.get_tokenizer()
__UpperCAmelCase : Tuple = self.get_image_processor()
__UpperCAmelCase : Tuple = VisionTextDualEncoderProcessor(tokenizer=_a , image_processor=_a )
processor.save_pretrained(self.tmpdirname )
__UpperCAmelCase : Tuple = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def _lowerCamelCase ( self: List[Any] ) -> Dict:
__UpperCAmelCase : Tuple = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__UpperCAmelCase : Union[str, Any] = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
__UpperCAmelCase : str = self.get_image_processor(do_normalize=_a , padding_value=1.0 )
__UpperCAmelCase : Union[str, Any] = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=_a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def _lowerCamelCase ( self: Any ) -> Dict:
__UpperCAmelCase : List[str] = self.get_image_processor()
__UpperCAmelCase : Dict = self.get_tokenizer()
__UpperCAmelCase : Tuple = VisionTextDualEncoderProcessor(tokenizer=_a , image_processor=_a )
__UpperCAmelCase : List[str] = self.prepare_image_inputs()
__UpperCAmelCase : Any = image_processor(_a , return_tensors="np" )
__UpperCAmelCase : Optional[int] = processor(images=_a , return_tensors="np" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _lowerCamelCase ( self: str ) -> Any:
__UpperCAmelCase : Any = self.get_image_processor()
__UpperCAmelCase : Dict = self.get_tokenizer()
__UpperCAmelCase : List[Any] = VisionTextDualEncoderProcessor(tokenizer=_a , image_processor=_a )
__UpperCAmelCase : int = "lower newer"
__UpperCAmelCase : List[Any] = processor(text=_a )
__UpperCAmelCase : Any = tokenizer(_a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _lowerCamelCase ( self: str ) -> List[str]:
__UpperCAmelCase : Dict = self.get_image_processor()
__UpperCAmelCase : List[Any] = self.get_tokenizer()
__UpperCAmelCase : Optional[int] = VisionTextDualEncoderProcessor(tokenizer=_a , image_processor=_a )
__UpperCAmelCase : int = "lower newer"
__UpperCAmelCase : Tuple = self.prepare_image_inputs()
__UpperCAmelCase : List[Any] = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "token_type_ids", "attention_mask", "pixel_values"] )
# test if it raises when no input is passed
with self.assertRaises(_a ):
processor()
def _lowerCamelCase ( self: str ) -> Union[str, Any]:
__UpperCAmelCase : Optional[int] = self.get_image_processor()
__UpperCAmelCase : List[str] = self.get_tokenizer()
__UpperCAmelCase : List[Any] = VisionTextDualEncoderProcessor(tokenizer=_a , image_processor=_a )
__UpperCAmelCase : Optional[int] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__UpperCAmelCase : Optional[int] = processor.batch_decode(_a )
__UpperCAmelCase : Union[str, Any] = tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
def _lowerCamelCase ( self: Any ) -> Optional[int]:
__UpperCAmelCase : Dict = self.get_image_processor()
__UpperCAmelCase : Optional[Any] = self.get_tokenizer()
__UpperCAmelCase : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=_a , image_processor=_a )
__UpperCAmelCase : Union[str, Any] = "lower newer"
__UpperCAmelCase : Union[str, Any] = self.prepare_image_inputs()
__UpperCAmelCase : Any = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 157 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case : Dict = logging.get_logger(__name__)
snake_case : Union[str, Any] = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
snake_case : Dict = {
"vocab_file": {
"vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
"vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
},
"merges_file": {
"vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
"vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
},
}
snake_case : Union[str, Any] = {
"vinai/phobert-base": 256,
"vinai/phobert-large": 256,
}
def lowerCAmelCase_ ( _snake_case : str ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : List[str] = set()
__magic_name__ : Any = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__magic_name__ : int = char
__magic_name__ : List[str] = set(_snake_case )
return pairs
class _snake_case ( snake_case ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , _a , _a , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , **_a , ):
super().__init__(
bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , cls_token=_a , pad_token=_a , mask_token=_a , **_a , )
__magic_name__ : Dict = vocab_file
__magic_name__ : Tuple = merges_file
__magic_name__ : List[Any] = {}
__magic_name__ : List[Any] = 0
__magic_name__ : Tuple = 1
__magic_name__ : int = 2
__magic_name__ : Union[str, Any] = 3
self.add_from_file(_a )
__magic_name__ : Optional[int] = {v: k for k, v in self.encoder.items()}
with open(_a , encoding="utf-8" ) as merges_handle:
__magic_name__ : List[str] = merges_handle.read().split("\n" )[:-1]
__magic_name__ : Union[str, Any] = [tuple(merge.split()[:-1] ) for merge in merges]
__magic_name__ : Union[str, Any] = dict(zip(_a , range(len(_a ) ) ) )
__magic_name__ : Optional[int] = {}
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__magic_name__ : Optional[Any] = [self.cls_token_id]
__magic_name__ : Union[str, Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self , _a , _a = None , _a = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is None:
return [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1, 1] + ([0] * len(_a )) + [1]
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
__magic_name__ : Optional[Any] = [self.sep_token_id]
__magic_name__ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def SCREAMING_SNAKE_CASE ( self ):
return len(self.encoder )
def SCREAMING_SNAKE_CASE ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def SCREAMING_SNAKE_CASE ( self , _a ):
if token in self.cache:
return self.cache[token]
__magic_name__ : List[Any] = tuple(_a )
__magic_name__ : List[Any] = tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
__magic_name__ : Any = get_pairs(_a )
if not pairs:
return token
while True:
__magic_name__ : str = min(_a , key=lambda _a : self.bpe_ranks.get(_a , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
__magic_name__ , __magic_name__ : List[str] = bigram
__magic_name__ : List[str] = []
__magic_name__ : List[str] = 0
while i < len(_a ):
try:
__magic_name__ : Any = word.index(_a , _a )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__magic_name__ : Tuple = j
if word[i] == first and i < len(_a ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__magic_name__ : Union[str, Any] = tuple(_a )
__magic_name__ : Optional[int] = new_word
if len(_a ) == 1:
break
else:
__magic_name__ : List[Any] = get_pairs(_a )
__magic_name__ : Optional[int] = "@@ ".join(_a )
__magic_name__ : Tuple = word[:-4]
__magic_name__ : str = word
return word
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Optional[Any] = []
__magic_name__ : Dict = re.findall(r"\S+\n?" , _a )
for token in words:
split_tokens.extend(list(self.bpe(_a ).split(" " ) ) )
return split_tokens
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.encoder.get(_a , self.encoder.get(self.unk_token ) )
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.decoder.get(_a , self.unk_token )
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Tuple = " ".join(_a ).replace("@@ " , "" ).strip()
return out_string
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__magic_name__ : Optional[int] = os.path.join(
_a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
__magic_name__ : Union[str, Any] = os.path.join(
_a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
if os.path.abspath(self.merges_file ) != os.path.abspath(_a ):
copyfile(self.merges_file , _a )
return out_vocab_file, out_merge_file
def SCREAMING_SNAKE_CASE ( self , _a ):
if isinstance(_a , _a ):
try:
with open(_a , "r" , encoding="utf-8" ) as fd:
self.add_from_file(_a )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset''' )
return
__magic_name__ : List[Any] = f.readlines()
for lineTmp in lines:
__magic_name__ : Optional[Any] = lineTmp.strip()
__magic_name__ : Union[str, Any] = line.rfind(" " )
if idx == -1:
raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'" )
__magic_name__ : Optional[int] = line[:idx]
__magic_name__ : Dict = len(self.encoder )
| 281 | 0 |
"""simple docstring"""
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class A__ ( _lowerCamelCase):
# to overwrite at feature extractactor specific tests
A_ : int = None
A_ : int = None
@property
def __lowerCamelCase ( self ):
return self.feat_extract_tester.prepare_feat_extract_dict()
def __lowerCamelCase ( self ):
__lowerCAmelCase : str = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(_a , 'feature_size' ) )
self.assertTrue(hasattr(_a , 'sampling_rate' ) )
self.assertTrue(hasattr(_a , 'padding_value' ) )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Dict = self.feat_extract_tester.prepare_inputs_for_common()
__lowerCAmelCase : Dict = self.feature_extraction_class(**self.feat_extract_dict )
__lowerCAmelCase : List[Any] = feat_extract.model_input_names[0]
__lowerCAmelCase : Dict = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(_a ) == len(_a ) for x, y in zip(_a , processed_features[input_name] ) ) )
__lowerCAmelCase : int = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a )
__lowerCAmelCase : str = BatchFeature({input_name: speech_inputs} , tensor_type='np' )
__lowerCAmelCase : int = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowerCAmelCase : List[str] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def __lowerCamelCase ( self ):
__lowerCAmelCase : List[str] = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a )
__lowerCAmelCase : str = self.feature_extraction_class(**self.feat_extract_dict )
__lowerCAmelCase : Tuple = feat_extract.model_input_names[0]
__lowerCAmelCase : Optional[int] = BatchFeature({input_name: speech_inputs} , tensor_type='pt' )
__lowerCAmelCase : str = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowerCAmelCase : Optional[int] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def __lowerCamelCase ( self ):
__lowerCAmelCase : Tuple = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a )
__lowerCAmelCase : Dict = self.feature_extraction_class(**self.feat_extract_dict )
__lowerCAmelCase : Dict = feat_extract.model_input_names[0]
__lowerCAmelCase : Optional[int] = BatchFeature({input_name: speech_inputs} , tensor_type='tf' )
__lowerCAmelCase : Union[str, Any] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowerCAmelCase : Union[str, Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE=False ):
def _inputs_have_equal_length(_SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Tuple = len(input[0] )
for input_slice in input[1:]:
if len(_a ) != length:
return False
return True
def _inputs_are_equal(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if len(_a ) != len(_a ):
return False
for input_slice_a, input_slice_a in zip(_a , _a ):
if not np.allclose(np.asarray(_a ) , np.asarray(_a ) , atol=1E-3 ):
return False
return True
__lowerCAmelCase : Dict = self.feature_extraction_class(**self.feat_extract_dict )
__lowerCAmelCase : List[Any] = self.feat_extract_tester.prepare_inputs_for_common(numpify=_a )
__lowerCAmelCase : List[Any] = feat_extract.model_input_names[0]
__lowerCAmelCase : Union[str, Any] = BatchFeature({input_name: speech_inputs} )
__lowerCAmelCase : Tuple = self.feat_extract_tester.seq_length_diff
__lowerCAmelCase : List[Any] = self.feat_extract_tester.max_seq_length + pad_diff
__lowerCAmelCase : Any = self.feat_extract_tester.min_seq_length
__lowerCAmelCase : Dict = self.feat_extract_tester.batch_size
__lowerCAmelCase : Optional[Any] = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
__lowerCAmelCase : int = feat_extract.pad(_a , padding=_a )
__lowerCAmelCase : int = input_a[input_name]
__lowerCAmelCase : int = feat_extract.pad(_a , padding='longest' )
__lowerCAmelCase : int = input_a[input_name]
__lowerCAmelCase : List[Any] = feat_extract.pad(_a , padding='max_length' , max_length=len(speech_inputs[-1] ) )
__lowerCAmelCase : List[Any] = input_a[input_name]
__lowerCAmelCase : str = feat_extract.pad(_a , padding='longest' , return_tensors='np' )
__lowerCAmelCase : Any = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(_a ):
feat_extract.pad(_a , padding='max_length' )[input_name]
__lowerCAmelCase : List[Any] = feat_extract.pad(
_a , padding='max_length' , max_length=_a , return_tensors='np' )
__lowerCAmelCase : Optional[int] = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(_a ) )
self.assertTrue(_inputs_have_equal_length(_a ) )
self.assertTrue(_inputs_have_equal_length(_a ) )
self.assertTrue(_inputs_are_equal(_a , _a ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
__lowerCAmelCase : str = feat_extract.pad(_a , pad_to_multiple_of=10 )
__lowerCAmelCase : int = input_a[input_name]
__lowerCAmelCase : Union[str, Any] = feat_extract.pad(_a , padding='longest' , pad_to_multiple_of=10 )
__lowerCAmelCase : Dict = input_a[input_name]
__lowerCAmelCase : List[Any] = feat_extract.pad(
_a , padding='max_length' , pad_to_multiple_of=10 , max_length=_a )
__lowerCAmelCase : int = input_a[input_name]
__lowerCAmelCase : Optional[Any] = feat_extract.pad(
_a , padding='max_length' , pad_to_multiple_of=10 , max_length=_a , return_tensors='np' , )
__lowerCAmelCase : str = input_a[input_name]
self.assertTrue(all(len(_a ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(_a , _a ) )
__lowerCAmelCase : List[str] = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(_a ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
__lowerCAmelCase : Optional[int] = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1E-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1E-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1E-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1E-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1E-3 )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE=False ):
def _inputs_have_equal_length(_SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Optional[Any] = len(input[0] )
for input_slice in input[1:]:
if len(_a ) != length:
return False
return True
def _inputs_are_equal(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if len(_a ) != len(_a ):
return False
for input_slice_a, input_slice_a in zip(_a , _a ):
if not np.allclose(np.asarray(_a ) , np.asarray(_a ) , atol=1E-3 ):
return False
return True
__lowerCAmelCase : List[str] = self.feature_extraction_class(**self.feat_extract_dict )
__lowerCAmelCase : Any = self.feat_extract_tester.prepare_inputs_for_common(numpify=_a )
__lowerCAmelCase : Any = feat_extract.model_input_names[0]
__lowerCAmelCase : Union[str, Any] = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
__lowerCAmelCase : Tuple = feat_extract.pad(
_a , padding='max_length' , max_length=len(speech_inputs[0] ) , truncation=_a )
__lowerCAmelCase : Union[str, Any] = input_a[input_name]
__lowerCAmelCase : List[Any] = feat_extract.pad(_a , padding='max_length' , max_length=len(speech_inputs[0] ) )
__lowerCAmelCase : Union[str, Any] = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_a ) )
self.assertFalse(_inputs_have_equal_length(_a ) )
# truncate to smallest with np
__lowerCAmelCase : Optional[int] = feat_extract.pad(
_a , padding='max_length' , max_length=len(speech_inputs[0] ) , return_tensors='np' , truncation=_a , )
__lowerCAmelCase : int = input_a[input_name]
__lowerCAmelCase : Optional[int] = feat_extract.pad(
_a , padding='max_length' , max_length=len(speech_inputs[0] ) , return_tensors='np' )
__lowerCAmelCase : Optional[Any] = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_a ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_a ) )
# truncate to middle
__lowerCAmelCase : Optional[int] = feat_extract.pad(
_a , padding='max_length' , max_length=len(speech_inputs[1] ) , truncation=_a , return_tensors='np' , )
__lowerCAmelCase : List[str] = input_a[input_name]
__lowerCAmelCase : Tuple = feat_extract.pad(
_a , padding='max_length' , max_length=len(speech_inputs[1] ) , truncation=_a )
__lowerCAmelCase : List[Any] = input_a[input_name]
__lowerCAmelCase : int = feat_extract.pad(
_a , padding='max_length' , max_length=len(speech_inputs[1] ) , return_tensors='np' )
__lowerCAmelCase : Union[str, Any] = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(_a ) )
self.assertTrue(_inputs_have_equal_length(_a ) )
self.assertTrue(_inputs_are_equal(_a , _a ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_a ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_a ):
feat_extract.pad(_a , truncation=_a )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_a ):
feat_extract.pad(_a , padding='longest' , truncation=_a )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_a ):
feat_extract.pad(_a , padding='longest' , truncation=_a )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(_a ):
feat_extract.pad(_a , padding='max_length' , truncation=_a )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
__lowerCAmelCase : List[str] = 12
__lowerCAmelCase : Tuple = feat_extract.pad(
_a , padding='max_length' , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_a , truncation=_a , )
__lowerCAmelCase : Optional[int] = input_a[input_name]
__lowerCAmelCase : Tuple = feat_extract.pad(
_a , padding='max_length' , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_a , )
__lowerCAmelCase : str = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
__lowerCAmelCase : List[Any] = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
__lowerCAmelCase : Tuple = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(_a ) )
self.assertFalse(_inputs_have_equal_length(_a ) )
def __lowerCamelCase ( self ):
self._check_padding(numpify=_a )
def __lowerCamelCase ( self ):
self._check_padding(numpify=_a )
def __lowerCamelCase ( self ):
self._check_truncation(numpify=_a )
def __lowerCamelCase ( self ):
self._check_truncation(numpify=_a )
@require_torch
def __lowerCamelCase ( self ):
__lowerCAmelCase : int = self.feature_extraction_class(**self.feat_extract_dict )
__lowerCAmelCase : str = self.feat_extract_tester.prepare_inputs_for_common()
__lowerCAmelCase : Optional[Any] = feat_extract.model_input_names[0]
__lowerCAmelCase : List[Any] = BatchFeature({input_name: speech_inputs} )
__lowerCAmelCase : List[str] = feat_extract.pad(_a , padding='longest' , return_tensors='np' )[input_name]
__lowerCAmelCase : Dict = feat_extract.pad(_a , padding='longest' , return_tensors='pt' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )
@require_tf
def __lowerCamelCase ( self ):
