code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
class lowerCamelCase__ :
def __init__(self ) -> Tuple:
_lowercase =''''''
_lowercase =''''''
_lowercase =[]
def __A (self , UpperCAmelCase , UpperCAmelCase ) -> int:
if m == -1:
return n + 1
elif n == -1:
return m + 1
elif self.dp[m][n] > -1:
return self.dp[m][n]
else:
if self.worda[m] == self.worda[n]:
_lowercase =self.__min_dist_top_down_dp(m - 1 , n - 1 )
else:
_lowercase =self.__min_dist_top_down_dp(UpperCAmelCase , n - 1 )
_lowercase =self.__min_dist_top_down_dp(m - 1 , UpperCAmelCase )
_lowercase =self.__min_dist_top_down_dp(m - 1 , n - 1 )
_lowercase =1 + min(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
return self.dp[m][n]
def __A (self , UpperCAmelCase , UpperCAmelCase ) -> int:
_lowercase =worda
_lowercase =worda
_lowercase =[[-1 for _ in range(len(UpperCAmelCase ) )] for _ in range(len(UpperCAmelCase ) )]
return self.__min_dist_top_down_dp(len(UpperCAmelCase ) - 1 , len(UpperCAmelCase ) - 1 )
def __A (self , UpperCAmelCase , UpperCAmelCase ) -> int:
_lowercase =worda
_lowercase =worda
_lowercase =len(UpperCAmelCase )
_lowercase =len(UpperCAmelCase )
_lowercase =[[0 for _ in range(n + 1 )] for _ in range(m + 1 )]
for i in range(m + 1 ):
for j in range(n + 1 ):
if i == 0: # first string is empty
_lowercase =j
elif j == 0: # second string is empty
_lowercase =i
elif worda[i - 1] == worda[j - 1]: # last characters are equal
_lowercase =self.dp[i - 1][j - 1]
else:
_lowercase =self.dp[i][j - 1]
_lowercase =self.dp[i - 1][j]
_lowercase =self.dp[i - 1][j - 1]
_lowercase =1 + min(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
return self.dp[m][n]
if __name__ == "__main__":
UpperCAmelCase__ = EditDistance()
print('''****************** Testing Edit Distance DP Algorithm ******************''')
print()
UpperCAmelCase__ = input('''Enter the first string: ''').strip()
UpperCAmelCase__ = input('''Enter the second string: ''').strip()
print()
print(f'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
print(f'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
print()
print('''*************** End of Testing Edit Distance DP Algorithm ***************''')
| 5 |
from math import isqrt
def UpperCAmelCase_ ( __snake_case ) -> list[int]:
"""simple docstring"""
_lowercase =[True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , __snake_case , __snake_case ):
_lowercase =False
return [i for i in range(2 , __snake_case ) if is_prime[i]]
def UpperCAmelCase_ ( __snake_case = 10**8 ) -> int:
"""simple docstring"""
_lowercase =calculate_prime_numbers(max_number // 2 )
_lowercase =0
_lowercase =0
_lowercase =len(__snake_case ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(f'''{solution() = }''')
| 5 | 1 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
UpperCamelCase = 2
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : List[Any] , *, # begin keyword-only arguments
SCREAMING_SNAKE_CASE__ : int="<s>" , SCREAMING_SNAKE_CASE__ : List[str]="<pad>" , SCREAMING_SNAKE_CASE__ : Any="</s>" , SCREAMING_SNAKE_CASE__ : Union[str, Any]="<unk>" , SCREAMING_SNAKE_CASE__ : Dict=None , ):
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = bos, unk, pad, eos
lowerCAmelCase__ = []
lowerCAmelCase__ = []
lowerCAmelCase__ = {}
lowerCAmelCase__ = self.add_symbol(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = self.add_symbol(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = self.add_symbol(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = self.add_symbol(SCREAMING_SNAKE_CASE__ )
if extra_special_symbols:
for s in extra_special_symbols:
self.add_symbol(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = len(self.symbols )
def __eq__( self : int , SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
return self.indices == other.indices
def __getitem__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str] ):
if idx < len(self.symbols ):
return self.symbols[idx]
return self.unk_word
def __len__( self : Optional[Any] ):
return len(self.symbols )
def __contains__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] ):
return sym in self.indices
@classmethod
def a ( cls : Tuple , SCREAMING_SNAKE_CASE__ : Any ):
lowerCAmelCase__ = cls()
d.add_from_file(SCREAMING_SNAKE_CASE__ )
return d
def a ( self : Any , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Any=1 , SCREAMING_SNAKE_CASE__ : Tuple=False ):
if word in self.indices and not overwrite:
lowerCAmelCase__ = self.indices[word]
lowerCAmelCase__ = self.count[idx] + n
return idx
else:
lowerCAmelCase__ = len(self.symbols )
lowerCAmelCase__ = idx
self.symbols.append(SCREAMING_SNAKE_CASE__ )
self.count.append(SCREAMING_SNAKE_CASE__ )
return idx
def a ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[int] ):
return 0
def a ( self : Dict , SCREAMING_SNAKE_CASE__ : int ):
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
try:
with open(SCREAMING_SNAKE_CASE__ , "r" , encoding="utf-8" ) as fd:
self.add_from_file(SCREAMING_SNAKE_CASE__ )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(SCREAMING_SNAKE_CASE__ ) )
return
lowerCAmelCase__ = f.readlines()
lowerCAmelCase__ = self._load_meta(SCREAMING_SNAKE_CASE__ )
for line in lines[indices_start_line:]:
try:
lowerCAmelCase__ , lowerCAmelCase__ = line.rstrip().rsplit(" " , 1 )
if field == "#fairseq:overwrite":
lowerCAmelCase__ = True
lowerCAmelCase__ , lowerCAmelCase__ = line.rsplit(" " , 1 )
else:
lowerCAmelCase__ = False
lowerCAmelCase__ = int(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = line
if word in self and not overwrite:
raise RuntimeError(
"Duplicate word found when loading Dictionary: '{}'. "
"Duplicate words can overwrite earlier ones by adding the "
"#fairseq:overwrite flag at the end of the corresponding row "
"in the dictionary file. If using the Camembert model, please "
"download an updated copy of the model file.".format(SCREAMING_SNAKE_CASE__ ) )
self.add_symbol(SCREAMING_SNAKE_CASE__ , n=SCREAMING_SNAKE_CASE__ , overwrite=SCREAMING_SNAKE_CASE__ )
except ValueError:
raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'" )
def _A ( lowerCAmelCase_ : Dict ):
"""simple docstring"""
lowerCAmelCase__ = dict((re.sub(r"@@$" , "" , lowerCAmelCase_ ), v) if k.endswith("@@" ) else (re.sub(r"$" , "</w>" , lowerCAmelCase_ ), v) for k, v in d.items() )
lowerCAmelCase__ = "<s> <pad> </s> <unk>".split()
# restore the special tokens
for k in keep_keys:
del da[F'{k}</w>']
lowerCAmelCase__ = d[k] # restore
return da
def _A ( lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str ):
"""simple docstring"""
if not os.path.exists(lowerCAmelCase_ ):
raise ValueError(F'path {biogpt_checkpoint_path} does not exist!' )
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
print(F'Writing results to {pytorch_dump_folder_path}' )
# handle various types of models
lowerCAmelCase__ = os.path.join(lowerCAmelCase_ , "checkpoint.pt" )
if not os.path.isfile(lowerCAmelCase_ ):
raise ValueError(F'path to the file {checkpoint_file} does not exist!' )
lowerCAmelCase__ = torch.load(lowerCAmelCase_ , map_location="cpu" )
lowerCAmelCase__ = chkpt["cfg"]["model"]
# dicts
lowerCAmelCase__ = os.path.join(lowerCAmelCase_ , "dict.txt" )
if not os.path.isfile(lowerCAmelCase_ ):
raise ValueError(F'path to the file {dict_file} does not exist!' )
lowerCAmelCase__ = Dictionary.load(lowerCAmelCase_ )
lowerCAmelCase__ = rewrite_dict_keys(src_dict.indices )
lowerCAmelCase__ = len(lowerCAmelCase_ )
lowerCAmelCase__ = os.path.join(lowerCAmelCase_ , VOCAB_FILES_NAMES["vocab_file"] )
print(F'Generating {src_vocab_file} of {src_vocab_size} records' )
with open(lowerCAmelCase_ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(lowerCAmelCase_ , ensure_ascii=lowerCAmelCase_ , indent=lowerCAmelCase_ ) )
# merges_file (bpecodes)
lowerCAmelCase__ = os.path.join(lowerCAmelCase_ , "bpecodes" )
if not os.path.isfile(lowerCAmelCase_ ):
raise ValueError(F'path to the file {bpecodes_file} does not exist!' )
lowerCAmelCase__ = os.path.join(lowerCAmelCase_ , VOCAB_FILES_NAMES["merges_file"] )
shutil.copyfile(lowerCAmelCase_ , lowerCAmelCase_ )
# model config
lowerCAmelCase__ = os.path.join(lowerCAmelCase_ , "config.json" )
lowerCAmelCase__ = {
"activation_dropout": args["activation_dropout"],
"architectures": ["BioGptForCausalLM"],
"attention_probs_dropout_prob": args["attention_dropout"],
"bos_token_id": 0,
"eos_token_id": 2,
"hidden_act": args["activation_fn"],
"hidden_dropout_prob": args["dropout"],
"hidden_size": args["decoder_embed_dim"],
"initializer_range": 0.02,
"intermediate_size": args["decoder_ffn_embed_dim"],
"layer_norm_eps": 1E-1_2,
"layerdrop": args["decoder_layerdrop"],
"max_position_embeddings": args["max_target_positions"],
"model_type": "biogpt",
"num_attention_heads": args["decoder_attention_heads"],
"num_hidden_layers": args["decoder_layers"],
"pad_token_id": 1,
"scale_embedding": not args["no_scale_embedding"],
"tie_word_embeddings": args["share_decoder_input_output_embed"],
"vocab_size": src_vocab_size,
}
# good hparam defaults to start with
print(F'Generating {biogpt_model_config_file}' )
with open(lowerCAmelCase_ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(lowerCAmelCase_ , ensure_ascii=lowerCAmelCase_ , indent=lowerCAmelCase_ ) )
# tokenizer config
lowerCAmelCase__ = os.path.join(lowerCAmelCase_ , lowerCAmelCase_ )
lowerCAmelCase__ = {
"bos_token": "<s>",
"eos_token": "</s>",
"model_max_length": 1024,
"pad_token": "<pad>",
"special_tokens_map_file": None,
"tokenizer_class": "BioGptTokenizer",
"unk_token": "<unk>",
}
print(F'Generating {biogpt_tokenizer_config_file}' )
with open(lowerCAmelCase_ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(lowerCAmelCase_ , ensure_ascii=lowerCAmelCase_ , indent=lowerCAmelCase_ ) )
# model
lowerCAmelCase__ = chkpt["model"]
# remove unneeded keys
lowerCAmelCase__ = [
"decoder.version",
]
for k in ignore_keys:
model_state_dict.pop(lowerCAmelCase_ , lowerCAmelCase_ )
lowerCAmelCase__ = list(model_state_dict.keys() )
for layer_name in layer_names:
if layer_name.endswith("output_projection.weight" ):
lowerCAmelCase__ = model_state_dict.pop(lowerCAmelCase_ )
else:
lowerCAmelCase__ = model_state_dict.pop(lowerCAmelCase_ )
lowerCAmelCase__ = BioGptConfig.from_pretrained(lowerCAmelCase_ )
lowerCAmelCase__ = BioGptForCausalLM(lowerCAmelCase_ )
# check that it loads ok
model_new.load_state_dict(lowerCAmelCase_ )
# save
lowerCAmelCase__ = os.path.join(lowerCAmelCase_ , lowerCAmelCase_ )
print(F'Generating {pytorch_weights_dump_path}' )
torch.save(lowerCAmelCase_ , lowerCAmelCase_ )
print("Conversion is done!" )
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--biogpt_checkpoint_path',
default=None,
type=str,
required=True,
help=(
'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
' bpecodes, etc.'
),
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
UpperCamelCase = parser.parse_args()
convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
| 364 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
UpperCamelCase = {
'vocab_file': {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-ctx_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-ctx_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
),
},
}
UpperCamelCase = {
'vocab_file': {
'facebook/dpr-question_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-question_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-question_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-question_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
),
},
}
UpperCamelCase = {
'vocab_file': {
'facebook/dpr-reader-single-nq-base': (
'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-reader-multiset-base': (
'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-reader-single-nq-base': (
'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-reader-multiset-base': (
'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
),
},
}
UpperCamelCase = {
'facebook/dpr-ctx_encoder-single-nq-base': 512,
'facebook/dpr-ctx_encoder-multiset-base': 512,
}
UpperCamelCase = {
'facebook/dpr-question_encoder-single-nq-base': 512,
'facebook/dpr-question_encoder-multiset-base': 512,
}
UpperCamelCase = {
'facebook/dpr-reader-single-nq-base': 512,
'facebook/dpr-reader-multiset-base': 512,
}
UpperCamelCase = {
'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
UpperCamelCase = {
'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
UpperCamelCase = {
'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
snake_case__ = VOCAB_FILES_NAMES
snake_case__ = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
snake_case__ = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
snake_case__ = DPRContextEncoderTokenizer
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
snake_case__ = VOCAB_FILES_NAMES
snake_case__ = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
snake_case__ = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
snake_case__ = DPRQuestionEncoderTokenizer
UpperCamelCase = collections.namedtuple(
'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)
UpperCamelCase = collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])
UpperCamelCase = R'\n Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n with the format:\n\n [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n Args:\n questions (`str` or `List[str]`):\n The questions to be encoded. You can specify one question for many passages. In this case, the question\n will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n `titles` or `texts`.\n titles (`str` or `List[str]`):\n The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n texts (`str` or `List[str]`):\n The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n Activates and controls padding. Accepts the following values:\n\n - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n if provided).\n - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided.\n - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n lengths).\n truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n Activates and controls truncation. Accepts the following values:\n\n - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n the maximum acceptable input length for the model if that argument is not provided. This will truncate\n token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n of pairs) is provided.\n - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the first\n sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the\n second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n greater than the model maximum admissible input size).\n max_length (`int`, *optional*):\n Controls the maximum length to use by one of the truncation/padding parameters.\n\n If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n is required by one of the truncation/padding parameters. If the model has no specific maximum input\n length (like XLNet) truncation/padding to a maximum length will be deactivated.\n return_tensors (`str` or [`~utils.TensorType`], *optional*):\n If set, will return tensors instead of list of python integers. Acceptable values are:\n\n - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n - `\'np\'`: Return Numpy `np.ndarray` objects.\n return_attention_mask (`bool`, *optional*):\n Whether or not to return the attention mask. If not set, will return the attention mask according to the\n specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Return:\n `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n - `input_ids`: List of token ids to be fed to a model.\n - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n '
@add_start_docstrings(UpperCamelCase__ )
class __lowerCamelCase :
"""simple docstring"""
def __call__( self : Tuple , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[str] = None , SCREAMING_SNAKE_CASE__ : Optional[str] = None , SCREAMING_SNAKE_CASE__ : Union[bool, str] = False , SCREAMING_SNAKE_CASE__ : Union[bool, str] = False , SCREAMING_SNAKE_CASE__ : Optional[int] = None , SCREAMING_SNAKE_CASE__ : Optional[Union[str, TensorType]] = None , SCREAMING_SNAKE_CASE__ : Optional[bool] = None , **SCREAMING_SNAKE_CASE__ : List[Any] , ) -> BatchEncoding:
if titles is None and texts is None:
return super().__call__(
SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
elif titles is None or texts is None:
lowerCAmelCase__ = titles if texts is None else texts
return super().__call__(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
lowerCAmelCase__ = titles if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else [titles]
lowerCAmelCase__ = texts if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else [texts]
lowerCAmelCase__ = len(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = questions if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else [questions] * n_passages
assert len(SCREAMING_SNAKE_CASE__ ) == len(
SCREAMING_SNAKE_CASE__ ), f'There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE__ )} titles and {len(SCREAMING_SNAKE_CASE__ )} texts.'
lowerCAmelCase__ = super().__call__(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ )["input_ids"]
lowerCAmelCase__ = super().__call__(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ )["input_ids"]
lowerCAmelCase__ = {
"input_ids": [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
]
}
if return_attention_mask is not False:
lowerCAmelCase__ = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
lowerCAmelCase__ = attention_mask
return self.pad(SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
def a ( self : Dict , SCREAMING_SNAKE_CASE__ : BatchEncoding , SCREAMING_SNAKE_CASE__ : DPRReaderOutput , SCREAMING_SNAKE_CASE__ : int = 16 , SCREAMING_SNAKE_CASE__ : int = 64 , SCREAMING_SNAKE_CASE__ : int = 4 , ) -> List[DPRSpanPrediction]:
lowerCAmelCase__ = reader_input["input_ids"]
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = reader_output[:3]
lowerCAmelCase__ = len(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = sorted(range(SCREAMING_SNAKE_CASE__ ) , reverse=SCREAMING_SNAKE_CASE__ , key=relevance_logits.__getitem__ )
lowerCAmelCase__ = []
for doc_id in sorted_docs:
lowerCAmelCase__ = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
lowerCAmelCase__ = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
lowerCAmelCase__ = sequence_ids.index(self.pad_token_id )
else:
lowerCAmelCase__ = len(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=SCREAMING_SNAKE_CASE__ , top_spans=SCREAMING_SNAKE_CASE__ , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=SCREAMING_SNAKE_CASE__ , start_index=SCREAMING_SNAKE_CASE__ , end_index=SCREAMING_SNAKE_CASE__ , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(SCREAMING_SNAKE_CASE__ ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def a ( self : int , SCREAMING_SNAKE_CASE__ : List[int] , SCREAMING_SNAKE_CASE__ : List[int] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , ) -> List[DPRSpanPrediction]:
lowerCAmelCase__ = []
for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE__ ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
lowerCAmelCase__ = sorted(SCREAMING_SNAKE_CASE__ , key=lambda SCREAMING_SNAKE_CASE__ : x[1] , reverse=SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = []
for (start_index, end_index), score in scores:
assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
lowerCAmelCase__ = end_index - start_index + 1
assert length <= max_answer_length, f'Span is too long: {length} > {max_answer_length}'
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(SCREAMING_SNAKE_CASE__ ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(UpperCamelCase__ )
class __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
snake_case__ = VOCAB_FILES_NAMES
snake_case__ = READER_PRETRAINED_VOCAB_FILES_MAP
snake_case__ = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ = READER_PRETRAINED_INIT_CONFIGURATION
snake_case__ = ["input_ids", "attention_mask"]
snake_case__ = DPRReaderTokenizer
| 221 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json',
'allenai/longformer-large-4096': 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json',
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'
),
}
class lowerCAmelCase__ ( lowerCAmelCase_ ):
lowerCAmelCase : int = "longformer"
def __init__( self : Optional[Any] , lowerCamelCase__ : Union[List[int], int] = 5_12 , lowerCamelCase__ : int = 2 , lowerCamelCase__ : int = 1 , lowerCamelCase__ : int = 0 , lowerCamelCase__ : int = 2 , lowerCamelCase__ : int = 3_05_22 , lowerCamelCase__ : int = 7_68 , lowerCamelCase__ : int = 12 , lowerCamelCase__ : int = 12 , lowerCamelCase__ : int = 30_72 , lowerCamelCase__ : str = "gelu" , lowerCamelCase__ : float = 0.1 , lowerCamelCase__ : float = 0.1 , lowerCamelCase__ : int = 5_12 , lowerCamelCase__ : int = 2 , lowerCamelCase__ : float = 0.0_2 , lowerCamelCase__ : float = 1E-12 , lowerCamelCase__ : bool = False , **lowerCamelCase__ : List[str] , ) ->int:
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase__ , **lowerCamelCase__ )
_UpperCAmelCase : Dict = attention_window
_UpperCAmelCase : int = sep_token_id
_UpperCAmelCase : Union[str, Any] = bos_token_id
_UpperCAmelCase : List[Any] = eos_token_id
_UpperCAmelCase : Tuple = vocab_size
_UpperCAmelCase : str = hidden_size
_UpperCAmelCase : Union[str, Any] = num_hidden_layers
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Optional[int] = hidden_act
_UpperCAmelCase : List[Any] = intermediate_size
_UpperCAmelCase : Dict = hidden_dropout_prob
_UpperCAmelCase : List[Any] = attention_probs_dropout_prob
_UpperCAmelCase : List[Any] = max_position_embeddings
_UpperCAmelCase : Union[str, Any] = type_vocab_size
_UpperCAmelCase : Dict = initializer_range
_UpperCAmelCase : Union[str, Any] = layer_norm_eps
_UpperCAmelCase : str = onnx_export
class lowerCAmelCase__ ( lowerCAmelCase_ ):
def __init__( self : List[Any] , lowerCamelCase__ : "PretrainedConfig" , lowerCamelCase__ : str = "default" , lowerCamelCase__ : "List[PatchingSpec]" = None ) ->int:
'''simple docstring'''
super().__init__(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
_UpperCAmelCase : Union[str, Any] = True
@property
def lowerCAmelCase__ ( self : Tuple ) ->Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
_UpperCAmelCase : List[str] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_UpperCAmelCase : str = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("global_attention_mask", dynamic_axis),
] )
@property
def lowerCAmelCase__ ( self : Dict ) ->Mapping[str, Mapping[int, str]]:
'''simple docstring'''
_UpperCAmelCase : List[Any] = super().outputs
if self.task == "default":
_UpperCAmelCase : str = {0: '''batch'''}
return outputs
@property
def lowerCAmelCase__ ( self : str ) ->float:
'''simple docstring'''
return 1E-4
@property
def lowerCAmelCase__ ( self : List[str] ) ->int:
'''simple docstring'''
return max(super().default_onnx_opset , 14 )
def lowerCAmelCase__ ( self : int , lowerCamelCase__ : "PreTrainedTokenizerBase" , lowerCamelCase__ : int = -1 , lowerCamelCase__ : int = -1 , lowerCamelCase__ : bool = False , lowerCamelCase__ : Optional[TensorType] = None , ) ->Mapping[str, Any]:
'''simple docstring'''
_UpperCAmelCase : str = super().generate_dummy_inputs(
preprocessor=lowerCamelCase__ , batch_size=lowerCamelCase__ , seq_length=lowerCamelCase__ , is_pair=lowerCamelCase__ , framework=lowerCamelCase__ )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
_UpperCAmelCase : Any = torch.zeros_like(inputs["input_ids"] )
# make every second token global
_UpperCAmelCase : Optional[int] = 1
return inputs
| 234 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Dict = logging.get_logger(__name__)
_A : Union[str, Any] = {
'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 __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
_UpperCAmelCase : Any = "vit_msn"
def __init__( self : Optional[Any] , A : Dict=7_6_8 , A : Union[str, Any]=1_2 , A : Optional[Any]=1_2 , A : List[Any]=3_0_7_2 , A : List[str]="gelu" , A : Optional[int]=0.0 , A : int=0.0 , A : int=0.02 , A : Tuple=1e-06 , A : int=2_2_4 , A : Union[str, Any]=1_6 , A : Dict=3 , A : Optional[Any]=True , **A : Optional[Any] , ) ->Dict:
super().__init__(**A )
lowerCamelCase__ : int = hidden_size
lowerCamelCase__ : Dict = num_hidden_layers
lowerCamelCase__ : str = num_attention_heads
lowerCamelCase__ : Tuple = intermediate_size
lowerCamelCase__ : str = hidden_act
lowerCamelCase__ : Optional[int] = hidden_dropout_prob
lowerCamelCase__ : Any = attention_probs_dropout_prob
lowerCamelCase__ : List[str] = initializer_range
lowerCamelCase__ : Optional[int] = layer_norm_eps
lowerCamelCase__ : Any = image_size
lowerCamelCase__ : Any = patch_size
lowerCamelCase__ : Union[str, Any] = num_channels
lowerCamelCase__ : Tuple = qkv_bias
| 142 | 0 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
snake_case = ['''image_processor''', '''tokenizer''']
snake_case = '''BlipImageProcessor'''
snake_case = '''AutoTokenizer'''
def __init__( self : str , __UpperCAmelCase : Any , __UpperCAmelCase : Any , __UpperCAmelCase : Any ):
'''simple docstring'''
super().__init__(__UpperCAmelCase , __UpperCAmelCase )
# add QFormer tokenizer
_A = qformer_tokenizer
def __call__( self : List[Any] , __UpperCAmelCase : ImageInput = None , __UpperCAmelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __UpperCAmelCase : bool = True , __UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , __UpperCAmelCase : Union[bool, str, TruncationStrategy] = None , __UpperCAmelCase : Optional[int] = None , __UpperCAmelCase : int = 0 , __UpperCAmelCase : Optional[int] = None , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : bool = False , __UpperCAmelCase : bool = False , __UpperCAmelCase : bool = False , __UpperCAmelCase : bool = False , __UpperCAmelCase : bool = False , __UpperCAmelCase : bool = True , __UpperCAmelCase : Optional[Union[str, TensorType]] = None , **__UpperCAmelCase : Union[str, Any] , ):
'''simple docstring'''
if images is None and text is None:
raise ValueError("You have to specify at least images or text." )
_A = BatchFeature()
if text is not None:
_A = self.tokenizer(
text=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , max_length=__UpperCAmelCase , stride=__UpperCAmelCase , pad_to_multiple_of=__UpperCAmelCase , return_attention_mask=__UpperCAmelCase , return_overflowing_tokens=__UpperCAmelCase , return_special_tokens_mask=__UpperCAmelCase , return_offsets_mapping=__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase , return_length=__UpperCAmelCase , verbose=__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase , )
encoding.update(__UpperCAmelCase )
_A = self.qformer_tokenizer(
text=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , max_length=__UpperCAmelCase , stride=__UpperCAmelCase , pad_to_multiple_of=__UpperCAmelCase , return_attention_mask=__UpperCAmelCase , return_overflowing_tokens=__UpperCAmelCase , return_special_tokens_mask=__UpperCAmelCase , return_offsets_mapping=__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase , return_length=__UpperCAmelCase , verbose=__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase , )
_A = qformer_text_encoding.pop("input_ids" )
_A = qformer_text_encoding.pop("attention_mask" )
if images is not None:
_A = self.image_processor(__UpperCAmelCase , return_tensors=__UpperCAmelCase )
encoding.update(__UpperCAmelCase )
return encoding
def lowerCAmelCase ( self : Tuple , *__UpperCAmelCase : Optional[int] , **__UpperCAmelCase : int ):
'''simple docstring'''
return self.tokenizer.batch_decode(*__UpperCAmelCase , **__UpperCAmelCase )
def lowerCAmelCase ( self : Any , *__UpperCAmelCase : List[Any] , **__UpperCAmelCase : Union[str, Any] ):
'''simple docstring'''
return self.tokenizer.decode(*__UpperCAmelCase , **__UpperCAmelCase )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def lowerCAmelCase ( self : Any ):
'''simple docstring'''
_A = self.tokenizer.model_input_names
_A = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : Dict , **__UpperCAmelCase : List[str] ):
'''simple docstring'''
if os.path.isfile(__UpperCAmelCase ):
raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
_A = os.path.join(__UpperCAmelCase , "qformer_tokenizer" )
self.qformer_tokenizer.save_pretrained(__UpperCAmelCase )
return super().save_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
@classmethod
def lowerCAmelCase ( cls : Tuple , __UpperCAmelCase : Optional[Any] , **__UpperCAmelCase : Optional[int] ):
'''simple docstring'''
_A = AutoTokenizer.from_pretrained(__UpperCAmelCase , subfolder="qformer_tokenizer" )
_A = cls._get_arguments_from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
args.append(__UpperCAmelCase )
return cls(*__UpperCAmelCase )
| 358 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def __lowercase ( __lowercase , __lowercase , __lowercase=None , **__lowercase ) -> Optional[int]:
'''simple docstring'''
_A = [x.strip() for x in open(__lowercase ).readlines()]
_A = [x.strip() for x in open(__lowercase ).readlines()][: len(__lowercase )]
_A = calculate_rouge(__lowercase , __lowercase , **__lowercase )
if save_path is not None:
save_json(__lowercase , __lowercase , indent=__lowercase )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 174 | 0 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
BertTokenizer,
ProcessorMixin,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
from test_module.custom_processing import CustomProcessor # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
_lowerCAmelCase : str = get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_lowerCAmelCase : List[Any] = get_tests_dir("fixtures/vocab.json")
_lowerCAmelCase : List[str] = get_tests_dir("fixtures")
class _UpperCamelCase ( unittest.TestCase ):
UpperCAmelCase_ = ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]
def UpperCAmelCase_ ( self :Dict ) -> Dict:
UpperCAmelCase__ = 0
def UpperCAmelCase_ ( self :List[str] ) -> Tuple:
UpperCAmelCase__ = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h" )
self.assertIsInstance(lowerCamelCase , lowerCamelCase )
def UpperCAmelCase_ ( self :str ) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase__ = WavaVecaConfig()
UpperCAmelCase__ = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h" )
# save in new folder
model_config.save_pretrained(lowerCamelCase )
processor.save_pretrained(lowerCamelCase )
UpperCAmelCase__ = AutoProcessor.from_pretrained(lowerCamelCase )
self.assertIsInstance(lowerCamelCase , lowerCamelCase )
def UpperCAmelCase_ ( self :List[Any] ) -> Any:
with tempfile.TemporaryDirectory() as tmpdirname:
# copy relevant files
copyfile(lowerCamelCase , os.path.join(lowerCamelCase , lowerCamelCase ) )
copyfile(lowerCamelCase , os.path.join(lowerCamelCase , "vocab.json" ) )
UpperCAmelCase__ = AutoProcessor.from_pretrained(lowerCamelCase )
self.assertIsInstance(lowerCamelCase , lowerCamelCase )
def UpperCAmelCase_ ( self :Any ) -> Any:
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase__ = WavaVecaFeatureExtractor()
UpperCAmelCase__ = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h" )
UpperCAmelCase__ = WavaVecaProcessor(lowerCamelCase , lowerCamelCase )
# save in new folder
processor.save_pretrained(lowerCamelCase )
# drop `processor_class` in tokenizer
with open(os.path.join(lowerCamelCase , lowerCamelCase ) , "r" ) as f:
UpperCAmelCase__ = json.load(lowerCamelCase )
config_dict.pop("processor_class" )
with open(os.path.join(lowerCamelCase , lowerCamelCase ) , "w" ) as f:
f.write(json.dumps(lowerCamelCase ) )
UpperCAmelCase__ = AutoProcessor.from_pretrained(lowerCamelCase )
self.assertIsInstance(lowerCamelCase , lowerCamelCase )
def UpperCAmelCase_ ( self :Optional[Any] ) -> Dict:
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase__ = WavaVecaFeatureExtractor()
UpperCAmelCase__ = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h" )
UpperCAmelCase__ = WavaVecaProcessor(lowerCamelCase , lowerCamelCase )
# save in new folder
processor.save_pretrained(lowerCamelCase )
# drop `processor_class` in feature extractor
with open(os.path.join(lowerCamelCase , lowerCamelCase ) , "r" ) as f:
UpperCAmelCase__ = json.load(lowerCamelCase )
config_dict.pop("processor_class" )
with open(os.path.join(lowerCamelCase , lowerCamelCase ) , "w" ) as f:
f.write(json.dumps(lowerCamelCase ) )
UpperCAmelCase__ = AutoProcessor.from_pretrained(lowerCamelCase )
self.assertIsInstance(lowerCamelCase , lowerCamelCase )
def UpperCAmelCase_ ( self :Union[str, Any] ) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase__ = WavaVecaConfig(processor_class="Wav2Vec2Processor" )
model_config.save_pretrained(lowerCamelCase )
# copy relevant files
copyfile(lowerCamelCase , os.path.join(lowerCamelCase , "vocab.json" ) )
# create emtpy sample processor
with open(os.path.join(lowerCamelCase , lowerCamelCase ) , "w" ) as f:
f.write("{}" )
UpperCAmelCase__ = AutoProcessor.from_pretrained(lowerCamelCase )
self.assertIsInstance(lowerCamelCase , lowerCamelCase )
def UpperCAmelCase_ ( self :List[Any] ) -> Tuple:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(lowerCamelCase ):
UpperCAmelCase__ = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowerCamelCase ):
UpperCAmelCase__ = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=lowerCamelCase )
UpperCAmelCase__ = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor" , trust_remote_code=lowerCamelCase )
self.assertTrue(processor.special_attribute_present )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
UpperCAmelCase__ = processor.feature_extractor
self.assertTrue(feature_extractor.special_attribute_present )
self.assertEqual(feature_extractor.__class__.__name__ , "NewFeatureExtractor" )
UpperCAmelCase__ = processor.tokenizer
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" )
# Test we can also load the slow version
UpperCAmelCase__ = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=lowerCamelCase , use_fast=lowerCamelCase )
UpperCAmelCase__ = new_processor.tokenizer
self.assertTrue(new_tokenizer.special_attribute_present )
self.assertEqual(new_tokenizer.__class__.__name__ , "NewTokenizer" )
else:
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
def UpperCAmelCase_ ( self :List[Any] ) -> List[Any]:
try:
AutoConfig.register("custom" , lowerCamelCase )
AutoFeatureExtractor.register(lowerCamelCase , lowerCamelCase )
AutoTokenizer.register(lowerCamelCase , slow_tokenizer_class=lowerCamelCase )
AutoProcessor.register(lowerCamelCase , lowerCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowerCamelCase ):
AutoProcessor.register(lowerCamelCase , lowerCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
UpperCAmelCase__ = CustomFeatureExtractor.from_pretrained(lowerCamelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase__ = os.path.join(lowerCamelCase , "vocab.txt" )
with open(lowerCamelCase , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
UpperCAmelCase__ = CustomTokenizer(lowerCamelCase )
UpperCAmelCase__ = CustomProcessor(lowerCamelCase , lowerCamelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(lowerCamelCase )
UpperCAmelCase__ = AutoProcessor.from_pretrained(lowerCamelCase )
self.assertIsInstance(lowerCamelCase , lowerCamelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def UpperCAmelCase_ ( self :Any ) -> Union[str, Any]:
class _UpperCamelCase ( lowerCAmelCase ):
UpperCAmelCase_ = False
class _UpperCamelCase ( lowerCAmelCase ):
UpperCAmelCase_ = False
class _UpperCamelCase ( lowerCAmelCase ):
UpperCAmelCase_ = """AutoFeatureExtractor"""
UpperCAmelCase_ = """AutoTokenizer"""
UpperCAmelCase_ = False
try:
AutoConfig.register("custom" , lowerCamelCase )
AutoFeatureExtractor.register(lowerCamelCase , lowerCamelCase )
AutoTokenizer.register(lowerCamelCase , slow_tokenizer_class=lowerCamelCase )
AutoProcessor.register(lowerCamelCase , lowerCamelCase )