__lowerCAmelCase : int = self.feature_extraction_class(**self.feat_extract_dict )
__lowerCAmelCase : Optional[int] = self.feat_extract_tester.prepare_inputs_for_common()
__lowerCAmelCase : Any = feat_extract.model_input_names[0]
__lowerCAmelCase : Union[str, Any] = BatchFeature({input_name: speech_inputs} )
__lowerCAmelCase : Tuple = feat_extract.pad(_a , padding='longest' , return_tensors='np' )[input_name]
__lowerCAmelCase : List[str] = feat_extract.pad(_a , padding='longest' , return_tensors='tf' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1E-2 )
def __lowerCamelCase ( self ):
__lowerCAmelCase : int = self.feat_extract_dict
__lowerCAmelCase : int = True
__lowerCAmelCase : Union[str, Any] = self.feature_extraction_class(**_a )
__lowerCAmelCase : List[Any] = self.feat_extract_tester.prepare_inputs_for_common()
__lowerCAmelCase : List[str] = [len(_a ) for x in speech_inputs]
__lowerCAmelCase : Dict = feat_extract.model_input_names[0]
__lowerCAmelCase : Union[str, Any] = BatchFeature({input_name: speech_inputs} )
__lowerCAmelCase : Optional[Any] = feat_extract.pad(_a , padding='longest' , return_tensors='np' )
self.assertIn('attention_mask' , _a )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , _a )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Optional[int] = self.feat_extract_dict
__lowerCAmelCase : Dict = True
__lowerCAmelCase : Any = self.feature_extraction_class(**_a )
__lowerCAmelCase : str = self.feat_extract_tester.prepare_inputs_for_common()
__lowerCAmelCase : Union[str, Any] = [len(_a ) for x in speech_inputs]
__lowerCAmelCase : Dict = feat_extract.model_input_names[0]
__lowerCAmelCase : List[Any] = BatchFeature({input_name: speech_inputs} )
__lowerCAmelCase : Dict = min(_a )
__lowerCAmelCase : Dict = feat_extract.pad(
_a , padding='max_length' , max_length=_a , truncation=_a , return_tensors='np' )
self.assertIn('attention_mask' , _a )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] ) | 86 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase_ ( _snake_case : str = "laptop" ) -> DataFrame:
'''simple docstring'''
__magic_name__ : Tuple = F'''https://www.amazon.in/laptop/s?k={product}'''
__magic_name__ : Dict = {
"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
"Accept-Language": "en-US, en;q=0.5",
}
__magic_name__ : Tuple = BeautifulSoup(requests.get(_snake_case , headers=_snake_case ).text )
# Initialize a Pandas dataframe with the column titles
__magic_name__ : int = DataFrame(
columns=[
"Product Title",
"Product Link",
"Current Price of the product",
"Product Rating",
"MRP of the product",
"Discount",
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
"div" , attrs={"class": "s-result-item", "data-component-type": "s-search-result"} , ) , soup.find_all("div" , attrs={"class": "a-row a-size-base a-color-base"} ) , ):
try:
__magic_name__ : Dict = item.ha.text
__magic_name__ : Optional[int] = "https://www.amazon.in/" + item.ha.a["href"]
__magic_name__ : Optional[Any] = item.find("span" , attrs={"class": "a-offscreen"} ).text
try:
__magic_name__ : Union[str, Any] = item.find("span" , attrs={"class": "a-icon-alt"} ).text
except AttributeError:
__magic_name__ : Dict = "Not available"
try:
__magic_name__ : Optional[int] = (
"₹"
+ item.find(
"span" , attrs={"class": "a-price a-text-price"} ).text.split("₹" )[1]
)
except AttributeError:
__magic_name__ : List[str] = ""
try:
__magic_name__ : int = float(
(
(
float(product_mrp.strip("₹" ).replace("," , "" ) )
- float(product_price.strip("₹" ).replace("," , "" ) )
)
/ float(product_mrp.strip("₹" ).replace("," , "" ) )
)
* 100 )
except ValueError:
__magic_name__ : str = float("nan" )
except AttributeError:
pass
__magic_name__ : Optional[int] = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
__magic_name__ : Optional[Any] = " "
__magic_name__ : str = " "
data_frame.index += 1
return data_frame
if __name__ == "__main__":
snake_case : Any = "headphones"
get_amazon_product_data(product).to_csv(F"Amazon Product Data for {product}.csv")
| 281 | 0 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __snake_case :
lowerCAmelCase_ = 42
lowerCAmelCase_ = None
lowerCAmelCase_ = None
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : TreeNode | None ) -> bool:
"""simple docstring"""
def is_valid_tree(__UpperCamelCase : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(_snake_case , _snake_case ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(_snake_case ):
raise ValueError(
"""Each node should be type of TreeNode and data should be float.""" )
def is_binary_search_tree_recursive_check(
__UpperCamelCase : TreeNode | None , __UpperCamelCase : float , __UpperCamelCase : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , _snake_case , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , _snake_case )
)
return is_binary_search_tree_recursive_check(_snake_case , -float("""inf""" ) , float("""inf""" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 219 |
from __future__ import annotations
class _snake_case :
def __init__( self , _a ):
__magic_name__ : Optional[Any] = data
__magic_name__ : Node | None = None
__magic_name__ : Node | None = None
def lowerCAmelCase_ ( _snake_case : Node | None ) -> None: # In Order traversal of the tree
'''simple docstring'''
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def lowerCAmelCase_ ( _snake_case : Node | None ) -> int:
'''simple docstring'''
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def lowerCAmelCase_ ( _snake_case : Node ) -> bool:
'''simple docstring'''
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def lowerCAmelCase_ ( ) -> None: # Main function for testing.
'''simple docstring'''
__magic_name__ : int = Node(1 )
__magic_name__ : Union[str, Any] = Node(2 )
__magic_name__ : Tuple = Node(3 )
__magic_name__ : Optional[Any] = Node(4 )
__magic_name__ : Union[str, Any] = Node(5 )
__magic_name__ : Any = Node(6 )
__magic_name__ : int = Node(7 )
__magic_name__ : List[str] = Node(8 )
__magic_name__ : Union[str, Any] = Node(9 )
print(is_full_binary_tree(_snake_case ) )
print(depth_of_tree(_snake_case ) )
print("Tree is: " )
display(_snake_case )
if __name__ == "__main__":
main()
| 281 | 0 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__snake_case = logging.getLogger(__name__)
class lowercase ( A__ ):
"""simple docstring"""
def __init__( self , UpperCamelCase_=-1 ):
'''simple docstring'''
UpperCamelCase__ :Dict = label_idx
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
if isinstance(_a , _a ):
UpperCamelCase__ :List[str] = mode.value
UpperCamelCase__ :str = os.path.join(_a , F'''{mode}.txt''' )
UpperCamelCase__ :List[Any] = 1
UpperCamelCase__ :Optional[Any] = []
with open(_a , encoding='''utf-8''' ) as f:
UpperCamelCase__ :List[str] = []
UpperCamelCase__ :Optional[Any] = []
for line in f:
if line.startswith('''-DOCSTART-''' ) or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=F'''{mode}-{guid_index}''' , words=_a , labels=_a ) )
guid_index += 1
UpperCamelCase__ :List[Any] = []
UpperCamelCase__ :Dict = []
else:
UpperCamelCase__ :Optional[Any] = line.split(''' ''' )
words.append(splits[0] )
if len(_a ) > 1:
labels.append(splits[self.label_idx].replace('''\n''' , '''''' ) )
else:
# Examples could have no label for mode = "test"
labels.append('''O''' )
if words:
examples.append(InputExample(guid=F'''{mode}-{guid_index}''' , words=_a , labels=_a ) )
return examples
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :Tuple = 0
for line in test_input_reader:
if line.startswith('''-DOCSTART-''' ) or line == "" or line == "\n":
writer.write(_a )
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
UpperCamelCase__ :List[Any] = line.split()[0] + " " + preds_list[example_id].pop(0 ) + "\n"
writer.write(_a )
else:
logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.''' , line.split()[0] )
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
if path:
with open(_a , '''r''' ) as f:
UpperCamelCase__ :Tuple = f.read().splitlines()
if "O" not in labels:
UpperCamelCase__ :Union[str, Any] = ["O"] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class lowercase ( A__ ):
"""simple docstring"""
def __init__( self ):
'''simple docstring'''
super().__init__(label_idx=-2 )
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
if path:
with open(_a , '''r''' ) as f:
UpperCamelCase__ :Tuple = f.read().splitlines()
if "O" not in labels:
UpperCamelCase__ :Union[str, Any] = ["O"] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class lowercase ( A__ ):
"""simple docstring"""
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
if isinstance(_a , _a ):
UpperCamelCase__ :List[Any] = mode.value
UpperCamelCase__ :Union[str, Any] = os.path.join(_a , F'''{mode}.txt''' )
UpperCamelCase__ :Optional[int] = 1
UpperCamelCase__ :List[str] = []
with open(_a , encoding='''utf-8''' ) as f:
for sentence in parse_incr(_a ):
UpperCamelCase__ :Union[str, Any] = []
UpperCamelCase__ :Union[str, Any] = []
for token in sentence:
words.append(token['''form'''] )
labels.append(token['''upos'''] )
assert len(_a ) == len(_a )
if words:
examples.append(InputExample(guid=F'''{mode}-{guid_index}''' , words=_a , labels=_a ) )
guid_index += 1
return examples
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :Any = 0
for sentence in parse_incr(_a ):
UpperCamelCase__ :List[Any] = preds_list[example_id]
UpperCamelCase__ :Optional[Any] = ""
for token in sentence:
out += F'''{token["form"]} ({token["upos"]}|{s_p.pop(0 )}) '''
out += "\n"
writer.write(_a )
example_id += 1
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
if path:
with open(_a , '''r''' ) as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
] | 97 |
def lowerCAmelCase_ ( _snake_case : str , _snake_case : str ) -> bool:
'''simple docstring'''
__magic_name__ : Union[str, Any] = len(_snake_case ) + 1
__magic_name__ : List[str] = len(_snake_case ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
__magic_name__ : str = [[0 for i in range(_snake_case )] for j in range(_snake_case )]
# since string of zero length match pattern of zero length
__magic_name__ : Optional[int] = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _snake_case ):
__magic_name__ : Optional[int] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _snake_case ):
__magic_name__ : Union[str, Any] = dp[0][j - 2] if pattern[j - 1] == "*" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _snake_case ):
for j in range(1 , _snake_case ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
__magic_name__ : Optional[int] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
__magic_name__ : Optional[Any] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
__magic_name__ : List[Any] = dp[i - 1][j]
else:
__magic_name__ : Union[str, Any] = 0
else:
__magic_name__ : Dict = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
snake_case : Optional[Any] = "aab"
snake_case : List[str] = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F"{input_string} matches the given pattern {pattern}")
else:
print(F"{input_string} does not match with the given pattern {pattern}")
| 281 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A: Tuple = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A: Tuple = [
"IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"IBertForMaskedLM",
"IBertForMultipleChoice",
"IBertForQuestionAnswering",
"IBertForSequenceClassification",
"IBertForTokenClassification",
"IBertModel",
"IBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
A: List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 109 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class _snake_case :
@staticmethod
def SCREAMING_SNAKE_CASE ( *_a , **_a ):
pass
def lowerCAmelCase_ ( _snake_case : Image ) -> str:
'''simple docstring'''
__magic_name__ : Optional[int] = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def lowerCAmelCase_ ( _snake_case : Image ) -> Dict:
'''simple docstring'''
__magic_name__ : List[Any] = np.array(_snake_case )
__magic_name__ : Optional[int] = npimg.shape
return {"hash": hashimage(_snake_case ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class _snake_case ( unittest.TestCase ):
UpperCamelCase__ = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
UpperCamelCase__ = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a ):
__magic_name__ : Dict = MaskGenerationPipeline(model=_a , image_processor=_a )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def SCREAMING_SNAKE_CASE ( self , _a , _a ):
pass
@require_tf
@unittest.skip("Image segmentation not implemented in TF" )
def SCREAMING_SNAKE_CASE ( self ):
pass
@slow
@require_torch
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = pipeline("mask-generation" , model="facebook/sam-vit-huge" )
__magic_name__ : str = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg" , points_per_batch=256 )
# Shortening by hashing
__magic_name__ : Dict = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(_a ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
{"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
{"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
{"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
{"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
{"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
{"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
{"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
{"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
{"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
{"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
{"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
{"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
{"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
{"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
{"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
{"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
{"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
{"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
{"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
{"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
{"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
{"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
{"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
{"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
{"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
{"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
{"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
{"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
{"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
] , )
# fmt: on
@require_torch
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : str = "facebook/sam-vit-huge"
__magic_name__ : str = pipeline("mask-generation" , model=_a )
__magic_name__ : Tuple = image_segmenter(
"http://images.cocodataset.org/val2017/000000039769.jpg" , pred_iou_thresh=1 , points_per_batch=256 )
# Shortening by hashing
__magic_name__ : Any = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(_a ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
{"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
{"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
{"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
{"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
] , )
| 281 | 0 |
class A :
def __init__( self ):
"""simple docstring"""
lowerCAmelCase_ = {} # Mapping from char to TrieNode
lowerCAmelCase_ = False
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
for word in words:
self.insert(_a )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = self
for char in word:
if char not in curr.nodes:
lowerCAmelCase_ = TrieNode()
lowerCAmelCase_ = curr.nodes[char]
lowerCAmelCase_ = True
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = self
for char in word:
if char not in curr.nodes:
return False
lowerCAmelCase_ = curr.nodes[char]
return curr.is_leaf
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
def _delete(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ) -> bool:
if index == len(_a ):
# If word does not exist
if not curr.is_leaf:
return False
lowerCAmelCase_ = False
return len(curr.nodes ) == 0
lowerCAmelCase_ = word[index]
lowerCAmelCase_ = curr.nodes.get(_a )
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
lowerCAmelCase_ = _delete(_a, _a, index + 1 )
if delete_curr:
del curr.nodes[char]
return len(curr.nodes ) == 0
return delete_curr
_delete(self, _a, 0 )
def __UpperCamelCase ( _A , _A ):
if node.is_leaf:
print(_snake_case , end=''' ''' )
for key, value in node.nodes.items():
print_words(_snake_case , word + key )
def __UpperCamelCase ( ):
lowerCAmelCase_ = "banana bananas bandana band apple all beast".split()
lowerCAmelCase_ = TrieNode()
root.insert_many(_snake_case )
# print_words(root, "")
assert all(root.find(_snake_case ) for word in words )
assert root.find('''banana''' )
assert not root.find('''bandanas''' )
assert not root.find('''apps''' )
assert root.find('''apple''' )
assert root.find('''all''' )
root.delete('''all''' )
assert not root.find('''all''' )
root.delete('''banana''' )
assert not root.find('''banana''' )
assert root.find('''bananas''' )
return True
def __UpperCamelCase ( _A , _A ):
print(str(_snake_case ) , '''works!''' if passes else '''doesn\'t work :(''' )
def __UpperCamelCase ( ):
assert test_trie()
def __UpperCamelCase ( ):
print_results('''Testing trie functionality''' , test_trie() )
if __name__ == "__main__":
main()
| 278 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
snake_case : List[Any] = "\\n@inproceedings{lin-2004-rouge,\n title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n author = \"Lin, Chin-Yew\",\n booktitle = \"Text Summarization Branches Out\",\n month = jul,\n year = \"2004\",\n address = \"Barcelona, Spain\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W04-1013\",\n pages = \"74--81\",\n}\n"
snake_case : Any = "\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"
snake_case : str = "\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n rouge_types: A list of rouge types to calculate.\n Valid names:\n `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n `\"rougeL\"`: Longest common subsequence based scoring.\n `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n See details in https://github.com/huggingface/datasets/issues/617\n use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n use_aggregator: Return aggregates if this is set to True\nReturns:\n rouge1: rouge_1 (precision, recall, f1),\n rouge2: rouge_2 (precision, recall, f1),\n rougeL: rouge_l (precision, recall, f1),\n rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n >>> rouge = datasets.load_metric('rouge')\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> results = rouge.compute(predictions=predictions, references=references)\n >>> print(list(results.keys()))\n ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n >>> print(results[\"rouge1\"])\n AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n >>> print(results[\"rouge1\"].mid.fmeasure)\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _snake_case ( datasets.Metric ):
def SCREAMING_SNAKE_CASE ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"] , reference_urls=[
"https://en.wikipedia.org/wiki/ROUGE_(metric)",
"https://github.com/google-research/google-research/tree/master/rouge",
] , )
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a=None , _a=True , _a=False ):
if rouge_types is None:
__magic_name__ : str = ["rouge1", "rouge2", "rougeL", "rougeLsum"]
__magic_name__ : List[str] = rouge_scorer.RougeScorer(rouge_types=_a , use_stemmer=_a )
if use_aggregator:
__magic_name__ : Dict = scoring.BootstrapAggregator()
else:
__magic_name__ : str = []
for ref, pred in zip(_a , _a ):
__magic_name__ : Union[str, Any] = scorer.score(_a , _a )
if use_aggregator:
aggregator.add_scores(_a )
else:
scores.append(_a )
if use_aggregator:
__magic_name__ : Any = aggregator.aggregate()
else:
__magic_name__ : List[Any] = {}
for key in scores[0]:
__magic_name__ : str = [score[key] for score in scores]
return result
| 281 | 0 |
"""simple docstring"""
import json
import sys
def lowercase (SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Optional[int]:
with open(_snake_case , encoding='utf-8' ) as f:
SCREAMING_SNAKE_CASE = json.load(_snake_case )
SCREAMING_SNAKE_CASE = ["<details>", "<summary>Show updated benchmarks!</summary>", " "]
for benchmark_name in sorted(_snake_case ):
SCREAMING_SNAKE_CASE = results[benchmark_name]
SCREAMING_SNAKE_CASE = benchmark_name.split('/' )[-1]
output_md.append(F'### Benchmark: {benchmark_file_name}' )
SCREAMING_SNAKE_CASE = "| metric |"
SCREAMING_SNAKE_CASE = "|--------|"
SCREAMING_SNAKE_CASE = "| new / old (diff) |"
for metric_name in sorted(_snake_case ):
SCREAMING_SNAKE_CASE = benchmark_res[metric_name]
SCREAMING_SNAKE_CASE = metric_vals["new"]
SCREAMING_SNAKE_CASE = metric_vals.get('old' , _snake_case )
SCREAMING_SNAKE_CASE = metric_vals.get('diff' , _snake_case )
SCREAMING_SNAKE_CASE = F' {new_val:f}' if isinstance(_snake_case , (int, float) ) else "None"
if old_val is not None:
val_str += F' / {old_val:f}' if isinstance(_snake_case , (int, float) ) else "None"
if dif_val is not None:
val_str += F' ({dif_val:f})' if isinstance(_snake_case , (int, float) ) else "None"
title += " " + metric_name + " |"
lines += "---|"
value += val_str + " |"
output_md += [title, lines, value, " "]
output_md.append('</details>' )
with open(_snake_case , 'w' , encoding='utf-8' ) as f:
f.writelines('\n'.join(_snake_case ) )
if __name__ == "__main__":
__UpperCamelCase = sys.argv[1]
__UpperCamelCase = sys.argv[2]
format_json_to_md(input_json_file, output_md_file)
| 113 |
snake_case : Optional[int] = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowerCAmelCase_ ( _snake_case : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(_snake_case , _snake_case ):
__magic_name__ : Tuple = F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
raise TypeError(_snake_case )
__magic_name__ : Optional[int] = "".join(bin(_snake_case )[2:].zfill(8 ) for byte in data )
__magic_name__ : List[Any] = len(_snake_case ) % 6 != 0
if padding_needed:
# The padding that will be added later
__magic_name__ : List[str] = B"=" * ((6 - len(_snake_case ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(_snake_case ) % 6)
else:
__magic_name__ : List[str] = B""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(_snake_case ) , 6 ) ).encode()
+ padding
)
def lowerCAmelCase_ ( _snake_case : str ) -> bytes:
'''simple docstring'''
if not isinstance(_snake_case , _snake_case ) and not isinstance(_snake_case , _snake_case ):
__magic_name__ : List[str] = (
"argument should be a bytes-like object or ASCII string, "
F'''not \'{encoded_data.__class__.__name__}\''''
)
raise TypeError(_snake_case )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(_snake_case , _snake_case ):
try:
__magic_name__ : List[Any] = encoded_data.decode("utf-8" )
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters" )
__magic_name__ : List[str] = encoded_data.count("=" )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(_snake_case ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
__magic_name__ : Optional[int] = encoded_data[:-padding]
__magic_name__ : Dict = "".join(
bin(B64_CHARSET.index(_snake_case ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
__magic_name__ : Union[str, Any] = "".join(
bin(B64_CHARSET.index(_snake_case ) )[2:].zfill(6 ) for char in encoded_data )
__magic_name__ : List[Any] = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(_snake_case ) , 8 )
]
return bytes(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 281 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ , lowercase__ ):
while a != 0:
_lowerCamelCase : List[str] = b % a, a
return b
def _snake_case ( lowercase__ , lowercase__ ):
if gcd(_snake_case , _snake_case ) != 1:
_lowerCamelCase : Union[str, Any] = f'''mod inverse of {a!r} and {m!r} does not exist'''
raise ValueError(_snake_case )
_lowerCamelCase : List[Any] = 1, 0, a
_lowerCamelCase : List[Any] = 0, 1, m
while va != 0:
_lowerCamelCase : List[Any] = ua // va
_lowerCamelCase : Optional[int] = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m | 96 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class _snake_case ( unittest.TestCase ):
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ):
__magic_name__ : List[Any] = parent
__magic_name__ : Optional[Any] = batch_size
__magic_name__ : Dict = seq_length
__magic_name__ : Union[str, Any] = is_training
__magic_name__ : Optional[Any] = use_attention_mask
__magic_name__ : Optional[Any] = use_token_type_ids
__magic_name__ : int = use_labels
__magic_name__ : List[Any] = vocab_size
__magic_name__ : Union[str, Any] = hidden_size
__magic_name__ : Optional[Any] = num_hidden_layers
__magic_name__ : int = num_attention_heads
__magic_name__ : Any = intermediate_size
__magic_name__ : List[Any] = hidden_act
__magic_name__ : List[Any] = hidden_dropout_prob
__magic_name__ : Optional[int] = attention_probs_dropout_prob
__magic_name__ : List[Any] = max_position_embeddings
__magic_name__ : Tuple = type_vocab_size
__magic_name__ : List[str] = type_sequence_label_size
__magic_name__ : Dict = initializer_range
__magic_name__ : List[Any] = num_choices
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__magic_name__ : List[Any] = None
if self.use_attention_mask:
__magic_name__ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
__magic_name__ : str = None
if self.use_token_type_ids:
__magic_name__ : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__magic_name__ : List[str] = RobertaPreLayerNormConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : int = self.prepare_config_and_inputs()
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : List[Any] = config_and_inputs
__magic_name__ : List[str] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = self.prepare_config_and_inputs()
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] = config_and_inputs
__magic_name__ : Tuple = True
__magic_name__ : int = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__magic_name__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class _snake_case ( snake_case , unittest.TestCase ):
UpperCamelCase__ = True
UpperCamelCase__ = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[Any] = FlaxRobertaPreLayerNormModelTester(self )
@slow
def SCREAMING_SNAKE_CASE ( self ):
for model_class_name in self.all_model_classes:
__magic_name__ : Optional[Any] = model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_a )
__magic_name__ : Dict = model(np.ones((1, 1) ) )
self.assertIsNotNone(_a )
@require_flax
class _snake_case ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_a )
__magic_name__ : Union[str, Any] = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__magic_name__ : List[str] = model(_a )[0]
__magic_name__ : str = [1, 11, 50_265]
self.assertEqual(list(output.shape ) , _a )
# compare the actual values for a slice.
__magic_name__ : List[str] = np.array(
[[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _a , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_a )
__magic_name__ : Tuple = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__magic_name__ : Tuple = model(_a )[0]
# compare the actual values for a slice.
__magic_name__ : Dict = np.array(
[[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _a , atol=1e-4 ) )
| 281 | 0 |
"""simple docstring"""
import math
def lowerCAmelCase__ ( ) -> None:
"""simple docstring"""
snake_case = input('Enter message: ' )
snake_case = int(input(f"""Enter key [2-{len(_snake_case ) - 1}]: """ ) )
snake_case = input('Encryption/Decryption [e/d]: ' )
if mode.lower().startswith('e' ):
snake_case = encrypt_message(_snake_case , _snake_case )
elif mode.lower().startswith('d' ):
snake_case = decrypt_message(_snake_case , _snake_case )
# Append pipe symbol (vertical bar) to identify spaces at the end.
print(f"""Output:\n{text + "|"}""" )
def lowerCAmelCase__ ( _UpperCamelCase : int , _UpperCamelCase : str ) -> str:
"""simple docstring"""
snake_case = [""] * key
for col in range(_snake_case ):
snake_case = col
while pointer < len(_snake_case ):
cipher_text[col] += message[pointer]
pointer += key
return "".join(_snake_case )
def lowerCAmelCase__ ( _UpperCamelCase : int , _UpperCamelCase : str ) -> str:
"""simple docstring"""
snake_case = math.ceil(len(_snake_case ) / key )
snake_case = key
snake_case = (num_cols * num_rows) - len(_snake_case )
snake_case = [""] * num_cols
snake_case = 0
snake_case = 0
for symbol in message:
plain_text[col] += symbol
col += 1
if (
(col == num_cols)
or (col == num_cols - 1)
and (row >= num_rows - num_shaded_boxes)
):
snake_case = 0
row += 1
return "".join(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 150 |
def lowerCAmelCase_ ( _snake_case : list[list[int | float]] ) -> int:
'''simple docstring'''
__magic_name__ : Any = len(_snake_case )
__magic_name__ : Optional[Any] = len(matrix[0] )
__magic_name__ : Union[str, Any] = min(_snake_case , _snake_case )
for row in range(_snake_case ):
# Check if diagonal element is not zero
if matrix[row][row] != 0:
# Eliminate all the elements below the diagonal
for col in range(row + 1 , _snake_case ):
__magic_name__ : Optional[Any] = matrix[col][row] / matrix[row][row]
for i in range(_snake_case , _snake_case ):
matrix[col][i] -= multiplier * matrix[row][i]
else:
# Find a non-zero diagonal element to swap rows
__magic_name__ : str = True
for i in range(row + 1 , _snake_case ):
if matrix[i][row] != 0:
__magic_name__ , __magic_name__ : List[str] = matrix[i], matrix[row]
__magic_name__ : Union[str, Any] = False
break
if reduce:
rank -= 1
for i in range(_snake_case ):
__magic_name__ : Any = matrix[i][rank]
# Reduce the row pointer by one to stay on the same row
row -= 1
return rank
if __name__ == "__main__":
import doctest
doctest.testmod()
| 281 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
| 115 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
snake_case : Dict = re.compile(R"\b(a|an|the)\b", re.UNICODE)
snake_case : Optional[int] = None
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : Any = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0." )
parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file." )
parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions." )
parser.add_argument(
"--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout)." )
parser.add_argument(
"--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer." )
parser.add_argument(
"--na-prob-thresh" , "-t" , type=_snake_case , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , )
parser.add_argument(
"--out-image-dir" , "-p" , metavar="out_images" , default=_snake_case , help="Save precision-recall curves to directory." )
parser.add_argument("--verbose" , "-v" , action="store_true" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def lowerCAmelCase_ ( _snake_case : Optional[Any] ) -> Tuple:
'''simple docstring'''
__magic_name__ : Optional[int] = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
__magic_name__ : str = bool(qa["answers"]["text"] )
return qid_to_has_ans
def lowerCAmelCase_ ( _snake_case : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
def remove_articles(_snake_case : List[str] ):
return ARTICLES_REGEX.sub(" " , _snake_case )
def white_space_fix(_snake_case : Optional[int] ):
return " ".join(text.split() )
def remove_punc(_snake_case : Optional[int] ):
__magic_name__ : Dict = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_snake_case : str ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_snake_case ) ) ) )
def lowerCAmelCase_ ( _snake_case : Any ) -> Optional[Any]:
'''simple docstring'''
if not s:
return []
return normalize_answer(_snake_case ).split()
def lowerCAmelCase_ ( _snake_case : str , _snake_case : Dict ) -> Tuple:
'''simple docstring'''
return int(normalize_answer(_snake_case ) == normalize_answer(_snake_case ) )
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : int ) -> str:
'''simple docstring'''
__magic_name__ : Any = get_tokens(_snake_case )
__magic_name__ : Optional[int] = get_tokens(_snake_case )
__magic_name__ : Tuple = collections.Counter(_snake_case ) & collections.Counter(_snake_case )
__magic_name__ : Tuple = sum(common.values() )
if len(_snake_case ) == 0 or len(_snake_case ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
__magic_name__ : Dict = 1.0 * num_same / len(_snake_case )
__magic_name__ : Optional[Any] = 1.0 * num_same / len(_snake_case )
__magic_name__ : List[Any] = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( _snake_case : Optional[Any] , _snake_case : List[Any] ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = {}
__magic_name__ : int = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
__magic_name__ : Union[str, Any] = qa["id"]
__magic_name__ : Any = [t for t in qa["answers"]["text"] if normalize_answer(_snake_case )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
__magic_name__ : Tuple = [""]
if qid not in preds:
print(F'''Missing prediction for {qid}''' )
continue
__magic_name__ : Any = preds[qid]
# Take max over all gold answers
__magic_name__ : List[Any] = max(compute_exact(_snake_case , _snake_case ) for a in gold_answers )
__magic_name__ : int = max(compute_fa(_snake_case , _snake_case ) for a in gold_answers )
return exact_scores, fa_scores
def lowerCAmelCase_ ( _snake_case : Optional[Any] , _snake_case : List[Any] , _snake_case : Optional[int] , _snake_case : Dict ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : str = {}
for qid, s in scores.items():
__magic_name__ : Dict = na_probs[qid] > na_prob_thresh
if pred_na:
__magic_name__ : str = float(not qid_to_has_ans[qid] )
else:
__magic_name__ : Optional[int] = s
return new_scores
def lowerCAmelCase_ ( _snake_case : List[Any] , _snake_case : List[str] , _snake_case : Tuple=None ) -> Tuple:
'''simple docstring'''
if not qid_list:
__magic_name__ : Any = len(_snake_case )
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores.values() ) / total),
("f1", 100.0 * sum(fa_scores.values() ) / total),
("total", total),
] )
else:
__magic_name__ : Tuple = len(_snake_case )
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
("f1", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
("total", total),
] )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : str , _snake_case : str ) -> Dict:
'''simple docstring'''
for k in new_eval:
__magic_name__ : int = new_eval[k]
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Dict , _snake_case : Optional[Any] , _snake_case : Union[str, Any] ) -> str:
'''simple docstring'''
plt.step(_snake_case , _snake_case , color="b" , alpha=0.2 , where="post" )
plt.fill_between(_snake_case , _snake_case , step="post" , alpha=0.2 , color="b" )
plt.xlabel("Recall" )
plt.ylabel("Precision" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(_snake_case )
plt.savefig(_snake_case )
plt.clf()
def lowerCAmelCase_ ( _snake_case : Dict , _snake_case : Any , _snake_case : Optional[int] , _snake_case : List[Any] , _snake_case : Optional[int]=None , _snake_case : int=None ) -> str:
'''simple docstring'''
__magic_name__ : Union[str, Any] = sorted(_snake_case , key=lambda _snake_case : na_probs[k] )
__magic_name__ : Optional[int] = 0.0
__magic_name__ : str = 1.0
__magic_name__ : str = 0.0
__magic_name__ : List[str] = [1.0]
__magic_name__ : str = [0.0]
__magic_name__ : Optional[Any] = 0.0
for i, qid in enumerate(_snake_case ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
__magic_name__ : List[str] = true_pos / float(i + 1 )
__magic_name__ : Any = true_pos / float(_snake_case )
if i == len(_snake_case ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(_snake_case )
recalls.append(_snake_case )
if out_image:
plot_pr_curve(_snake_case , _snake_case , _snake_case , _snake_case )
return {"ap": 100.0 * avg_prec}
def lowerCAmelCase_ ( _snake_case : Tuple , _snake_case : Optional[Any] , _snake_case : Optional[int] , _snake_case : Optional[Any] , _snake_case : Any , _snake_case : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
if out_image_dir and not os.path.exists(_snake_case ):
os.makedirs(_snake_case )
__magic_name__ : Any = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
__magic_name__ : str = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_exact.png" ) , title="Precision-Recall curve for Exact Match score" , )
__magic_name__ : Union[str, Any] = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_f1.png" ) , title="Precision-Recall curve for F1 score" , )
__magic_name__ : str = {k: float(_snake_case ) for k, v in qid_to_has_ans.items()}
__magic_name__ : str = make_precision_recall_eval(
_snake_case , _snake_case , _snake_case , _snake_case , out_image=os.path.join(_snake_case , "pr_oracle.png" ) , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , )
merge_eval(_snake_case , _snake_case , "pr_exact" )
merge_eval(_snake_case , _snake_case , "pr_f1" )
merge_eval(_snake_case , _snake_case , "pr_oracle" )