# If remote code is not set, the default is to use local classes.
UpperCAmelCase__ = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor" )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote code is disabled, we load the local ones.
UpperCAmelCase__ = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=lowerCamelCase )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub.
UpperCAmelCase__ = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=lowerCamelCase )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
self.assertTrue(processor.special_attribute_present )
self.assertTrue(processor.feature_extractor.special_attribute_present )
self.assertTrue(processor.tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def UpperCAmelCase_ ( self :str ) -> Union[str, Any]:
UpperCAmelCase__ = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert" )
self.assertEqual(processor.__class__.__name__ , "BertTokenizerFast" )
def UpperCAmelCase_ ( self :Union[str, Any] ) -> str:
UpperCAmelCase__ = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext" )
self.assertEqual(processor.__class__.__name__ , "ConvNextImageProcessor" )
@is_staging_test
class _UpperCamelCase ( unittest.TestCase ):
UpperCAmelCase_ = ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]
@classmethod
def UpperCAmelCase_ ( cls :Optional[int] ) -> int:
UpperCAmelCase__ = TOKEN
HfFolder.save_token(lowerCamelCase )
@classmethod
def UpperCAmelCase_ ( cls :List[str] ) -> Any:
try:
delete_repo(token=cls._token , repo_id="test-processor" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-processor-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-processor" )
except HTTPError:
pass
def UpperCAmelCase_ ( self :Dict ) -> Union[str, Any]:
UpperCAmelCase__ = WavaVecaProcessor.from_pretrained(lowerCamelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(lowerCamelCase , "test-processor" ) , push_to_hub=lowerCamelCase , use_auth_token=self._token )
UpperCAmelCase__ = WavaVecaProcessor.from_pretrained(f'''{USER}/test-processor''' )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(lowerCamelCase , getattr(new_processor.feature_extractor , lowerCamelCase ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def UpperCAmelCase_ ( self :str ) -> List[str]:
UpperCAmelCase__ = WavaVecaProcessor.from_pretrained(lowerCamelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(lowerCamelCase , "test-processor-org" ) , push_to_hub=lowerCamelCase , use_auth_token=self._token , organization="valid_org" , )
UpperCAmelCase__ = WavaVecaProcessor.from_pretrained("valid_org/test-processor-org" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(lowerCamelCase , getattr(new_processor.feature_extractor , lowerCamelCase ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def UpperCAmelCase_ ( self :List[str] ) -> Optional[int]:
CustomFeatureExtractor.register_for_auto_class()
CustomTokenizer.register_for_auto_class()
CustomProcessor.register_for_auto_class()
UpperCAmelCase__ = CustomFeatureExtractor.from_pretrained(lowerCamelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase__ = os.path.join(lowerCamelCase , "vocab.txt" )
with open(lowerCamelCase , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
UpperCAmelCase__ = CustomTokenizer(lowerCamelCase )
UpperCAmelCase__ = CustomProcessor(lowerCamelCase , lowerCamelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
create_repo(f'''{USER}/test-dynamic-processor''' , token=self._token )
UpperCAmelCase__ = Repository(lowerCamelCase , clone_from=f'''{USER}/test-dynamic-processor''' , token=self._token )
processor.save_pretrained(lowerCamelCase )
# This has added the proper auto_map field to the feature extractor config
self.assertDictEqual(
processor.feature_extractor.auto_map , {
"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor",
"AutoProcessor": "custom_processing.CustomProcessor",
} , )
# This has added the proper auto_map field to the tokenizer config
with open(os.path.join(lowerCamelCase , "tokenizer_config.json" ) ) as f:
UpperCAmelCase__ = json.load(lowerCamelCase )
self.assertDictEqual(
tokenizer_config["auto_map"] , {
"AutoTokenizer": ["custom_tokenization.CustomTokenizer", None],
"AutoProcessor": "custom_processing.CustomProcessor",
} , )
# The code has been copied from fixtures
self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase , "custom_feature_extraction.py" ) ) )
self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase , "custom_tokenization.py" ) ) )
self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase , "custom_processing.py" ) ) )
repo.push_to_hub()
UpperCAmelCase__ = AutoProcessor.from_pretrained(f'''{USER}/test-dynamic-processor''' , trust_remote_code=lowerCamelCase )
# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
self.assertEqual(new_processor.__class__.__name__ , "CustomProcessor" )
| 169 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class _UpperCamelCase ( lowerCAmelCase ):
def __init__( self :int , lowerCamelCase :AutoencoderKL , lowerCamelCase :CLIPTextModel , lowerCamelCase :CLIPTokenizer , lowerCamelCase :UNetaDConditionModel , lowerCamelCase :Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , lowerCamelCase :StableDiffusionSafetyChecker , lowerCamelCase :CLIPImageProcessor , ) -> Optional[int]:
super().__init__()
self.register_modules(
vae=lowerCamelCase , text_encoder=lowerCamelCase , tokenizer=lowerCamelCase , unet=lowerCamelCase , scheduler=lowerCamelCase , safety_checker=lowerCamelCase , feature_extractor=lowerCamelCase , )
def UpperCAmelCase_ ( self :Tuple , lowerCamelCase :Optional[Union[str, int]] = "auto" ) -> int:
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
UpperCAmelCase__ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowerCamelCase )
def UpperCAmelCase_ ( self :Optional[int] ) -> Union[str, Any]:
self.enable_attention_slicing(lowerCamelCase )
@torch.no_grad()
def __call__( self :int , lowerCamelCase :Union[str, List[str]] , lowerCamelCase :int = 512 , lowerCamelCase :int = 512 , lowerCamelCase :int = 50 , lowerCamelCase :float = 7.5 , lowerCamelCase :Optional[Union[str, List[str]]] = None , lowerCamelCase :Optional[int] = 1 , lowerCamelCase :float = 0.0 , lowerCamelCase :Optional[torch.Generator] = None , lowerCamelCase :Optional[torch.FloatTensor] = None , lowerCamelCase :Optional[str] = "pil" , lowerCamelCase :bool = True , lowerCamelCase :Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowerCamelCase :int = 1 , lowerCamelCase :Optional[torch.FloatTensor] = None , **lowerCamelCase :List[str] , ) -> str:
if isinstance(lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = 1
elif isinstance(lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = len(lowerCamelCase )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase )}''' )
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 (callback_steps is None) or (
callback_steps is not None and (not isinstance(lowerCamelCase , lowerCamelCase ) or callback_steps <= 0)
):
raise ValueError(
f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
f''' {type(lowerCamelCase )}.''' )
# get prompt text embeddings
UpperCAmelCase__ = self.tokenizer(
lowerCamelCase , padding="max_length" , max_length=self.tokenizer.model_max_length , return_tensors="pt" , )
UpperCAmelCase__ = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
UpperCAmelCase__ = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
UpperCAmelCase__ = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
UpperCAmelCase__ = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = text_embeddings.shape
UpperCAmelCase__ = text_embeddings.repeat(1 , lowerCamelCase , 1 )
UpperCAmelCase__ = text_embeddings.view(bs_embed * num_images_per_prompt , lowerCamelCase , -1 )
# 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.
UpperCAmelCase__ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
UpperCAmelCase__ = 42
if negative_prompt is None:
UpperCAmelCase__ = [""]
elif type(lowerCamelCase ) is not type(lowerCamelCase ):
raise TypeError(
f'''`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase )} !='''
f''' {type(lowerCamelCase )}.''' )
elif isinstance(lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = [negative_prompt]
elif batch_size != len(lowerCamelCase ):
raise ValueError(
f'''`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase )}, but `prompt`:'''
f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
" the batch size of `prompt`." )
else:
UpperCAmelCase__ = negative_prompt
UpperCAmelCase__ = text_input_ids.shape[-1]
UpperCAmelCase__ = self.tokenizer(
lowerCamelCase , padding="max_length" , max_length=lowerCamelCase , truncation=lowerCamelCase , return_tensors="pt" , )
UpperCAmelCase__ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
UpperCAmelCase__ = uncond_embeddings.shape[1]
UpperCAmelCase__ = uncond_embeddings.repeat(lowerCamelCase , lowerCamelCase , 1 )
UpperCAmelCase__ = uncond_embeddings.view(batch_size * num_images_per_prompt , lowerCamelCase , -1 )
# 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
UpperCAmelCase__ = 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`.
UpperCAmelCase__ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
UpperCAmelCase__ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
UpperCAmelCase__ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
UpperCAmelCase__ = torch.randn(
lowerCamelCase , generator=lowerCamelCase , device="cpu" , dtype=lowerCamelCase ).to(self.device )
UpperCAmelCase__ = torch.randn(lowerCamelCase , generator=lowerCamelCase , device="cpu" , dtype=lowerCamelCase ).to(
self.device )
else:
UpperCAmelCase__ = torch.randn(
lowerCamelCase , generator=lowerCamelCase , device=self.device , dtype=lowerCamelCase )
UpperCAmelCase__ = torch.randn(lowerCamelCase , generator=lowerCamelCase , device=self.device , dtype=lowerCamelCase )
else:
if latents_reference.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
UpperCAmelCase__ = latents_reference.to(self.device )
UpperCAmelCase__ = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
UpperCAmelCase__ = (latents_shape[3] - latents_shape_reference[3]) // 2
UpperCAmelCase__ = (latents_shape[2] - latents_shape_reference[2]) // 2
UpperCAmelCase__ = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
UpperCAmelCase__ = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
UpperCAmelCase__ = 0 if dx < 0 else dx
UpperCAmelCase__ = 0 if dy < 0 else dy
UpperCAmelCase__ = max(-dx , 0 )
UpperCAmelCase__ = max(-dy , 0 )
# import pdb
# pdb.set_trace()
UpperCAmelCase__ = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(lowerCamelCase )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
UpperCAmelCase__ = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
UpperCAmelCase__ = 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]
UpperCAmelCase__ = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
UpperCAmelCase__ = {}
if accepts_eta:
UpperCAmelCase__ = eta
for i, t in enumerate(self.progress_bar(lowerCamelCase ) ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase__ = self.scheduler.scale_model_input(lowerCamelCase , lowerCamelCase )
# predict the noise residual
UpperCAmelCase__ = self.unet(lowerCamelCase , lowerCamelCase , encoder_hidden_states=lowerCamelCase ).sample
# perform guidance
if do_classifier_free_guidance:
UpperCAmelCase__ , UpperCAmelCase__ = noise_pred.chunk(2 )
UpperCAmelCase__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase__ = self.scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(lowerCamelCase , lowerCamelCase , lowerCamelCase )
UpperCAmelCase__ = 1 / 0.1_82_15 * latents
UpperCAmelCase__ = self.vae.decode(lowerCamelCase ).sample
UpperCAmelCase__ = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
UpperCAmelCase__ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
UpperCAmelCase__ = self.feature_extractor(self.numpy_to_pil(lowerCamelCase ) , return_tensors="pt" ).to(
self.device )
UpperCAmelCase__ , UpperCAmelCase__ = self.safety_checker(
images=lowerCamelCase , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
UpperCAmelCase__ = None
if output_type == "pil":
UpperCAmelCase__ = self.numpy_to_pil(lowerCamelCase )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=lowerCamelCase , nsfw_content_detected=lowerCamelCase )
| 169 | 1 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
__snake_case = input('''Enter image url: ''').strip()
print(F"""Downloading image from {url} ...""")
__snake_case = BeautifulSoup(requests.get(url).content, '''html.parser''')
# The image URL is in the content field of the first meta tag with property og:image
__snake_case = soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
__snake_case = requests.get(image_url).content
__snake_case = F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
with open(file_name, '''wb''') as fp:
fp.write(image_data)
print(F"""Done. Image saved to disk as {file_name}.""") | 219 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
),
'''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
),
'''distilbert-base-uncased-finetuned-sst-2-english''': (
'''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
),
}
class lowercase ( A__ ):
"""simple docstring"""
_a = 'distilbert'
_a = {
'hidden_size': 'dim',
'num_attention_heads': 'n_heads',
'num_hidden_layers': 'n_layers',
}
def __init__( self , UpperCamelCase_=30522 , UpperCamelCase_=512 , UpperCamelCase_=False , UpperCamelCase_=6 , UpperCamelCase_=12 , UpperCamelCase_=768 , UpperCamelCase_=4 * 768 , UpperCamelCase_=0.1 , UpperCamelCase_=0.1 , UpperCamelCase_="gelu" , UpperCamelCase_=0.02 , UpperCamelCase_=0.1 , UpperCamelCase_=0.2 , UpperCamelCase_=0 , **UpperCamelCase_ , ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = vocab_size
UpperCamelCase__ :Dict = max_position_embeddings
UpperCamelCase__ :str = sinusoidal_pos_embds
UpperCamelCase__ :Any = n_layers
UpperCamelCase__ :str = n_heads
UpperCamelCase__ :Tuple = dim
UpperCamelCase__ :str = hidden_dim
UpperCamelCase__ :Dict = dropout
UpperCamelCase__ :int = attention_dropout
UpperCamelCase__ :Optional[Any] = activation
UpperCamelCase__ :Optional[int] = initializer_range
UpperCamelCase__ :Union[str, Any] = qa_dropout
UpperCamelCase__ :Dict = seq_classif_dropout
super().__init__(**UpperCamelCase_ , pad_token_id=UpperCamelCase_ )
class lowercase ( A__ ):
"""simple docstring"""
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
UpperCamelCase__ :str = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
UpperCamelCase__ :str = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] ) | 219 | 1 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
__UpperCAmelCase ="pt"
elif is_tf_available():
__UpperCAmelCase ="tf"
else:
__UpperCAmelCase ="jax"
class a__ ( UpperCAmelCase__ , unittest.TestCase ):
lowerCamelCase : Union[str, Any] =PerceiverTokenizer
lowerCamelCase : Tuple =False
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
super().setUp()
__lowerCamelCase = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''' )
def SCREAMING_SNAKE_CASE__ ( self : Dict , **a : str ):
"""simple docstring"""
return self.tokenizer_class.from_pretrained(self.tmpdirname , **a )
def SCREAMING_SNAKE_CASE__ ( self : Any , a : str , a : Any=False , a : Any=20 , a : Union[str, Any]=5 ):
"""simple docstring"""
__lowerCamelCase = []
for i in range(len(a ) ):
try:
__lowerCamelCase = tokenizer.decode([i] , clean_up_tokenization_spaces=a )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
__lowerCamelCase = list(filter(lambda a : re.match(R'''^[ a-zA-Z]+$''' , t[1] ) , a ) )
__lowerCamelCase = list(filter(lambda a : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=a ) , a ) )
if max_length is not None and len(a ) > max_length:
__lowerCamelCase = toks[:max_length]
if min_length is not None and len(a ) < min_length and len(a ) > 0:
while len(a ) < min_length:
__lowerCamelCase = toks + toks
# toks_str = [t[1] for t in toks]
__lowerCamelCase = [t[0] for t in toks]
# Ensure consistency
__lowerCamelCase = tokenizer.decode(a , clean_up_tokenization_spaces=a )
if " " not in output_txt and len(a ) > 1:
__lowerCamelCase = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=a )
+ ''' '''
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=a )
)
if with_prefix_space:
__lowerCamelCase = ''' ''' + output_txt
__lowerCamelCase = tokenizer.encode(a , add_special_tokens=a )
return output_txt, output_ids
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
__lowerCamelCase = self.perceiver_tokenizer
__lowerCamelCase = '''Unicode €.'''
__lowerCamelCase = tokenizer(a )
__lowerCamelCase = [4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
self.assertEqual(encoded['''input_ids'''] , a )
# decoding
__lowerCamelCase = tokenizer.decode(a )
self.assertEqual(a , '''[CLS]Unicode €.[SEP]''' )
__lowerCamelCase = tokenizer('''e è é ê ë''' )
__lowerCamelCase = [4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
self.assertEqual(encoded['''input_ids'''] , a )
# decoding
__lowerCamelCase = tokenizer.decode(a )
self.assertEqual(a , '''[CLS]e è é ê ë[SEP]''' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë''' ) ) , '''[CLS]e è é ê ë[SEP]''' )
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
__lowerCamelCase = self.perceiver_tokenizer
__lowerCamelCase = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
# fmt: off
__lowerCamelCase = [4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
# fmt: on
__lowerCamelCase = tokenizer(a , padding=a , return_tensors=a )
self.assertIsInstance(a , a )
if FRAMEWORK != "jax":
__lowerCamelCase = list(batch.input_ids.numpy()[0] )
else:
__lowerCamelCase = list(batch.input_ids.tolist()[0] )
self.assertListEqual(a , a )
self.assertEqual((2, 38) , batch.input_ids.shape )
self.assertEqual((2, 38) , batch.attention_mask.shape )
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
__lowerCamelCase = self.perceiver_tokenizer
__lowerCamelCase = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
__lowerCamelCase = tokenizer(a , padding=a , return_tensors=a )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('''input_ids''' , a )
self.assertIn('''attention_mask''' , a )
self.assertNotIn('''decoder_input_ids''' , a )
self.assertNotIn('''decoder_attention_mask''' , a )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase = self.perceiver_tokenizer
__lowerCamelCase = [
'''Summary of the text.''',
'''Another summary.''',
]
__lowerCamelCase = tokenizer(
text_target=a , max_length=32 , padding='''max_length''' , truncation=a , return_tensors=a )
self.assertEqual(32 , targets['''input_ids'''].shape[1] )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
__lowerCamelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
__lowerCamelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
__lowerCamelCase = tempfile.mkdtemp()
__lowerCamelCase = ''' He is very happy, UNwant\u00E9d,running'''
__lowerCamelCase = tokenizer.encode(a , add_special_tokens=a )
tokenizer.save_pretrained(a )
__lowerCamelCase = tokenizer.__class__.from_pretrained(a )
__lowerCamelCase = after_tokenizer.encode(a , add_special_tokens=a )
self.assertListEqual(a , a )
shutil.rmtree(a )
__lowerCamelCase = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
__lowerCamelCase = tempfile.mkdtemp()
__lowerCamelCase = ''' He is very happy, UNwant\u00E9d,running'''
tokenizer.add_tokens(['''bim''', '''bambam'''] )
__lowerCamelCase = tokenizer.additional_special_tokens
additional_special_tokens.append('''new_additional_special_token''' )
tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
__lowerCamelCase = tokenizer.encode(a , add_special_tokens=a )
tokenizer.save_pretrained(a )
__lowerCamelCase = tokenizer.__class__.from_pretrained(a )
__lowerCamelCase = after_tokenizer.encode(a , add_special_tokens=a )
self.assertListEqual(a , a )
self.assertIn('''new_additional_special_token''' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
__lowerCamelCase = tokenizer.__class__.from_pretrained(a , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(a )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
__lowerCamelCase = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(a )
with open(os.path.join(a , '''special_tokens_map.json''' ) , encoding='''utf-8''' ) as json_file:
__lowerCamelCase = json.load(a )
with open(os.path.join(a , '''tokenizer_config.json''' ) , encoding='''utf-8''' ) as json_file:
__lowerCamelCase = json.load(a )
__lowerCamelCase = [f"""<extra_id_{i}>""" for i in range(1_25 )]
__lowerCamelCase = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
__lowerCamelCase = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
with open(os.path.join(a , '''special_tokens_map.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(a , a )
with open(os.path.join(a , '''tokenizer_config.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(a , a )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
__lowerCamelCase = tokenizer_class.from_pretrained(
a , )
self.assertIn(
'''an_additional_special_token''' , tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
['''an_additional_special_token'''] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token'''] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
__lowerCamelCase = added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' , lstrip=a )]
__lowerCamelCase = tokenizer_class.from_pretrained(
a , additional_special_tokens=a , )
self.assertIn('''a_new_additional_special_token''' , tokenizer.additional_special_tokens )
self.assertEqual(
['''a_new_additional_special_token'''] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token'''] ) ) , )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([1_78] ) , '''�''' )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
__lowerCamelCase = self.get_tokenizers(fast=a , do_lower_case=a )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
__lowerCamelCase = ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
__lowerCamelCase = tokenizer.convert_tokens_to_string(a )
self.assertIsInstance(a , a )
| 67 |
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,
)
| 303 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> list:
lowerCamelCase__ : Dict = int(UpperCamelCase )
if n_element < 1:
lowerCamelCase__ : Tuple = ValueError("""a should be a positive number""" )
raise my_error
lowerCamelCase__ : Any = [1]
lowerCamelCase__ : Optional[int] = (0, 0, 0)
lowerCamelCase__ : Dict = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
_A : Dict =input('''Enter the last number (nth term) of the Hamming Number Series: ''')
print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
_A : Dict =hamming(int(n))
print('''-----------------------------------------------------''')
print(F'The list with nth numbers is: {hamming_numbers}')
print('''-----------------------------------------------------''')
| 370 |
'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
_A : List[Any] =True
except ImportError:
_A : int =False
_A : Union[str, Any] =logging.get_logger(__name__) # pylint: disable=invalid-name
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> Tuple:
return AddNewModelCommand(args.testing , args.testing_file , path=args.path )
class _lowercase ( _lowercase ):
@staticmethod
def lowerCamelCase_ ( UpperCamelCase__: ArgumentParser ):
lowerCamelCase__ : List[str] = parser.add_parser("""add-new-model""" )
add_new_model_parser.add_argument("""--testing""" , action="""store_true""" , help="""If in testing mode.""" )
add_new_model_parser.add_argument("""--testing_file""" , type=UpperCamelCase__ , help="""Configuration file on which to run.""" )
add_new_model_parser.add_argument(
"""--path""" , type=UpperCamelCase__ , help="""Path to cookiecutter. Should only be used for testing purposes.""" )
add_new_model_parser.set_defaults(func=UpperCamelCase__ )
def __init__( self: Optional[int] , UpperCamelCase__: bool , UpperCamelCase__: str , UpperCamelCase__: str=None , *UpperCamelCase__: Optional[int] ):
lowerCamelCase__ : List[Any] = testing
lowerCamelCase__ : Tuple = testing_file
lowerCamelCase__ : int = path
def lowerCamelCase_ ( self: int ):
warnings.warn(
"""The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. """
"""It is not actively maintained anymore, so might give a result that won't pass all tests and quality """
"""checks, you should use `transformers-cli add-new-model-like` instead.""" )
if not _has_cookiecutter:
raise ImportError(
"""Model creation dependencies are required to use the `add_new_model` command. Install them by running """
"""the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n""" )
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
lowerCamelCase__ : List[Any] = [directory for directory in os.listdir() if """cookiecutter-template-""" == directory[:22]]
if len(UpperCamelCase__ ) > 0:
raise ValueError(
"""Several directories starting with `cookiecutter-template-` in current working directory. """
"""Please clean your directory by removing all folders starting with `cookiecutter-template-` or """
"""change your working directory.""" )
lowerCamelCase__ : int = (
Path(UpperCamelCase__ ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
)
lowerCamelCase__ : int = path_to_transformer_root / """templates""" / """adding_a_new_model"""
# Execute cookiecutter
if not self._testing:
cookiecutter(str(UpperCamelCase__ ) )
else:
with open(self._testing_file , """r""" ) as configuration_file:
lowerCamelCase__ : List[str] = json.load(UpperCamelCase__ )
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path ) , no_input=UpperCamelCase__ , extra_context=UpperCamelCase__ , )
lowerCamelCase__ : Optional[Any] = [directory for directory in os.listdir() if """cookiecutter-template-""" in directory[:22]][0]
# Retrieve configuration
with open(directory + """/configuration.json""" , """r""" ) as configuration_file:
lowerCamelCase__ : int = json.load(UpperCamelCase__ )
lowerCamelCase__ : Tuple = configuration["""lowercase_modelname"""]
lowerCamelCase__ : int = configuration["""generate_tensorflow_pytorch_and_flax"""]
os.remove(F'''{directory}/configuration.json''' )
lowerCamelCase__ : Union[str, Any] = """PyTorch""" in generate_tensorflow_pytorch_and_flax
lowerCamelCase__ : Union[str, Any] = """TensorFlow""" in generate_tensorflow_pytorch_and_flax
lowerCamelCase__ : Tuple = """Flax""" in generate_tensorflow_pytorch_and_flax
lowerCamelCase__ : List[str] = F'''{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'''
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
os.makedirs(F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}''' , exist_ok=UpperCamelCase__ )
# Tests require submodules as they have parent imports
with open(F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py''' , """w""" ):
pass
shutil.move(
F'''{directory}/__init__.py''' , F'''{model_dir}/__init__.py''' , )
shutil.move(
F'''{directory}/configuration_{lowercase_model_name}.py''' , F'''{model_dir}/configuration_{lowercase_model_name}.py''' , )
def remove_copy_lines(UpperCamelCase__: Optional[int] ):
with open(UpperCamelCase__ , """r""" ) as f:
lowerCamelCase__ : Union[str, Any] = f.readlines()
with open(UpperCamelCase__ , """w""" ) as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(UpperCamelCase__ )
if output_pytorch:
if not self._testing:
remove_copy_lines(F'''{directory}/modeling_{lowercase_model_name}.py''' )
shutil.move(
F'''{directory}/modeling_{lowercase_model_name}.py''' , F'''{model_dir}/modeling_{lowercase_model_name}.py''' , )
shutil.move(
F'''{directory}/test_modeling_{lowercase_model_name}.py''' , F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py''' , )
else:
os.remove(F'''{directory}/modeling_{lowercase_model_name}.py''' )
os.remove(F'''{directory}/test_modeling_{lowercase_model_name}.py''' )
if output_tensorflow:
if not self._testing:
remove_copy_lines(F'''{directory}/modeling_tf_{lowercase_model_name}.py''' )
shutil.move(
F'''{directory}/modeling_tf_{lowercase_model_name}.py''' , F'''{model_dir}/modeling_tf_{lowercase_model_name}.py''' , )
shutil.move(
F'''{directory}/test_modeling_tf_{lowercase_model_name}.py''' , F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py''' , )
else:
os.remove(F'''{directory}/modeling_tf_{lowercase_model_name}.py''' )
os.remove(F'''{directory}/test_modeling_tf_{lowercase_model_name}.py''' )
if output_flax:
if not self._testing:
remove_copy_lines(F'''{directory}/modeling_flax_{lowercase_model_name}.py''' )
shutil.move(
F'''{directory}/modeling_flax_{lowercase_model_name}.py''' , F'''{model_dir}/modeling_flax_{lowercase_model_name}.py''' , )
shutil.move(
F'''{directory}/test_modeling_flax_{lowercase_model_name}.py''' , F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py''' , )
else:
os.remove(F'''{directory}/modeling_flax_{lowercase_model_name}.py''' )
os.remove(F'''{directory}/test_modeling_flax_{lowercase_model_name}.py''' )
shutil.move(
F'''{directory}/{lowercase_model_name}.md''' , F'''{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md''' , )
shutil.move(
F'''{directory}/tokenization_{lowercase_model_name}.py''' , F'''{model_dir}/tokenization_{lowercase_model_name}.py''' , )
shutil.move(
F'''{directory}/tokenization_fast_{lowercase_model_name}.py''' , F'''{model_dir}/tokenization_{lowercase_model_name}_fast.py''' , )
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(UpperCamelCase__: str , UpperCamelCase__: str , UpperCamelCase__: List[str] ):
# Create temp file
lowerCamelCase__ , lowerCamelCase__ : Any = mkstemp()
lowerCamelCase__ : Tuple = False
with fdopen(UpperCamelCase__ , """w""" ) as new_file:
with open(UpperCamelCase__ ) as old_file:
for line in old_file:
new_file.write(UpperCamelCase__ )
if line_to_copy_below in line:
lowerCamelCase__ : int = True
for line_to_copy in lines_to_copy:
new_file.write(UpperCamelCase__ )
if not line_found:
raise ValueError(F'''Line {line_to_copy_below} was not found in file.''' )
# Copy the file permissions from the old file to the new file
copymode(UpperCamelCase__ , UpperCamelCase__ )
# Remove original file
remove(UpperCamelCase__ )
# Move new file
move(UpperCamelCase__ , UpperCamelCase__ )
def skip_units(UpperCamelCase__: Optional[int] ):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(UpperCamelCase__: List[str] ):
with open(UpperCamelCase__ ) as datafile:
lowerCamelCase__ : int = []
lowerCamelCase__ : Tuple = False
lowerCamelCase__ : int = False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
lowerCamelCase__ : List[str] = line.split("""\"""" )[1]
lowerCamelCase__ : List[str] = skip_units(UpperCamelCase__ )
elif "# Below: " in line and "##" not in line:
lowerCamelCase__ : List[Any] = line.split("""\"""" )[1]
lowerCamelCase__ : Union[str, Any] = skip_units(UpperCamelCase__ )
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase__ : Union[str, Any] = []
elif "# Replace with" in line and "##" not in line:
lowerCamelCase__ : str = []
elif "##" not in line:
lines_to_copy.append(UpperCamelCase__ )
remove(UpperCamelCase__ )
replace_in_files(F'''{directory}/to_replace_{lowercase_model_name}.py''' )
os.rmdir(UpperCamelCase__ )
| 129 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
a__ : Dict = {
'google/tapas-base-finetuned-sqa': (
'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
),
'google/tapas-base-finetuned-wtq': (
'https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'
),
'google/tapas-base-finetuned-wikisql-supervised': (
'https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'
),
'google/tapas-base-finetuned-tabfact': (
'https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'
),
}
class lowercase_ ( a__ ):
__UpperCAmelCase = 'tapas'
def __init__( self , a=3_05_22 , a=7_68 , a=12 , a=12 , a=30_72 , a="gelu" , a=0.1 , a=0.1 , a=10_24 , a=[3, 2_56, 2_56, 2, 2_56, 2_56, 10] , a=0.02 , a=1e-12 , a=0 , a=10.0 , a=0 , a=1.0 , a=None , a=1.0 , a=False , a=None , a=1.0 , a=1.0 , a=False , a=False , a="ratio" , a=None , a=None , a=64 , a=32 , a=False , a=True , a=False , a=False , a=True , a=False , a=None , a=None , **a , ):
super().__init__(pad_token_id=a , **a )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
UpperCamelCase__ = vocab_size
UpperCamelCase__ = hidden_size
UpperCamelCase__ = num_hidden_layers
UpperCamelCase__ = num_attention_heads
UpperCamelCase__ = hidden_act
UpperCamelCase__ = intermediate_size
UpperCamelCase__ = hidden_dropout_prob
UpperCamelCase__ = attention_probs_dropout_prob
UpperCamelCase__ = max_position_embeddings
UpperCamelCase__ = type_vocab_sizes
UpperCamelCase__ = initializer_range
UpperCamelCase__ = layer_norm_eps
# Fine-tuning task hyperparameters
UpperCamelCase__ = positive_label_weight
UpperCamelCase__ = num_aggregation_labels
UpperCamelCase__ = aggregation_loss_weight
UpperCamelCase__ = use_answer_as_supervision
UpperCamelCase__ = answer_loss_importance
UpperCamelCase__ = use_normalized_answer_loss
UpperCamelCase__ = huber_loss_delta
UpperCamelCase__ = temperature
UpperCamelCase__ = aggregation_temperature
UpperCamelCase__ = use_gumbel_for_cells
UpperCamelCase__ = use_gumbel_for_aggregation
UpperCamelCase__ = average_approximation_function
UpperCamelCase__ = cell_selection_preference
UpperCamelCase__ = answer_loss_cutoff
UpperCamelCase__ = max_num_rows
UpperCamelCase__ = max_num_columns
UpperCamelCase__ = average_logits_per_cell
UpperCamelCase__ = select_one_column
UpperCamelCase__ = allow_empty_column_selection
UpperCamelCase__ = init_cell_selection_weights_to_zero
UpperCamelCase__ = reset_position_index_per_cell
UpperCamelCase__ = disable_per_token_loss
# Aggregation hyperparameters
UpperCamelCase__ = aggregation_labels
UpperCamelCase__ = no_aggregation_label_index
if isinstance(self.aggregation_labels , a ):
UpperCamelCase__ = {int(a ): v for k, v in aggregation_labels.items()}
| 80 |
"""simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class __snake_case ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = 42
_lowerCamelCase = jnp.floataa
_lowerCamelCase = True
def UpperCamelCase__( self ):
'''simple docstring'''
super().setup()
__A : List[Any] = nn.Dense(5 , dtype=self.dtype )
def __call__( self , *__lowerCamelCase , **__lowerCamelCase ):
'''simple docstring'''
__A : Tuple = super().__call__(*__lowerCamelCase , **__lowerCamelCase )
__A : Optional[int] = self.cls(outputs[2] )
return outputs[:2] + (cls_out,)
class __snake_case ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = FlaxBigBirdForNaturalQuestionsModule
def __lowercase ( snake_case_ : Tuple ,snake_case_ : Optional[int] ,snake_case_ : int ,snake_case_ : Tuple ,snake_case_ : Any ,snake_case_ : Any ) ->List[Any]:
'''simple docstring'''
def cross_entropy(snake_case_ : str ,snake_case_ : Optional[Any] ,snake_case_ : Tuple=None ):
__A : Dict = logits.shape[-1]
__A : Dict = (labels[..., None] == jnp.arange(snake_case_ )[None]).astype('''f4''' )
__A : int = jax.nn.log_softmax(snake_case_ ,axis=-1 )
__A : Optional[int] = -jnp.sum(labels * logits ,axis=-1 )
if reduction is not None:
__A : Optional[int] = reduction(snake_case_ )
return loss
__A : str = partial(snake_case_ ,reduction=jnp.mean )
__A : Dict = cross_entropy(snake_case_ ,snake_case_ )
__A : List[str] = cross_entropy(snake_case_ ,snake_case_ )
__A : str = cross_entropy(snake_case_ ,snake_case_ )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class __snake_case :
"""simple docstring"""
_lowerCamelCase = "google/bigbird-roberta-base"
_lowerCamelCase = 30_00
_lowerCamelCase = 1_05_00
_lowerCamelCase = 1_28
_lowerCamelCase = 3
_lowerCamelCase = 1
_lowerCamelCase = 5
# tx_args
_lowerCamelCase = 3e-5
_lowerCamelCase = 0.0
_lowerCamelCase = 2_00_00
_lowerCamelCase = 0.0_0_9_5
_lowerCamelCase = "bigbird-roberta-natural-questions"
_lowerCamelCase = "training-expt"
_lowerCamelCase = "data/nq-training.jsonl"
_lowerCamelCase = "data/nq-validation.jsonl"
def UpperCamelCase__( self ):
'''simple docstring'''
os.makedirs(self.base_dir , exist_ok=__lowerCamelCase )
__A : Dict = os.path.join(self.base_dir , self.save_dir )
__A : Dict = self.batch_size_per_device * jax.device_count()
@dataclass
class __snake_case :
"""simple docstring"""
_lowerCamelCase = 42
_lowerCamelCase = 40_96 # no dynamic padding on TPUs
def __call__( self , __lowerCamelCase ):
'''simple docstring'''
__A : Optional[int] = self.collate_fn(__lowerCamelCase )
__A : Tuple = jax.tree_util.tree_map(__lowerCamelCase , __lowerCamelCase )
return batch
def UpperCamelCase__( self , __lowerCamelCase ):
'''simple docstring'''
__A , __A : List[Any] = self.fetch_inputs(features['''input_ids'''] )
__A : Union[str, Any] = {
'''input_ids''': jnp.array(__lowerCamelCase , dtype=jnp.intaa ),
'''attention_mask''': jnp.array(__lowerCamelCase , dtype=jnp.intaa ),
'''start_labels''': jnp.array(features['''start_token'''] , dtype=jnp.intaa ),
'''end_labels''': jnp.array(features['''end_token'''] , dtype=jnp.intaa ),
'''pooled_labels''': jnp.array(features['''category'''] , dtype=jnp.intaa ),
}
return batch
def UpperCamelCase__( self , __lowerCamelCase ):
'''simple docstring'''
__A : Any = [self._fetch_inputs(__lowerCamelCase ) for ids in input_ids]
return zip(*__lowerCamelCase )
def UpperCamelCase__( self , __lowerCamelCase ):
'''simple docstring'''
__A : Any = [1 for _ in range(len(__lowerCamelCase ) )]
while len(__lowerCamelCase ) < self.max_length:
input_ids.append(self.pad_id )
attention_mask.append(0 )
return input_ids, attention_mask
def __lowercase ( snake_case_ : List[Any] ,snake_case_ : Optional[Any] ,snake_case_ : List[str]=None ) ->Optional[int]:
'''simple docstring'''
if seed is not None:
__A : List[Any] = dataset.shuffle(seed=snake_case_ )
for i in range(len(snake_case_ ) // batch_size ):
__A : Tuple = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(snake_case_ )
@partial(jax.pmap ,axis_name='''batch''' )
def __lowercase ( snake_case_ : str ,snake_case_ : Union[str, Any] ,**snake_case_ : List[str] ) ->Tuple:
'''simple docstring'''
def loss_fn(snake_case_ : List[str] ):
__A : str = model_inputs.pop('''start_labels''' )
__A : str = model_inputs.pop('''end_labels''' )
__A : int = model_inputs.pop('''pooled_labels''' )
__A : Dict = state.apply_fn(**snake_case_ ,params=snake_case_ ,dropout_rng=snake_case_ ,train=snake_case_ )
__A , __A , __A : Union[str, Any] = outputs
return state.loss_fn(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,)
__A , __A : int = jax.random.split(snake_case_ )
__A : str = jax.value_and_grad(snake_case_ )
__A , __A : Optional[int] = grad_fn(state.params )
__A : List[str] = jax.lax.pmean({'''loss''': loss} ,axis_name='''batch''' )
__A : List[str] = jax.lax.pmean(snake_case_ ,'''batch''' )
__A : str = state.apply_gradients(grads=snake_case_ )
return state, metrics, new_drp_rng
@partial(jax.pmap ,axis_name='''batch''' )
def __lowercase ( snake_case_ : int ,**snake_case_ : Union[str, Any] ) ->List[str]:
'''simple docstring'''
__A : Tuple = model_inputs.pop('''start_labels''' )
__A : Dict = model_inputs.pop('''end_labels''' )
__A : int = model_inputs.pop('''pooled_labels''' )
__A : List[str] = state.apply_fn(**snake_case_ ,params=state.params ,train=snake_case_ )
__A , __A , __A : Dict = outputs
__A : Optional[int] = state.loss_fn(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
__A : List[str] = jax.lax.pmean({'''loss''': loss} ,axis_name='''batch''' )
return metrics
class __snake_case ( train_state.TrainState ):
"""simple docstring"""
_lowerCamelCase = struct.field(pytree_node=SCREAMING_SNAKE_CASE__ )
@dataclass
class __snake_case :
"""simple docstring"""
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = None
def UpperCamelCase__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=None ):
'''simple docstring'''
__A : Tuple = model.params
__A : Union[str, Any] = TrainState.create(
apply_fn=model.__call__ , params=__lowerCamelCase , tx=__lowerCamelCase , loss_fn=__lowerCamelCase , )
if ckpt_dir is not None:
__A , __A , __A , __A , __A : Optional[Any] = restore_checkpoint(__lowerCamelCase , __lowerCamelCase )
__A : List[Any] = {
'''lr''': args.lr,
'''init_lr''': args.init_lr,
'''warmup_steps''': args.warmup_steps,
'''num_train_steps''': num_train_steps,
'''weight_decay''': args.weight_decay,
}
__A , __A : List[str] = build_tx(**__lowerCamelCase )
__A : int = train_state.TrainState(
step=__lowerCamelCase , apply_fn=model.__call__ , params=__lowerCamelCase , tx=__lowerCamelCase , opt_state=__lowerCamelCase , )
__A : int = args
__A : Optional[Any] = data_collator
__A : Tuple = lr
__A : List[Any] = params
__A : Dict = jax_utils.replicate(__lowerCamelCase )
return state
def UpperCamelCase__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
__A : List[Any] = self.args
__A : Dict = len(__lowerCamelCase ) // args.batch_size
__A : List[Any] = jax.random.PRNGKey(0 )
__A : Optional[Any] = jax.random.split(__lowerCamelCase , jax.device_count() )
for epoch in range(args.max_epochs ):
__A : Tuple = jnp.array(0 , dtype=jnp.floataa )
__A : Optional[Any] = get_batched_dataset(__lowerCamelCase , args.batch_size , seed=__lowerCamelCase )
__A : Union[str, Any] = 0
for batch in tqdm(__lowerCamelCase , total=__lowerCamelCase , desc=F"""Running EPOCH-{epoch}""" ):
__A : Optional[Any] = self.data_collator(__lowerCamelCase )
__A , __A , __A : Union[str, Any] = self.train_step_fn(__lowerCamelCase , __lowerCamelCase , **__lowerCamelCase )
running_loss += jax_utils.unreplicate(metrics['''loss'''] )
i += 1
if i % args.logging_steps == 0:
__A : Union[str, Any] = jax_utils.unreplicate(state.step )
__A : Optional[int] = running_loss.item() / i
__A : List[Any] = self.scheduler_fn(state_step - 1 )
__A : Union[str, Any] = self.evaluate(__lowerCamelCase , __lowerCamelCase )
__A : Optional[Any] = {
'''step''': state_step.item(),
'''eval_loss''': eval_loss.item(),
'''tr_loss''': tr_loss,
'''lr''': lr.item(),
}
tqdm.write(str(__lowerCamelCase ) )
self.logger.log(__lowerCamelCase , commit=__lowerCamelCase )
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + F"""-e{epoch}-s{i}""" , state=__lowerCamelCase )
def UpperCamelCase__( self , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
__A : Union[str, Any] = get_batched_dataset(__lowerCamelCase , self.args.batch_size )
__A : int = len(__lowerCamelCase ) // self.args.batch_size
__A : Optional[Any] = jnp.array(0 , dtype=jnp.floataa )
__A : Dict = 0
for batch in tqdm(__lowerCamelCase , total=__lowerCamelCase , desc='''Evaluating ... ''' ):
__A : List[str] = self.data_collator(__lowerCamelCase )
__A : Union[str, Any] = self.val_step_fn(__lowerCamelCase , **__lowerCamelCase )
running_loss += jax_utils.unreplicate(metrics['''loss'''] )
i += 1
return running_loss / i
def UpperCamelCase__( self , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
__A : Dict = jax_utils.unreplicate(__lowerCamelCase )
print(F"""SAVING CHECKPOINT IN {save_dir}""" , end=''' ... ''' )
self.model_save_fn(__lowerCamelCase , params=state.params )
with open(os.path.join(__lowerCamelCase , '''opt_state.msgpack''' ) , '''wb''' ) as f:
f.write(to_bytes(state.opt_state ) )
joblib.dump(self.args , os.path.join(__lowerCamelCase , '''args.joblib''' ) )
joblib.dump(self.data_collator , os.path.join(__lowerCamelCase , '''data_collator.joblib''' ) )
with open(os.path.join(__lowerCamelCase , '''training_state.json''' ) , '''w''' ) as f:
json.dump({'''step''': state.step.item()} , __lowerCamelCase )
print('''DONE''' )
def __lowercase ( snake_case_ : int ,snake_case_ : Dict ) ->Optional[int]:
'''simple docstring'''
print(F"""RESTORING CHECKPOINT FROM {save_dir}""" ,end=''' ... ''' )
with open(os.path.join(snake_case_ ,'''flax_model.msgpack''' ) ,'''rb''' ) as f:
__A : List[Any] = from_bytes(state.params ,f.read() )
with open(os.path.join(snake_case_ ,'''opt_state.msgpack''' ) ,'''rb''' ) as f:
__A : Optional[int] = from_bytes(state.opt_state ,f.read() )
__A : Tuple = joblib.load(os.path.join(snake_case_ ,'''args.joblib''' ) )
__A : List[str] = joblib.load(os.path.join(snake_case_ ,'''data_collator.joblib''' ) )
with open(os.path.join(snake_case_ ,'''training_state.json''' ) ,'''r''' ) as f:
__A : Dict = json.load(snake_case_ )
__A : int = training_state['''step''']
print('''DONE''' )
return params, opt_state, step, args, data_collator
def __lowercase ( snake_case_ : List[str] ,snake_case_ : Any ,snake_case_ : Dict ,snake_case_ : str ) ->List[str]:
'''simple docstring'''
__A : str = num_train_steps - warmup_steps
__A : Union[str, Any] = optax.linear_schedule(init_value=snake_case_ ,end_value=snake_case_ ,transition_steps=snake_case_ )
__A : Optional[Any] = optax.linear_schedule(init_value=snake_case_ ,end_value=1e-7 ,transition_steps=snake_case_ )
__A : Any = optax.join_schedules(schedules=[warmup_fn, decay_fn] ,boundaries=[warmup_steps] )
return lr
def __lowercase ( snake_case_ : List[str] ,snake_case_ : List[Any] ,snake_case_ : Union[str, Any] ,snake_case_ : List[Any] ,snake_case_ : str ) ->List[str]:
'''simple docstring'''
def weight_decay_mask(snake_case_ : List[Any] ):
__A : List[Any] = traverse_util.flatten_dict(snake_case_ )
__A : int = {k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
return traverse_util.unflatten_dict(snake_case_ )
__A : List[Any] = scheduler_fn(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
__A : List[str] = optax.adamw(learning_rate=snake_case_ ,weight_decay=snake_case_ ,mask=snake_case_ )
return tx, lr
| 179 | 0 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
UpperCAmelCase_ : int = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
UpperCAmelCase_ : Optional[Any] = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
UpperCAmelCase_ : Optional[int] = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str , __magic_name__ : str ) -> tuple[str, float]:
"""simple docstring"""
UpperCamelCase :Tuple = len([g for position, g in enumerate(__magic_name__ ) if g == main_target[position]] )
return (item, float(__magic_name__ ))
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str , __magic_name__ : str ) -> tuple[str, str]:
"""simple docstring"""
UpperCamelCase :str = random.randint(0 , len(__magic_name__ ) - 1 )
UpperCamelCase :Tuple = parent_a[:random_slice] + parent_a[random_slice:]
UpperCamelCase :int = parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str , __magic_name__ : list[str] ) -> str:
"""simple docstring"""
UpperCamelCase :Union[str, Any] = list(__magic_name__ )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
UpperCamelCase :int = random.choice(__magic_name__ )
return "".join(__magic_name__ )
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : tuple[str, float] , __magic_name__ : list[tuple[str, float]] , __magic_name__ : list[str] , ) -> list[str]:
"""simple docstring"""
UpperCamelCase :str = []
# Generate more children proportionally to the fitness score.
UpperCamelCase :List[Any] = int(parent_a[1] * 100 ) + 1
UpperCamelCase :int = 10 if child_n >= 10 else child_n
for _ in range(__magic_name__ ):
UpperCamelCase :Union[str, Any] = population_score[random.randint(0 , __magic_name__ )][0]
UpperCamelCase , UpperCamelCase :Optional[int] = crossover(parent_a[0] , __magic_name__ )
# Append new string to the population list.
pop.append(mutate(__magic_name__ , __magic_name__ ) )
pop.append(mutate(__magic_name__ , __magic_name__ ) )
return pop
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str , __magic_name__ : list[str] , __magic_name__ : bool = True ) -> tuple[int, int, str]:
"""simple docstring"""
if N_POPULATION < N_SELECTED:
UpperCamelCase :Dict = f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
raise ValueError(__magic_name__ )
# Verify that the target contains no genes besides the ones inside genes variable.
UpperCamelCase :List[str] = sorted({c for c in target if c not in genes} )
if not_in_genes_list:
UpperCamelCase :str = f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
raise ValueError(__magic_name__ )
# Generate random starting population.
UpperCamelCase :Union[str, Any] = []
for _ in range(__magic_name__ ):
population.append("""""".join([random.choice(__magic_name__ ) for i in range(len(__magic_name__ ) )] ) )
# Just some logs to know what the algorithms is doing.
UpperCamelCase , UpperCamelCase :Optional[int] = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(__magic_name__ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
UpperCamelCase :int = [evaluate(__magic_name__ , __magic_name__ ) for item in population]
# Check if there is a matching evolution.
UpperCamelCase :Tuple = sorted(__magic_name__ , key=lambda __magic_name__ : x[1] , reverse=__magic_name__ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
f"""\nGeneration: {generation}"""
f"""\nTotal Population:{total_population}"""
f"""\nBest score: {population_score[0][1]}"""
f"""\nBest string: {population_score[0][0]}""" )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
UpperCamelCase :List[str] = population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(__magic_name__ )