def lowerCAmelCase_ ( _snake_case : int , _snake_case : Optional[Any] , _snake_case : List[str] , _snake_case : Optional[Any] ) -> Dict:
'''simple docstring'''
if not qid_list:
return
__magic_name__ : Dict = [na_probs[k] for k in qid_list]
__magic_name__ : str = np.ones_like(_snake_case ) / float(len(_snake_case ) )
plt.hist(_snake_case , weights=_snake_case , bins=20 , range=(0.0, 1.0) )
plt.xlabel("Model probability of no-answer" )
plt.ylabel("Proportion of dataset" )
plt.title(F'''Histogram of no-answer probability: {name}''' )
plt.savefig(os.path.join(_snake_case , F'''na_prob_hist_{name}.png''' ) )
plt.clf()
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Tuple , _snake_case : List[str] , _snake_case : Dict ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
__magic_name__ : List[str] = num_no_ans
__magic_name__ : Dict = cur_score
__magic_name__ : Dict = 0.0
__magic_name__ : Any = sorted(_snake_case , key=lambda _snake_case : na_probs[k] )
for i, qid in enumerate(_snake_case ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
__magic_name__ : Union[str, Any] = scores[qid]
else:
if preds[qid]:
__magic_name__ : List[Any] = -1
else:
__magic_name__ : Optional[int] = 0
cur_score += diff
if cur_score > best_score:
__magic_name__ : Optional[int] = cur_score
__magic_name__ : List[Any] = na_probs[qid]
return 100.0 * best_score / len(_snake_case ), best_thresh
def lowerCAmelCase_ ( _snake_case : int , _snake_case : str , _snake_case : List[str] , _snake_case : Tuple , _snake_case : List[Any] , _snake_case : Dict ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ , __magic_name__ : List[str] = find_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case )
__magic_name__ , __magic_name__ : int = find_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case )
__magic_name__ : Optional[int] = best_exact
__magic_name__ : List[Any] = exact_thresh
__magic_name__ : Dict = best_fa
__magic_name__ : Any = fa_thresh
def lowerCAmelCase_ ( ) -> int:
'''simple docstring'''
with open(OPTS.data_file ) as f:
__magic_name__ : Optional[Any] = json.load(_snake_case )
__magic_name__ : List[Any] = dataset_json["data"]
with open(OPTS.pred_file ) as f:
__magic_name__ : Optional[Any] = json.load(_snake_case )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
__magic_name__ : Any = json.load(_snake_case )
else:
__magic_name__ : Any = {k: 0.0 for k in preds}
__magic_name__ : str = make_qid_to_has_ans(_snake_case ) # maps qid to True/False
__magic_name__ : Tuple = [k for k, v in qid_to_has_ans.items() if v]
__magic_name__ : Optional[Any] = [k for k, v in qid_to_has_ans.items() if not v]
__magic_name__ , __magic_name__ : Union[str, Any] = get_raw_scores(_snake_case , _snake_case )
__magic_name__ : Optional[Any] = apply_no_ans_threshold(_snake_case , _snake_case , _snake_case , OPTS.na_prob_thresh )
__magic_name__ : Optional[Any] = apply_no_ans_threshold(_snake_case , _snake_case , _snake_case , OPTS.na_prob_thresh )
__magic_name__ : List[Any] = make_eval_dict(_snake_case , _snake_case )
if has_ans_qids:
__magic_name__ : int = make_eval_dict(_snake_case , _snake_case , qid_list=_snake_case )
merge_eval(_snake_case , _snake_case , "HasAns" )
if no_ans_qids:
__magic_name__ : List[Any] = make_eval_dict(_snake_case , _snake_case , qid_list=_snake_case )
merge_eval(_snake_case , _snake_case , "NoAns" )
if OPTS.na_prob_file:
find_all_best_thresh(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , OPTS.out_image_dir )
histogram_na_prob(_snake_case , _snake_case , OPTS.out_image_dir , "hasAns" )
histogram_na_prob(_snake_case , _snake_case , OPTS.out_image_dir , "noAns" )
if OPTS.out_file:
with open(OPTS.out_file , "w" ) as f:
json.dump(_snake_case , _snake_case )
else:
print(json.dumps(_snake_case , indent=2 ) )
if __name__ == "__main__":
snake_case : int = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
main()
| 281 | 0 |
import unittest
import numpy as np
def _a ( SCREAMING_SNAKE_CASE : np.ndarray , SCREAMING_SNAKE_CASE : np.ndarray , SCREAMING_SNAKE_CASE : np.ndarray , SCREAMING_SNAKE_CASE : np.ndarray | None = None , ) -> np.ndarray:
"""simple docstring"""
__lowerCAmelCase: Any = np.shape(_snake_case )
__lowerCAmelCase: List[str] = np.shape(_snake_case )
__lowerCAmelCase: Dict = np.shape(_snake_case )
if shape_a[0] != shape_b[0]:
__lowerCAmelCase: List[Any] = (
"Expected the same number of rows for A and B. "
f'''Instead found A of size {shape_a} and B of size {shape_b}'''
)
raise ValueError(_snake_case )
if shape_b[1] != shape_c[1]:
__lowerCAmelCase: Any = (
"Expected the same number of columns for B and C. "
f'''Instead found B of size {shape_b} and C of size {shape_c}'''
)
raise ValueError(_snake_case )
__lowerCAmelCase: str = pseudo_inv
if a_inv is None:
try:
__lowerCAmelCase: int = np.linalg.inv(_snake_case )
except np.linalg.LinAlgError:
raise ValueError(
'Input matrix A is not invertible. Cannot compute Schur complement.' )
return mat_c - mat_b.T @ a_inv @ mat_b
class A_ ( unittest.TestCase ):
def UpperCAmelCase ( self : Tuple ) -> Union[str, Any]:
__lowerCAmelCase: Any = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
__lowerCAmelCase: Dict = np.array([[0, 3], [3, 0], [2, 3]] )
__lowerCAmelCase: List[Any] = np.array([[2, 1], [6, 3]] )
__lowerCAmelCase: Dict = schur_complement(_a , _a , _a )
__lowerCAmelCase: int = np.block([[a, b], [b.T, c]] )
__lowerCAmelCase: Dict = np.linalg.det(_a )
__lowerCAmelCase: str = np.linalg.det(_a )
__lowerCAmelCase: Dict = np.linalg.det(_a )
self.assertAlmostEqual(_a , det_a * det_s )
def UpperCAmelCase ( self : Tuple ) -> Optional[int]:
__lowerCAmelCase: Any = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
__lowerCAmelCase: str = np.array([[0, 3], [3, 0], [2, 3]] )
__lowerCAmelCase: Union[str, Any] = np.array([[2, 1], [6, 3]] )
with self.assertRaises(_a ):
schur_complement(_a , _a , _a )
def UpperCAmelCase ( self : Union[str, Any] ) -> Optional[int]:
__lowerCAmelCase: Dict = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
__lowerCAmelCase: Optional[Any] = np.array([[0, 3], [3, 0], [2, 3]] )
__lowerCAmelCase: List[Any] = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(_a ):
schur_complement(_a , _a , _a )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main()
| 322 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
snake_case : str = "▁"
snake_case : List[Any] = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class _snake_case ( snake_case , unittest.TestCase ):
UpperCamelCase__ = BigBirdTokenizer
UpperCamelCase__ = BigBirdTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = True
def SCREAMING_SNAKE_CASE ( self ):
super().setUp()
__magic_name__ : Optional[Any] = self.tokenizer_class(_a , keep_accents=_a )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Union[str, Any] = "<s>"
__magic_name__ : Dict = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<unk>" )
self.assertEqual(vocab_keys[1] , "<s>" )
self.assertEqual(vocab_keys[-1] , "[MASK]" )
self.assertEqual(len(_a ) , 1_004 )
def SCREAMING_SNAKE_CASE ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_000 )
def SCREAMING_SNAKE_CASE ( self ):
if not self.test_rust_tokenizer:
return
__magic_name__ : Dict = self.get_tokenizer()
__magic_name__ : str = self.get_rust_tokenizer()
__magic_name__ : Any = "I was born in 92000, and this is falsé."
__magic_name__ : Dict = tokenizer.tokenize(_a )
__magic_name__ : Any = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
__magic_name__ : List[Any] = tokenizer.encode(_a , add_special_tokens=_a )
__magic_name__ : List[str] = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
__magic_name__ : str = self.get_rust_tokenizer()
__magic_name__ : Dict = tokenizer.encode(_a )
__magic_name__ : Optional[int] = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = BigBirdTokenizer(_a , keep_accents=_a )
__magic_name__ : str = tokenizer.tokenize("This is a test" )
self.assertListEqual(_a , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_a ) , [285, 46, 10, 170, 382] , )
__magic_name__ : Dict = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
__magic_name__ : Union[str, Any] = tokenizer.convert_tokens_to_ids(_a )
self.assertListEqual(
_a , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
__magic_name__ : int = tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
@cached_property
def SCREAMING_SNAKE_CASE ( self ):
return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base" )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Any = "Hello World!"
__magic_name__ : Dict = [65, 18_536, 2_260, 101, 66]
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = (
"This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
)
# fmt: off
__magic_name__ : List[str] = [65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66] # noqa: E231
# fmt: on
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@require_torch
@slow
def SCREAMING_SNAKE_CASE ( self ):
import torch
from transformers import BigBirdConfig, BigBirdModel
# Build sequence
__magic_name__ : Optional[Any] = list(self.big_tokenizer.get_vocab().keys() )[:10]
__magic_name__ : List[Any] = " ".join(_a )
__magic_name__ : Any = self.big_tokenizer.encode_plus(_a , return_tensors="pt" , return_token_type_ids=_a )
__magic_name__ : Union[str, Any] = self.big_tokenizer.batch_encode_plus(
[sequence + " " + sequence] , return_tensors="pt" , return_token_type_ids=_a )
__magic_name__ : List[str] = BigBirdConfig(attention_type="original_full" )
__magic_name__ : Optional[int] = BigBirdModel(_a )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_a )
model(**_a )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : int = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base" )
__magic_name__ : int = tokenizer.decode(tokenizer("Paris is the [MASK]." ).input_ids )
self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]" )
@slow
def SCREAMING_SNAKE_CASE ( self ):
# fmt: off
__magic_name__ : Optional[Any] = {"input_ids": [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_a , model_name="google/bigbird-roberta-base" , revision="215c99f1600e06f83acce68422f2035b2b5c3510" , )
| 281 | 0 |
def _UpperCamelCase ( snake_case__ ) -> list:
__UpperCAmelCase : int = len(_snake_case )
for i in range(1, _snake_case ):
__UpperCAmelCase : Tuple = collection[i]
__UpperCAmelCase : int = 0
__UpperCAmelCase : int = i - 1
while low <= high:
__UpperCAmelCase : Optional[int] = (low + high) // 2
if val < collection[mid]:
__UpperCAmelCase : Dict = mid - 1
else:
__UpperCAmelCase : List[str] = mid + 1
for j in range(_snake_case, _snake_case, -1 ):
__UpperCAmelCase : Any = collection[j - 1]
__UpperCAmelCase : int = val
return collection
if __name__ == "__main__":
_snake_case = input('''Enter numbers separated by a comma:\n''').strip()
_snake_case = [int(item) for item in user_input.split(''',''')]
print(binary_insertion_sort(unsorted))
| 157 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
snake_case : int = logging.get_logger(__name__)
snake_case : List[str] = {"vocab_file": "spiece.model"}
snake_case : List[str] = {
"vocab_file": {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
}
}
snake_case : Tuple = {
"albert-base-v1": 512,
"albert-large-v1": 512,
"albert-xlarge-v1": 512,
"albert-xxlarge-v1": 512,
"albert-base-v2": 512,
"albert-large-v2": 512,
"albert-xlarge-v2": 512,
"albert-xxlarge-v2": 512,
}
snake_case : List[str] = "▁"
class _snake_case ( snake_case ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , _a , _a=True , _a=True , _a=False , _a="[CLS]" , _a="[SEP]" , _a="<unk>" , _a="[SEP]" , _a="<pad>" , _a="[CLS]" , _a="[MASK]" , _a = None , **_a , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
__magic_name__ : str = (
AddedToken(_a , lstrip=_a , rstrip=_a , normalized=_a )
if isinstance(_a , _a )
else mask_token
)
__magic_name__ : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , )
__magic_name__ : Dict = do_lower_case
__magic_name__ : Tuple = remove_space
__magic_name__ : Union[str, Any] = keep_accents
__magic_name__ : Tuple = vocab_file
__magic_name__ : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_a )
@property
def SCREAMING_SNAKE_CASE ( self ):
return len(self.sp_model )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
__magic_name__ : List[str] = self.__dict__.copy()
__magic_name__ : Any = None
return state
def __setstate__( self , _a ):
__magic_name__ : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
__magic_name__ : str = {}
__magic_name__ : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE ( self , _a ):
if self.remove_space:
__magic_name__ : List[Any] = " ".join(inputs.strip().split() )
else:
__magic_name__ : str = inputs
__magic_name__ : int = outputs.replace("``" , "\"" ).replace("''" , "\"" )
if not self.keep_accents:
__magic_name__ : str = unicodedata.normalize("NFKD" , _a )
__magic_name__ : Tuple = "".join([c for c in outputs if not unicodedata.combining(_a )] )
if self.do_lower_case:
__magic_name__ : int = outputs.lower()
return outputs
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Optional[Any] = self.preprocess_text(_a )
__magic_name__ : Dict = self.sp_model.encode(_a , out_type=_a )
__magic_name__ : Any = []
for piece in pieces:
if len(_a ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
__magic_name__ : Optional[Any] = self.sp_model.EncodeAsPieces(piece[:-1].replace(_a , "" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
__magic_name__ : List[str] = cur_pieces[1:]
else:
__magic_name__ : Optional[int] = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_a )
else:
new_pieces.append(_a )
return new_pieces
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.sp_model.PieceToId(_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
return self.sp_model.IdToPiece(_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Any = []
__magic_name__ : Union[str, Any] = ""
__magic_name__ : int = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_a ) + token
__magic_name__ : List[Any] = True
__magic_name__ : Optional[int] = []
else:
current_sub_tokens.append(_a )
__magic_name__ : Optional[Any] = False
out_string += self.sp_model.decode(_a )
return out_string.strip()
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
__magic_name__ : List[str] = [self.sep_token_id]
__magic_name__ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self , _a , _a = None , _a = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is not None:
return [1] + ([0] * len(_a )) + [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1]
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
__magic_name__ : Optional[int] = [self.sep_token_id]
__magic_name__ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def SCREAMING_SNAKE_CASE ( self , _a , _a = None ):
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__magic_name__ : List[str] = os.path.join(
_a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _a )
elif not os.path.isfile(self.vocab_file ):
with open(_a , "wb" ) as fi:
__magic_name__ : Union[str, Any] = self.sp_model.serialized_model_proto()
fi.write(_a )
return (out_vocab_file,)
| 281 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
from transformers import TvltProcessor
@require_torch
class A__ ( unittest.TestCase):
def __lowerCamelCase ( self ):
__lowerCAmelCase : Any = "ZinengTang/tvlt-base"
__lowerCAmelCase : int = tempfile.mkdtemp()
def __lowerCamelCase ( self , **_SCREAMING_SNAKE_CASE ):
return TvltImageProcessor.from_pretrained(self.checkpoint , **_a )
def __lowerCamelCase ( self , **_SCREAMING_SNAKE_CASE ):
return TvltFeatureExtractor.from_pretrained(self.checkpoint , **_a )
def __lowerCamelCase ( self ):
shutil.rmtree(self.tmpdirname )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Tuple = self.get_image_processor()
__lowerCAmelCase : str = self.get_feature_extractor()
__lowerCAmelCase : List[Any] = TvltProcessor(image_processor=_a , feature_extractor=_a )
processor.save_pretrained(self.tmpdirname )
__lowerCAmelCase : Any = TvltProcessor.from_pretrained(self.tmpdirname )
self.assertIsInstance(processor.feature_extractor , _a )
self.assertIsInstance(processor.image_processor , _a )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Union[str, Any] = self.get_image_processor()
__lowerCAmelCase : Union[str, Any] = self.get_feature_extractor()
__lowerCAmelCase : Any = TvltProcessor(image_processor=_a , feature_extractor=_a )
__lowerCAmelCase : List[Any] = np.ones([1_20_00] )
__lowerCAmelCase : Optional[int] = feature_extractor(_a , return_tensors='np' )
__lowerCAmelCase : List[str] = processor(audio=_a , return_tensors='np' )
for key in audio_dict.keys():
self.assertAlmostEqual(audio_dict[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __lowerCamelCase ( self ):
__lowerCAmelCase : int = self.get_image_processor()
__lowerCAmelCase : Union[str, Any] = self.get_feature_extractor()
__lowerCAmelCase : int = TvltProcessor(image_processor=_a , feature_extractor=_a )
__lowerCAmelCase : int = np.ones([3, 2_24, 2_24] )
__lowerCAmelCase : int = image_processor(_a , return_tensors='np' )
__lowerCAmelCase : List[str] = processor(images=_a , return_tensors='np' )
for key in image_dict.keys():
self.assertAlmostEqual(image_dict[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Union[str, Any] = self.get_image_processor()
__lowerCAmelCase : Dict = self.get_feature_extractor()
__lowerCAmelCase : Tuple = TvltProcessor(image_processor=_a , feature_extractor=_a )
__lowerCAmelCase : Tuple = np.ones([1_20_00] )
__lowerCAmelCase : List[Any] = np.ones([3, 2_24, 2_24] )
__lowerCAmelCase : str = processor(audio=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ['audio_values', 'audio_mask', 'pixel_values', 'pixel_mask'] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def __lowerCamelCase ( self ):
__lowerCAmelCase : Tuple = self.get_image_processor()
__lowerCAmelCase : str = self.get_feature_extractor()
__lowerCAmelCase : List[Any] = TvltProcessor(image_processor=_a , feature_extractor=_a )
self.assertListEqual(
processor.model_input_names , image_processor.model_input_names + feature_extractor.model_input_names , msg='`processor` and `image_processor`+`feature_extractor` model input names do not match' , ) | 86 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : Tuple , _snake_case : List[Any] , _snake_case : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if isinstance(_snake_case , _snake_case ):
__magic_name__ : Union[str, Any] = np.full((len(_snake_case ), sequence_length, 2) , _snake_case )
else:
__magic_name__ : List[Any] = np.full((len(_snake_case ), sequence_length) , _snake_case )
for i, tensor in enumerate(_snake_case ):
if padding_side == "right":
if isinstance(_snake_case , _snake_case ):
__magic_name__ : Optional[Any] = tensor[:sequence_length]
else:
__magic_name__ : Union[str, Any] = tensor[:sequence_length]
else:
if isinstance(_snake_case , _snake_case ):
__magic_name__ : List[Any] = tensor[:sequence_length]
else:
__magic_name__ : Optional[Any] = tensor[:sequence_length]
return out_tensor.tolist()
def lowerCAmelCase_ ( _snake_case : Optional[int] ) -> Tuple:
'''simple docstring'''
__magic_name__ : Union[str, Any] = ord(_snake_case )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
__magic_name__ : Any = unicodedata.category(_snake_case )
if cat.startswith("P" ):
return True
return False
@dataclass
class _snake_case ( snake_case ):
UpperCamelCase__ = 42
UpperCamelCase__ = True
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = -100
UpperCamelCase__ = "pt"
def SCREAMING_SNAKE_CASE ( self , _a ):
import torch
__magic_name__ : List[str] = "label" if "label" in features[0].keys() else "labels"
__magic_name__ : Union[str, Any] = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
__magic_name__ : Optional[int] = self.tokenizer.pad(
_a , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" if labels is None else None , )
if labels is None:
return batch
__magic_name__ : Dict = torch.tensor(batch["entity_ids"] ).shape[1]
__magic_name__ : List[Any] = self.tokenizer.padding_side
if padding_side == "right":
__magic_name__ : str = [
list(_a ) + [self.label_pad_token_id] * (sequence_length - len(_a )) for label in labels
]
else:
__magic_name__ : int = [
[self.label_pad_token_id] * (sequence_length - len(_a )) + list(_a ) for label in labels
]
__magic_name__ : Dict = [feature["ner_tags"] for feature in features]
__magic_name__ : List[Any] = padding_tensor(_a , -1 , _a , _a )
__magic_name__ : Any = [feature["original_entity_spans"] for feature in features]
__magic_name__ : Any = padding_tensor(_a , (-1, -1) , _a , _a )
__magic_name__ : List[Any] = {k: torch.tensor(_a , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 281 | 0 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
__lowerCamelCase : Any = get_logger(__name__)
class __snake_case :
lowerCAmelCase_ = "dummy_data"
lowerCAmelCase_ = "datasets"
lowerCAmelCase_ = False
def __init__( self : Optional[Any] , _lowercase : List[str] , _lowercase : List[Any] , _lowercase : List[str] , _lowercase : Any = None , _lowercase : List[str] = False , _lowercase : Optional[Any] = True , _lowercase : Dict = None , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = dataset_name
SCREAMING_SNAKE_CASE__ = cache_dir
SCREAMING_SNAKE_CASE__ = use_local_dummy_data
SCREAMING_SNAKE_CASE__ = config
# download_callbacks take a single url as input
SCREAMING_SNAKE_CASE__ = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
SCREAMING_SNAKE_CASE__ = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
SCREAMING_SNAKE_CASE__ = str(_a )
# to be downloaded
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
@property
def __a ( self : Optional[int] ):
"""simple docstring"""
if self._dummy_file is None:
SCREAMING_SNAKE_CASE__ = self.download_dummy_data()
return self._dummy_file
@property
def __a ( self : Optional[int] ):
"""simple docstring"""
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join("""dummy""" , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join("""dummy""" , self.version_name )
@property
def __a ( self : Dict ):
"""simple docstring"""
return os.path.join(self.dummy_data_folder , """dummy_data.zip""" )
def __a ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
SCREAMING_SNAKE_CASE__ = cached_path(
_a , cache_dir=self.cache_dir , extract_compressed_file=_a , force_extract=_a )
return os.path.join(_a , self.dummy_file_name )
@property
def __a ( self : Tuple ):
"""simple docstring"""
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def __a ( self : Any ):
"""simple docstring"""
if self._bucket_url is None:
SCREAMING_SNAKE_CASE__ = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , """/""" ) )
return self._bucket_url
@property
def __a ( self : Any ):
"""simple docstring"""
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , """/""" ).split("""/""" )[:-1] )
def __a ( self : Optional[Any] , _lowercase : List[Any] , *_lowercase : Tuple ):
"""simple docstring"""
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
SCREAMING_SNAKE_CASE__ = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
SCREAMING_SNAKE_CASE__ = self.dummy_file_name
# special case when data_url is a dict
if isinstance(_a , _a ):
return self.create_dummy_data_dict(_a , _a )
elif isinstance(_a , (list, tuple) ):
return self.create_dummy_data_list(_a , _a )
else:
return self.create_dummy_data_single(_a , _a )
def __a ( self : List[Any] , _lowercase : Tuple , *_lowercase : Dict ):
"""simple docstring"""
return self.download_and_extract(_a )
def __a ( self : List[Any] , _lowercase : Any , _lowercase : Any ):
"""simple docstring"""
return self.download_and_extract(_a )
def __a ( self : List[Any] , _lowercase : Optional[Any] , *_lowercase : List[Any] , **_lowercase : Any ):
"""simple docstring"""
return path
def __a ( self : Dict ):
"""simple docstring"""
return {}
def __a ( self : List[str] , _lowercase : Optional[int] , _lowercase : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(_a , _a ):
for single_url in single_urls:
download_callback(_a )
else:
SCREAMING_SNAKE_CASE__ = single_urls
download_callback(_a )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(_a , _a ):
SCREAMING_SNAKE_CASE__ = [os.path.join(_a , urllib.parse.quote_plus(Path(_a ).name ) ) for x in single_urls]
else:
SCREAMING_SNAKE_CASE__ = single_urls
SCREAMING_SNAKE_CASE__ = os.path.join(_a , urllib.parse.quote_plus(Path(_a ).name ) )
SCREAMING_SNAKE_CASE__ = value
# make sure that values are unique
if all(isinstance(_a , _a ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
SCREAMING_SNAKE_CASE__ = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def __a ( self : Any , _lowercase : Dict , _lowercase : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
SCREAMING_SNAKE_CASE__ = all(bool(re.findall("""[0-9]{3,}-of-[0-9]{3,}""" , _a ) ) for url in data_url )
SCREAMING_SNAKE_CASE__ = all(
url.startswith("""https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed""" ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
SCREAMING_SNAKE_CASE__ = [data_url[0]] * len(_a )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(_a )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
SCREAMING_SNAKE_CASE__ = os.path.join(_a , urllib.parse.quote_plus(single_url.split("""/""" )[-1] ) )
dummy_data_list.append(_a )
return dummy_data_list
def __a ( self : Tuple , _lowercase : int , _lowercase : Any ):
"""simple docstring"""
for download_callback in self.download_callbacks:
download_callback(_a )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
SCREAMING_SNAKE_CASE__ = os.path.join(_a , urllib.parse.quote_plus(data_url.split("""/""" )[-1] ) )
if os.path.exists(_a ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def __a ( self : Dict ):
"""simple docstring"""
pass
def __a ( self : Any ):
"""simple docstring"""
pass
def __a ( self : Tuple , _lowercase : Optional[Any] ):
"""simple docstring"""
def _iter_archive_members(_lowercase : Optional[int] ):
# this preserves the order of the members inside the ZIP archive
SCREAMING_SNAKE_CASE__ = Path(self.dummy_file ).parent
SCREAMING_SNAKE_CASE__ = path.relative_to(_a )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
SCREAMING_SNAKE_CASE__ = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(_a )
SCREAMING_SNAKE_CASE__ = Path(_a )
SCREAMING_SNAKE_CASE__ = _iter_archive_members(_a ) if self.use_local_dummy_data else path.rglob("""*""" )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith((""".""", """__""") ):
yield file_path.relative_to(_a ).as_posix(), file_path.open("""rb""" )
def __a ( self : int , _lowercase : Any ):
"""simple docstring"""
if not isinstance(_a , _a ):
SCREAMING_SNAKE_CASE__ = [paths]
for path in paths:
if os.path.isfile(_a ):
if os.path.basename(_a ).startswith((""".""", """__""") ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(_a ):
if os.path.basename(_a ).startswith((""".""", """__""") ):
continue
dirnames.sort()
for filename in sorted(_a ):
if filename.startswith((""".""", """__""") ):
continue
yield os.path.join(_a , _a )
| 219 |
import math
def lowerCAmelCase_ ( _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
return math.pow(_snake_case , 2 ) - a
def lowerCAmelCase_ ( _snake_case : float ) -> float:
'''simple docstring'''
return 2 * x
def lowerCAmelCase_ ( _snake_case : float ) -> float:
'''simple docstring'''
__magic_name__ : Optional[int] = 2.0
while start <= a:
__magic_name__ : str = math.pow(_snake_case , 2 )
return start
def lowerCAmelCase_ ( _snake_case : float , _snake_case : int = 9999 , _snake_case : float = 0.00_000_000_000_001 ) -> float:
'''simple docstring'''
if a < 0:
raise ValueError("math domain error" )
__magic_name__ : Optional[int] = get_initial_point(_snake_case )
for _ in range(_snake_case ):
__magic_name__ : int = value
__magic_name__ : str = value - fx(_snake_case , _snake_case ) / fx_derivative(_snake_case )
if abs(prev_value - value ) < tolerance:
return value
return value
if __name__ == "__main__":
from doctest import testmod
testmod()
| 281 | 0 |
'''simple docstring'''
__snake_case = frozenset(
[
'''prompt''',
'''height''',
'''width''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
'''cross_attention_kwargs''',
]
)
__snake_case = frozenset(['''prompt''', '''negative_prompt'''])
__snake_case = frozenset([])
__snake_case = frozenset(['''image'''])
__snake_case = frozenset(
[
'''image''',
'''height''',
'''width''',
'''guidance_scale''',
]
)
__snake_case = frozenset(['''image'''])
__snake_case = frozenset(
[
'''prompt''',
'''image''',
'''height''',
'''width''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
]
)
__snake_case = frozenset(['''prompt''', '''image''', '''negative_prompt'''])
__snake_case = frozenset(
[
# Text guided image variation with an image mask
'''prompt''',
'''image''',
'''mask_image''',
'''height''',
'''width''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
]
)
__snake_case = frozenset(['''prompt''', '''image''', '''mask_image''', '''negative_prompt'''])
__snake_case = frozenset(
[
# image variation with an image mask
'''image''',
'''mask_image''',
'''height''',
'''width''',
'''guidance_scale''',
]
)
__snake_case = frozenset(['''image''', '''mask_image'''])
__snake_case = frozenset(
[
'''example_image''',
'''image''',
'''mask_image''',
'''height''',
'''width''',
'''guidance_scale''',
]
)
__snake_case = frozenset(['''example_image''', '''image''', '''mask_image'''])
__snake_case = frozenset(['''class_labels'''])
__snake_case = frozenset(['''class_labels'''])
__snake_case = frozenset(['''batch_size'''])
__snake_case = frozenset([])
__snake_case = frozenset(['''batch_size'''])
__snake_case = frozenset([])
__snake_case = frozenset(
[
'''prompt''',
'''audio_length_in_s''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
'''cross_attention_kwargs''',
]
)
__snake_case = frozenset(['''prompt''', '''negative_prompt'''])
__snake_case = frozenset(['''input_tokens'''])
__snake_case = frozenset(['''input_tokens''']) | 97 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class _snake_case :
UpperCamelCase__ = LEDConfig
UpperCamelCase__ = {}
UpperCamelCase__ = 'gelu'
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=False , _a=99 , _a=32 , _a=2 , _a=4 , _a=37 , _a=0.1 , _a=0.1 , _a=20 , _a=2 , _a=1 , _a=0 , _a=4 , ):
__magic_name__ : int = parent
__magic_name__ : Optional[int] = batch_size
__magic_name__ : Tuple = seq_length
__magic_name__ : List[Any] = is_training
__magic_name__ : Dict = use_labels
__magic_name__ : Optional[Any] = vocab_size
__magic_name__ : int = hidden_size
__magic_name__ : Optional[int] = num_hidden_layers
__magic_name__ : Optional[int] = num_attention_heads
__magic_name__ : Tuple = intermediate_size
__magic_name__ : Any = hidden_dropout_prob
__magic_name__ : Optional[int] = attention_probs_dropout_prob
__magic_name__ : List[str] = max_position_embeddings
__magic_name__ : Any = eos_token_id
__magic_name__ : str = pad_token_id
__magic_name__ : int = bos_token_id
__magic_name__ : Optional[int] = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
__magic_name__ : Tuple = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
__magic_name__ : Tuple = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__magic_name__ : int = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__magic_name__ : int = tf.concat([input_ids, eos_tensor] , axis=1 )
__magic_name__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__magic_name__ : Dict = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
__magic_name__ : List[str] = prepare_led_inputs_dict(_a , _a , _a )
__magic_name__ : Union[str, Any] = tf.concat(
[tf.zeros_like(_a )[:, :-1], tf.ones_like(_a )[:, -1:]] , axis=-1 , )
__magic_name__ : List[Any] = global_attention_mask
return config, inputs_dict
def SCREAMING_SNAKE_CASE ( self , _a , _a ):
__magic_name__ : Dict = TFLEDModel(config=_a ).get_decoder()
__magic_name__ : Optional[int] = inputs_dict["input_ids"]
__magic_name__ : Union[str, Any] = input_ids[:1, :]
__magic_name__ : str = inputs_dict["attention_mask"][:1, :]
__magic_name__ : int = 1
# first forward pass
__magic_name__ : Tuple = model(_a , attention_mask=_a , use_cache=_a )
__magic_name__ , __magic_name__ : str = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__magic_name__ : Optional[int] = ids_tensor((self.batch_size, 3) , config.vocab_size )
__magic_name__ : Any = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
__magic_name__ : Optional[Any] = tf.concat([input_ids, next_tokens] , axis=-1 )
__magic_name__ : List[Any] = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
__magic_name__ : List[str] = model(_a , attention_mask=_a )[0]
__magic_name__ : Dict = model(_a , attention_mask=_a , past_key_values=_a )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__magic_name__ : List[Any] = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
__magic_name__ : Union[str, Any] = output_from_no_past[:, -3:, random_slice_idx]
__magic_name__ : List[str] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_a , _a , rtol=1e-3 )
def lowerCAmelCase_ ( _snake_case : Any , _snake_case : List[Any] , _snake_case : Any , _snake_case : str=None , _snake_case : List[str]=None , _snake_case : int=None , _snake_case : Any=None , ) -> int:
'''simple docstring'''
if attention_mask is None:
__magic_name__ : str = tf.cast(tf.math.not_equal(_snake_case , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__magic_name__ : List[Any] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__magic_name__ : Dict = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__magic_name__ : Any = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class _snake_case ( snake_case , snake_case , unittest.TestCase ):
UpperCamelCase__ = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
UpperCamelCase__ = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
UpperCamelCase__ = (
{
'conversational': TFLEDForConditionalGeneration,
'feature-extraction': TFLEDModel,
'summarization': TFLEDForConditionalGeneration,
'text2text-generation': TFLEDForConditionalGeneration,
'translation': TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
UpperCamelCase__ = True
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = TFLEDModelTester(self )
__magic_name__ : List[Any] = ConfigTester(self , config_class=_a )
def SCREAMING_SNAKE_CASE ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ , __magic_name__ : int = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ : List[str] = tf.zeros_like(inputs_dict["attention_mask"] )
__magic_name__ : Optional[Any] = 2
__magic_name__ : Tuple = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict["global_attention_mask"] , )
__magic_name__ : Any = True
__magic_name__ : str = self.model_tester.seq_length
__magic_name__ : Dict = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(_a ):
__magic_name__ : str = outputs.decoder_attentions
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(_a ):
__magic_name__ : Any = [t.numpy() for t in outputs.encoder_attentions]
__magic_name__ : Tuple = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(_a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
__magic_name__ : Union[str, Any] = True
__magic_name__ : List[str] = False
__magic_name__ : Tuple = False
__magic_name__ : Optional[int] = model_class(_a )
__magic_name__ : str = model(self._prepare_for_class(_a , _a ) )
__magic_name__ : Any = len(_a )
self.assertEqual(config.output_hidden_states , _a )
check_encoder_attentions_output(_a )
if self.is_encoder_decoder:
__magic_name__ : Tuple = model_class(_a )
__magic_name__ : Optional[Any] = model(self._prepare_for_class(_a , _a ) )
self.assertEqual(config.output_hidden_states , _a )
check_decoder_attentions_output(_a )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
__magic_name__ : Dict = True
__magic_name__ : str = model_class(_a )
__magic_name__ : Any = model(self._prepare_for_class(_a , _a ) )
self.assertEqual(config.output_hidden_states , _a )
check_encoder_attentions_output(_a )
# Check attention is always last and order is fine
__magic_name__ : Union[str, Any] = True
__magic_name__ : Union[str, Any] = True
__magic_name__ : List[str] = model_class(_a )
__magic_name__ : Any = model(self._prepare_for_class(_a , _a ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_a ) )
self.assertEqual(model.config.output_hidden_states , _a )
check_encoder_attentions_output(_a )
@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing." )
def SCREAMING_SNAKE_CASE ( self ):
pass
def SCREAMING_SNAKE_CASE ( self ):
# TODO: Head-masking not yet implement
pass
def lowerCAmelCase_ ( _snake_case : int ) -> Optional[int]:
'''simple docstring'''
return tf.constant(_snake_case , dtype=tf.intaa )
snake_case : Optional[int] = 1E-4
@slow
@require_tf
class _snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[Any] = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" ).led
# change to intended input here
__magic_name__ : Optional[int] = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : str = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : Any = prepare_led_inputs_dict(model.config , _a , _a )
__magic_name__ : List[Any] = model(**_a )[0]
__magic_name__ : List[str] = (1, 1_024, 768)
self.assertEqual(output.shape , _a )