# Normalize population score to be between 0 and 1.
UpperCamelCase :Tuple = [
(item, score / len(__magic_name__ )) for item, score in population_score
]
# This is selection
for i in range(__magic_name__ ):
population.extend(select(population_score[int(__magic_name__ )] , __magic_name__ , __magic_name__ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(__magic_name__ ) > N_POPULATION:
break
if __name__ == "__main__":
UpperCAmelCase_ : Union[str, Any] = (
'''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
)
UpperCAmelCase_ : List[str] = list(
''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
'''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
)
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Union[str, Any] = basic(target_str, genes_list)
print(
F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
)
| 62 |
from string import ascii_uppercase
UpperCAmelCase_ : str = {str(ord(c) - 55): c for c in ascii_uppercase}
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int , __magic_name__ : int ) -> str:
"""simple docstring"""
if isinstance(__magic_name__ , __magic_name__ ):
raise TypeError("""int() can't convert non-string with explicit base""" )
if num < 0:
raise ValueError("""parameter must be positive int""" )
if isinstance(__magic_name__ , __magic_name__ ):
raise TypeError("""'str' object cannot be interpreted as an integer""" )
if isinstance(__magic_name__ , __magic_name__ ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if base in (0, 1):
raise ValueError("""base must be >= 2""" )
if base > 36:
raise ValueError("""base must be <= 36""" )
UpperCamelCase :Any = """"""
UpperCamelCase :Any = 0
UpperCamelCase :int = 0
while div != 1:
UpperCamelCase , UpperCamelCase :str = divmod(__magic_name__ , __magic_name__ )
if base >= 11 and 9 < mod < 36:
UpperCamelCase :List[str] = ALPHABET_VALUES[str(__magic_name__ )]
else:
UpperCamelCase :Dict = str(__magic_name__ )
new_value += actual_value
UpperCamelCase :int = num // base
UpperCamelCase :Any = div
if div == 0:
return str(new_value[::-1] )
elif div == 1:
new_value += str(__magic_name__ )
return str(new_value[::-1] )
return new_value[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for base in range(2, 37):
for num in range(10_00):
assert int(decimal_to_any(num, base), base) == num, (
num,
base,
decimal_to_any(num, base),
int(decimal_to_any(num, base), base),
)
| 62 | 1 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler
@dataclass
class A_ ( _lowerCamelCase ):
lowerCAmelCase__ = 0
lowerCAmelCase__ = False
lowerCAmelCase__ = 3.0
class A_ ( unittest.TestCase ):
def _lowerCAmelCase (self :Optional[Any] )-> Optional[int]:
# If no defaults are changed, `to_kwargs` returns an empty dict.
self.assertDictEqual(MockClass().to_kwargs() , {} )
self.assertDictEqual(MockClass(a=2 ).to_kwargs() , {'''a''': 2} )
self.assertDictEqual(MockClass(a=2 , b=_UpperCamelCase ).to_kwargs() , {'''a''': 2, '''b''': True} )
self.assertDictEqual(MockClass(a=2 , c=2.2_5 ).to_kwargs() , {'''a''': 2, '''c''': 2.2_5} )
@require_cuda
def _lowerCAmelCase (self :Optional[Any] )-> Optional[Any]:
# If no defaults are changed, `to_kwargs` returns an empty dict.
__A = GradScalerKwargs(init_scale=1024 , growth_factor=2 )
AcceleratorState._reset_state()
__A = Accelerator(mixed_precision='''fp16''' , kwargs_handlers=[scaler_handler] )
print(accelerator.use_fpaa )
__A = accelerator.scaler
# Check the kwargs have been applied
self.assertEqual(scaler._init_scale , 1_0_2_4.0 )
self.assertEqual(scaler._growth_factor , 2.0 )
# Check the other values are at the default
self.assertEqual(scaler._backoff_factor , 0.5 )
self.assertEqual(scaler._growth_interval , 2000 )
self.assertEqual(scaler._enabled , _UpperCamelCase )
@require_multi_gpu
def _lowerCAmelCase (self :Tuple )-> Optional[Any]:
__A = ['''torchrun''', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
execute_subprocess_async(_UpperCamelCase , env=os.environ.copy() )
if __name__ == "__main__":
snake_case__ : Optional[int] = DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
snake_case__ : Optional[int] = Accelerator(kwargs_handlers=[ddp_scaler])
snake_case__ : Dict = torch.nn.Linear(100, 200)
snake_case__ : int = accelerator.prepare(model)
# Check the values changed in kwargs
snake_case__ : Optional[Any] = ''
snake_case__ : int = model.bucket_bytes_cap // (1024 * 1024)
if observed_bucket_cap_map != 15:
error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
if model.find_unused_parameters is not True:
error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"
# Check the values of the defaults
if model.dim != 0:
error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
if model.broadcast_buffers is not True:
error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
if model.gradient_as_bucket_view is not False:
error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 117 |
import os
from datetime import datetime as dt
from github import Github
snake_case__ : Union[str, Any] = [
'good first issue',
'feature request',
'wip',
]
def _a ( ) -> List[Any]:
'''simple docstring'''
__A = Github(os.environ['''GITHUB_TOKEN'''] )
__A = g.get_repo('''huggingface/accelerate''' )
__A = repo.get_issues(state='''open''' )
for issue in open_issues:
__A = sorted([comment for comment in issue.get_comments()] , key=lambda lowerCamelCase : i.created_at , reverse=lowerCamelCase )
__A = comments[0] if len(lowerCamelCase ) > 0 else None
__A = dt.utcnow()
__A = (current_time - issue.updated_at).days
__A = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state='''closed''' )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
issue.create_comment(
'''This issue has been automatically marked as stale because it has not had '''
'''recent activity. If you think this still needs to be addressed '''
'''please comment on this thread.\n\nPlease note that issues that do not follow the '''
'''[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '''
'''are likely to be ignored.''' )
if __name__ == "__main__":
main()
| 117 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}
class lowerCamelCase__( __snake_case):
UpperCAmelCase__ : List[Any] = """xlnet"""
UpperCAmelCase__ : Union[str, Any] = ["""mems"""]
UpperCAmelCase__ : str = {
"""n_token""": """vocab_size""", # Backward compatibility
"""hidden_size""": """d_model""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self: Optional[Any] , UpperCamelCase_: Any=3_20_00 , UpperCamelCase_: Any=10_24 , UpperCamelCase_: Any=24 , UpperCamelCase_: Tuple=16 , UpperCamelCase_: List[str]=40_96 , UpperCamelCase_: Optional[Any]="gelu" , UpperCamelCase_: List[Any]=True , UpperCamelCase_: Optional[int]="bi" , UpperCamelCase_: Any=0.02 , UpperCamelCase_: Optional[Any]=1E-12 , UpperCamelCase_: Optional[int]=0.1 , UpperCamelCase_: Optional[Any]=5_12 , UpperCamelCase_: int=None , UpperCamelCase_: str=True , UpperCamelCase_: Union[str, Any]=False , UpperCamelCase_: Any=False , UpperCamelCase_: Any=-1 , UpperCamelCase_: int=False , UpperCamelCase_: int="last" , UpperCamelCase_: Tuple=True , UpperCamelCase_: Dict="tanh" , UpperCamelCase_: Optional[int]=0.1 , UpperCamelCase_: List[Any]=5 , UpperCamelCase_: int=5 , UpperCamelCase_: str=5 , UpperCamelCase_: Union[str, Any]=1 , UpperCamelCase_: Optional[Any]=2 , **UpperCamelCase_: Union[str, Any] , ):
__lowerCamelCase = vocab_size
__lowerCamelCase = d_model
__lowerCamelCase = n_layer
__lowerCamelCase = n_head
if d_model % n_head != 0:
raise ValueError(F'\'d_model % n_head\' ({d_model % n_head}) should be equal to 0' )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
F'`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})' )
__lowerCamelCase = d_model // n_head
__lowerCamelCase = ff_activation
__lowerCamelCase = d_inner
__lowerCamelCase = untie_r
__lowerCamelCase = attn_type
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = dropout
__lowerCamelCase = mem_len
__lowerCamelCase = reuse_len
__lowerCamelCase = bi_data
__lowerCamelCase = clamp_len
__lowerCamelCase = same_length
__lowerCamelCase = summary_type
__lowerCamelCase = summary_use_proj
__lowerCamelCase = summary_activation
__lowerCamelCase = summary_last_dropout
__lowerCamelCase = start_n_top
__lowerCamelCase = end_n_top
__lowerCamelCase = bos_token_id
__lowerCamelCase = pad_token_id
__lowerCamelCase = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
"""The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"""
""" instead.""" , UpperCamelCase__ , )
__lowerCamelCase = kwargs["use_cache"]
__lowerCamelCase = use_mems_eval
__lowerCamelCase = use_mems_train
super().__init__(pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__ )
@property
def lowerCAmelCase__ ( self: List[str] ):
logger.info(F'The model {self.model_type} is one of the few models that has no sequence length limit.' )
return -1
@max_position_embeddings.setter
def lowerCAmelCase__ ( self: int , UpperCamelCase_: int ):
raise NotImplementedError(
F'The model {self.model_type} is one of the few models that has no sequence length limit.' )
| 369 |
import warnings
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
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'nvidia/segformer-b0-finetuned-ade-512-512': (
'https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class lowerCamelCase__( __lowerCamelCase):
UpperCAmelCase__ : str = 'segformer'
def __init__( self: Union[str, Any] , UpperCamelCase_: Optional[int]=3 , UpperCamelCase_: Any=4 , UpperCamelCase_: int=[2, 2, 2, 2] , UpperCamelCase_: Optional[Any]=[8, 4, 2, 1] , UpperCamelCase_: Union[str, Any]=[32, 64, 1_60, 2_56] , UpperCamelCase_: int=[7, 3, 3, 3] , UpperCamelCase_: Dict=[4, 2, 2, 2] , UpperCamelCase_: str=[1, 2, 5, 8] , UpperCamelCase_: List[str]=[4, 4, 4, 4] , UpperCamelCase_: Optional[int]="gelu" , UpperCamelCase_: List[Any]=0.0 , UpperCamelCase_: List[Any]=0.0 , UpperCamelCase_: Tuple=0.1 , UpperCamelCase_: Optional[int]=0.02 , UpperCamelCase_: List[Any]=0.1 , UpperCamelCase_: Optional[int]=1E-6 , UpperCamelCase_: Optional[int]=2_56 , UpperCamelCase_: Optional[Any]=2_55 , **UpperCamelCase_: List[Any] , ):
super().__init__(**UpperCamelCase_ )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
"""Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
""" removed, as the behaviour will default to that of reshape_last_stage = True.""" , UpperCamelCase_ , )
__lowerCamelCase = num_channels
__lowerCamelCase = num_encoder_blocks
__lowerCamelCase = depths
__lowerCamelCase = sr_ratios
__lowerCamelCase = hidden_sizes
__lowerCamelCase = patch_sizes
__lowerCamelCase = strides
__lowerCamelCase = mlp_ratios
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = classifier_dropout_prob
__lowerCamelCase = initializer_range
__lowerCamelCase = drop_path_rate
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = decoder_hidden_size
__lowerCamelCase = kwargs.get("""reshape_last_stage""" , UpperCamelCase_ )
__lowerCamelCase = semantic_loss_ignore_index
class lowerCamelCase__( __lowerCamelCase):
UpperCAmelCase__ : Any = version.parse('1.11')
@property
def lowerCAmelCase__ ( self: Any ):
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def lowerCAmelCase__ ( self: Union[str, Any] ):
return 1E-4
@property
def lowerCAmelCase__ ( self: Dict ):
return 12
| 29 | 0 |
"""simple docstring"""
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowercase ( __snake_case : str = "" ):
lowercase_ : str = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
lowercase_ : int = BeautifulSoup(requests.get(_lowerCamelCase ).text , '''html.parser''' )
lowercase_ : int = soup.find_all('''td''' , attrs='''titleColumn''' )
lowercase_ : Any = soup.find_all('''td''' , class_='''ratingColumn imdbRating''' )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(_lowerCamelCase , _lowerCamelCase )
}
def lowercase ( __snake_case : str = "IMDb_Top_250_Movies.csv" ):
lowercase_ : Dict = get_imdb_top_aaa_movies()
with open(_lowerCamelCase , '''w''' , newline='''''' ) as out_file:
lowercase_ : Optional[int] = csv.writer(_lowerCamelCase )
writer.writerow(['''Movie title''', '''IMDb rating'''] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 33 | import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snake_case_ ( __A ,unittest.TestCase ):
__A : Union[str, Any] = LEDTokenizer
__A : Union[str, Any] = LEDTokenizerFast
__A : Optional[Any] = True
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
super().setUp()
lowercase__ : List[str] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
lowercase__ : Optional[int] = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
lowercase__ : str = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
lowercase__ : Tuple = {"unk_token": "<unk>"}
lowercase__ : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
lowercase__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(lowercase_ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(lowercase_ ) )
def __UpperCamelCase ( self : int , **lowercase_ : str ) -> List[Any]:
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowercase_ )
def __UpperCamelCase ( self : List[Any] , **lowercase_ : Any ) -> List[Any]:
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **lowercase_ )
def __UpperCamelCase ( self : str , lowercase_ : Any ) -> Tuple:
return "lower newer", "lower newer"
@cached_property
def __UpperCamelCase ( self : Tuple ) -> Optional[Any]:
return LEDTokenizer.from_pretrained("allenai/led-base-16384" )
@cached_property
def __UpperCamelCase ( self : Tuple ) -> int:
return LEDTokenizerFast.from_pretrained("allenai/led-base-16384" )
@require_torch
def __UpperCamelCase ( self : int ) -> List[Any]:
lowercase__ : Union[str, Any] = ["A long paragraph for summarization.", "Another paragraph for summarization."]
lowercase__ : str = [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowercase__ : Dict = tokenizer(lowercase_ , max_length=len(lowercase_ ) , padding=lowercase_ , return_tensors="pt" )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
lowercase__ : Union[str, Any] = batch.input_ids.tolist()[0]
self.assertListEqual(lowercase_ , lowercase_ )
@require_torch
def __UpperCamelCase ( self : List[str] ) -> Tuple:
lowercase__ : Dict = ["A long paragraph for summarization.", "Another paragraph for summarization."]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowercase__ : Optional[int] = tokenizer(lowercase_ , padding=lowercase_ , return_tensors="pt" )
self.assertIn("input_ids" , lowercase_ )
self.assertIn("attention_mask" , lowercase_ )
self.assertNotIn("labels" , lowercase_ )
self.assertNotIn("decoder_attention_mask" , lowercase_ )
@require_torch
def __UpperCamelCase ( self : Optional[Any] ) -> Any:
lowercase__ : Dict = [
"Summary of the text.",
"Another summary.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowercase__ : Dict = tokenizer(text_target=lowercase_ , max_length=32 , padding="max_length" , return_tensors="pt" )
self.assertEqual(32 , targets["input_ids"].shape[1] )
@require_torch
def __UpperCamelCase ( self : Optional[int] ) -> Tuple:
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowercase__ : int = tokenizer(
["I am a small frog" * 10_24, "I am a small frog"] , padding=lowercase_ , truncation=lowercase_ , return_tensors="pt" )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertEqual(batch.input_ids.shape , (2, 51_22) )
@require_torch
def __UpperCamelCase ( self : List[str] ) -> Any:
lowercase__ : Union[str, Any] = ["A long paragraph for summarization."]
lowercase__ : List[Any] = [
"Summary of the text.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowercase__ : List[Any] = tokenizer(lowercase_ , return_tensors="pt" )
lowercase__ : Dict = tokenizer(text_target=lowercase_ , return_tensors="pt" )
lowercase__ : Optional[int] = inputs["input_ids"]
lowercase__ : str = targets["input_ids"]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
@require_torch
def __UpperCamelCase ( self : Union[str, Any] ) -> Dict:
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowercase__ : int = ["Summary of the text.", "Another summary."]
lowercase__ : List[str] = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
lowercase__ : Tuple = tokenizer(lowercase_ , padding=lowercase_ )
lowercase__ : int = [[0] * len(lowercase_ ) for x in encoded_output["input_ids"]]
lowercase__ : Any = tokenizer.pad(lowercase_ )
self.assertSequenceEqual(outputs["global_attention_mask"] , lowercase_ )
def __UpperCamelCase ( self : int ) -> Union[str, Any]:
pass
def __UpperCamelCase ( self : int ) -> Optional[Any]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase__ : List[Any] = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
lowercase__ : List[str] = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
lowercase__ : List[Any] = "A, <mask> AllenNLP sentence."
lowercase__ : Tuple = tokenizer_r.encode_plus(lowercase_ , add_special_tokens=lowercase_ , return_token_type_ids=lowercase_ )
lowercase__ : List[str] = tokenizer_p.encode_plus(lowercase_ , add_special_tokens=lowercase_ , return_token_type_ids=lowercase_ )
self.assertEqual(sum(tokens_r["token_type_ids"] ) , sum(tokens_p["token_type_ids"] ) )
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) , sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) , )
lowercase__ : Any = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
lowercase__ : Optional[Any] = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
self.assertSequenceEqual(tokens_p["input_ids"] , [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] , [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
lowercase_ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
lowercase_ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
| 87 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import GLPNImageProcessor
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=7 , lowerCAmelCase__=3 , lowerCAmelCase__=18 , lowerCAmelCase__=30 , lowerCAmelCase__=400 , lowerCAmelCase__=True , lowerCAmelCase__=32 , lowerCAmelCase__=True , ) -> Optional[Any]:
a : str = parent
a : str = batch_size
a : Union[str, Any] = num_channels
a : Optional[Any] = image_size
a : int = min_resolution
a : Optional[int] = max_resolution
a : List[Any] = do_resize
a : List[Any] = size_divisor
a : Tuple = do_rescale
def __a ( self ) -> Optional[int]:
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
@require_torch
@require_vision
class __UpperCamelCase ( a__ , unittest.TestCase ):
lowerCamelCase : Dict =GLPNImageProcessor if is_vision_available() else None
def __a ( self ) -> int:
a : Any = GLPNImageProcessingTester(self )
@property
def __a ( self ) -> Tuple:
return self.image_processor_tester.prepare_image_processor_dict()
def __a ( self ) -> str:
a : List[str] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_resize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "size_divisor" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "resample" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , "do_rescale" ) )
def __a ( self ) -> List[str]:
pass
def __a ( self ) -> Union[str, Any]:
# Initialize image_processing
a : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
a : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input (GLPNImageProcessor doesn't support batching)
a : Optional[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def __a ( self ) -> str:
# Initialize image_processing
a : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
a : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
# Test not batched input (GLPNImageProcessor doesn't support batching)
a : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def __a ( self ) -> int:
# Initialize image_processing
a : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
a : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input (GLPNImageProcessor doesn't support batching)
a : str = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
| 352 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester
if is_flax_available():
from transformers import (
FlaxBertModel,
FlaxCLIPVisionModel,
FlaxVisionTextDualEncoderModel,
FlaxViTModel,
VisionTextDualEncoderConfig,
VisionTextDualEncoderProcessor,
)
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_torch_available():
import torch
from transformers import VisionTextDualEncoderModel
if is_vision_available():
from PIL import Image
def _SCREAMING_SNAKE_CASE ( _lowercase : List[Any] ) ->Union[str, Any]:
'''simple docstring'''
if isinstance(_lowercase , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class __UpperCamelCase :
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> str:
pass
def __a ( self ) -> List[Any]:
pass
def __a ( self ) -> str:
pass
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Any:
a : Dict = np.abs((a - b) ).max()
self.assertLessEqual(lowerCAmelCase__ , lowerCAmelCase__ , f"""Difference between torch and flax is {diff} (>= {tol}).""" )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=None , **lowerCAmelCase__ ) -> Dict:
a : Dict = VisionTextDualEncoderConfig.from_vision_text_configs(lowerCAmelCase__ , lowerCAmelCase__ )
a : List[str] = FlaxVisionTextDualEncoderModel(lowerCAmelCase__ )
a : int = model(input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], config.projection_dim) )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=None , **lowerCAmelCase__ ) -> Optional[Any]:
a, a : Optional[int] = self.get_vision_text_model(lowerCAmelCase__ , lowerCAmelCase__ )
a : Dict = {"vision_model": vision_model, "text_model": text_model}
a : Any = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCAmelCase__ )
a : List[str] = model(input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=None , **lowerCAmelCase__ ) -> Union[str, Any]:
a, a : Dict = self.get_vision_text_model(lowerCAmelCase__ , lowerCAmelCase__ )
a : Tuple = {"vision_model": vision_model, "text_model": text_model}
a : Tuple = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCAmelCase__ )
a : List[str] = model(input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )
a : Any = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCAmelCase__ )
a : str = FlaxVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase__ )
a : Dict = model(input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )
a : List[Any] = after_output[0]
a : Optional[Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowerCAmelCase__ , 1E-3 )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=None , **lowerCAmelCase__ ) -> List[Any]:
a, a : Union[str, Any] = self.get_vision_text_model(lowerCAmelCase__ , lowerCAmelCase__ )
a : List[Any] = {"vision_model": vision_model, "text_model": text_model}
a : int = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCAmelCase__ )
a : Tuple = model(
input_ids=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , output_attentions=lowerCAmelCase__ )
a : int = output.vision_model_output.attentions
self.assertEqual(len(lowerCAmelCase__ ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
a : Optional[int] = to_atuple(vision_model.config.image_size )
a : Tuple = to_atuple(vision_model.config.patch_size )
a : Any = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
a : Dict = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
a : str = output.text_model_output.attentions
self.assertEqual(len(lowerCAmelCase__ ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[Any]:
pt_model.to(lowerCAmelCase__ )
pt_model.eval()
# prepare inputs
a : List[Any] = inputs_dict
a : Any = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
a : int = pt_model(**lowerCAmelCase__ ).to_tuple()
a : Union[str, Any] = fx_model(**lowerCAmelCase__ ).to_tuple()
self.assertEqual(len(lowerCAmelCase__ ) , len(lowerCAmelCase__ ) , "Output lengths differ between Flax and PyTorch" )
for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ):
self.assert_almost_equals(lowerCAmelCase__ , pt_output.numpy() , 4E-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(lowerCAmelCase__ )
a : Dict = FlaxVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase__ , from_pt=lowerCAmelCase__ )
a : Optional[int] = fx_model_loaded(**lowerCAmelCase__ ).to_tuple()
self.assertEqual(len(lowerCAmelCase__ ) , len(lowerCAmelCase__ ) , "Output lengths differ between Flax and PyTorch" )
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] , pt_outputs[:4] ):
self.assert_almost_equals(lowerCAmelCase__ , pt_output.numpy() , 4E-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(lowerCAmelCase__ )
a : Optional[int] = VisionTextDualEncoderModel.from_pretrained(lowerCAmelCase__ , from_flax=lowerCAmelCase__ )
pt_model_loaded.to(lowerCAmelCase__ )
pt_model_loaded.eval()
with torch.no_grad():
a : int = pt_model_loaded(**lowerCAmelCase__ ).to_tuple()
self.assertEqual(len(lowerCAmelCase__ ) , len(lowerCAmelCase__ ) , "Output lengths differ between Flax and PyTorch" )
for fx_output, pt_output_loaded in zip(fx_outputs[:4] , pt_outputs_loaded[:4] ):
self.assert_almost_equals(lowerCAmelCase__ , pt_output_loaded.numpy() , 4E-2 )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> List[Any]:
a : List[Any] = VisionTextDualEncoderConfig.from_vision_text_configs(lowerCAmelCase__ , lowerCAmelCase__ )
a : Dict = VisionTextDualEncoderModel(lowerCAmelCase__ )
a : Optional[int] = FlaxVisionTextDualEncoderModel(lowerCAmelCase__ )
a : Dict = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , lowerCAmelCase__ )
a : List[str] = fx_state
self.check_pt_flax_equivalence(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[int]:
a : Optional[int] = VisionTextDualEncoderConfig.from_vision_text_configs(lowerCAmelCase__ , lowerCAmelCase__ )
a : Optional[int] = VisionTextDualEncoderModel(lowerCAmelCase__ )
a : List[Any] = FlaxVisionTextDualEncoderModel(lowerCAmelCase__ )
a : int = load_flax_weights_in_pytorch_model(lowerCAmelCase__ , fx_model.params )
self.check_pt_flax_equivalence(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def __a ( self ) -> Dict:
a : Any = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**lowerCAmelCase__ )
def __a ( self ) -> Dict:
a : List[str] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**lowerCAmelCase__ )
def __a ( self ) -> List[str]:
a : int = self.prepare_config_and_inputs()
self.check_save_load(**lowerCAmelCase__ )
def __a ( self ) -> List[str]:
a : Tuple = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**lowerCAmelCase__ )
@is_pt_flax_cross_test
def __a ( self ) -> Any:
a : List[Any] = self.prepare_config_and_inputs()
a : Tuple = config_inputs_dict.pop("vision_config" )
a : int = config_inputs_dict.pop("text_config" )
a : List[str] = config_inputs_dict
self.check_equivalence_pt_to_flax(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
self.check_equivalence_flax_to_pt(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def __a ( self ) -> List[Any]:
a, a : Optional[int] = self.get_pretrained_model_and_inputs()
a : Optional[int] = model_a(**lowerCAmelCase__ )
a : Optional[int] = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(lowerCAmelCase__ )
a : Any = FlaxVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase__ )
a : str = model_a(**lowerCAmelCase__ )
a : Dict = after_outputs[0]
a : Optional[Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowerCAmelCase__ , 1E-5 )
@require_flax
class __UpperCamelCase ( a__ , unittest.TestCase ):
def __a ( self ) -> List[Any]:
a : str = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"hf-internal-testing/tiny-random-vit" , "hf-internal-testing/tiny-bert" , vision_from_pt=lowerCAmelCase__ , text_from_pt=lowerCAmelCase__ , )
a : Any = 13
a : str = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
a : str = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
a : Optional[Any] = random_attention_mask([batch_size, 4] )
a : Optional[Any] = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Dict:
a : Dict = FlaxViTModel(lowerCAmelCase__ )
a : Dict = FlaxBertModel(lowerCAmelCase__ )
return vision_model, text_model
def __a ( self ) -> str:
a : Union[str, Any] = FlaxViTModelTester(self )
a : Dict = FlaxBertModelTester(self )
a : str = vit_model_tester.prepare_config_and_inputs()
a : Any = bert_model_tester.prepare_config_and_inputs()
a, a : Optional[int] = vision_config_and_inputs
a, a, a, a : Dict = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_torch
class __UpperCamelCase ( a__ , unittest.TestCase ):
def __a ( self ) -> List[Any]:
a : str = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"hf-internal-testing/tiny-random-clip" , "hf-internal-testing/tiny-bert" , vision_from_pt=lowerCAmelCase__ , text_from_pt=lowerCAmelCase__ , )
a : Tuple = 13
a : Union[str, Any] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
a : Union[str, Any] = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
a : Tuple = random_attention_mask([batch_size, 4] )
a : str = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Tuple:
a : List[Any] = FlaxCLIPVisionModel(lowerCAmelCase__ )
a : Tuple = FlaxBertModel(lowerCAmelCase__ )
return vision_model, text_model
def __a ( self ) -> List[Any]:
a : Tuple = FlaxCLIPVisionModelTester(self )
a : Union[str, Any] = FlaxBertModelTester(self )
a : Dict = clip_model_tester.prepare_config_and_inputs()
a : Optional[int] = bert_model_tester.prepare_config_and_inputs()
a, a : Dict = vision_config_and_inputs
a, a, a, a : Union[str, Any] = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_flax
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@slow
def __a ( self ) -> Dict:
a : str = FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian" , logit_scale_init_value=1.0 )
a : Optional[Any] = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian" )
a : Any = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
a : Optional[int] = processor(
text=["una foto di un gatto", "una foto di un cane"] , images=lowerCAmelCase__ , padding=lowerCAmelCase__ , return_tensors="np" )
a : Optional[Any] = model(**lowerCAmelCase__ )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
a : List[str] = np.array([[1.2_284_727, 0.3_104_122]] )
self.assertTrue(np.allclose(outputs.logits_per_image , lowerCAmelCase__ , atol=1E-3 ) )
| 79 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = CanineTokenizer
lowerCAmelCase__ = False
def UpperCAmelCase__ ( self : Any ):
super().setUp()
__snake_case: str = CanineTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def UpperCAmelCase__ ( self : Optional[int] ):
return CanineTokenizer.from_pretrained("""google/canine-s""" )
def UpperCAmelCase__ ( self : str , **A : List[str] ):
__snake_case: Tuple = self.tokenizer_class.from_pretrained(self.tmpdirname , **A )
__snake_case: int = 1_024
return tokenizer
@require_torch
def UpperCAmelCase__ ( self : int ):
__snake_case: Dict = self.canine_tokenizer
__snake_case: int = ["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
# fmt: off
__snake_case: List[str] = [57_344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57_345, 0, 0, 0, 0]
# fmt: on
__snake_case: int = tokenizer(A , padding=A , return_tensors="""pt""" )
self.assertIsInstance(A , A )
__snake_case: int = list(batch.input_ids.numpy()[0] )
self.assertListEqual(A , A )
self.assertEqual((2, 39) , batch.input_ids.shape )
self.assertEqual((2, 39) , batch.attention_mask.shape )
@require_torch
def UpperCAmelCase__ ( self : int ):
__snake_case: Optional[Any] = self.canine_tokenizer
__snake_case: Tuple = ["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
__snake_case: Any = tokenizer(A , padding=A , return_tensors="""pt""" )
# check if input_ids, attention_mask and token_type_ids are returned
self.assertIn("""input_ids""" , A )
self.assertIn("""attention_mask""" , A )
self.assertIn("""token_type_ids""" , A )
@require_torch
def UpperCAmelCase__ ( self : int ):
__snake_case: int = self.canine_tokenizer
__snake_case: Optional[int] = [
"""What's the weater?""",
"""It's about 25 degrees.""",
]
__snake_case: str = tokenizer(
text_target=A , max_length=32 , padding="""max_length""" , truncation=A , return_tensors="""pt""" )
self.assertEqual(32 , targets["""input_ids"""].shape[1] )
def UpperCAmelCase__ ( self : int ):
# safety check on max_len default value so we are sure the test works
__snake_case: Any = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
__snake_case: List[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
__snake_case: str = tempfile.mkdtemp()
__snake_case: List[Any] = """ He is very happy, UNwant\u00E9d,running"""
__snake_case: Optional[Any] = tokenizer.encode(A , add_special_tokens=A )
tokenizer.save_pretrained(A )
__snake_case: int = tokenizer.__class__.from_pretrained(A )
__snake_case: Dict = after_tokenizer.encode(A , add_special_tokens=A )
self.assertListEqual(A , A )
shutil.rmtree(A )
__snake_case: Union[str, Any] = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
__snake_case: str = tempfile.mkdtemp()
__snake_case: Tuple = """ He is very happy, UNwant\u00E9d,running"""
__snake_case: Dict = tokenizer.additional_special_tokens
# We can add a new special token for Canine as follows:
__snake_case: Union[str, Any] = chr(0xe0_07 )
additional_special_tokens.append(A )
tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens} )
__snake_case: Optional[int] = tokenizer.encode(A , add_special_tokens=A )
tokenizer.save_pretrained(A )
__snake_case: str = tokenizer.__class__.from_pretrained(A )
__snake_case: List[str] = after_tokenizer.encode(A , add_special_tokens=A )
self.assertListEqual(A , A )
self.assertIn(A , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
__snake_case: Tuple = tokenizer.__class__.from_pretrained(A , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(A )
def UpperCAmelCase__ ( self : int ):
__snake_case: int = self.get_tokenizers(do_lower_case=A )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
__snake_case , __snake_case: List[Any] = self.get_clean_sequence(A )
# a special token for Canine can be defined as follows:
__snake_case: List[Any] = 0xe0_05
__snake_case: Optional[int] = chr(A )
tokenizer.add_special_tokens({"""cls_token""": special_token} )
__snake_case: str = tokenizer.encode(A , add_special_tokens=A )
self.assertEqual(len(A ) , 1 )
__snake_case: Tuple = tokenizer.decode(ids + encoded_special_token , clean_up_tokenization_spaces=A )
__snake_case: List[str] = tokenizer.encode(A , add_special_tokens=A )
__snake_case: Dict = tokenizer.encode(A , add_special_tokens=A )
__snake_case: List[Any] = tokenizer.encode(A , add_special_tokens=A )
self.assertEqual(A , input_encoded + special_token_id )
__snake_case: int = tokenizer.decode(A , skip_special_tokens=A )
self.assertTrue(special_token not in decoded )
def UpperCAmelCase__ ( self : Union[str, Any] ):
__snake_case: str = self.get_tokenizers(do_lower_case=A )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
__snake_case: Any = chr(0xe0_05 )
__snake_case: List[Any] = chr(0xe0_06 )
# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
tokenizer.add_tokens([SPECIAL_TOKEN_1] , special_tokens=A )
# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
tokenizer.add_special_tokens({"""additional_special_tokens""": [SPECIAL_TOKEN_2]} )
__snake_case: str = tokenizer.tokenize(A )
__snake_case: int = tokenizer.tokenize(A )
self.assertEqual(len(A ) , 1 )
self.assertEqual(len(A ) , 1 )
self.assertEqual(token_a[0] , A )
self.assertEqual(token_a[0] , A )
@require_tokenizers
def UpperCAmelCase__ ( self : Tuple ):
__snake_case: Optional[int] = self.get_tokenizers(do_lower_case=A )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# a special token for Canine can be defined as follows:
__snake_case: Tuple = 0xe0_06
__snake_case: Union[str, Any] = chr(A )
__snake_case: Tuple = AddedToken(A , lstrip=A )
tokenizer.add_special_tokens({"""additional_special_tokens""": [new_token]} )
with tempfile.TemporaryDirectory() as tmp_dir_name:
tokenizer.save_pretrained(A )
tokenizer.from_pretrained(A )
def UpperCAmelCase__ ( self : Union[str, Any] ):
__snake_case: List[Any] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(A )
with open(os.path.join(A , """special_tokens_map.json""" ) , encoding="""utf-8""" ) as json_file:
__snake_case: Optional[Any] = json.load(A )
with open(os.path.join(A , """tokenizer_config.json""" ) , encoding="""utf-8""" ) as json_file:
__snake_case: Dict = json.load(A )
# a special token for Canine can be defined as follows:
__snake_case: str = 0xe0_06
__snake_case: int = chr(A )
__snake_case: int = [new_token_a]
__snake_case: List[Any] = [new_token_a]
with open(os.path.join(A , """special_tokens_map.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(A , A )
with open(os.path.join(A , """tokenizer_config.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(A , A )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
__snake_case: int = tokenizer_class.from_pretrained(A , extra_ids=0 )
self.assertIn(A , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a] ) ) , )
__snake_case: int = 0xe0_07
__snake_case: List[str] = chr(A )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
__snake_case: int = [AddedToken(A , lstrip=A )]
__snake_case: Dict = tokenizer_class.from_pretrained(
A , additional_special_tokens=A , extra_ids=0 )
self.assertIn(A , tokenizer.additional_special_tokens )
# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a] ) ) )
@require_tokenizers
def UpperCAmelCase__ ( self : List[Any] ):
__snake_case: int = self.get_tokenizers(do_lower_case=A )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
__snake_case: List[str] = """hello world"""
if self.space_between_special_tokens:
__snake_case: Any = """[CLS] hello world [SEP]"""
else:
__snake_case: Dict = input
__snake_case: Any = tokenizer.encode(A , add_special_tokens=A )
__snake_case: Optional[Any] = tokenizer.decode(A , spaces_between_special_tokens=self.space_between_special_tokens )
self.assertIn(A , [output, output.lower()] )
def UpperCAmelCase__ ( self : Dict ):
__snake_case: Optional[int] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
__snake_case: Dict = [
"""bos_token""",
"""eos_token""",
"""unk_token""",
"""sep_token""",
"""pad_token""",
"""cls_token""",
"""mask_token""",
]
__snake_case: Optional[int] = """a"""
__snake_case: Tuple = ord(A )
for attr in attributes_list:
setattr(A , attr + """_id""" , A )
self.assertEqual(getattr(A , A ) , A )
self.assertEqual(getattr(A , attr + """_id""" ) , A )
setattr(A , attr + """_id""" , A )
self.assertEqual(getattr(A , A ) , A )
self.assertEqual(getattr(A , attr + """_id""" ) , A )
setattr(A , """additional_special_tokens_ids""" , [] )
self.assertListEqual(getattr(A , """additional_special_tokens""" ) , [] )
self.assertListEqual(getattr(A , """additional_special_tokens_ids""" ) , [] )
__snake_case: Tuple = 0xe0_06
__snake_case: Tuple = chr(A )
setattr(A , """additional_special_tokens_ids""" , [additional_special_token_id] )
self.assertListEqual(getattr(A , """additional_special_tokens""" ) , [additional_special_token] )
self.assertListEqual(getattr(A , """additional_special_tokens_ids""" ) , [additional_special_token_id] )
def UpperCAmelCase__ ( self : int ):
pass
def UpperCAmelCase__ ( self : Dict ):
pass
def UpperCAmelCase__ ( self : Tuple ):
pass
def UpperCAmelCase__ ( self : Tuple ):
pass
def UpperCAmelCase__ ( self : Tuple ):
pass
def UpperCAmelCase__ ( self : int ):
pass
def UpperCAmelCase__ ( self : Any ):
pass
def UpperCAmelCase__ ( self : Any ):
pass
| 111 |
import pytest
__UpperCAmelCase : Optional[Any] = "__dummy_dataset1__"
__UpperCAmelCase : List[str] = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n def _info(self):\n features = datasets.Features(\n {\n \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n \"ner_tags\": datasets.Sequence(\n datasets.features.ClassLabel(\n names=[\n \"O\",\n \"B-PER\",\n \"I-PER\",\n \"B-ORG\",\n \"I-ORG\",\n \"B-LOC\",\n \"I-LOC\",\n ]\n )\n ),\n \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n }\n )\n return datasets.DatasetInfo(features=features)\n\n def _split_generators(self, dl_manager):\n dl_path = dl_manager.download(URLS)\n return [\n datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n ]\n\n def _generate_examples(self, filepath):\n with open(filepath, \"r\", encoding=\"utf-8\") as f:\n for i, line in enumerate(f):\n yield i, json.loads(line)\n"
@pytest.fixture
def A__ ( ) -> Optional[int]:
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def A__ ( ) -> Tuple:
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> Tuple:
__snake_case: List[Any] = dataset_loading_script_name
__snake_case: Any = tmp_path / """datasets""" / script_name
script_dir.mkdir(parents=SCREAMING_SNAKE_CASE__)
__snake_case: int = script_dir / F'''{script_name}.py'''
with open(SCREAMING_SNAKE_CASE__ , """w""") as f:
f.write(SCREAMING_SNAKE_CASE__)
return str(SCREAMING_SNAKE_CASE__)
| 111 | 1 |
"""simple docstring"""
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
_lowerCAmelCase : Any = logging.get_logger(__name__)
_lowerCAmelCase : str = {
"facebook/data2vec-vision-base-ft": (
"https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
),
}
class UpperCAmelCase_ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE : List[Any] = 'data2vec-vision'
def __init__( self : Dict , A : int=7_6_8 , A : List[Any]=1_2 , A : List[str]=1_2 , A : Union[str, Any]=3_0_7_2 , A : Tuple="gelu" , A : List[Any]=0.0 , A : List[Any]=0.0 , A : Optional[Any]=0.02 , A : Any=1e-12 , A : Union[str, Any]=2_2_4 , A : Union[str, Any]=1_6 , A : Union[str, Any]=3 , A : Tuple=False , A : List[Any]=False , A : str=False , A : Optional[Any]=False , A : List[str]=0.1 , A : List[Any]=0.1 , A : Tuple=True , A : Optional[Any]=[3, 5, 7, 1_1] , A : Any=[1, 2, 3, 6] , A : int=True , A : Dict=0.4 , A : Any=2_5_6 , A : List[str]=1 , A : str=False , A : Optional[Any]=2_5_5 , **A : Optional[int] , ):
super().__init__(**A )
_UpperCAmelCase : str = hidden_size
_UpperCAmelCase : Optional[Any] = num_hidden_layers
_UpperCAmelCase : Dict = num_attention_heads
_UpperCAmelCase : Optional[Any] = intermediate_size
_UpperCAmelCase : List[str] = hidden_act
_UpperCAmelCase : str = hidden_dropout_prob
_UpperCAmelCase : List[Any] = attention_probs_dropout_prob
_UpperCAmelCase : Union[str, Any] = initializer_range
_UpperCAmelCase : int = layer_norm_eps
_UpperCAmelCase : Optional[int] = image_size
_UpperCAmelCase : Optional[Any] = patch_size
_UpperCAmelCase : Dict = num_channels
_UpperCAmelCase : Optional[Any] = use_mask_token
_UpperCAmelCase : str = use_absolute_position_embeddings
_UpperCAmelCase : Optional[int] = use_relative_position_bias
_UpperCAmelCase : Optional[int] = use_shared_relative_position_bias
_UpperCAmelCase : List[str] = layer_scale_init_value
_UpperCAmelCase : Dict = drop_path_rate
_UpperCAmelCase : Union[str, Any] = use_mean_pooling
# decode head attributes (semantic segmentation)
_UpperCAmelCase : Dict = out_indices
_UpperCAmelCase : Optional[Any] = pool_scales
# auxiliary head attributes (semantic segmentation)
_UpperCAmelCase : Dict = use_auxiliary_head
_UpperCAmelCase : Dict = auxiliary_loss_weight
_UpperCAmelCase : str = auxiliary_channels
_UpperCAmelCase : Dict = auxiliary_num_convs
_UpperCAmelCase : Optional[int] = auxiliary_concat_input
_UpperCAmelCase : Tuple = semantic_loss_ignore_index
class UpperCAmelCase_ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE : Any = version.parse('1.11' )
@property
def snake_case_ ( self : List[Any] ):
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def snake_case_ ( self : List[Any] ):
return 1e-4
| 202 |
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_lowerCAmelCase : Tuple = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n"
_lowerCAmelCase : Optional[int] = "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"
_lowerCAmelCase : Any = "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n 'google_bleu': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.4\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
def snake_case_ ( self : List[str] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ),
"references": datasets.Sequence(
datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ) , id="references" ),
} ) , )
def snake_case_ ( self : str , A : List[List[List[str]]] , A : List[List[str]] , A : int = 1 , A : int = 4 , ):
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=A , hypotheses=A , min_len=A , max_len=A )
}
| 202 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileNetVaForImageClassification, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class _SCREAMING_SNAKE_CASE ( __A ):
def _A ( self : str ):
UpperCamelCase :Tuple = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__lowercase , """tf_padding""" ) )
self.parent.assertTrue(hasattr(__lowercase , """depth_multiplier""" ) )
class _SCREAMING_SNAKE_CASE :
def __init__( self : Any , __lowerCamelCase : List[str] , __lowerCamelCase : List[Any]=13 , __lowerCamelCase : Optional[int]=3 , __lowerCamelCase : str=32 , __lowerCamelCase : Optional[int]=0.25 , __lowerCamelCase : List[Any]=8 , __lowerCamelCase : Any=True , __lowerCamelCase : Any=1_024 , __lowerCamelCase : List[Any]=32 , __lowerCamelCase : Tuple="relu6" , __lowerCamelCase : Dict=0.1 , __lowerCamelCase : Optional[int]=0.02 , __lowerCamelCase : str=True , __lowerCamelCase : List[Any]=True , __lowerCamelCase : Any=10 , __lowerCamelCase : Optional[int]=None , ):
UpperCamelCase :Any = parent
UpperCamelCase :Union[str, Any] = batch_size
UpperCamelCase :Optional[Any] = num_channels
UpperCamelCase :Optional[Any] = image_size
UpperCamelCase :Union[str, Any] = depth_multiplier
UpperCamelCase :List[Any] = min_depth
UpperCamelCase :List[str] = tf_padding
UpperCamelCase :List[Any] = int(last_hidden_size * depth_multiplier )
UpperCamelCase :int = output_stride
UpperCamelCase :str = hidden_act
UpperCamelCase :List[str] = classifier_dropout_prob
UpperCamelCase :List[str] = use_labels
UpperCamelCase :Tuple = is_training
UpperCamelCase :Optional[Any] = num_labels
UpperCamelCase :Dict = initializer_range
UpperCamelCase :Dict = scope
def _A ( self : Tuple ):
UpperCamelCase :Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase :Any = None
UpperCamelCase :str = None
if self.use_labels:
UpperCamelCase :Dict = ids_tensor([self.batch_size] , self.num_labels )
UpperCamelCase :Optional[Any] = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
UpperCamelCase :str = self.get_config()
return config, pixel_values, labels, pixel_labels
def _A ( self : Tuple ):
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , min_depth=self.min_depth , tf_padding=self.tf_padding , hidden_act=self.hidden_act , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def _A ( self : Any , __lowerCamelCase : int , __lowerCamelCase : Tuple , __lowerCamelCase : str , __lowerCamelCase : List[Any] ):
UpperCamelCase :Dict = MobileNetVaModel(config=__lowercase )
model.to(__lowercase )
model.eval()
UpperCamelCase :List[Any] = model(__lowercase )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def _A ( self : Optional[Any] , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : str , __lowerCamelCase : Dict ):
UpperCamelCase :List[Any] = self.num_labels
UpperCamelCase :Optional[int] = MobileNetVaForImageClassification(__lowercase )
model.to(__lowercase )
model.eval()
UpperCamelCase :Dict = model(__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _A ( self : Tuple ):
UpperCamelCase :str = self.prepare_config_and_inputs()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase :List[Any] = config_and_inputs
UpperCamelCase :Union[str, Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _SCREAMING_SNAKE_CASE ( __A , __A , unittest.TestCase ):
snake_case__ : Optional[Any] = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
snake_case__ : Tuple = (
{'feature-extraction': MobileNetVaModel, 'image-classification': MobileNetVaForImageClassification}
if is_torch_available()
else {}
)
snake_case__ : Dict = False
snake_case__ : Optional[int] = False
snake_case__ : Optional[Any] = False
snake_case__ : List[str] = False
def _A ( self : Optional[int] ):
UpperCamelCase :Any = MobileNetVaModelTester(self )
UpperCamelCase :int = MobileNetVaConfigTester(self , config_class=__lowercase , has_text_modality=__lowercase )
def _A ( self : Dict ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""MobileNetV1 does not use inputs_embeds""" )
def _A ( self : Optional[int] ):
pass
@unittest.skip(reason="""MobileNetV1 does not support input and output embeddings""" )
def _A ( self : Dict ):
pass
@unittest.skip(reason="""MobileNetV1 does not output attentions""" )
def _A ( self : Tuple ):
pass
def _A ( self : int ):
UpperCamelCase , UpperCamelCase :str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase :List[str] = model_class(__lowercase )
UpperCamelCase :List[str] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase :str = [*signature.parameters.keys()]
UpperCamelCase :str = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __lowercase )
def _A ( self : List[Any] ):
UpperCamelCase :Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase )
def _A ( self : int ):
def check_hidden_states_output(__lowerCamelCase : Dict , __lowerCamelCase : List[Any] , __lowerCamelCase : str ):
UpperCamelCase :Tuple = model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
UpperCamelCase :Optional[int] = model(**self._prepare_for_class(__lowercase , __lowercase ) )
UpperCamelCase :List[str] = outputs.hidden_states
UpperCamelCase :Dict = 26
self.assertEqual(len(__lowercase ) , __lowercase )
UpperCamelCase , UpperCamelCase :Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase :Union[str, Any] = True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCamelCase :List[Any] = True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
def _A ( self : int ):
UpperCamelCase :Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowercase )
@slow
def _A ( self : str ):
for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase :Dict = MobileNetVaModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
def SCREAMING_SNAKE_CASE_ ( ) -> List[Any]:
"""simple docstring"""
UpperCamelCase :List[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def _A ( self : str ):
return (
MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224""" ) if is_vision_available() else None
)
@slow
def _A ( self : Tuple ):
UpperCamelCase :Union[str, Any] = MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224""" ).to(__lowercase )
UpperCamelCase :List[Any] = self.default_image_processor
UpperCamelCase :List[Any] = prepare_img()
UpperCamelCase :str = image_processor(images=__lowercase , return_tensors="""pt""" ).to(__lowercase )
# forward pass
with torch.no_grad():
UpperCamelCase :Tuple = model(**__lowercase )
# verify the logits
UpperCamelCase :List[str] = torch.Size((1, 1_001) )
self.assertEqual(outputs.logits.shape , __lowercase )
UpperCamelCase :Optional[int] = torch.tensor([-4.1739, -1.1233, 3.1205] ).to(__lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowercase , atol=1E-4 ) )
| 38 |
'''simple docstring'''
def __magic_name__( lowerCamelCase, lowerCamelCase):
assert x is not None
assert y is not None
__lowerCAmelCase = len(lowerCamelCase)
__lowerCAmelCase = len(lowerCamelCase)
# declaring the array for storing the dp values
__lowerCAmelCase = [[0] * (n + 1) for _ in range(m + 1)] # noqa: E741
for i in range(1, m + 1):
for j in range(1, n + 1):
__lowerCAmelCase = 1 if x[i - 1] == y[j - 1] else 0
__lowerCAmelCase = max(l[i - 1][j], l[i][j - 1], l[i - 1][j - 1] + match)
__lowerCAmelCase = ''''''
__lowerCAmelCase , __lowerCAmelCase = m, n
while i > 0 and j > 0:
__lowerCAmelCase = 1 if x[i - 1] == y[j - 1] else 0
if l[i][j] == l[i - 1][j - 1] + match:
if match == 1:
__lowerCAmelCase = x[i - 1] + seq
i -= 1
j -= 1
elif l[i][j] == l[i - 1][j]:
i -= 1
else:
j -= 1
return l[m][n], seq
if __name__ == "__main__":
_UpperCAmelCase : List[Any] = """AGGTAB"""
_UpperCAmelCase : int = """GXTXAYB"""
_UpperCAmelCase : Any = 4
_UpperCAmelCase : List[Any] = """GTAB"""
_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = longest_common_subsequence(a, b)
print("""len =""", ln, """, sub-sequence =""", subseq)
import doctest
doctest.testmod()
| 174 | 0 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __magic_name__ ( lowerCAmelCase ,lowerCAmelCase ,unittest.TestCase ):
UpperCAmelCase =IFInpaintingPipeline
UpperCAmelCase =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
UpperCAmelCase =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
UpperCAmelCase =PipelineTesterMixin.required_optional_params - {"latents"}
def lowerCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
return self._get_dummy_components()
def lowerCAmelCase ( self , snake_case , snake_case=0) -> Any:
'''simple docstring'''
if str(snake_case).startswith('mps'):
_UpperCAmelCase : str =torch.manual_seed(snake_case)
else:
_UpperCAmelCase : List[str] =torch.Generator(device=snake_case).manual_seed(snake_case)
_UpperCAmelCase : int =floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(snake_case)).to(snake_case)
_UpperCAmelCase : List[str] =floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(snake_case)).to(snake_case)
_UpperCAmelCase : Dict ={
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def lowerCAmelCase ( self) -> int:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)
def lowerCAmelCase ( self) -> List[str]:
'''simple docstring'''
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA')
def lowerCAmelCase ( self) -> Any:
'''simple docstring'''
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1E-1)
def lowerCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)
def lowerCAmelCase ( self) -> List[Any]:
'''simple docstring'''
self._test_save_load_local()
def lowerCAmelCase ( self) -> List[str]:
'''simple docstring'''
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 242 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
lowercase =logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase )
class __magic_name__ ( lowerCAmelCase ):
def __init__( self , **snake_case) -> Optional[int]:
'''simple docstring'''
super().__init__(**snake_case)
requires_backends(self , 'vision')
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == 'tf'
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING)
def __call__( self , snake_case , **snake_case) -> str:
'''simple docstring'''
return super().__call__(snake_case , **snake_case)
def lowerCAmelCase ( self , **snake_case) -> int:
'''simple docstring'''
_UpperCAmelCase : str ={}
if "candidate_labels" in kwargs:
_UpperCAmelCase : Union[str, Any] =kwargs['candidate_labels']
if "hypothesis_template" in kwargs:
_UpperCAmelCase : List[Any] =kwargs['hypothesis_template']
return preprocess_params, {}, {}
def lowerCAmelCase ( self , snake_case , snake_case=None , snake_case="This is a photo of {}.") -> Any:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] =load_image(snake_case)
_UpperCAmelCase : Union[str, Any] =self.image_processor(images=[image] , return_tensors=self.framework)
_UpperCAmelCase : Union[str, Any] =candidate_labels
_UpperCAmelCase : List[Any] =[hypothesis_template.format(snake_case) for x in candidate_labels]
_UpperCAmelCase : str =self.tokenizer(snake_case , return_tensors=self.framework , padding=snake_case)
_UpperCAmelCase : Any =[text_inputs]
return inputs
def lowerCAmelCase ( self , snake_case) -> str:
'''simple docstring'''
_UpperCAmelCase : List[str] =model_inputs.pop('candidate_labels')
_UpperCAmelCase : Tuple =model_inputs.pop('text_inputs')
if isinstance(text_inputs[0] , snake_case):
_UpperCAmelCase : Any =text_inputs[0]
else:
# Batching case.
_UpperCAmelCase : str =text_inputs[0][0]
_UpperCAmelCase : Any =self.model(**snake_case , **snake_case)
_UpperCAmelCase : List[str] ={
'candidate_labels': candidate_labels,
'logits': outputs.logits_per_image,
}
return model_outputs
def lowerCAmelCase ( self , snake_case) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : str =model_outputs.pop('candidate_labels')
_UpperCAmelCase : Union[str, Any] =model_outputs['logits'][0]
if self.framework == "pt":
_UpperCAmelCase : Dict =logits.softmax(dim=-1).squeeze(-1)
_UpperCAmelCase : Union[str, Any] =probs.tolist()
if not isinstance(snake_case , snake_case):
_UpperCAmelCase : Union[str, Any] =[scores]
elif self.framework == "tf":
_UpperCAmelCase : Dict =stable_softmax(snake_case , axis=-1)
_UpperCAmelCase : str =probs.numpy().tolist()
else:
raise ValueError(f"Unsupported framework: {self.framework}")
_UpperCAmelCase : List[str] =[
{'score': score, 'label': candidate_label}
for score, candidate_label in sorted(zip(snake_case , snake_case) , key=lambda snake_case: -x[0])
]
return result
| 242 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
a : Optional[Any] = logging.get_logger(__name__)
a : int = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
def _SCREAMING_SNAKE_CASE ( _lowercase : int , _lowercase : int , _lowercase : List[str] , _lowercase : List[str] , _lowercase : Dict ) ->Optional[Any]:
'''simple docstring'''
for attribute in key.split("." ):
a : Dict = getattr(_lowercase , _lowercase )
if weight_type is not None:
a : Optional[Any] = getattr(_lowercase , _lowercase ).shape
else:
a : Tuple = hf_pointer.shape
assert hf_shape == value.shape, (
F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
a : List[str] = value
elif weight_type == "weight_g":
a : int = value
elif weight_type == "weight_v":
a : int = value
elif weight_type == "bias":
a : Tuple = value
else:
a : Union[str, Any] = value
logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def _SCREAMING_SNAKE_CASE ( _lowercase : Any , _lowercase : List[str] , _lowercase : int ) ->Any:
'''simple docstring'''
a : Dict = []
a : int = fairseq_model.state_dict()
a : Tuple = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
a : int = False
if "conv_layers" in name:
load_conv_layer(
_lowercase , _lowercase , _lowercase , _lowercase , hf_model.config.feat_extract_norm == "group" , )
a : Optional[Any] = True
else:
for key, mapped_key in MAPPING.items():
a : Optional[Any] = "hubert." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key
if key in name or (key.split("w2v_model." )[-1] == name.split("." )[0] and not is_finetuned):
a : List[str] = True
if "*" in mapped_key:
a : Union[str, Any] = name.split(_lowercase )[0].split("." )[-2]
a : str = mapped_key.replace("*" , _lowercase )
if "weight_g" in name:
a : Optional[int] = "weight_g"
elif "weight_v" in name:
a : Optional[Any] = "weight_v"
elif "weight" in name:
a : Tuple = "weight"
elif "bias" in name:
a : Tuple = "bias"
else:
a : Union[str, Any] = None
set_recursively(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
continue
if not is_used:
unused_weights.append(_lowercase )
logger.warning(F"""Unused weights: {unused_weights}""" )
def _SCREAMING_SNAKE_CASE ( _lowercase : Optional[int] , _lowercase : Optional[Any] , _lowercase : Optional[Any] , _lowercase : str , _lowercase : Optional[Any] ) ->List[Any]:
'''simple docstring'''
a : List[Any] = full_name.split("conv_layers." )[-1]
a : Any = name.split("." )
a : List[str] = int(items[0] )
a : Any = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
a : Tuple = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
a : str = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
a : str = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
a : str = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_lowercase )
@torch.no_grad()
def _SCREAMING_SNAKE_CASE ( _lowercase : Any , _lowercase : Tuple , _lowercase : List[str]=None , _lowercase : Dict=None , _lowercase : int=True ) ->List[Any]:
'''simple docstring'''
if config_path is not None:
a : Tuple = HubertConfig.from_pretrained(_lowercase )
else:
a : Any = HubertConfig()
if is_finetuned:
if dict_path:
a : str = Dictionary.load(_lowercase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
a : int = target_dict.pad_index
a : Optional[int] = target_dict.bos_index
a : Dict = target_dict.eos_index
a : Optional[int] = len(target_dict.symbols )
a : List[str] = os.path.join(_lowercase , "vocab.json" )
if not os.path.isdir(_lowercase ):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_lowercase ) )
return
os.makedirs(_lowercase , exist_ok=_lowercase )
with open(_lowercase , "w" , encoding="utf-8" ) as vocab_handle:
json.dump(target_dict.indices , _lowercase )
a : Optional[int] = WavaVecaCTCTokenizer(
_lowercase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="|" , do_lower_case=_lowercase , )
a : int = True if config.feat_extract_norm == "layer" else False
a : Any = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=_lowercase , return_attention_mask=_lowercase , )
a : str = WavaVecaProcessor(feature_extractor=_lowercase , tokenizer=_lowercase )
processor.save_pretrained(_lowercase )
a : int = HubertForCTC(_lowercase )
else:
a : str = HubertModel(_lowercase )
if is_finetuned:
a, a, a : List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
else:
a, a, a : int = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
a : Optional[int] = model[0].eval()
recursively_load_weights(_lowercase , _lowercase , _lowercase )
hf_wavavec.save_pretrained(_lowercase )
if __name__ == "__main__":
a : Tuple = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
a : Optional[Any] = parser.parse_args()
convert_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 105 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__magic_name__ = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_ ( __a , unittest.TestCase ):
"""simple docstring"""
__lowercase : Optional[Any] = XLNetTokenizer
__lowercase : List[str] = XLNetTokenizerFast
__lowercase : List[Any] = True
__lowercase : int = True
def snake_case_ ( self):
super().setUp()
# We have a SentencePiece fixture for testing
__SCREAMING_SNAKE_CASE = XLNetTokenizer(lowerCAmelCase__ , keep_accents=lowerCAmelCase__)
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname)
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = """<s>"""
__SCREAMING_SNAKE_CASE = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__) , lowerCAmelCase__)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__) , lowerCAmelCase__)
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , """<unk>""")
self.assertEqual(vocab_keys[1] , """<s>""")
self.assertEqual(vocab_keys[-1] , """<eod>""")
self.assertEqual(len(lowerCAmelCase__) , 1_0_0_6)
def snake_case_ ( self):
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_0)
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = XLNetTokenizer(lowerCAmelCase__ , keep_accents=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = tokenizer.tokenize("""This is a test""")
self.assertListEqual(lowerCAmelCase__ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__) , [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2])
__SCREAMING_SNAKE_CASE = tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
self.assertListEqual(
lowerCAmelCase__ , [
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""",
""".""",
] , )
__SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(lowerCAmelCase__)
self.assertListEqual(lowerCAmelCase__ , [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4])
__SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(lowerCAmelCase__)
self.assertListEqual(
lowerCAmelCase__ , [
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>""",
""".""",
] , )
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = XLNetTokenizer(lowerCAmelCase__ , do_lower_case=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
self.assertListEqual(
lowerCAmelCase__ , [
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""",
"""se""",
""".""",
] , )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""") , ["""▁he""", """ll""", """o"""])
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = XLNetTokenizer(lowerCAmelCase__ , do_lower_case=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
self.assertListEqual(
lowerCAmelCase__ , [
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""",
"""se""",
""".""",
] , )
@slow
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = XLNetTokenizer.from_pretrained("""xlnet-base-cased""")
__SCREAMING_SNAKE_CASE = tokenizer.encode("""sequence builders""" , add_special_tokens=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ , lowerCAmelCase__)
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def snake_case_ ( self):
# fmt: off
__SCREAMING_SNAKE_CASE = {"""input_ids""": [[1_7, 2_1_4_4_2, 2_7_0, 1_7, 1_0, 1_4_6_4_5, 3_1_8, 3_4, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 7_7_5_2, 2_2_0_1_8, 2_3, 2_1, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 3_3_5_2, 1_4_4_3_1, 1_3, 5_5_0_0, 1_1, 1_1_7_6, 5_8_0, 1_3, 1_6_8_1_9, 4_7_9_7, 2_3, 1_7, 1_0, 1_7_1_3_5, 6_5_8, 1_9, 4_5_7, 7_9_3_2, 1_3, 1_8_4, 1_9, 3_1_5_4, 1_7_1_3_5, 6_4_6_8, 1_9, 1_4_0_4, 1_2_2_6_9, 1_9, 4_2_2_9, 5_3_5_6, 1_6_2_6_4, 4_6, 1_9, 1_7, 2_0_5_4_5, 1_0_3_9_5, 9, 9, 9, 1_1, 2_8, 6_4_2_1, 9_5_3_1, 2_0_7_2_9, 1_7, 1_0, 3_5_3, 1_7_0_2_2, 1_1, 2_1, 6_4_2_1, 9_5_3_1, 1_6_9_4_9, 1_7, 1_0, 1_1_5_0_9, 7_5_3, 1_1, 3_3, 9_5, 2_4_2_1, 7_3_8_5, 9_5_6, 1_4_4_3_1, 2_6_2_6, 2_5, 8_4_2, 7_3_8_5, 4_8_3_6, 2_1, 1_4_2_9, 2_2_7_2, 9_8_5_5, 3_1_2_0, 1_6_1, 2_4_7_3_8, 1_9, 1_3_2_0_3, 6_5_8, 2_1_8, 7_8_7, 2_1, 4_3_0, 1_8_4_8_2, 8_4_7, 2_6_3_7, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2_2, 2_2_1_7_8, 2_7, 1_0_6_4, 2_2, 9_5_6, 1_3, 1_1_1_0_1, 1_4_2_9, 5_8_5_4, 2_4_3_1_3, 1_8_9_5_3, 4_0, 4_2_2, 2_4_3_6_6, 6_8, 1_7_5_8, 3_7, 1_0_4_8_3, 1_4_2_5_7, 3_1, 2_0_7, 2_6_3, 2_1, 2_0_3, 3_7_7_3, 2_5, 7_1, 9_7_3_5, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2, 2_0_4_9, 3_4_4_2, 1_7, 1_3_8_9_4, 3_3_8_0, 2_3, 9_5, 1_8, 1_7_6_3_4, 2_2_8_8, 9, 4, 3]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], """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], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase__ , model_name="""xlnet-base-cased""" , revision="""c841166438c31ec7ca9a106dee7bb312b73ae511""" , )
| 100 | 0 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def A_ ( ):
'''simple docstring'''
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
UpperCamelCase : Optional[Any] = [1, 2, 3]
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=2 )
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" ,[2, -1] )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : List[Any] = [1, 2]
UpperCamelCase : List[Any] = {"""a""": 1, """b""": 2}
UpperCamelCase : List[str] = {"""a""": [1, 2], """b""": [3, 4]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 1}, """b""": 2}
UpperCamelCase : Any = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
UpperCamelCase : Optional[int] = [2, 3]
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3}
UpperCamelCase : Any = {"""a""": [2, 3], """b""": [4, 5]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 2}, """b""": 3}
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
| 356 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( snake_case_ : Dataset ,snake_case_ : Dict[str, str] ):
'''simple docstring'''
UpperCamelCase : List[str] = args.log_outputs
UpperCamelCase : Tuple = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
UpperCamelCase : List[Any] = load_metric("""wer""" )
UpperCamelCase : Any = load_metric("""cer""" )
# compute metrics
UpperCamelCase : str = wer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
UpperCamelCase : Dict = cer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
# print & log results
UpperCamelCase : Optional[int] = f'WER: {wer_result}\nCER: {cer_result}'
print(snake_case_ )
with open(f'{dataset_id}_eval_results.txt' ,"""w""" ) as f:
f.write(snake_case_ )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
UpperCamelCase : Optional[Any] = f'log_{dataset_id}_predictions.txt'
UpperCamelCase : str = f'log_{dataset_id}_targets.txt'
with open(snake_case_ ,"""w""" ) as p, open(snake_case_ ,"""w""" ) as t:
# mapping function to write output
def write_to_file(snake_case_ : Union[str, Any] ,snake_case_ : Tuple ):
p.write(f'{i}' + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(f'{i}' + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(snake_case_ ,with_indices=snake_case_ )
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Dict = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
UpperCamelCase : str = re.sub(snake_case_ ,"""""" ,text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
UpperCamelCase : List[str] = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
UpperCamelCase : Tuple = """ """.join(text.split(snake_case_ ) )
return text
def A_ ( snake_case_ : str ):
'''simple docstring'''
# load dataset
UpperCamelCase : Union[str, Any] = load_dataset(args.dataset ,args.config ,split=args.split ,use_auth_token=snake_case_ )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
UpperCamelCase : List[Any] = AutoFeatureExtractor.from_pretrained(args.model_id )
UpperCamelCase : Dict = feature_extractor.sampling_rate
# resample audio
UpperCamelCase : Optional[Any] = dataset.cast_column("""audio""" ,Audio(sampling_rate=snake_case_ ) )
# load eval pipeline
if args.device is None:
UpperCamelCase : int = 0 if torch.cuda.is_available() else -1
UpperCamelCase : Union[str, Any] = pipeline("""automatic-speech-recognition""" ,model=args.model_id ,device=args.device )
# map function to decode audio
def map_to_pred(snake_case_ : Union[str, Any] ):
UpperCamelCase : List[Any] = asr(
batch["""audio"""]["""array"""] ,chunk_length_s=args.chunk_length_s ,stride_length_s=args.stride_length_s )
UpperCamelCase : Union[str, Any] = prediction["""text"""]
UpperCamelCase : Optional[Any] = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
UpperCamelCase : Any = dataset.map(snake_case_ ,remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(snake_case_ ,snake_case_ )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
__A : Optional[Any] = parser.parse_args()
main(args)
| 27 | 0 |
def lowerCamelCase__ ( a , a , a , a ) -> Dict:
# Return True if there is node that has not iterated.
_A: int = [False] * len(a )
_A: Any = []
queue.append(a )
_A: List[Any] = True
while queue:
_A: Dict = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(a )
_A: Dict = True
_A: Optional[int] = u
return visited[t]
def lowerCamelCase__ ( a , a , a ) -> List[Any]:
# This array is filled by BFS and to store path
_A: Any = [-1] * (len(a ))
_A: Union[str, Any] = 0
while bfs(a , a , a , a ):
_A: str = float('''Inf''' )
_A: Any = sink
while s != source:
# Find the minimum value in select path
_A: Optional[int] = min(a , graph[parent[s]][s] )
_A: List[str] = parent[s]
max_flow += path_flow
_A: Union[str, Any] = sink
while v != source:
_A: List[Any] = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
_A: List[Any] = parent[v]
return max_flow
UpperCAmelCase__ : Optional[int] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
UpperCAmelCase__ ,UpperCAmelCase__ : List[str] = 0, 5
print(ford_fulkerson(graph, source, sink))
| 121 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' , [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.md''', '''dataset_infos.json'''],
['''dataset_infos.json'''],
['''full:README.md'''],
] , )
def lowerCamelCase__ ( a , a ) -> Any:
_A: Any = tmp_path_factory.mktemp('''dset_infos_dir''' )
if "full:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''---\ndataset_info:\n dataset_size: 42\n---''' )
if "empty:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''''' )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / '''dataset_infos.json''' , '''w''' ) as f:
f.write('''{"default": {"dataset_size": 42}}''' )
_A: Optional[int] = DatasetInfosDict.from_directory(a )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
'''dataset_info''' , [
DatasetInfo(),
DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , ),
] , )
def lowerCamelCase__ ( a , a ) -> Any:
_A: int = str(a )
dataset_info.write_to_directory(a )
_A: str = DatasetInfo.from_directory(a )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(a , '''dataset_info.json''' ) )
def lowerCamelCase__ ( ) -> Any:
_A: int = DatasetInfo(
description='''foo''' , citation='''bar''' , homepage='''https://foo.bar''' , license='''CC0''' , features=Features({'''a''': Value('''int32''' )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train''', '''num_examples''': 42}] , download_checksums={} , download_size=13_37 , post_processing_size=4_42 , dataset_size=12_34 , size_in_bytes=13_37 + 4_42 + 12_34 , )
_A: Optional[Any] = dataset_info._to_yaml_dict()
assert sorted(a ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
_A: str = yaml.safe_dump(a )
_A: Optional[int] = yaml.safe_load(a )
assert dataset_info_yaml_dict == reloaded
def lowerCamelCase__ ( ) -> int:
_A: Union[str, Any] = DatasetInfo()
_A: Union[str, Any] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
'''dataset_infos_dict''' , [
DatasetInfosDict(),
DatasetInfosDict({'''default''': DatasetInfo()} ),
DatasetInfosDict({'''my_config_name''': DatasetInfo()} ),
DatasetInfosDict(
{
'''default''': DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , )
} ),
DatasetInfosDict(
{
'''v1''': DatasetInfo(dataset_size=42 ),
'''v2''': DatasetInfo(dataset_size=13_37 ),
} ),
] , )
def lowerCamelCase__ ( a , a ) -> Optional[int]:
_A: Optional[int] = str(a )
dataset_infos_dict.write_to_directory(a )
_A: Union[str, Any] = DatasetInfosDict.from_directory(a )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_A: Optional[Any] = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
_A: Any = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(a , '''README.md''' ) )
| 121 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase = {
"""configuration_conditional_detr""": [
"""CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ConditionalDetrConfig""",
"""ConditionalDetrOnnxConfig""",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ["""ConditionalDetrFeatureExtractor"""]
_lowercase = ["""ConditionalDetrImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ConditionalDetrForObjectDetection""",
"""ConditionalDetrForSegmentation""",
"""ConditionalDetrModel""",
"""ConditionalDetrPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 229 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""VanForImageClassification""",
"""VanModel""",
"""VanPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 229 | 1 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
_A = logging.get_logger(__name__)
_A = {
'b0': efficientnet.EfficientNetBa,
'b1': efficientnet.EfficientNetBa,
'b2': efficientnet.EfficientNetBa,
'b3': efficientnet.EfficientNetBa,
'b4': efficientnet.EfficientNetBa,
'b5': efficientnet.EfficientNetBa,
'b6': efficientnet.EfficientNetBa,
'b7': efficientnet.EfficientNetBa,
}
_A = {
'b0': {
'hidden_dim': 1280,
'width_coef': 1.0,
'depth_coef': 1.0,
'image_size': 224,
'dropout_rate': 0.2,
'dw_padding': [],
},
'b1': {
'hidden_dim': 1280,
'width_coef': 1.0,
'depth_coef': 1.1,
'image_size': 240,
'dropout_rate': 0.2,
'dw_padding': [16],
},
'b2': {
'hidden_dim': 1408,
'width_coef': 1.1,
'depth_coef': 1.2,
'image_size': 260,
'dropout_rate': 0.3,
'dw_padding': [5, 8, 16],
},
'b3': {
'hidden_dim': 1536,
'width_coef': 1.2,
'depth_coef': 1.4,
'image_size': 300,
'dropout_rate': 0.3,
'dw_padding': [5, 18],
},
'b4': {
'hidden_dim': 1792,
'width_coef': 1.4,
'depth_coef': 1.8,
'image_size': 380,
'dropout_rate': 0.4,
'dw_padding': [6],
},
'b5': {
'hidden_dim': 2048,
'width_coef': 1.6,
'depth_coef': 2.2,
'image_size': 456,
'dropout_rate': 0.4,
'dw_padding': [13, 27],
},
'b6': {
'hidden_dim': 2304,
'width_coef': 1.8,
'depth_coef': 2.6,
'image_size': 528,
'dropout_rate': 0.5,
'dw_padding': [31],
},
'b7': {
'hidden_dim': 2560,
'width_coef': 2.0,
'depth_coef': 3.1,
'image_size': 600,
'dropout_rate': 0.5,
'dw_padding': [18],
},
}
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : List[Any] ):
__UpperCamelCase =EfficientNetConfig()
__UpperCamelCase =CONFIG_MAP[model_name]['hidden_dim']
__UpperCamelCase =CONFIG_MAP[model_name]['width_coef']
__UpperCamelCase =CONFIG_MAP[model_name]['depth_coef']
__UpperCamelCase =CONFIG_MAP[model_name]['image_size']
__UpperCamelCase =CONFIG_MAP[model_name]['dropout_rate']
__UpperCamelCase =CONFIG_MAP[model_name]['dw_padding']
__UpperCamelCase ='huggingface/label-files'
__UpperCamelCase ='imagenet-1k-id2label.json'
__UpperCamelCase =10_00
__UpperCamelCase =json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , repo_type='dataset' ) , 'r' ) )
__UpperCamelCase ={int(SCREAMING_SNAKE_CASE__ ): v for k, v in idalabel.items()}
__UpperCamelCase =idalabel
__UpperCamelCase ={v: k for k, v in idalabel.items()}
return config
def _UpperCAmelCase ( ):
__UpperCamelCase ='http://images.cocodataset.org/val2017/000000039769.jpg'
__UpperCamelCase =Image.open(requests.get(SCREAMING_SNAKE_CASE__ , stream=SCREAMING_SNAKE_CASE__ ).raw )
return im
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
__UpperCamelCase =CONFIG_MAP[model_name]['image_size']
__UpperCamelCase =EfficientNetImageProcessor(
size={'height': size, 'width': size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.47853944, 0.4732864, 0.47434163] , do_center_crop=SCREAMING_SNAKE_CASE__ , )
return preprocessor
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : str ):
__UpperCamelCase =[v.split('_' )[0].split('block' )[1] for v in original_param_names if v.startswith('block' )]
__UpperCamelCase =sorted(set(SCREAMING_SNAKE_CASE__ ) )
__UpperCamelCase =len(SCREAMING_SNAKE_CASE__ )
__UpperCamelCase ={b: str(SCREAMING_SNAKE_CASE__ ) for b, i in zip(SCREAMING_SNAKE_CASE__ , range(SCREAMING_SNAKE_CASE__ ) )}
__UpperCamelCase =[]
rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight') )
rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight') )
rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias') )
rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean') )
rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var') )
for b in block_names:
__UpperCamelCase =block_name_mapping[b]
rename_keys.append((F'block{b}_expand_conv/kernel:0', F'encoder.blocks.{hf_b}.expansion.expand_conv.weight') )
rename_keys.append((F'block{b}_expand_bn/gamma:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.weight') )
rename_keys.append((F'block{b}_expand_bn/beta:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.bias') )
rename_keys.append(
(F'block{b}_expand_bn/moving_mean:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean') )
rename_keys.append(
(F'block{b}_expand_bn/moving_variance:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_var') )
rename_keys.append(
(F'block{b}_dwconv/depthwise_kernel:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight') )
rename_keys.append((F'block{b}_bn/gamma:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight') )
rename_keys.append((F'block{b}_bn/beta:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias') )
rename_keys.append(
(F'block{b}_bn/moving_mean:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean') )
rename_keys.append(
(F'block{b}_bn/moving_variance:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var') )
rename_keys.append((F'block{b}_se_reduce/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight') )
rename_keys.append((F'block{b}_se_reduce/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias') )
rename_keys.append((F'block{b}_se_expand/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.weight') )
rename_keys.append((F'block{b}_se_expand/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.bias') )
rename_keys.append(
(F'block{b}_project_conv/kernel:0', F'encoder.blocks.{hf_b}.projection.project_conv.weight') )
rename_keys.append((F'block{b}_project_bn/gamma:0', F'encoder.blocks.{hf_b}.projection.project_bn.weight') )
rename_keys.append((F'block{b}_project_bn/beta:0', F'encoder.blocks.{hf_b}.projection.project_bn.bias') )
rename_keys.append(
(F'block{b}_project_bn/moving_mean:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_mean') )
rename_keys.append(
(F'block{b}_project_bn/moving_variance:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_var') )
rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight') )
rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight') )
rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias') )
rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean') )
rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var') )
__UpperCamelCase ={}
for item in rename_keys:
if item[0] in original_param_names:
__UpperCamelCase ='efficientnet.' + item[1]
__UpperCamelCase ='classifier.weight'
__UpperCamelCase ='classifier.bias'
return key_mapping
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Tuple ):
for key, value in tf_params.items():
if "normalization" in key:
continue
__UpperCamelCase =key_mapping[key]
if "_conv" in key and "kernel" in key:
__UpperCamelCase =torch.from_numpy(SCREAMING_SNAKE_CASE__ ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
__UpperCamelCase =torch.from_numpy(SCREAMING_SNAKE_CASE__ ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
__UpperCamelCase =torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE__ ) )
else:
__UpperCamelCase =torch.from_numpy(SCREAMING_SNAKE_CASE__ )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(SCREAMING_SNAKE_CASE__ )
@torch.no_grad()
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
__UpperCamelCase =model_classes[model_name](
include_top=SCREAMING_SNAKE_CASE__ , weights='imagenet' , input_tensor=SCREAMING_SNAKE_CASE__ , input_shape=SCREAMING_SNAKE_CASE__ , pooling=SCREAMING_SNAKE_CASE__ , classes=10_00 , classifier_activation='softmax' , )
__UpperCamelCase =original_model.trainable_variables
__UpperCamelCase =original_model.non_trainable_variables
__UpperCamelCase ={param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
__UpperCamelCase =param.numpy()
__UpperCamelCase =list(tf_params.keys() )
# Load HuggingFace model
__UpperCamelCase =get_efficientnet_config(SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =EfficientNetForImageClassification(SCREAMING_SNAKE_CASE__ ).eval()
__UpperCamelCase =hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('Converting parameters...' )
__UpperCamelCase =rename_keys(SCREAMING_SNAKE_CASE__ )
replace_params(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Initialize preprocessor and preprocess input image
__UpperCamelCase =convert_image_processor(SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =preprocessor(images=prepare_img() , return_tensors='pt' )
# HF model inference
hf_model.eval()
with torch.no_grad():
__UpperCamelCase =hf_model(**SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =outputs.logits.detach().numpy()
# Original model inference
__UpperCamelCase =False
__UpperCamelCase =CONFIG_MAP[model_name]['image_size']
__UpperCamelCase =prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
__UpperCamelCase =image.img_to_array(SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =np.expand_dims(SCREAMING_SNAKE_CASE__ , axis=0 )
__UpperCamelCase =original_model.predict(SCREAMING_SNAKE_CASE__ )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , atol=1E-3 ), "The predicted logits are not the same."
print('Model outputs match!' )
if save_model:
# Create folder to save model
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
os.mkdir(SCREAMING_SNAKE_CASE__ )
# Save converted model and image processor
hf_model.save_pretrained(SCREAMING_SNAKE_CASE__ )
preprocessor.save_pretrained(SCREAMING_SNAKE_CASE__ )
if push_to_hub:
# Push model and image processor to hub
print(F'Pushing converted {model_name} to the hub...' )
__UpperCamelCase =F'efficientnet-{model_name}'
preprocessor.push_to_hub(SCREAMING_SNAKE_CASE__ )
hf_model.push_to_hub(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='b0',
type=str,
help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='hf_model',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--save_model', action='store_true', help='Save model to local')
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
_A = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 62 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase__ ( A_ , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Dict = GPTaTokenizer
UpperCAmelCase__ : Any = GPTaTokenizerFast
UpperCAmelCase__ : Tuple = True
UpperCAmelCase__ : int = {"add_prefix_space": True}
UpperCAmelCase__ : Any = False
def _a ( self ) -> Optional[int]:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__UpperCamelCase =[
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
'<|endoftext|>',
]
__UpperCamelCase =dict(zip(A_ , range(len(A_ ) ) ) )
__UpperCamelCase =['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
__UpperCamelCase ={'unk_token': '<unk>'}
__UpperCamelCase =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__UpperCamelCase =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(A_ ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(A_ ) )
def _a ( self , **A_ ) -> str:
kwargs.update(self.special_tokens_map )
return GPTaTokenizer.from_pretrained(self.tmpdirname , **A_ )
def _a ( self , **A_ ) -> Optional[Any]:
kwargs.update(self.special_tokens_map )
return GPTaTokenizerFast.from_pretrained(self.tmpdirname , **A_ )
def _a ( self , A_ ) -> Tuple:
__UpperCamelCase ='lower newer'
__UpperCamelCase ='lower newer'
return input_text, output_text
def _a ( self ) -> List[Any]:
__UpperCamelCase =GPTaTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__UpperCamelCase ='lower newer'
__UpperCamelCase =['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
__UpperCamelCase =tokenizer.tokenize(A_ , add_prefix_space=A_ )
self.assertListEqual(A_ , A_ )
__UpperCamelCase =tokens + [tokenizer.unk_token]
__UpperCamelCase =[14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ ) , A_ )
def _a ( self ) -> int:
if not self.test_rust_tokenizer:
return
__UpperCamelCase =self.get_tokenizer()
__UpperCamelCase =self.get_rust_tokenizer(add_prefix_space=A_ )
__UpperCamelCase ='lower newer'
# Testing tokenization
__UpperCamelCase =tokenizer.tokenize(A_ , add_prefix_space=A_ )
__UpperCamelCase =rust_tokenizer.tokenize(A_ )
self.assertListEqual(A_ , A_ )
# Testing conversion to ids without special tokens
__UpperCamelCase =tokenizer.encode(A_ , add_special_tokens=A_ , add_prefix_space=A_ )
__UpperCamelCase =rust_tokenizer.encode(A_ , add_special_tokens=A_ )
self.assertListEqual(A_ , A_ )
# Testing conversion to ids with special tokens
__UpperCamelCase =self.get_rust_tokenizer(add_prefix_space=A_ )
__UpperCamelCase =tokenizer.encode(A_ , add_prefix_space=A_ )
__UpperCamelCase =rust_tokenizer.encode(A_ )
self.assertListEqual(A_ , A_ )
# Testing the unknown token
__UpperCamelCase =tokens + [rust_tokenizer.unk_token]
__UpperCamelCase =[14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A_ ) , A_ )
def _a ( self , *A_ , **A_ ) -> Optional[int]:
# It's very difficult to mix/test pretokenization with byte-level
# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def _a ( self , A_=15 ) -> List[str]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__UpperCamelCase =self.rust_tokenizer_class.from_pretrained(A_ , **A_ )
# Simple input
__UpperCamelCase ='This is a simple input'
__UpperCamelCase =['This is a simple input 1', 'This is a simple input 2']
__UpperCamelCase =('This is a simple input', 'This is a pair')
__UpperCamelCase =[
('This is a simple input 1', 'This is a simple input 2'),
('This is a simple pair 1', 'This is a simple pair 2'),
]
# Simple input tests
self.assertRaises(A_ , tokenizer_r.encode , A_ , max_length=A_ , padding='max_length' )
# Simple input
self.assertRaises(A_ , tokenizer_r.encode_plus , A_ , max_length=A_ , padding='max_length' )
# Simple input
self.assertRaises(
A_ , tokenizer_r.batch_encode_plus , A_ , max_length=A_ , padding='max_length' , )
# Pair input
self.assertRaises(A_ , tokenizer_r.encode , A_ , max_length=A_ , padding='max_length' )
# Pair input
self.assertRaises(A_ , tokenizer_r.encode_plus , A_ , max_length=A_ , padding='max_length' )
# Pair input
self.assertRaises(
A_ , tokenizer_r.batch_encode_plus , A_ , max_length=A_ , padding='max_length' , )
def _a ( self ) -> int:
__UpperCamelCase =GPTaTokenizer.from_pretrained(self.tmpdirname , pad_token='<pad>' )
# Simple input
__UpperCamelCase ='This is a simple input'
__UpperCamelCase =['This is a simple input looooooooong', 'This is a simple input']
__UpperCamelCase =('This is a simple input', 'This is a pair')
__UpperCamelCase =[
('This is a simple input loooooong', 'This is a simple input'),
('This is a simple pair loooooong', 'This is a simple pair'),
]
__UpperCamelCase =tokenizer.pad_token_id
__UpperCamelCase =tokenizer(A_ , padding='max_length' , max_length=30 , return_tensors='np' )
__UpperCamelCase =tokenizer(A_ , padding=A_ , truncate=A_ , return_tensors='np' )
__UpperCamelCase =tokenizer(*A_ , padding='max_length' , max_length=60 , return_tensors='np' )
__UpperCamelCase =tokenizer(A_ , padding=A_ , truncate=A_ , return_tensors='np' )
# s
# test single string max_length padding
self.assertEqual(out_s['input_ids'].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['input_ids'] )
self.assertTrue(0 in out_s['attention_mask'] )
# s2
# test automatic padding
self.assertEqual(out_sa['input_ids'].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['input_ids'][0] )
self.assertFalse(0 in out_sa['attention_mask'][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['input_ids'][1] )
self.assertTrue(0 in out_sa['attention_mask'][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['input_ids'].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['input_ids'] )
self.assertTrue(0 in out_p['attention_mask'] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['input_ids'].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['input_ids'][0] )
self.assertFalse(0 in out_pa['attention_mask'][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['input_ids'][1] )
self.assertTrue(0 in out_pa['attention_mask'][1] )
def _a ( self ) -> Union[str, Any]:
__UpperCamelCase ='$$$'
__UpperCamelCase =GPTaTokenizer.from_pretrained(self.tmpdirname , bos_token=A_ , add_bos_token=A_ )
__UpperCamelCase ='This is a simple input'
__UpperCamelCase =['This is a simple input 1', 'This is a simple input 2']
__UpperCamelCase =tokenizer.bos_token_id
__UpperCamelCase =tokenizer(A_ )
__UpperCamelCase =tokenizer(A_ )
self.assertEqual(out_s.input_ids[0] , A_ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
__UpperCamelCase =tokenizer.decode(out_s.input_ids )
__UpperCamelCase =tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , A_ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
def _a ( self ) -> Optional[int]:
pass
def _a ( self ) -> Any:
# TODO: change to self.get_tokenizers() when the fast version is implemented
__UpperCamelCase =[self.get_tokenizer(do_lower_case=A_ , add_bos_token=A_ )]
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
__UpperCamelCase ='Encode this.'
__UpperCamelCase ='This one too please.'
__UpperCamelCase =tokenizer.encode(A_ , add_special_tokens=A_ )
encoded_sequence += tokenizer.encode(A_ , add_special_tokens=A_ )
__UpperCamelCase =tokenizer.encode_plus(
A_ , A_ , add_special_tokens=A_ , return_special_tokens_mask=A_ , )
__UpperCamelCase =encoded_sequence_dict['input_ids']
__UpperCamelCase =encoded_sequence_dict['special_tokens_mask']
self.assertEqual(len(A_ ) , len(A_ ) )
__UpperCamelCase =[
(x if not special_tokens_mask[i] else None) for i, x in enumerate(A_ )
]
__UpperCamelCase =[x for x in filtered_sequence if x is not None]
self.assertEqual(A_ , A_ )
@require_tokenizers
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _a ( self ) -> Optional[Any]:
# More context:
# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439
__UpperCamelCase =AutoTokenizer.from_pretrained('facebook/opt-350m' , from_slow=A_ )
__UpperCamelCase ='A photo of a cat'
__UpperCamelCase =tokenizer.encode(
A_ , )
self.assertEqual(A_ , [2, 250, 1345, 9, 10, 4758] )
tokenizer.save_pretrained('test_opt' )
__UpperCamelCase =AutoTokenizer.from_pretrained('./test_opt' )
__UpperCamelCase =tokenizer.encode(
A_ , )
self.assertEqual(A_ , [2, 250, 1345, 9, 10, 4758] )
def _a ( self ) -> Dict:
__UpperCamelCase =AutoTokenizer.from_pretrained('facebook/opt-350m' , use_slow=A_ )
__UpperCamelCase ='A photo of a cat'
__UpperCamelCase =tokenizer.encode(
A_ , )
# Same as above
self.assertEqual(A_ , [2, 250, 1345, 9, 10, 4758] )
@unittest.skip('This test is failing because of a bug in the fast tokenizer' )
def _a ( self ) -> List[Any]:
__UpperCamelCase =AutoTokenizer.from_pretrained('facebook/opt-350m' , from_slow=A_ )
__UpperCamelCase ='bos'
__UpperCamelCase =tokenizer.get_vocab()['bos']
__UpperCamelCase ='A photo of a cat'
__UpperCamelCase =tokenizer.encode(
A_ , )
# We changed the bos token
self.assertEqual(A_ , [31957, 250, 1345, 9, 10, 4758] )
tokenizer.save_pretrained('./tok' )
__UpperCamelCase =AutoTokenizer.from_pretrained('./tok' )
self.assertTrue(tokenizer.is_fast )
__UpperCamelCase =tokenizer.encode(
A_ , )
self.assertEqual(A_ , [31957, 250, 1345, 9, 10, 4758] )
| 62 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
def _a( UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
if "resnet-50" in model_name:
SCREAMING_SNAKE_CASE__ : List[str] =ResNetConfig.from_pretrained('''microsoft/resnet-50''' )
elif "resnet-101" in model_name:
SCREAMING_SNAKE_CASE__ : Dict =ResNetConfig.from_pretrained('''microsoft/resnet-101''' )
else:
raise ValueError('''Model name should include either resnet50 or resnet101''' )
SCREAMING_SNAKE_CASE__ : List[str] =DetrConfig(use_timm_backbone=UpperCamelCase__, backbone_config=UpperCamelCase__ )
# set label attributes
SCREAMING_SNAKE_CASE__ : Tuple ='''panoptic''' in model_name
if is_panoptic:
SCREAMING_SNAKE_CASE__ : List[str] =2_5_0
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =9_1
SCREAMING_SNAKE_CASE__ : Any ='''huggingface/label-files'''
SCREAMING_SNAKE_CASE__ : str ='''coco-detection-id2label.json'''
SCREAMING_SNAKE_CASE__ : int =json.load(open(hf_hub_download(UpperCamelCase__, UpperCamelCase__, repo_type='''dataset''' ), '''r''' ) )
SCREAMING_SNAKE_CASE__ : int ={int(UpperCamelCase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Optional[int] =idalabel
SCREAMING_SNAKE_CASE__ : List[Any] ={v: k for k, v in idalabel.items()}
return config, is_panoptic
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =[]
# stem
# fmt: off
rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''') )
rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''') )
rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''') )
rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''') )
rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var''') )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
) )
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
) )
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
) )
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
) )
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
) )
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
) )
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
) )
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
) )
rename_keys.append(
(
f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
f"transformer.encoder.layers.{i}.self_attn.out_proj.weight",
f"encoder.layers.{i}.self_attn.out_proj.weight",
) )
rename_keys.append(
(f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias") )
rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias") )
rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias") )
rename_keys.append(
(f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight") )
rename_keys.append(
(f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias") )
rename_keys.append(
(f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight") )
rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias") )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
f"transformer.decoder.layers.{i}.self_attn.out_proj.weight",
f"decoder.layers.{i}.self_attn.out_proj.weight",
) )
rename_keys.append(
(f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias") )
rename_keys.append(
(
f"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
f"decoder.layers.{i}.encoder_attn.out_proj.weight",
) )
rename_keys.append(
(
f"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
f"decoder.layers.{i}.encoder_attn.out_proj.bias",
) )
rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias") )
rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias") )
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight") )
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias") )
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight") )
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias") )
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight") )
rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias") )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('''input_proj.weight''', '''input_projection.weight'''),
('''input_proj.bias''', '''input_projection.bias'''),
('''query_embed.weight''', '''query_position_embeddings.weight'''),
('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
('''class_embed.weight''', '''class_labels_classifier.weight'''),
('''class_embed.bias''', '''class_labels_classifier.bias'''),
('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
] )
return rename_keys
def _a( UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Optional[int], UpperCamelCase__ : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple =state_dict.pop(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] =val
def _a( UpperCamelCase__ : Optional[int], UpperCamelCase__ : Tuple=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] =''''''
if is_panoptic:
SCREAMING_SNAKE_CASE__ : str ='''detr.'''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
SCREAMING_SNAKE_CASE__ : List[Any] =state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight" )
SCREAMING_SNAKE_CASE__ : Optional[Any] =state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias" )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE__ : Optional[int] =in_proj_weight[:2_5_6, :]
SCREAMING_SNAKE_CASE__ : Union[str, Any] =in_proj_bias[:2_5_6]
SCREAMING_SNAKE_CASE__ : Optional[int] =in_proj_weight[2_5_6:5_1_2, :]
SCREAMING_SNAKE_CASE__ : List[Any] =in_proj_bias[2_5_6:5_1_2]
SCREAMING_SNAKE_CASE__ : Dict =in_proj_weight[-2_5_6:, :]
SCREAMING_SNAKE_CASE__ : Optional[Any] =in_proj_bias[-2_5_6:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
SCREAMING_SNAKE_CASE__ : Optional[Any] =state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight" )
SCREAMING_SNAKE_CASE__ : int =state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias" )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE__ : Optional[Any] =in_proj_weight[:2_5_6, :]
SCREAMING_SNAKE_CASE__ : Any =in_proj_bias[:2_5_6]
SCREAMING_SNAKE_CASE__ : Tuple =in_proj_weight[2_5_6:5_1_2, :]
SCREAMING_SNAKE_CASE__ : str =in_proj_bias[2_5_6:5_1_2]
SCREAMING_SNAKE_CASE__ : Optional[Any] =in_proj_weight[-2_5_6:, :]
SCREAMING_SNAKE_CASE__ : Any =in_proj_bias[-2_5_6:]
# read in weights + bias of input projection layer of cross-attention
SCREAMING_SNAKE_CASE__ : Union[str, Any] =state_dict.pop(
f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight" )
SCREAMING_SNAKE_CASE__ : int =state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias" )
# next, add query, keys and values (in that order) of cross-attention to the state dict
SCREAMING_SNAKE_CASE__ : Any =in_proj_weight_cross_attn[:2_5_6, :]
SCREAMING_SNAKE_CASE__ : Any =in_proj_bias_cross_attn[:2_5_6]
SCREAMING_SNAKE_CASE__ : Tuple =in_proj_weight_cross_attn[2_5_6:5_1_2, :]
SCREAMING_SNAKE_CASE__ : int =in_proj_bias_cross_attn[2_5_6:5_1_2]
SCREAMING_SNAKE_CASE__ : List[Any] =in_proj_weight_cross_attn[-2_5_6:, :]
SCREAMING_SNAKE_CASE__ : Any =in_proj_bias_cross_attn[-2_5_6:]
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
SCREAMING_SNAKE_CASE__ : List[str] =Image.open(requests.get(UpperCamelCase__, stream=UpperCamelCase__ ).raw )
return im
@torch.no_grad()
def _a( UpperCamelCase__ : Optional[Any], UpperCamelCase__ : List[str]=None, UpperCamelCase__ : str=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict =get_detr_config(UpperCamelCase__ )
# load original model from torch hub
SCREAMING_SNAKE_CASE__ : List[Any] ={
'''detr-resnet-50''': '''detr_resnet50''',
'''detr-resnet-101''': '''detr_resnet101''',
}
logger.info(f"Converting model {model_name}..." )
SCREAMING_SNAKE_CASE__ : int =torch.hub.load('''facebookresearch/detr''', model_name_to_original_name[model_name], pretrained=UpperCamelCase__ ).eval()
SCREAMING_SNAKE_CASE__ : str =detr.state_dict()
# rename keys
for src, dest in create_rename_keys(UpperCamelCase__ ):
if is_panoptic:
SCREAMING_SNAKE_CASE__ : Union[str, Any] ='''detr.''' + src
rename_key(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
# query, key and value matrices need special treatment
read_in_q_k_v(UpperCamelCase__, is_panoptic=UpperCamelCase__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
SCREAMING_SNAKE_CASE__ : str ='''detr.model.''' if is_panoptic else '''model.'''
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith('''detr''' )
and not key.startswith('''class_labels_classifier''' )
and not key.startswith('''bbox_predictor''' )
):
SCREAMING_SNAKE_CASE__ : Optional[int] =state_dict.pop(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Tuple =val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
SCREAMING_SNAKE_CASE__ : Dict =state_dict.pop(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[Any] =val
elif key.startswith('''bbox_attention''' ) or key.startswith('''mask_head''' ):
continue
else:
SCREAMING_SNAKE_CASE__ : Any =state_dict.pop(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] =val
else:
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] =state_dict.pop(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[Any] =val
# finally, create HuggingFace model and load state dict
SCREAMING_SNAKE_CASE__ : Optional[int] =DetrForSegmentation(UpperCamelCase__ ) if is_panoptic else DetrForObjectDetection(UpperCamelCase__ )
model.load_state_dict(UpperCamelCase__ )
model.eval()
# verify our conversion on an image
SCREAMING_SNAKE_CASE__ : int ='''coco_panoptic''' if is_panoptic else '''coco_detection'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =DetrImageProcessor(format=UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =processor(images=prepare_img(), return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : Dict =encoding['''pixel_values''']
SCREAMING_SNAKE_CASE__ : str =detr(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[str] =model(UpperCamelCase__ )
assert torch.allclose(outputs.logits, original_outputs['''pred_logits'''], atol=1e-3 )
assert torch.allclose(outputs.pred_boxes, original_outputs['''pred_boxes'''], atol=1e-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks, original_outputs['''pred_masks'''], atol=1e-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..." )
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
model.save_pretrained(UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
if push_to_hub:
# Upload model and image processor to the hub
logger.info('''Uploading PyTorch model and image processor to the hub...''' )
model.push_to_hub(f"nielsr/{model_name}" )
processor.push_to_hub(f"nielsr/{model_name}" )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='detr-resnet-50',
type=str,
choices=['detr-resnet-50', 'detr-resnet-101'],
help='Name of the DETR model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
a_ = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | 222 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'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
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 222 | 1 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __magic_name__ ( A : NDArray[floataa], A : NDArray[floataa], A : list[int], A : int, ):
'''simple docstring'''
a , a = coefficient_matrix.shape
a , a = constant_matrix.shape
if rowsa != colsa:
a = F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
raise ValueError(A )
if colsa != 1:
a = F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
raise ValueError(A )
if rowsa != rowsa:
a = (
"Coefficient and constant matrices dimensions must be nxn and nx1 but "
F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
)
raise ValueError(A )
if len(A ) != rowsa:
a = (
"Number of initial values must be equal to number of rows in coefficient "
F"""matrix but received {len(A )} and {rowsa}"""
)
raise ValueError(A )
if iterations <= 0:
raise ValueError("Iterations must be at least 1" )
a = np.concatenate(
(coefficient_matrix, constant_matrix), axis=1 )
a , a = table.shape
strictly_diagonally_dominant(A )
# Iterates the whole matrix for given number of times
for _ in range(A ):
a = []
for row in range(A ):
a = 0
for col in range(A ):
if col == row:
a = table[row][col]
elif col == cols - 1:
a = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
a = (temp + val) / denom
new_val.append(A )
a = new_val
return [float(A ) for i in new_val]
def __magic_name__ ( A : NDArray[floataa] ):
'''simple docstring'''
a , a = table.shape
a = True
for i in range(0, A ):
a = 0
for j in range(0, cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError("Coefficient matrix is not strictly diagonally dominant" )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 107 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
__UpperCAmelCase = logging.get_logger(__name__)
@add_end_docstrings(_snake_case )
class lowerCamelCase (_snake_case ):
'''simple docstring'''
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ) -> int:
super().__init__(*_UpperCamelCase , **_UpperCamelCase )
self.check_model_type(_UpperCamelCase )
def __UpperCAmelCase ( self , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase=None , **_UpperCamelCase ) -> List[Any]:
UpperCAmelCase_ , UpperCAmelCase_ : Tuple = {}, {}
if padding is not None:
UpperCAmelCase_ : List[str] = padding
if truncation is not None:
UpperCAmelCase_ : Tuple = truncation
if top_k is not None:
UpperCAmelCase_ : Dict = top_k
return preprocess_params, {}, postprocess_params
def __call__( self , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase ) -> int:
if isinstance(_UpperCamelCase , (Image.Image, str) ) and isinstance(_UpperCamelCase , _UpperCamelCase ):
UpperCAmelCase_ : Optional[Any] = {'image': image, 'question': question}
else:
UpperCAmelCase_ : List[str] = image
UpperCAmelCase_ : Optional[Any] = super().__call__(_UpperCamelCase , **_UpperCamelCase )
return results
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase=False , _UpperCamelCase=False ) -> Optional[Any]:
UpperCAmelCase_ : List[Any] = load_image(inputs['image'] )
UpperCAmelCase_ : Dict = self.tokenizer(
inputs['question'] , return_tensors=self.framework , padding=_UpperCamelCase , truncation=_UpperCamelCase )
UpperCAmelCase_ : int = self.image_processor(images=_UpperCamelCase , return_tensors=self.framework )
model_inputs.update(_UpperCamelCase )
return model_inputs
def __UpperCAmelCase ( self , _UpperCamelCase ) -> Optional[int]:
UpperCAmelCase_ : Any = self.model(**_UpperCamelCase )
return model_outputs
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase=5 ) -> str:
if top_k > self.model.config.num_labels:
UpperCAmelCase_ : Union[str, Any] = self.model.config.num_labels
if self.framework == "pt":
UpperCAmelCase_ : List[str] = model_outputs.logits.sigmoid()[0]
UpperCAmelCase_ , UpperCAmelCase_ : str = probs.topk(_UpperCamelCase )
else:
raise ValueError(f"Unsupported framework: {self.framework}" )
UpperCAmelCase_ : Optional[Any] = scores.tolist()
UpperCAmelCase_ : Tuple = ids.tolist()
return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCamelCase , _UpperCamelCase )]
| 29 | 0 |
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
__A = [1]
__A , __A , __A = 0, 0, 0
__A = ugly_nums[ia] * 2
__A = ugly_nums[ia] * 3
__A = ugly_nums[ia] * 5
for _ in range(1 , a_ ):
__A = min(a_ , a_ , a_ )
ugly_nums.append(a_ )
if next_num == next_a:
ia += 1
__A = ugly_nums[ia] * 2
if next_num == next_a:
ia += 1
__A = ugly_nums[ia] * 3
if next_num == next_a:
ia += 1
__A = ugly_nums[ia] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(f'''{ugly_numbers(200) = }''')
| 124 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case_ = BertJapaneseTokenizer
snake_case_ = False
snake_case_ = True
def UpperCamelCase_ ( self : List[Any] ):
super().setUp()
__A = [
"[UNK]",
"[CLS]",
"[SEP]",
"こんにちは",
"こん",
"にちは",
"ばんは",
"##こん",
"##にちは",
"##ばんは",
"世界",
"##世界",
"、",
"##、",
"。",
"##。",
]
__A = 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] ) )
def UpperCamelCase_ ( self : Union[str, Any] ,A : Optional[Any] ):
__A = "こんにちは、世界。 \nこんばんは、世界。"
__A = "こんにちは 、 世界 。 こんばんは 、 世界 。"
return input_text, output_text
def UpperCamelCase_ ( self : Any ,A : Optional[int] ):
__A , __A = self.get_input_output_texts(A )
__A = tokenizer.encode(A ,add_special_tokens=A )
__A = tokenizer.decode(A ,clean_up_tokenization_spaces=A )
return text, ids
def UpperCamelCase_ ( self : int ):
pass # TODO add if relevant
def UpperCamelCase_ ( self : int ):
pass # TODO add if relevant
def UpperCamelCase_ ( self : Optional[int] ):
pass # TODO add if relevant
def UpperCamelCase_ ( self : List[Any] ):
__A = self.tokenizer_class(self.vocab_file )
__A = tokenizer.tokenize("こんにちは、世界。\nこんばんは、世界。" )
self.assertListEqual(A ,["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) ,[3, 12, 10, 14, 4, 9, 12, 10, 14] )
def UpperCamelCase_ ( self : int ):
__A = self.tokenizer_class(self.vocab_file ,word_tokenizer_type="mecab" )
self.assertIsNotNone(A )
__A = "こんにちは、世界。\nこんばんは、世界。"
__A = tokenizer.tokenize(A )
self.assertListEqual(A ,["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) ,[3, 12, 10, 14, 4, 9, 12, 10, 14] )
__A = os.path.join(self.tmpdirname ,"tokenizer.bin" )
with open(A ,"wb" ) as handle:
pickle.dump(A ,A )
with open(A ,"rb" ) as handle:
__A = pickle.load(A )
__A = tokenizer_new.tokenize(A )
self.assertListEqual(A ,A )
def UpperCamelCase_ ( self : Any ):
__A = MecabTokenizer(mecab_dic="ipadic" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) ,["アップルストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"] ,)
def UpperCamelCase_ ( self : List[str] ):
try:
__A = MecabTokenizer(mecab_dic="unidic_lite" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) ,["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"] ,)
def UpperCamelCase_ ( self : Tuple ):
try:
__A = MecabTokenizer(mecab_dic="unidic" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) ,["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"] ,)
def UpperCamelCase_ ( self : Tuple ):
__A = MecabTokenizer(do_lower_case=A ,mecab_dic="ipadic" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) ,["アップルストア", "で", "iphone", "8", "が", "発売", "さ", "れ", "た", "。"] ,)
def UpperCamelCase_ ( self : Any ):
try:
__A = MecabTokenizer(
do_lower_case=A ,normalize_text=A ,mecab_option="-d /usr/local/lib/mecab/dic/jumandic" )
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) ,["ｱｯﾌﾟﾙストア", "で", "iPhone", "８", "が", "発売", "さ", "れた", "\u3000", "。"] ,)
def UpperCamelCase_ ( self : int ):
__A = MecabTokenizer(normalize_text=A ,mecab_dic="ipadic" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) ,["ｱｯﾌﾟﾙストア", "で", "iPhone", "８", "が", "発売", "さ", "れ", "た", " ", "。"] ,)
@require_sudachi
def UpperCamelCase_ ( self : int ):
__A = self.tokenizer_class(self.vocab_file ,word_tokenizer_type="sudachi" )
self.assertIsNotNone(A )
__A = "こんにちは、世界。\nこんばんは、世界。"
__A = tokenizer.tokenize(A )
self.assertListEqual(A ,["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) ,[3, 12, 10, 14, 4, 9, 12, 10, 14] )
__A = os.path.join(self.tmpdirname ,"tokenizer.bin" )
with open(A ,"wb" ) as handle:
pickle.dump(A ,A )
with open(A ,"rb" ) as handle:
__A = pickle.load(A )
__A = tokenizer_new.tokenize(A )
self.assertListEqual(A ,A )
@require_sudachi
def UpperCamelCase_ ( self : Optional[int] ):
__A = SudachiTokenizer(sudachi_dict_type="core" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) ,[" ", "\t", "アップル", "ストア", "で", "iPhone", "8", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", " ", "。", " ", " "] ,)
@require_sudachi
def UpperCamelCase_ ( self : List[Any] ):
__A = SudachiTokenizer(sudachi_dict_type="core" ,sudachi_split_mode="A" )
self.assertListEqual(tokenizer.tokenize("外国人参政権" ) ,["外国", "人", "参政", "権"] )
@require_sudachi
def UpperCamelCase_ ( self : int ):
__A = SudachiTokenizer(sudachi_dict_type="core" ,sudachi_split_mode="B" )
self.assertListEqual(tokenizer.tokenize("外国人参政権" ) ,["外国人", "参政権"] )
@require_sudachi
def UpperCamelCase_ ( self : Tuple ):
__A = SudachiTokenizer(sudachi_dict_type="core" ,sudachi_split_mode="C" )
self.assertListEqual(tokenizer.tokenize("外国人参政権" ) ,["外国人参政権"] )
@require_sudachi
def UpperCamelCase_ ( self : int ):
__A = SudachiTokenizer(do_lower_case=A ,sudachi_dict_type="core" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) ,[" ", "\t", "アップル", "ストア", "で", "iphone", "8", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", " ", "。", " ", " "] ,)
@require_sudachi
def UpperCamelCase_ ( self : List[str] ):
__A = SudachiTokenizer(normalize_text=A ,sudachi_dict_type="core" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) ,[" ", "\t", "ｱｯﾌﾟﾙ", "ストア", "で", "iPhone", "８", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", "\u3000", "。", " ", " "] ,)
@require_sudachi
def UpperCamelCase_ ( self : str ):
__A = SudachiTokenizer(trim_whitespace=A ,sudachi_dict_type="core" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) ,["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"] ,)
@require_jumanpp
def UpperCamelCase_ ( self : Optional[Any] ):
__A = self.tokenizer_class(self.vocab_file ,word_tokenizer_type="jumanpp" )
self.assertIsNotNone(A )
__A = "こんにちは、世界。\nこんばんは、世界。"
__A = tokenizer.tokenize(A )
self.assertListEqual(A ,["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) ,[3, 12, 10, 14, 4, 9, 12, 10, 14] )
__A = os.path.join(self.tmpdirname ,"tokenizer.bin" )
with open(A ,"wb" ) as handle:
pickle.dump(A ,A )
with open(A ,"rb" ) as handle:
__A = pickle.load(A )
__A = tokenizer_new.tokenize(A )
self.assertListEqual(A ,A )
@require_jumanpp
def UpperCamelCase_ ( self : int ):
__A = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) ,["アップル", "ストア", "で", "iPhone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"] ,)
@require_jumanpp
def UpperCamelCase_ ( self : str ):
__A = JumanppTokenizer(do_lower_case=A )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) ,["アップル", "ストア", "で", "iphone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"] ,)
@require_jumanpp
def UpperCamelCase_ ( self : Any ):
__A = JumanppTokenizer(normalize_text=A )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) ,["ｱ", "ｯ", "ﾌ", "ﾟ", "ﾙ", "ストア", "で", "iPhone", "８", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"] ,)
@require_jumanpp
def UpperCamelCase_ ( self : List[str] ):
__A = JumanppTokenizer(trim_whitespace=A )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) ,["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れた", "。"] ,)
@require_jumanpp
def UpperCamelCase_ ( self : Dict ):
__A = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize("ありがとうございますm(_ _)ｍ見つけるのが大変です。" ) ,["ありがとう", "ございます", "m(_ _)m", "見つける", "の", "が", "大変です", "。"] ,)
def UpperCamelCase_ ( self : str ):
__A = ["[UNK]", "[CLS]", "[SEP]", "こんにちは", "こん", "にちは", "ばんは", "##こん", "##にちは", "##ばんは"]
__A = {}
for i, token in enumerate(A ):
__A = i
__A = WordpieceTokenizer(vocab=A ,unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) ,[] )
self.assertListEqual(tokenizer.tokenize("こんにちは" ) ,["こんにちは"] )
self.assertListEqual(tokenizer.tokenize("こんばんは" ) ,["こん", "##ばんは"] )
self.assertListEqual(tokenizer.tokenize("こんばんは こんばんにちは こんにちは" ) ,["こん", "##ばんは", "[UNK]", "こんにちは"] )
def UpperCamelCase_ ( self : Any ):
__A = BertJapaneseTokenizer.from_pretrained("nlp-waseda/roberta-base-japanese-with-auto-jumanpp" )
__A = tokenizer.subword_tokenizer
__A = subword_tokenizer.tokenize("国境 の 長い トンネル を 抜ける と 雪国 であった 。" )
self.assertListEqual(A ,["▁国境", "▁の", "▁長い", "▁トンネル", "▁を", "▁抜ける", "▁と", "▁雪", "国", "▁であった", "▁。"] )
__A = subword_tokenizer.tokenize("こんばんは こんばん にち は こんにちは" )
self.assertListEqual(A ,["▁こん", "ばん", "は", "▁こん", "ばん", "▁に", "ち", "▁は", "▁こんにちは"] )
def UpperCamelCase_ ( self : int ):
__A = self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese" )
__A = tokenizer.encode("ありがとう。" ,add_special_tokens=A )
__A = tokenizer.encode("どういたしまして。" ,add_special_tokens=A )
__A = tokenizer.build_inputs_with_special_tokens(A )
__A = tokenizer.build_inputs_with_special_tokens(A ,A )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case_ = BertJapaneseTokenizer
snake_case_ = False
def UpperCamelCase_ ( self : Any ):
super().setUp()
__A = ["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界", "、", "。"]
__A = 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] ) )
def UpperCamelCase_ ( self : int ,**A : str ):
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname ,subword_tokenizer_type="character" ,**A )
def UpperCamelCase_ ( self : List[str] ,A : str ):
__A = "こんにちは、世界。 \nこんばんは、世界。"
__A = "こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"
return input_text, output_text
def UpperCamelCase_ ( self : str ):
pass # TODO add if relevant
def UpperCamelCase_ ( self : Optional[Any] ):
pass # TODO add if relevant
def UpperCamelCase_ ( self : Any ):
pass # TODO add if relevant
def UpperCamelCase_ ( self : str ):
__A = self.tokenizer_class(self.vocab_file ,subword_tokenizer_type="character" )
__A = tokenizer.tokenize("こんにちは、世界。 \nこんばんは、世界。" )
self.assertListEqual(
A ,["こ", "ん", "に", "ち", "は", "、", "世", "界", "。", "こ", "ん", "ば", "ん", "は", "、", "世", "界", "。"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(A ) ,[3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] )
def UpperCamelCase_ ( self : Union[str, Any] ):
__A = ["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界", "、", "。"]
__A = {}
for i, token in enumerate(A ):
__A = i
__A = CharacterTokenizer(vocab=A ,unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) ,[] )
self.assertListEqual(tokenizer.tokenize("こんにちは" ) ,["こ", "ん", "に", "ち", "は"] )
self.assertListEqual(tokenizer.tokenize("こんにちほ" ) ,["こ", "ん", "に", "ち", "[UNK]"] )
def UpperCamelCase_ ( self : Tuple ):