# change to expected output here
__magic_name__ : int = tf.convert_to_tensor(
[[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]] , )
tf.debugging.assert_near(output[:, :3, :3] , _a , atol=1e-3 )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Tuple = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" )
# change to intended input here
__magic_name__ : int = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : Tuple = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
__magic_name__ : Optional[Any] = prepare_led_inputs_dict(model.config , _a , _a )
__magic_name__ : Union[str, Any] = model(**_a )[0]
__magic_name__ : Optional[int] = (1, 1_024, model.config.vocab_size)
self.assertEqual(output.shape , _a )
# change to expected output here
__magic_name__ : str = tf.convert_to_tensor(
[[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]] , )
tf.debugging.assert_near(output[:, :3, :3] , _a , atol=1e-3 , rtol=1e-3 )
| 281 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A: Dict = logging.get_logger(__name__)
A: Optional[int] = {"vocab_file": "sentencepiece.bpe.model"}
A: List[str] = {
"vocab_file": {
"moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
"moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
"moussaKam/barthez-orangesum-title": (
"https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
),
},
}
A: Optional[int] = {
"moussaKam/mbarthez": 1_0_2_4,
"moussaKam/barthez": 1_0_2_4,
"moussaKam/barthez-orangesum-title": 1_0_2_4,
}
A: Optional[Any] = "▁"
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
__lowerCAmelCase : List[str] = VOCAB_FILES_NAMES
__lowerCAmelCase : Tuple = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase : Tuple = ['input_ids', 'attention_mask']
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="<s>" , _SCREAMING_SNAKE_CASE="</s>" , _SCREAMING_SNAKE_CASE="</s>" , _SCREAMING_SNAKE_CASE="<s>" , _SCREAMING_SNAKE_CASE="<unk>" , _SCREAMING_SNAKE_CASE="<pad>" , _SCREAMING_SNAKE_CASE="<mask>" , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> Dict:
'''simple docstring'''
UpperCAmelCase : List[str] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
UpperCAmelCase : List[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , cls_token=_a , pad_token=_a , mask_token=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , )
UpperCAmelCase : Union[str, Any] = vocab_file
UpperCAmelCase : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_a ) )
UpperCAmelCase : List[Any] = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
UpperCAmelCase : str = len(self.sp_model ) - 1
UpperCAmelCase : Optional[int] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> Any:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase : Any = [self.cls_token_id]
UpperCAmelCase : Any = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False ) -> List[Any]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is None:
return [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1, 1] + ([0] * len(_a )) + [1]
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> str:
'''simple docstring'''
UpperCAmelCase : str = [self.sep_token_id]
UpperCAmelCase : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
return len(self.sp_model )
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Optional[int] = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
'''simple docstring'''
return self.sp_model.encode(_a , out_type=_a )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCAmelCase : List[str] = self.sp_model.PieceToId(_a )
return spm_id if spm_id else self.unk_token_id
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(_a )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
UpperCAmelCase : Any = []
UpperCAmelCase : Tuple = ""
UpperCAmelCase : Any = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_a ) + token
UpperCAmelCase : List[str] = True
UpperCAmelCase : Union[str, Any] = []
else:
current_sub_tokens.append(_a )
UpperCAmelCase : Optional[int] = False
out_string += self.sp_model.decode(_a )
return out_string.strip()
def __getstate__( self ) -> str:
'''simple docstring'''
UpperCAmelCase : str = self.__dict__.copy()
UpperCAmelCase : Tuple = None
return state
def __setstate__( self , _SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
UpperCAmelCase : str = {}
UpperCAmelCase : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> Optional[Any]:
'''simple docstring'''
if not os.path.isdir(_a ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
UpperCAmelCase : Dict = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _a )
elif not os.path.isfile(self.vocab_file ):
with open(_a , """wb""" ) as fi:
UpperCAmelCase : Dict = self.sp_model.serialized_model_proto()
fi.write(_a )
return (out_vocab_file,)
| 109 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
snake_case : Optional[Any] = logging.get_logger(__name__)
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Union[str, Any]=False ) -> List[str]:
'''simple docstring'''
__magic_name__ : Union[str, Any] = []
# fmt: off
# stem:
rename_keys.append(("cls_token", "vit.embeddings.cls_token") )
rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings") )
rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias") )
# backbone
rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias") )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''') )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''') )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
("pre_logits.fc.weight", "pooler.dense.weight"),
("pre_logits.fc.bias", "pooler.dense.bias"),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
__magic_name__ : int = [(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
# fmt: on
return rename_keys
def lowerCAmelCase_ ( _snake_case : Any , _snake_case : Any , _snake_case : Dict=False ) -> int:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
__magic_name__ : int = ""
else:
__magic_name__ : Union[str, Any] = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__magic_name__ : Optional[Any] = state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
__magic_name__ : int = state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
__magic_name__ : Dict = in_proj_weight[
: config.hidden_size, :
]
__magic_name__ : List[str] = in_proj_bias[: config.hidden_size]
__magic_name__ : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__magic_name__ : Optional[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__magic_name__ : Optional[Any] = in_proj_weight[
-config.hidden_size :, :
]
__magic_name__ : int = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase_ ( _snake_case : List[str] ) -> List[str]:
'''simple docstring'''
__magic_name__ : List[str] = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(_snake_case , _snake_case )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : int , _snake_case : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
__magic_name__ : int = dct.pop(_snake_case )
__magic_name__ : List[Any] = val
def lowerCAmelCase_ ( ) -> Dict:
'''simple docstring'''
__magic_name__ : List[str] = "http://images.cocodataset.org/val2017/000000039769.jpg"
__magic_name__ : List[str] = Image.open(requests.get(_snake_case , stream=_snake_case ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : Any , _snake_case : int=False ) -> Dict:
'''simple docstring'''
__magic_name__ : List[str] = BitConfig(
global_padding="same" , layer_type="bottleneck" , depths=(3, 4, 9) , out_features=["stage3"] , embedding_dynamic_padding=_snake_case , )
__magic_name__ : List[str] = ViTHybridConfig(backbone_config=_snake_case , image_size=384 , num_labels=1000 )
__magic_name__ : str = False
# load original model from timm
__magic_name__ : Union[str, Any] = timm.create_model(_snake_case , pretrained=_snake_case )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
__magic_name__ : List[Any] = timm_model.state_dict()
if base_model:
remove_classification_head_(_snake_case )
__magic_name__ : Tuple = create_rename_keys(_snake_case , _snake_case )
for src, dest in rename_keys:
rename_key(_snake_case , _snake_case , _snake_case )
read_in_q_k_v(_snake_case , _snake_case , _snake_case )
__magic_name__ : List[str] = "huggingface/label-files"
__magic_name__ : int = "imagenet-1k-id2label.json"
__magic_name__ : Optional[int] = json.load(open(hf_hub_download(_snake_case , _snake_case , repo_type="dataset" ) , "r" ) )
__magic_name__ : int = {int(_snake_case ): v for k, v in idalabel.items()}
__magic_name__ : List[str] = idalabel
__magic_name__ : List[str] = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
__magic_name__ : List[str] = ViTHybridModel(_snake_case ).eval()
else:
__magic_name__ : str = ViTHybridForImageClassification(_snake_case ).eval()
model.load_state_dict(_snake_case )
# create image processor
__magic_name__ : List[Any] = create_transform(**resolve_data_config({} , model=_snake_case ) )
__magic_name__ : int = transform.transforms
__magic_name__ : List[str] = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
__magic_name__ : int = ViTHybridImageProcessor(
do_resize=_snake_case , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_snake_case , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=_snake_case , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
__magic_name__ : List[Any] = prepare_img()
__magic_name__ : Any = transform(_snake_case ).unsqueeze(0 )
__magic_name__ : Tuple = processor(_snake_case , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(_snake_case , _snake_case )
# verify logits
with torch.no_grad():
__magic_name__ : Optional[int] = model(_snake_case )
__magic_name__ : List[str] = outputs.logits
print("Predicted class:" , logits.argmax(-1 ).item() )
if base_model:
__magic_name__ : List[str] = timm_model.forward_features(_snake_case )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_snake_case , outputs.pooler_output , atol=1E-3 )
else:
__magic_name__ : Any = timm_model(_snake_case )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_snake_case , outputs.logits , atol=1E-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(_snake_case ).mkdir(exist_ok=_snake_case )
print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_snake_case )
print(F'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(_snake_case )
if push_to_hub:
print(F'''Pushing model and processor to the hub {vit_name}''' )
model.push_to_hub(F'''ybelkada/{vit_name}''' )
processor.push_to_hub(F'''ybelkada/{vit_name}''' )
if __name__ == "__main__":
snake_case : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--vit_name",
default="vit_base_r50_s16_384",
type=str,
help="Name of the hybrid ViT timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
)
snake_case : List[Any] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 281 | 0 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def __UpperCamelCase ( _A , _A , _A ):
if isinstance(_snake_case , torch.Tensor ):
return image
elif isinstance(_snake_case , PIL.Image.Image ):
lowerCAmelCase_ = [image]
if isinstance(image[0] , PIL.Image.Image ):
lowerCAmelCase_ = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['''lanczos'''] ) )[None, :] for i in image]
lowerCAmelCase_ = np.concatenate(_snake_case , axis=0 )
lowerCAmelCase_ = np.array(_snake_case ).astype(np.floataa ) / 2_5_5.0
lowerCAmelCase_ = image.transpose(0 , 3 , 1 , 2 )
lowerCAmelCase_ = 2.0 * image - 1.0
lowerCAmelCase_ = torch.from_numpy(_snake_case )
elif isinstance(image[0] , torch.Tensor ):
lowerCAmelCase_ = torch.cat(_snake_case , dim=0 )
return image
def __UpperCamelCase ( _A , _A , _A , _A=0.9_9_9_5 ):
if not isinstance(_snake_case , np.ndarray ):
lowerCAmelCase_ = True
lowerCAmelCase_ = va.device
lowerCAmelCase_ = va.cpu().numpy()
lowerCAmelCase_ = va.cpu().numpy()
lowerCAmelCase_ = np.sum(va * va / (np.linalg.norm(_snake_case ) * np.linalg.norm(_snake_case )) )
if np.abs(_snake_case ) > DOT_THRESHOLD:
lowerCAmelCase_ = (1 - t) * va + t * va
else:
lowerCAmelCase_ = np.arccos(_snake_case )
lowerCAmelCase_ = np.sin(_snake_case )
lowerCAmelCase_ = theta_a * t
lowerCAmelCase_ = np.sin(_snake_case )
lowerCAmelCase_ = np.sin(theta_a - theta_t ) / sin_theta_a
lowerCAmelCase_ = sin_theta_t / sin_theta_a
lowerCAmelCase_ = sa * va + sa * va
if inputs_are_torch:
lowerCAmelCase_ = torch.from_numpy(_snake_case ).to(_snake_case )
return va
def __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = F.normalize(_snake_case , dim=-1 )
lowerCAmelCase_ = F.normalize(_snake_case , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def __UpperCamelCase ( _A , _A ):
for param in model.parameters():
lowerCAmelCase_ = value
class A ( __UpperCAmelCase ):
def __init__( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__=None, UpperCamelCase__=None, UpperCamelCase__=None, ):
"""simple docstring"""
super().__init__()
self.register_modules(
vae=_a, text_encoder=_a, clip_model=_a, tokenizer=_a, unet=_a, scheduler=_a, feature_extractor=_a, coca_model=_a, coca_tokenizer=_a, coca_transform=_a, )
lowerCAmelCase_ = (
feature_extractor.size
if isinstance(feature_extractor.size, _a )
else feature_extractor.size["shortest_edge"]
)
lowerCAmelCase_ = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std )
set_requires_grad(self.text_encoder, _a )
set_requires_grad(self.clip_model, _a )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ = "auto" ):
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowerCAmelCase_ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(_a )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
self.enable_attention_slicing(_a )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
set_requires_grad(self.vae, _a )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
set_requires_grad(self.vae, _a )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
set_requires_grad(self.unet, _a )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
set_requires_grad(self.unet, _a )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = min(int(num_inference_steps * strength ), _a )
lowerCAmelCase_ = max(num_inference_steps - init_timestep, 0 )
lowerCAmelCase_ = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__=None ):
"""simple docstring"""
if not isinstance(_a, torch.Tensor ):
raise ValueError(f"`image` has to be of type `torch.Tensor` but is {type(_a )}" )
lowerCAmelCase_ = image.to(device=_a, dtype=_a )
if isinstance(_a, _a ):
lowerCAmelCase_ = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(_a )
]
lowerCAmelCase_ = torch.cat(_a, dim=0 )
else:
lowerCAmelCase_ = self.vae.encode(_a ).latent_dist.sample(_a )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowerCAmelCase_ = 0.18_215 * init_latents
lowerCAmelCase_ = init_latents.repeat_interleave(_a, dim=0 )
lowerCAmelCase_ = randn_tensor(init_latents.shape, generator=_a, device=_a, dtype=_a )
# get latents
lowerCAmelCase_ = self.scheduler.add_noise(_a, _a, _a )
lowerCAmelCase_ = init_latents
return latents
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = self.coca_transform(_a ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
lowerCAmelCase_ = self.coca_model.generate(transformed_image.to(device=self.device, dtype=self.coca_model.dtype ) )
lowerCAmelCase_ = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split('''<end_of_text>''' )[0].replace('''<start_of_text>''', '''''' ).rstrip(''' .,''' )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = self.feature_extractor.preprocess(_a )
lowerCAmelCase_ = torch.from_numpy(clip_image_input['''pixel_values'''][0] ).unsqueeze(0 ).to(self.device ).half()
lowerCAmelCase_ = self.clip_model.get_image_features(_a )
lowerCAmelCase_ = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=_a )
lowerCAmelCase_ = image_embeddings_clip.repeat_interleave(_a, dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, ):
"""simple docstring"""
lowerCAmelCase_ = latents.detach().requires_grad_()
lowerCAmelCase_ = self.scheduler.scale_model_input(_a, _a )
# predict the noise residual
lowerCAmelCase_ = self.unet(_a, _a, encoder_hidden_states=_a ).sample
if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
lowerCAmelCase_ = self.scheduler.alphas_cumprod[timestep]
lowerCAmelCase_ = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
lowerCAmelCase_ = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
lowerCAmelCase_ = torch.sqrt(_a )
lowerCAmelCase_ = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler, _a ):
lowerCAmelCase_ = self.scheduler.sigmas[index]
lowerCAmelCase_ = latents - sigma * noise_pred
else:
raise ValueError(f"scheduler type {type(self.scheduler )} not supported" )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowerCAmelCase_ = 1 / 0.18_215 * sample
lowerCAmelCase_ = self.vae.decode(_a ).sample
lowerCAmelCase_ = (image / 2 + 0.5).clamp(0, 1 )
lowerCAmelCase_ = transforms.Resize(self.feature_extractor_size )(_a )
lowerCAmelCase_ = self.normalize(_a ).to(latents.dtype )
lowerCAmelCase_ = self.clip_model.get_image_features(_a )
lowerCAmelCase_ = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=_a )
lowerCAmelCase_ = spherical_dist_loss(_a, _a ).mean() * clip_guidance_scale
lowerCAmelCase_ = -torch.autograd.grad(_a, _a )[0]
if isinstance(self.scheduler, _a ):
lowerCAmelCase_ = latents.detach() + grads * (sigma**2)
lowerCAmelCase_ = noise_pred_original
else:
lowerCAmelCase_ = noise_pred_original - torch.sqrt(_a ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ = None, UpperCamelCase__ = None, UpperCamelCase__ = 512, UpperCamelCase__ = 512, UpperCamelCase__ = 0.6, UpperCamelCase__ = 50, UpperCamelCase__ = 7.5, UpperCamelCase__ = 1, UpperCamelCase__ = 0.0, UpperCamelCase__ = 100, UpperCamelCase__ = None, UpperCamelCase__ = "pil", UpperCamelCase__ = True, UpperCamelCase__ = 0.8, UpperCamelCase__ = 0.1, UpperCamelCase__ = 0.1, ):
"""simple docstring"""
if isinstance(_a, _a ) and len(_a ) != batch_size:
raise ValueError(f"You have passed {batch_size} batch_size, but only {len(_a )} generators." )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}." )
if isinstance(_a, torch.Generator ) and batch_size > 1:
lowerCAmelCase_ = [generator] + [None] * (batch_size - 1)
lowerCAmelCase_ = [
("model", self.coca_model is None),
("tokenizer", self.coca_tokenizer is None),
("transform", self.coca_transform is None),
]
lowerCAmelCase_ = [x[0] for x in coca_is_none if x[1]]
lowerCAmelCase_ = ", ".join(_a )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(_a ):
raise ValueError(
f"Content prompt is None and CoCa [{coca_is_none_str}] is None."
f"Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline." )
lowerCAmelCase_ = self.get_image_description(_a )
if style_prompt is None:
if len(_a ):
raise ValueError(
f"Style prompt is None and CoCa [{coca_is_none_str}] is None."
f" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline." )
lowerCAmelCase_ = self.get_image_description(_a )
# get prompt text embeddings for content and style
lowerCAmelCase_ = self.tokenizer(
_a, padding='''max_length''', max_length=self.tokenizer.model_max_length, truncation=_a, return_tensors='''pt''', )
lowerCAmelCase_ = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
lowerCAmelCase_ = self.tokenizer(
_a, padding='''max_length''', max_length=self.tokenizer.model_max_length, truncation=_a, return_tensors='''pt''', )
lowerCAmelCase_ = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
lowerCAmelCase_ = slerp(_a, _a, _a )
# duplicate text embeddings for each generation per prompt
lowerCAmelCase_ = text_embeddings.repeat_interleave(_a, dim=0 )
# set timesteps
lowerCAmelCase_ = "offset" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
lowerCAmelCase_ = {}
if accepts_offset:
lowerCAmelCase_ = 1
self.scheduler.set_timesteps(_a, **_a )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
lowerCAmelCase_ = self.get_timesteps(_a, _a, self.device )
lowerCAmelCase_ = timesteps[:1].repeat(_a )
# Preprocess image
lowerCAmelCase_ = preprocess(_a, _a, _a )
lowerCAmelCase_ = self.prepare_latents(
_a, _a, _a, text_embeddings.dtype, self.device, _a )
lowerCAmelCase_ = preprocess(_a, _a, _a )
lowerCAmelCase_ = self.prepare_latents(
_a, _a, _a, text_embeddings.dtype, self.device, _a )
lowerCAmelCase_ = slerp(_a, _a, _a )
if clip_guidance_scale > 0:
lowerCAmelCase_ = self.get_clip_image_embeddings(_a, _a )
lowerCAmelCase_ = self.get_clip_image_embeddings(_a, _a )
lowerCAmelCase_ = slerp(
_a, _a, _a )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
lowerCAmelCase_ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowerCAmelCase_ = content_text_input.input_ids.shape[-1]
lowerCAmelCase_ = self.tokenizer([''''''], padding='''max_length''', max_length=_a, return_tensors='''pt''' )
lowerCAmelCase_ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
lowerCAmelCase_ = uncond_embeddings.repeat_interleave(_a, dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowerCAmelCase_ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
lowerCAmelCase_ = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
lowerCAmelCase_ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
lowerCAmelCase_ = torch.randn(_a, generator=_a, device='''cpu''', dtype=_a ).to(
self.device )
else:
lowerCAmelCase_ = torch.randn(_a, generator=_a, device=self.device, dtype=_a )
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}" )
lowerCAmelCase_ = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowerCAmelCase_ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
lowerCAmelCase_ = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowerCAmelCase_ = {}
if accepts_eta:
lowerCAmelCase_ = eta
# check if the scheduler accepts generator
lowerCAmelCase_ = "generator" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
lowerCAmelCase_ = generator
with self.progress_bar(total=_a ):
for i, t in enumerate(_a ):
# expand the latents if we are doing classifier free guidance
lowerCAmelCase_ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCAmelCase_ = self.scheduler.scale_model_input(_a, _a )
# predict the noise residual
lowerCAmelCase_ = self.unet(_a, _a, encoder_hidden_states=_a ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
lowerCAmelCase_ = noise_pred.chunk(2 )
lowerCAmelCase_ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
lowerCAmelCase_ = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
lowerCAmelCase_ = self.cond_fn(
_a, _a, _a, _a, _a, _a, _a, )
# compute the previous noisy sample x_t -> x_t-1
lowerCAmelCase_ = self.scheduler.step(_a, _a, _a, **_a ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowerCAmelCase_ = 1 / 0.18_215 * latents
lowerCAmelCase_ = self.vae.decode(_a ).sample
lowerCAmelCase_ = (image / 2 + 0.5).clamp(0, 1 )
lowerCAmelCase_ = image.cpu().permute(0, 2, 3, 1 ).numpy()
if output_type == "pil":
lowerCAmelCase_ = self.numpy_to_pil(_a )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=_a, nsfw_content_detected=_a )
| 278 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
snake_case : List[str] = "facebook/wmt19-en-de"
snake_case : Dict = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
snake_case : List[str] = FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
snake_case : int = FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")
# Test
snake_case : Optional[Any] = tokenizer(["Making tiny model"], return_tensors="pt")
snake_case : List[str] = tiny_model(**batch)
print("test output:", len(outputs.logits[0]))
# Save
snake_case : Dict = "tiny-wmt19-en-de"
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F"Generated {mname_tiny}")
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 281 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase (SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : list[str] | None = None , SCREAMING_SNAKE_CASE_ : dict[str, float] | None = None , SCREAMING_SNAKE_CASE_ : bool = False , ) -> tuple[int, float, str]:
SCREAMING_SNAKE_CASE = cipher_alphabet or [chr(_snake_case ) for i in range(97 , 1_23 )]
# If the argument is None or the user provided an empty dictionary
if not frequencies_dict:
# Frequencies of letters in the english language (how much they show up)
SCREAMING_SNAKE_CASE = {
"a": 0.0_84_97,
"b": 0.0_14_92,
"c": 0.0_22_02,
"d": 0.0_42_53,
"e": 0.1_11_62,
"f": 0.0_22_28,
"g": 0.0_20_15,
"h": 0.0_60_94,
"i": 0.0_75_46,
"j": 0.0_01_53,
"k": 0.0_12_92,
"l": 0.0_40_25,
"m": 0.0_24_06,
"n": 0.0_67_49,
"o": 0.0_75_07,
"p": 0.0_19_29,
"q": 0.0_00_95,
"r": 0.0_75_87,
"s": 0.0_63_27,
"t": 0.0_93_56,
"u": 0.0_27_58,
"v": 0.0_09_78,
"w": 0.0_25_60,
"x": 0.0_01_50,
"y": 0.0_19_94,
"z": 0.0_00_77,
}
else:
# Custom frequencies dictionary
SCREAMING_SNAKE_CASE = frequencies_dict
if not case_sensitive:
SCREAMING_SNAKE_CASE = ciphertext.lower()
# Chi squared statistic values
SCREAMING_SNAKE_CASE = {}
# cycle through all of the shifts
for shift in range(len(_snake_case ) ):
SCREAMING_SNAKE_CASE = ""
# decrypt the message with the shift
for letter in ciphertext:
try:
# Try to index the letter in the alphabet
SCREAMING_SNAKE_CASE = (alphabet_letters.index(letter.lower() ) - shift) % len(
_snake_case )
decrypted_with_shift += (
alphabet_letters[new_key].upper()
if case_sensitive and letter.isupper()
else alphabet_letters[new_key]
)
except ValueError:
# Append the character if it isn't in the alphabet
decrypted_with_shift += letter
SCREAMING_SNAKE_CASE = 0.0
# Loop through each letter in the decoded message with the shift
for letter in decrypted_with_shift:
if case_sensitive:
SCREAMING_SNAKE_CASE = letter.lower()
if letter in frequencies:
# Get the amount of times the letter occurs in the message
SCREAMING_SNAKE_CASE = decrypted_with_shift.lower().count(_snake_case )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
SCREAMING_SNAKE_CASE = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
SCREAMING_SNAKE_CASE = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
else:
if letter.lower() in frequencies:
# Get the amount of times the letter occurs in the message
SCREAMING_SNAKE_CASE = decrypted_with_shift.count(_snake_case )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
SCREAMING_SNAKE_CASE = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
SCREAMING_SNAKE_CASE = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
# Add the data to the chi_squared_statistic_values dictionary
SCREAMING_SNAKE_CASE = (
chi_squared_statistic,
decrypted_with_shift,
)
# Get the most likely cipher by finding the cipher with the smallest chi squared
# statistic
def chi_squared_statistic_values_sorting_key(SCREAMING_SNAKE_CASE_ : int ) -> tuple[float, str]:
return chi_squared_statistic_values[key]
SCREAMING_SNAKE_CASE = min(
_snake_case , key=_snake_case , )
# Get all the data from the most likely cipher (key, decoded message)
(
SCREAMING_SNAKE_CASE
) = chi_squared_statistic_values[most_likely_cipher]
# Return the data on the most likely shift
return (
most_likely_cipher,
most_likely_cipher_chi_squared_value,
decoded_most_likely_cipher,
)
| 113 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
snake_case : Optional[int] = logging.getLogger(__name__)
def lowerCAmelCase_ ( _snake_case : Dict , _snake_case : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__magic_name__ : List[str] = np.argmax(_snake_case , axis=1 )
return np.sum(outputs == labels )
def lowerCAmelCase_ ( _snake_case : Optional[Any] ) -> Dict:
'''simple docstring'''
with open(_snake_case , encoding="utf_8" ) as f:
__magic_name__ : List[str] = csv.reader(_snake_case )
__magic_name__ : List[Any] = []
next(_snake_case ) # skip the first line
for line in tqdm(_snake_case ):
output.append((" ".join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def lowerCAmelCase_ ( _snake_case : str , _snake_case : Tuple , _snake_case : Union[str, Any] , _snake_case : List[Any] , _snake_case : Tuple , _snake_case : Optional[int] ) -> int:
'''simple docstring'''
__magic_name__ : Optional[int] = []
for dataset in encoded_datasets:
__magic_name__ : Union[str, Any] = len(_snake_case )
__magic_name__ : Dict = np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
__magic_name__ : List[str] = np.zeros((n_batch, 2) , dtype=np.intaa )
__magic_name__ : Optional[int] = np.full((n_batch, 2, input_len) , fill_value=-100 , dtype=np.intaa )
__magic_name__ : int = np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(_snake_case ):
__magic_name__ : Dict = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__magic_name__ : Dict = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__magic_name__ : str = with_conta
__magic_name__ : Tuple = with_conta
__magic_name__ : Union[str, Any] = len(_snake_case ) - 1
__magic_name__ : int = len(_snake_case ) - 1
__magic_name__ : Optional[Any] = with_conta
__magic_name__ : Optional[Any] = with_conta
__magic_name__ : Optional[int] = mc_label
__magic_name__ : str = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(_snake_case ) for t in all_inputs ) )
return tensor_datasets
def lowerCAmelCase_ ( ) -> List[Any]:
'''simple docstring'''
__magic_name__ : Any = argparse.ArgumentParser()
parser.add_argument("--model_name" , type=_snake_case , default="openai-gpt" , help="pretrained model name" )
parser.add_argument("--do_train" , action="store_true" , help="Whether to run training." )
parser.add_argument("--do_eval" , action="store_true" , help="Whether to run eval on the dev set." )
parser.add_argument(
"--output_dir" , default=_snake_case , type=_snake_case , required=_snake_case , help="The output directory where the model predictions and checkpoints will be written." , )
parser.add_argument("--train_dataset" , type=_snake_case , default="" )
parser.add_argument("--eval_dataset" , type=_snake_case , default="" )
parser.add_argument("--seed" , type=_snake_case , default=42 )
parser.add_argument("--num_train_epochs" , type=_snake_case , default=3 )
parser.add_argument("--train_batch_size" , type=_snake_case , default=8 )
parser.add_argument("--eval_batch_size" , type=_snake_case , default=16 )
parser.add_argument("--adam_epsilon" , default=1E-8 , type=_snake_case , help="Epsilon for Adam optimizer." )
parser.add_argument("--max_grad_norm" , type=_snake_case , default=1 )
parser.add_argument(
"--max_steps" , default=-1 , type=_snake_case , help=(
"If > 0: set total number of training steps to perform. Override num_train_epochs."
) , )
parser.add_argument(
"--gradient_accumulation_steps" , type=_snake_case , default=1 , help="Number of updates steps to accumulate before performing a backward/update pass." , )
parser.add_argument("--learning_rate" , type=_snake_case , default=6.25E-5 )
parser.add_argument("--warmup_steps" , default=0 , type=_snake_case , help="Linear warmup over warmup_steps." )
parser.add_argument("--lr_schedule" , type=_snake_case , default="warmup_linear" )
parser.add_argument("--weight_decay" , type=_snake_case , default=0.01 )
parser.add_argument("--lm_coef" , type=_snake_case , default=0.9 )
parser.add_argument("--n_valid" , type=_snake_case , default=374 )
parser.add_argument("--server_ip" , type=_snake_case , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=_snake_case , default="" , help="Can be used for distant debugging." )
__magic_name__ : List[Any] = parser.parse_args()
print(_snake_case )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=_snake_case )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
__magic_name__ : Dict = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
__magic_name__ : Optional[int] = torch.cuda.device_count()
logger.info("device: {}, n_gpu {}".format(_snake_case , _snake_case ) )
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True." )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
__magic_name__ : List[Any] = ["_start_", "_delimiter_", "_classify_"]
__magic_name__ : Optional[int] = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(_snake_case )
__magic_name__ : Optional[Any] = tokenizer.convert_tokens_to_ids(_snake_case )
__magic_name__ : List[str] = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(_snake_case ) )
model.to(_snake_case )
# Load and encode the datasets
def tokenize_and_encode(_snake_case : str ):
if isinstance(_snake_case , _snake_case ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_snake_case ) )
elif isinstance(_snake_case , _snake_case ):
return obj
return [tokenize_and_encode(_snake_case ) for o in obj]
logger.info("Encoding dataset..." )
__magic_name__ : Optional[int] = load_rocstories_dataset(args.train_dataset )
__magic_name__ : str = load_rocstories_dataset(args.eval_dataset )
__magic_name__ : int = (train_dataset, eval_dataset)
__magic_name__ : List[str] = tokenize_and_encode(_snake_case )
# Compute the max input length for the Transformer
__magic_name__ : Optional[Any] = model.config.n_positions // 2 - 2
__magic_name__ : Optional[int] = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
__magic_name__ : List[str] = min(_snake_case , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
__magic_name__ : List[Any] = pre_process_datasets(_snake_case , _snake_case , _snake_case , *_snake_case )
__magic_name__ , __magic_name__ : Optional[int] = tensor_datasets[0], tensor_datasets[1]
__magic_name__ : Tuple = TensorDataset(*_snake_case )
__magic_name__ : Union[str, Any] = RandomSampler(_snake_case )
__magic_name__ : Dict = DataLoader(_snake_case , sampler=_snake_case , batch_size=args.train_batch_size )
__magic_name__ : Any = TensorDataset(*_snake_case )
__magic_name__ : Optional[Any] = SequentialSampler(_snake_case )
__magic_name__ : int = DataLoader(_snake_case , sampler=_snake_case , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
__magic_name__ : Tuple = args.max_steps
__magic_name__ : List[str] = args.max_steps // (len(_snake_case ) // args.gradient_accumulation_steps) + 1
else:
__magic_name__ : List[str] = len(_snake_case ) // args.gradient_accumulation_steps * args.num_train_epochs
__magic_name__ : str = list(model.named_parameters() )
__magic_name__ : Dict = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
__magic_name__ : str = [
{
"params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
"weight_decay": args.weight_decay,
},
{"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], "weight_decay": 0.0},
]
__magic_name__ : str = AdamW(_snake_case , lr=args.learning_rate , eps=args.adam_epsilon )
__magic_name__ : List[str] = get_linear_schedule_with_warmup(
_snake_case , num_warmup_steps=args.warmup_steps , num_training_steps=_snake_case )
if args.do_train:
__magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc="Epoch" ):
__magic_name__ : List[str] = 0
__magic_name__ : Tuple = 0
__magic_name__ : Dict = tqdm(_snake_case , desc="Training" )
for step, batch in enumerate(_snake_case ):
__magic_name__ : Optional[Any] = tuple(t.to(_snake_case ) for t in batch )
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Dict = batch
__magic_name__ : Optional[Any] = model(_snake_case , mc_token_ids=_snake_case , lm_labels=_snake_case , mc_labels=_snake_case )
__magic_name__ : Optional[Any] = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
__magic_name__ : List[str] = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
__magic_name__ : int = "Training loss: {:.2e} lr: {:.2e}".format(_snake_case , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
__magic_name__ : Dict = model.module if hasattr(_snake_case , "module" ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
__magic_name__ : List[Any] = os.path.join(args.output_dir , _snake_case )
__magic_name__ : Dict = os.path.join(args.output_dir , _snake_case )
torch.save(model_to_save.state_dict() , _snake_case )
model_to_save.config.to_json_file(_snake_case )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
__magic_name__ : Dict = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
__magic_name__ : Optional[int] = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(_snake_case )
if args.do_eval:
model.eval()
__magic_name__ , __magic_name__ : Any = 0, 0
__magic_name__ , __magic_name__ : Union[str, Any] = 0, 0
for batch in tqdm(_snake_case , desc="Evaluating" ):
__magic_name__ : int = tuple(t.to(_snake_case ) for t in batch )
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] = batch
with torch.no_grad():
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Dict = model(
_snake_case , mc_token_ids=_snake_case , lm_labels=_snake_case , mc_labels=_snake_case )
__magic_name__ : Tuple = mc_logits.detach().cpu().numpy()
__magic_name__ : Any = mc_labels.to("cpu" ).numpy()
__magic_name__ : str = accuracy(_snake_case , _snake_case )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
__magic_name__ : Tuple = eval_loss / nb_eval_steps
__magic_name__ : List[Any] = eval_accuracy / nb_eval_examples
__magic_name__ : int = tr_loss / nb_tr_steps if args.do_train else None
__magic_name__ : Any = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}
__magic_name__ : int = os.path.join(args.output_dir , "eval_results.txt" )
with open(_snake_case , "w" ) as writer:
logger.info("***** Eval results *****" )
for key in sorted(result.keys() ):
logger.info(" %s = %s" , _snake_case , str(result[key] ) )
writer.write("%s = %s\n" % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 281 | 0 |
"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline # noqa: F401
deprecate(
"""stable diffusion controlnet""",
"""0.22.0""",
"""Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.""",
standard_warn=False,
stacklevel=3,
) | 96 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 281 | 0 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class lowerCAmelCase_ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase : int = 42
_lowerCAmelCase : str = None
def lowerCAmelCase__ ( _UpperCamelCase : Dict , _UpperCamelCase : List[str]=0.9_99 , _UpperCamelCase : Optional[Any]="cosine" , ) -> Union[str, Any]:
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(_UpperCamelCase : Optional[int] ):
return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(_UpperCamelCase : Any ):
return math.exp(t * -12.0 )
else:
raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
snake_case = []
for i in range(_snake_case ):
snake_case = i / num_diffusion_timesteps
snake_case = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(_snake_case ) / alpha_bar_fn(_snake_case ) , _snake_case ) )
return torch.tensor(_snake_case , dtype=torch.floataa )
class lowerCAmelCase_ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
@register_to_config
def __init__( self , lowerCAmelCase = 10_00 , lowerCAmelCase = "fixed_small_log" , lowerCAmelCase = True , lowerCAmelCase = 1.0 , lowerCAmelCase = "epsilon" , lowerCAmelCase = "squaredcos_cap_v2" , ):
"""simple docstring"""
if beta_schedule != "squaredcos_cap_v2":
raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'' )
snake_case = betas_for_alpha_bar(_a )
snake_case = 1.0 - self.betas
snake_case = torch.cumprod(self.alphas , dim=0 )
snake_case = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
snake_case = 1.0
# setable values
snake_case = None
snake_case = torch.from_numpy(np.arange(0 , _a )[::-1].copy() )
snake_case = variance_type
def snake_case ( self , lowerCAmelCase , lowerCAmelCase = None ):
"""simple docstring"""
return sample
def snake_case ( self , lowerCAmelCase , lowerCAmelCase = None ):
"""simple docstring"""
snake_case = num_inference_steps
snake_case = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
snake_case = (np.arange(0 , _a ) * step_ratio).round()[::-1].copy().astype(np.intaa )
snake_case = torch.from_numpy(_a ).to(_a )
def snake_case ( self , lowerCAmelCase , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=None ):
"""simple docstring"""
if prev_timestep is None:
snake_case = t - 1
snake_case = self.alphas_cumprod[t]
snake_case = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
snake_case = 1 - alpha_prod_t
snake_case = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
snake_case = self.betas[t]
else:
snake_case = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
snake_case = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
snake_case = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
snake_case = torch.log(torch.clamp(_a , min=1E-20 ) )
snake_case = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
snake_case = variance.log()
snake_case = beta.log()
snake_case = (predicted_variance + 1) / 2
snake_case = frac * max_log + (1 - frac) * min_log
return variance
def snake_case ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase=None , lowerCAmelCase = True , ):
"""simple docstring"""
snake_case = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
snake_case = torch.split(_a , sample.shape[1] , dim=1 )
else:
snake_case = None
# 1. compute alphas, betas
if prev_timestep is None:
snake_case = t - 1
snake_case = self.alphas_cumprod[t]
snake_case = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
snake_case = 1 - alpha_prod_t
snake_case = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
snake_case = self.betas[t]
snake_case = self.alphas[t]
else:
snake_case = 1 - alpha_prod_t / alpha_prod_t_prev
snake_case = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
snake_case = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
snake_case = model_output
else:
raise ValueError(
F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
' for the UnCLIPScheduler.' )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
snake_case = torch.clamp(
_a , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
snake_case = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
snake_case = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
snake_case = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
snake_case = 0
if t > 0:
snake_case = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=_a , device=model_output.device )
snake_case = self._get_variance(
_a , predicted_variance=_a , prev_timestep=_a , )
if self.variance_type == "fixed_small_log":
snake_case = variance
elif self.variance_type == "learned_range":
snake_case = (0.5 * variance).exp()
else:
raise ValueError(
F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
' for the UnCLIPScheduler.' )
snake_case = variance * variance_noise
snake_case = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=_a , pred_original_sample=_a )
def snake_case ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , ):
"""simple docstring"""
snake_case = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
snake_case = timesteps.to(original_samples.device )
snake_case = alphas_cumprod[timesteps] ** 0.5
snake_case = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
snake_case = sqrt_alpha_prod.unsqueeze(-1 )
snake_case = (1 - alphas_cumprod[timesteps]) ** 0.5
snake_case = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
snake_case = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
snake_case = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 150 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCAmelCase_ ( _snake_case : List[Any] ) -> List[Any]:
'''simple docstring'''
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ) -> Tuple:
'''simple docstring'''
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ : Dict = "mock-s3-bucket"
__magic_name__ : Any = F'''s3://{mock_bucket}'''
__magic_name__ : str = extract_path_from_uri(_snake_case )
assert dataset_path.startswith("s3://" ) is False
__magic_name__ : Tuple = "./local/path"
__magic_name__ : Optional[Any] = extract_path_from_uri(_snake_case )
assert dataset_path == new_dataset_path
def lowerCAmelCase_ ( _snake_case : List[str] ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ : str = is_remote_filesystem(_snake_case )
assert is_remote is True
__magic_name__ : Optional[int] = fsspec.filesystem("file" )
__magic_name__ : int = is_remote_filesystem(_snake_case )
assert is_remote is False
@pytest.mark.parametrize("compression_fs_class" , _snake_case )
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : Optional[Any] , _snake_case : int , _snake_case : Tuple , _snake_case : Any , _snake_case : Union[str, Any] , _snake_case : Any ) -> int:
'''simple docstring'''
__magic_name__ : Any = {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
__magic_name__ : str = input_paths[compression_fs_class.protocol]
if input_path is None:
__magic_name__ : Dict = F'''for \'{compression_fs_class.protocol}\' compression protocol, '''
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_snake_case )
__magic_name__ : str = fsspec.filesystem(compression_fs_class.protocol , fo=_snake_case )
assert isinstance(_snake_case , _snake_case )
__magic_name__ : int = os.path.basename(_snake_case )
__magic_name__ : Optional[int] = expected_filename[: expected_filename.rindex("." )]
assert fs.glob("*" ) == [expected_filename]
with fs.open(_snake_case , "r" , encoding="utf-8" ) as f, open(_snake_case , encoding="utf-8" ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize("protocol" , ["zip", "gzip"] )
def lowerCAmelCase_ ( _snake_case : List[Any] , _snake_case : Optional[Any] , _snake_case : Optional[Any] ) -> str:
'''simple docstring'''
__magic_name__ : int = {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
__magic_name__ : int = compressed_file_paths[protocol]
__magic_name__ : Tuple = "dataset.jsonl"
__magic_name__ : List[str] = F'''{protocol}://{member_file_path}::{compressed_file_path}'''
__magic_name__ , *__magic_name__ : Optional[Any] = fsspec.get_fs_token_paths(_snake_case )
assert fs.isfile(_snake_case )
assert not fs.isfile("non_existing_" + member_file_path )
@pytest.mark.integration
def lowerCAmelCase_ ( _snake_case : Union[str, Any] , _snake_case : Dict , _snake_case : List[str] , _snake_case : Tuple ) -> str:
'''simple docstring'''
__magic_name__ : int = hf_api.dataset_info(_snake_case , token=_snake_case )
__magic_name__ : Optional[Any] = HfFileSystem(repo_info=_snake_case , token=_snake_case )
assert sorted(hffs.glob("*" ) ) == [".gitattributes", "data"]
assert hffs.isdir("data" )
assert hffs.isfile(".gitattributes" ) and hffs.isfile("data/text_data.txt" )
with open(_snake_case ) as f:
assert hffs.open("data/text_data.txt" , "r" ).read() == f.read()
def lowerCAmelCase_ ( ) -> Optional[int]:
'''simple docstring'''
__magic_name__ : Optional[Any] = "bz2"
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(_snake_case , _snake_case , clobber=_snake_case )
with pytest.warns(_snake_case ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(_snake_case ) == 1
assert (
str(warning_info[0].message )
== F'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
)
| 281 | 0 |
"""simple docstring"""
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] , UpperCamelCase : int , UpperCamelCase : Dict=None , UpperCamelCase : List[str]=None ):
'''simple docstring'''
__UpperCAmelCase : Dict = data
__UpperCAmelCase : List[str] = previous
__UpperCAmelCase : Any = next_node
def __str__( self : List[str] ):
'''simple docstring'''
return f'''{self.data}'''
def lowerCamelCase__ ( self : List[str] ):
'''simple docstring'''
return self.data
def lowerCamelCase__ ( self : Dict ):
'''simple docstring'''
return self.next
def lowerCamelCase__ ( self : List[str] ):
'''simple docstring'''
return self.previous
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] , UpperCamelCase : Tuple ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = head
def __iter__( self : Any ):
'''simple docstring'''
return self
def lowerCamelCase__ ( self : Any ):
'''simple docstring'''
if not self.current:
raise StopIteration
else:
__UpperCAmelCase : List[Any] = self.current.get_data()
__UpperCAmelCase : int = self.current.get_next()
return value
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : List[str] ):
'''simple docstring'''
__UpperCAmelCase : int = None # First node in list
__UpperCAmelCase : Union[str, Any] = None # Last node in list
def __str__( self : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Optional[int] = self.head
__UpperCAmelCase : str = []
while current is not None:
nodes.append(current.get_data() )
__UpperCAmelCase : List[Any] = current.get_next()
return " ".join(str(_a ) for node in nodes )
def __contains__( self : str , UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
__UpperCAmelCase : List[str] = self.head
while current:
if current.get_data() == value:
return True
__UpperCAmelCase : str = current.get_next()
return False
def __iter__( self : List[Any] ):
'''simple docstring'''
return LinkedListIterator(self.head )
def lowerCamelCase__ ( self : List[str] ):
'''simple docstring'''
if self.head:
return self.head.get_data()
return None
def lowerCamelCase__ ( self : List[Any] ):
'''simple docstring'''
if self.tail:
return self.tail.get_data()
return None
def lowerCamelCase__ ( self : Tuple , UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
if self.head is None:
__UpperCAmelCase : str = node
__UpperCAmelCase : int = node
else:
self.insert_before_node(self.head , _a )
def lowerCamelCase__ ( self : Tuple , UpperCamelCase : Any ):
'''simple docstring'''
if self.head is None:
self.set_head(_a )
else:
self.insert_after_node(self.tail , _a )
def lowerCamelCase__ ( self : Any , UpperCamelCase : Optional[int] ):
'''simple docstring'''
__UpperCAmelCase : List[str] = Node(_a )
if self.head is None:
self.set_head(_a )
else:
self.set_tail(_a )
def lowerCamelCase__ ( self : Optional[Any] , UpperCamelCase : str , UpperCamelCase : Dict ):
'''simple docstring'''
__UpperCAmelCase : Tuple = node
__UpperCAmelCase : str = node.previous
if node.get_previous() is None:
__UpperCAmelCase : Dict = node_to_insert
else:
__UpperCAmelCase : str = node_to_insert
__UpperCAmelCase : str = node_to_insert
def lowerCamelCase__ ( self : str , UpperCamelCase : str , UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = node
__UpperCAmelCase : Tuple = node.next
if node.get_next() is None:
__UpperCAmelCase : Optional[int] = node_to_insert
else:
__UpperCAmelCase : List[Any] = node_to_insert
__UpperCAmelCase : int = node_to_insert
def lowerCamelCase__ ( self : Union[str, Any] , UpperCamelCase : Optional[Any] , UpperCamelCase : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : str = 1
__UpperCAmelCase : str = Node(_a )
__UpperCAmelCase : str = self.head
while node:
if current_position == position:
self.insert_before_node(_a , _a )
return
current_position += 1
__UpperCAmelCase : Optional[int] = node.next
self.insert_after_node(self.tail , _a )
def lowerCamelCase__ ( self : Any , UpperCamelCase : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = self.head
while node:
if node.get_data() == item:
return node
__UpperCAmelCase : Any = node.get_next()
raise Exception("""Node not found""" )
def lowerCamelCase__ ( self : Union[str, Any] , UpperCamelCase : Tuple ):
'''simple docstring'''
if (node := self.get_node(_a )) is not None:
if node == self.head:
__UpperCAmelCase : List[str] = self.head.get_next()
if node == self.tail:
__UpperCAmelCase : int = self.tail.get_previous()
self.remove_node_pointers(_a )
@staticmethod
def lowerCamelCase__ ( UpperCamelCase : List[Any] ):
'''simple docstring'''
if node.get_next():
__UpperCAmelCase : Dict = node.previous
if node.get_previous():
__UpperCAmelCase : int = node.next
__UpperCAmelCase : int = None
__UpperCAmelCase : Tuple = None
def lowerCamelCase__ ( self : Optional[int] ):
'''simple docstring'''
return self.head is None
def lowerCamelCase ( ) -> None:
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 115 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case : Dict = logging.get_logger(__name__)
snake_case : List[Any] = {
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
"YituTech/conv-bert-medium-small": (
"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
),
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class _snake_case ( snake_case ):
UpperCamelCase__ = 'convbert'
def __init__( self , _a=30_522 , _a=768 , _a=12 , _a=12 , _a=3_072 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=2 , _a=0.02 , _a=1e-12 , _a=1 , _a=0 , _a=2 , _a=768 , _a=2 , _a=9 , _a=1 , _a=None , **_a , ):
super().__init__(
pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a , )
__magic_name__ : Tuple = vocab_size
__magic_name__ : List[Any] = hidden_size
__magic_name__ : Union[str, Any] = num_hidden_layers
__magic_name__ : List[Any] = num_attention_heads
__magic_name__ : str = intermediate_size
__magic_name__ : Any = hidden_act
__magic_name__ : List[Any] = hidden_dropout_prob
__magic_name__ : Optional[int] = attention_probs_dropout_prob
__magic_name__ : Tuple = max_position_embeddings
__magic_name__ : str = type_vocab_size
__magic_name__ : List[str] = initializer_range
__magic_name__ : Tuple = layer_norm_eps
__magic_name__ : List[Any] = embedding_size
__magic_name__ : List[Any] = head_ratio
__magic_name__ : str = conv_kernel_size
__magic_name__ : Dict = num_groups
__magic_name__ : str = classifier_dropout
class _snake_case ( snake_case ):
@property
def SCREAMING_SNAKE_CASE ( self ):
if self.task == "multiple-choice":
__magic_name__ : Dict = {0: "batch", 1: "choice", 2: "sequence"}
else:
__magic_name__ : Dict = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 281 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.