__A = self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese-char" )
__A = tokenizer.encode("ありがとう。" ,add_special_tokens=A )
__A = tokenizer.encode("どういたしまして。" ,add_special_tokens=A )
__A = tokenizer.build_inputs_with_special_tokens(A )
__A = tokenizer.build_inputs_with_special_tokens(A ,A )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self : int ):
__A = "cl-tohoku/bert-base-japanese"
__A = AutoTokenizer.from_pretrained(A )
self.assertIsInstance(A ,A )
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self : Tuple ):
__A = "cl-tohoku/bert-base-japanese"
with self.assertLogs("transformers" ,level="WARNING" ) as cm:
BertTokenizer.from_pretrained(A )
self.assertTrue(
cm.records[0].message.startswith(
"The tokenizer class you load from this checkpoint is not the same type as the class this function"
" is called from." ) )
__A = "bert-base-cased"
with self.assertLogs("transformers" ,level="WARNING" ) as cm:
BertJapaneseTokenizer.from_pretrained(A )
self.assertTrue(
cm.records[0].message.startswith(
"The tokenizer class you load from this checkpoint is not the same type as the class this function"
" is called from." ) )
| 124 | 1 |
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
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''microsoft/table-transformer-detection''': (
'''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
),
}
class lowerCAmelCase_ ( snake_case_ ):
UpperCAmelCase__ : int = "table-transformer"
UpperCAmelCase__ : int = ["past_key_values"]
UpperCAmelCase__ : Optional[Any] = {
"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_=100, 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_=1, SCREAMING_SNAKE_CASE_=5, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=5, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=0.1, **SCREAMING_SNAKE_CASE_, ) -> int:
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 : Optional[Any] = CONFIG_MAPPING['resnet'](out_features=['stage4'] )
elif isinstance(__UpperCAmelCase, __UpperCAmelCase ):
UpperCamelCase : Optional[Any] = backbone_config.get('model_type' )
UpperCamelCase : Union[str, Any] = CONFIG_MAPPING[backbone_model_type]
UpperCamelCase : Tuple = config_class.from_dict(__UpperCAmelCase )
# set timm attributes to None
UpperCamelCase , UpperCamelCase , UpperCamelCase : int = None, None, None
UpperCamelCase : int = use_timm_backbone
UpperCamelCase : str = backbone_config
UpperCamelCase : str = num_channels
UpperCamelCase : Any = num_queries
UpperCamelCase : List[str] = d_model
UpperCamelCase : List[Any] = encoder_ffn_dim
UpperCamelCase : Tuple = encoder_layers
UpperCamelCase : List[str] = encoder_attention_heads
UpperCamelCase : Tuple = decoder_ffn_dim
UpperCamelCase : str = decoder_layers
UpperCamelCase : Tuple = decoder_attention_heads
UpperCamelCase : Any = dropout
UpperCamelCase : Dict = attention_dropout
UpperCamelCase : Union[str, Any] = activation_dropout
UpperCamelCase : Tuple = activation_function
UpperCamelCase : List[str] = init_std
UpperCamelCase : Dict = init_xavier_std
UpperCamelCase : int = encoder_layerdrop
UpperCamelCase : List[Any] = decoder_layerdrop
UpperCamelCase : List[str] = encoder_layers
UpperCamelCase : List[Any] = auxiliary_loss
UpperCamelCase : Dict = position_embedding_type
UpperCamelCase : int = backbone
UpperCamelCase : Optional[Any] = use_pretrained_backbone
UpperCamelCase : Any = dilation
# Hungarian matcher
UpperCamelCase : Dict = class_cost
UpperCamelCase : Any = bbox_cost
UpperCamelCase : Optional[int] = giou_cost
# Loss coefficients
UpperCamelCase : List[str] = mask_loss_coefficient
UpperCamelCase : List[Any] = dice_loss_coefficient
UpperCamelCase : Tuple = bbox_loss_coefficient
UpperCamelCase : Tuple = giou_loss_coefficient
UpperCamelCase : Tuple = eos_coefficient
super().__init__(is_encoder_decoder=__UpperCAmelCase, **__UpperCAmelCase )
@property
def snake_case_ ( self ) -> Union[str, Any]:
return self.encoder_attention_heads
@property
def snake_case_ ( self ) -> List[str]:
return self.d_model
class lowerCAmelCase_ ( snake_case_ ):
UpperCAmelCase__ : int = version.parse("1.11" )
@property
def snake_case_ ( self ) -> str:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
('pixel_mask', {0: 'batch'}),
] )
@property
def snake_case_ ( self ) -> List[str]:
return 1e-5
@property
def snake_case_ ( self ) -> Dict:
return 12
| 119 |
'''simple docstring'''
def __lowercase ( __lowercase , __lowercase , __lowercase=False ) -> Union[str, Any]:
'''simple docstring'''
if isinstance(__lowercase , __lowercase ) and isinstance(__lowercase , __lowercase ):
_A = len(set_a.intersection(__lowercase ) )
if alternative_union:
_A = len(__lowercase ) + len(__lowercase )
else:
_A = len(set_a.union(__lowercase ) )
return intersection / union
if isinstance(__lowercase , (list, tuple) ) and isinstance(__lowercase , (list, tuple) ):
_A = [element for element in set_a if element in set_b]
if alternative_union:
_A = len(__lowercase ) + len(__lowercase )
return len(__lowercase ) / union
else:
_A = set_a + [element for element in set_b if element not in set_a]
return len(__lowercase ) / len(__lowercase )
return len(__lowercase ) / len(__lowercase )
return None
if __name__ == "__main__":
lowerCamelCase_ = {'''a''', '''b''', '''c''', '''d''', '''e'''}
lowerCamelCase_ = {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
print(jaccard_similarity(set_a, set_b))
| 79 | 0 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _SCREAMING_SNAKE_CASE :
@staticmethod
def SCREAMING_SNAKE_CASE_( *lowercase , **lowercase ) -> Optional[Any]:
pass
@is_pipeline_test
@require_vision
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
lowerCAmelCase__ = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase ) -> int:
lowerCamelCase_ = pipeline(
"zero-shot-object-detection" , model="hf-internal-testing/tiny-random-owlvit-object-detection" )
lowerCamelCase_ = [
{
"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
"candidate_labels": ["cat", "remote", "couch"],
}
]
return object_detector, examples
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase ) -> Dict:
lowerCamelCase_ = object_detector(examples[0] , threshold=0.0 )
lowerCamelCase_ = len(lowercase )
self.assertGreater(lowercase , 0 )
self.assertEqual(
lowercase , [
{
"score": ANY(lowercase ),
"label": ANY(lowercase ),
"box": {"xmin": ANY(lowercase ), "ymin": ANY(lowercase ), "xmax": ANY(lowercase ), "ymax": ANY(lowercase )},
}
for i in range(lowercase )
] , )
@require_tf
@unittest.skip("Zero Shot Object Detection not implemented in TF" )
def SCREAMING_SNAKE_CASE_( self ) -> Tuple:
pass
@require_torch
def SCREAMING_SNAKE_CASE_( self ) -> str:
lowerCamelCase_ = pipeline(
"zero-shot-object-detection" , model="hf-internal-testing/tiny-random-owlvit-object-detection" )
lowerCamelCase_ = object_detector(
"./tests/fixtures/tests_samples/COCO/000000039769.png" , candidate_labels=["cat", "remote", "couch"] , threshold=0.6_4 , )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
{"score": 0.7_2_3_5, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.7_2_1_8, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.7_1_8_4, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.6_7_4_8, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6_6_5_6, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6_6_1_4, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6_4_5_6, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
{"score": 0.6_4_2, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
{"score": 0.6_4_1_9, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
] , )
lowerCamelCase_ = object_detector(
[
{
"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
"candidate_labels": ["cat", "remote", "couch"],
}
] , threshold=0.6_4 , )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
[
{"score": 0.7_2_3_5, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.7_2_1_8, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.7_1_8_4, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.6_7_4_8, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6_6_5_6, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6_6_1_4, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6_4_5_6, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
{"score": 0.6_4_2, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
{"score": 0.6_4_1_9, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
]
] , )
@require_torch
@slow
def SCREAMING_SNAKE_CASE_( self ) -> int:
lowerCamelCase_ = pipeline("zero-shot-object-detection" )
lowerCamelCase_ = object_detector(
"http://images.cocodataset.org/val2017/000000039769.jpg" , candidate_labels=["cat", "remote", "couch"] , )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
{"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
{"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
{"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
{"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
] , )
lowerCamelCase_ = object_detector(
[
{
"image": "http://images.cocodataset.org/val2017/000000039769.jpg",
"candidate_labels": ["cat", "remote", "couch"],
},
{
"image": "http://images.cocodataset.org/val2017/000000039769.jpg",
"candidate_labels": ["cat", "remote", "couch"],
},
] , )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
[
{"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
{"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
{"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
{"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
],
[
{"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
{"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
{"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
{"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
],
] , )
@require_tf
@unittest.skip("Zero Shot Object Detection not implemented in TF" )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[int]:
pass
@require_torch
@slow
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
lowerCamelCase_ = 0.2
lowerCamelCase_ = pipeline("zero-shot-object-detection" )
lowerCamelCase_ = object_detector(
"http://images.cocodataset.org/val2017/000000039769.jpg" , candidate_labels=["cat", "remote", "couch"] , threshold=lowercase , )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
{"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
{"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
] , )
@require_torch
@slow
def SCREAMING_SNAKE_CASE_( self ) -> Tuple:
lowerCamelCase_ = 2
lowerCamelCase_ = pipeline("zero-shot-object-detection" )
lowerCamelCase_ = object_detector(
"http://images.cocodataset.org/val2017/000000039769.jpg" , candidate_labels=["cat", "remote", "couch"] , top_k=lowercase , )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
{"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
] , )
| 47 |
from collections import defaultdict
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = first_str.lower().strip()
lowerCamelCase_ = second_str.lower().strip()
# Remove whitespace
lowerCamelCase_ = first_str.replace(" " , "" )
lowerCamelCase_ = second_str.replace(" " , "" )
# Strings of different lengths are not anagrams
if len(lowerCamelCase__ ) != len(lowerCamelCase__ ):
return False
# Default values for count should be 0
lowerCamelCase_ = defaultdict(lowerCamelCase__ )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(lowerCamelCase__ ) ):
count[first_str[i]] += 1
count[second_str[i]] -= 1
return all(_count == 0 for _count in count.values() )
if __name__ == "__main__":
from doctest import testmod
testmod()
__A =input('''Enter the first string ''').strip()
__A =input('''Enter the second string ''').strip()
__A =check_anagrams(input_a, input_b)
print(F"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")
| 47 | 1 |
def __lowerCamelCase ( __a :int , __a :int ) -> int:
"""simple docstring"""
return int(input_a == input_a == 0 )
def __lowerCamelCase ( ) -> None:
"""simple docstring"""
print("""Truth Table of NOR Gate:""" )
print("""| Input 1 | Input 2 | Output |""" )
print(F'| 0 | 0 | {nor_gate(0 , 0 )} |' )
print(F'| 0 | 1 | {nor_gate(0 , 1 )} |' )
print(F'| 1 | 0 | {nor_gate(1 , 0 )} |' )
print(F'| 1 | 1 | {nor_gate(1 , 1 )} |' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 274 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
A : Dict = logging.get_logger(__name__)
def __lowerCamelCase ( __a :int=None , __a :Optional[Any]=None ) -> int:
"""simple docstring"""
return field(default_factory=lambda: default , metadata=__a )
@dataclass
class A :
'''simple docstring'''
__lowerCamelCase : List[str] = 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'''
)
} , )
__lowerCamelCase : List[int] = list_field(
default=[8] , metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''} )
__lowerCamelCase : List[int] = list_field(
default=[8, 32, 128, 512] , metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''} , )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''} , )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''} , )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Use FP16 to accelerate inference.'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Benchmark training of model'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Verbose memory tracing'''} )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''} , )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={
'''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
} , )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Trace memory line by line'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Save result to a CSV file'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Save all print statements in a log file'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to print environment information'''} )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , 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.'''
)
} , )
__lowerCamelCase : str = field(
default=F'''inference_time_{round(time() )}.csv''' , metadata={'''help''': '''CSV filename used if saving time results to csv.'''} , )
__lowerCamelCase : str = field(
default=F'''inference_memory_{round(time() )}.csv''' , metadata={'''help''': '''CSV filename used if saving memory results to csv.'''} , )
__lowerCamelCase : str = field(
default=F'''train_time_{round(time() )}.csv''' , metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''} , )
__lowerCamelCase : str = field(
default=F'''train_memory_{round(time() )}.csv''' , metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''} , )
__lowerCamelCase : str = field(
default=F'''env_info_{round(time() )}.csv''' , metadata={'''help''': '''CSV filename used if saving environment information.'''} , )
__lowerCamelCase : str = field(
default=F'''log_{round(time() )}.csv''' , metadata={'''help''': '''Log filename used if print statements are saved in log.'''} , )
__lowerCamelCase : int = field(default=3 , metadata={'''help''': '''Times an experiment will be run.'''} )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={
'''help''': (
'''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
''' model weights.'''
)
} , )
def a_ ( self : Dict ) -> Union[str, Any]:
"""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.""" , __lowerCAmelCase , )
def a_ ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def a_ ( self : Tuple ) -> List[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 a_ ( self : Union[str, Any] ) -> Optional[Any]:
"""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
| 274 | 1 |
"""simple docstring"""
import re
def __lowerCamelCase ( __UpperCamelCase ) -> bool:
"""simple docstring"""
lowerCAmelCase_ : Optional[Any] = re.compile(r"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$" )
if match := re.search(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return match.string == phone
return False
if __name__ == "__main__":
print(indian_phone_validator("""+918827897895"""))
| 367 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def __lowerCamelCase ( __UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : int = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b
def __lowerCamelCase ( __UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
return (gray > 127) & (gray <= 255)
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
lowerCAmelCase_ : List[str] = np.zeros_like(__UpperCamelCase )
lowerCAmelCase_ : Dict = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
lowerCAmelCase_ : List[Any] = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
lowerCAmelCase_ : List[str] = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
lowerCAmelCase_ : int = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
lowercase__ = Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
lowercase__ = np.array(Image.open(lena_path))
# kernel to be applied
lowercase__ = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
lowercase__ = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
lowercase__ = Image.fromarray(output).convert("""RGB""")
pil_img.save("""result_dilation.png""")
| 161 | 0 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CASE ( snake_case_ , unittest.TestCase ):
lowerCAmelCase__ = PhobertTokenizer
lowerCAmelCase__ = False
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCamelCase_ = ["T@@", "i", "I", "R@@", "r", "e@@"]
lowerCamelCase_ = dict(zip(lowercase , range(len(lowercase ) ) ) )
lowerCamelCase_ = ["#version: 0.2", "l à</w>"]
lowerCamelCase_ = {"unk_token": "<unk>"}
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
for token in vocab_tokens:
fp.write(f'{token} {vocab_tokens[token]}\n' )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(lowercase ) )
def SCREAMING_SNAKE_CASE_( self , **lowercase ) -> List[str]:
kwargs.update(self.special_tokens_map )
return PhobertTokenizer.from_pretrained(self.tmpdirname , **lowercase )
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Optional[Any]:
lowerCamelCase_ = "Tôi là VinAI Research"
lowerCamelCase_ = "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
return input_text, output_text
def SCREAMING_SNAKE_CASE_( self ) -> Any:
lowerCamelCase_ = PhobertTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
lowerCamelCase_ = "Tôi là VinAI Research"
lowerCamelCase_ = "T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
lowerCamelCase_ = tokenizer.tokenize(lowercase )
print(lowercase )
self.assertListEqual(lowercase , lowercase )
lowerCamelCase_ = tokens + [tokenizer.unk_token]
lowerCamelCase_ = [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase ) , lowercase )
| 19 |
def __SCREAMING_SNAKE_CASE ( snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = len(snake_case_ )
for i in range(snake_case_ ):
for j in range(i + 1 , snake_case_ ):
if numbers[j] < numbers[i]:
_UpperCAmelCase , _UpperCAmelCase = numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
lowercase_ : Optional[Any] = input('Enter numbers separated by a comma:\n').strip()
lowercase_ : Dict = [int(item) for item in user_input.split(',')]
print(exchange_sort(unsorted))
| 133 | 0 |
from __future__ import annotations
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> Any: # noqa: E741
while r - l > 1:
__snake_case: Optional[Any] = (l + r) // 2
if v[m] >= key:
__snake_case: Optional[int] = m
else:
__snake_case: Optional[int] = m # noqa: E741
return r
def A__ ( SCREAMING_SNAKE_CASE__) -> int:
if len(_UpperCamelCase) == 0:
return 0
__snake_case: Tuple = [0] * len(_UpperCamelCase)
__snake_case: Any = 1
__snake_case: Tuple = v[0]
for i in range(1 , len(_UpperCamelCase)):
if v[i] < tail[0]:
__snake_case: Any = v[i]
elif v[i] > tail[length - 1]:
__snake_case: List[Any] = v[i]
length += 1
else:
__snake_case: Optional[Any] = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 368 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = DownBlockaD # noqa F405
lowerCAmelCase__ = """down"""
def UpperCAmelCase__ ( self : Any ):
__snake_case: str = [-0.0232, -0.9869, 0.8054, -0.0637, -0.1688, -1.4264, 0.4470, -1.3394, 0.0904]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = ResnetDownsampleBlockaD # noqa F405
lowerCAmelCase__ = """down"""
def UpperCAmelCase__ ( self : Optional[int] ):
__snake_case: Union[str, Any] = [0.0710, 0.2410, -0.7320, -1.0757, -1.1343, 0.3540, -0.0133, -0.2576, 0.0948]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = AttnDownBlockaD # noqa F405
lowerCAmelCase__ = """down"""
def UpperCAmelCase__ ( self : Any ):
__snake_case: Union[str, Any] = [0.0636, 0.8964, -0.6234, -1.0131, 0.0844, 0.4935, 0.3437, 0.0911, -0.2957]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = CrossAttnDownBlockaD # noqa F405
lowerCAmelCase__ = """down"""
def UpperCAmelCase__ ( self : List[str] ):
__snake_case , __snake_case: List[str] = super().prepare_init_args_and_inputs_for_common()
__snake_case: List[Any] = 32
return init_dict, inputs_dict
def UpperCAmelCase__ ( self : Optional[Any] ):
__snake_case: Optional[Any] = [0.2238, -0.7396, -0.2255, -0.3829, 0.1925, 1.1665, 0.0603, -0.7295, 0.1983]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = SimpleCrossAttnDownBlockaD # noqa F405
lowerCAmelCase__ = """down"""
@property
def UpperCAmelCase__ ( self : Tuple ):
return super().get_dummy_input(include_encoder_hidden_states=A )
def UpperCAmelCase__ ( self : int ):
__snake_case , __snake_case: Union[str, Any] = super().prepare_init_args_and_inputs_for_common()
__snake_case: Optional[Any] = 32
return init_dict, inputs_dict
@unittest.skipIf(torch_device == """mps""" , """MPS result is not consistent""" )
def UpperCAmelCase__ ( self : List[Any] ):
__snake_case: Optional[Any] = [0.7921, -0.0992, -0.1962, -0.7695, -0.4242, 0.7804, 0.4737, 0.2765, 0.3338]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = SkipDownBlockaD # noqa F405
lowerCAmelCase__ = """down"""
@property
def UpperCAmelCase__ ( self : Any ):
return super().get_dummy_input(include_skip_sample=A )
def UpperCAmelCase__ ( self : Any ):
__snake_case: Optional[Any] = [-0.0845, -0.2087, -0.2465, 0.0971, 0.1900, -0.0484, 0.2664, 0.4179, 0.5069]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = AttnSkipDownBlockaD # noqa F405
lowerCAmelCase__ = """down"""
@property
def UpperCAmelCase__ ( self : List[Any] ):
return super().get_dummy_input(include_skip_sample=A )
def UpperCAmelCase__ ( self : int ):
__snake_case: str = [0.5539, 0.1609, 0.4924, 0.0537, -0.1995, 0.4050, 0.0979, -0.2721, -0.0642]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = DownEncoderBlockaD # noqa F405
lowerCAmelCase__ = """down"""
@property
def UpperCAmelCase__ ( self : Union[str, Any] ):
return super().get_dummy_input(include_temb=A )
def UpperCAmelCase__ ( self : Optional[int] ):
__snake_case: str = {
"""in_channels""": 32,
"""out_channels""": 32,
}
__snake_case: Dict = self.dummy_input
return init_dict, inputs_dict
def UpperCAmelCase__ ( self : str ):
__snake_case: Optional[int] = [1.1102, 0.5302, 0.4872, -0.0023, -0.8042, 0.0483, -0.3489, -0.5632, 0.7626]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = AttnDownEncoderBlockaD # noqa F405
lowerCAmelCase__ = """down"""
@property
def UpperCAmelCase__ ( self : List[str] ):
return super().get_dummy_input(include_temb=A )
def UpperCAmelCase__ ( self : Optional[int] ):
__snake_case: Optional[Any] = {
"""in_channels""": 32,
"""out_channels""": 32,
}
__snake_case: Tuple = self.dummy_input
return init_dict, inputs_dict
def UpperCAmelCase__ ( self : Optional[int] ):
__snake_case: Dict = [0.8966, -0.1486, 0.8568, 0.8141, -0.9046, -0.1342, -0.0972, -0.7417, 0.1538]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = UNetMidBlockaD # noqa F405
lowerCAmelCase__ = """mid"""
def UpperCAmelCase__ ( self : str ):
__snake_case: Optional[int] = {
"""in_channels""": 32,
"""temb_channels""": 128,
}
__snake_case: List[str] = self.dummy_input
return init_dict, inputs_dict
def UpperCAmelCase__ ( self : str ):
__snake_case: Tuple = [-0.1062, 1.7248, 0.3494, 1.4569, -0.0910, -1.2421, -0.9984, 0.6736, 1.0028]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = UNetMidBlockaDCrossAttn # noqa F405
lowerCAmelCase__ = """mid"""
def UpperCAmelCase__ ( self : str ):
__snake_case , __snake_case: int = super().prepare_init_args_and_inputs_for_common()
__snake_case: int = 32
return init_dict, inputs_dict
def UpperCAmelCase__ ( self : Dict ):
__snake_case: Optional[Any] = [0.0187, 2.4220, 0.4484, 1.1203, -0.6121, -1.5122, -0.8270, 0.7851, 1.8335]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = UNetMidBlockaDSimpleCrossAttn # noqa F405
lowerCAmelCase__ = """mid"""
@property
def UpperCAmelCase__ ( self : Optional[int] ):
return super().get_dummy_input(include_encoder_hidden_states=A )
def UpperCAmelCase__ ( self : str ):
__snake_case , __snake_case: Any = super().prepare_init_args_and_inputs_for_common()
__snake_case: str = 32
return init_dict, inputs_dict
def UpperCAmelCase__ ( self : Dict ):
__snake_case: Optional[Any] = [0.7143, 1.9974, 0.5448, 1.3977, 0.1282, -1.1237, -1.4238, 0.5530, 0.8880]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = UpBlockaD # noqa F405
lowerCAmelCase__ = """up"""
@property
def UpperCAmelCase__ ( self : Tuple ):
return super().get_dummy_input(include_res_hidden_states_tuple=A )
def UpperCAmelCase__ ( self : Tuple ):
__snake_case: Tuple = [-0.2041, -0.4165, -0.3022, 0.0041, -0.6628, -0.7053, 0.1928, -0.0325, 0.0523]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = ResnetUpsampleBlockaD # noqa F405
lowerCAmelCase__ = """up"""
@property
def UpperCAmelCase__ ( self : Tuple ):
return super().get_dummy_input(include_res_hidden_states_tuple=A )
def UpperCAmelCase__ ( self : Union[str, Any] ):
__snake_case: int = [0.2287, 0.3549, -0.1346, 0.4797, -0.1715, -0.9649, 0.7305, -0.5864, -0.6244]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = CrossAttnUpBlockaD # noqa F405
lowerCAmelCase__ = """up"""
@property
def UpperCAmelCase__ ( self : Optional[int] ):
return super().get_dummy_input(include_res_hidden_states_tuple=A )
def UpperCAmelCase__ ( self : Dict ):
__snake_case , __snake_case: Any = super().prepare_init_args_and_inputs_for_common()
__snake_case: Optional[int] = 32
return init_dict, inputs_dict
def UpperCAmelCase__ ( self : Union[str, Any] ):
__snake_case: List[Any] = [-0.1403, -0.3515, -0.0420, -0.1425, 0.3167, 0.5094, -0.2181, 0.5931, 0.5582]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = SimpleCrossAttnUpBlockaD # noqa F405
lowerCAmelCase__ = """up"""
@property
def UpperCAmelCase__ ( self : Optional[Any] ):
return super().get_dummy_input(include_res_hidden_states_tuple=A , include_encoder_hidden_states=A )
def UpperCAmelCase__ ( self : Dict ):
__snake_case , __snake_case: Optional[Any] = super().prepare_init_args_and_inputs_for_common()
__snake_case: str = 32
return init_dict, inputs_dict
def UpperCAmelCase__ ( self : List[Any] ):
__snake_case: Union[str, Any] = [0.2645, 0.1480, 0.0909, 0.8044, -0.9758, -0.9083, 0.0994, -1.1453, -0.7402]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = AttnUpBlockaD # noqa F405
lowerCAmelCase__ = """up"""
@property
def UpperCAmelCase__ ( self : int ):
return super().get_dummy_input(include_res_hidden_states_tuple=A )
@unittest.skipIf(torch_device == """mps""" , """MPS result is not consistent""" )
def UpperCAmelCase__ ( self : List[str] ):
__snake_case: Optional[Any] = [0.0979, 0.1326, 0.0021, 0.0659, 0.2249, 0.0059, 0.1132, 0.5952, 0.1033]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = SkipUpBlockaD # noqa F405
lowerCAmelCase__ = """up"""
@property
def UpperCAmelCase__ ( self : str ):
return super().get_dummy_input(include_res_hidden_states_tuple=A )
def UpperCAmelCase__ ( self : Dict ):
__snake_case: Optional[int] = [-0.0893, -0.1234, -0.1506, -0.0332, 0.0123, -0.0211, 0.0566, 0.0143, 0.0362]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = AttnSkipUpBlockaD # noqa F405
lowerCAmelCase__ = """up"""
@property
def UpperCAmelCase__ ( self : str ):
return super().get_dummy_input(include_res_hidden_states_tuple=A )
def UpperCAmelCase__ ( self : Optional[Any] ):
__snake_case: Optional[Any] = [0.0361, 0.0617, 0.2787, -0.0350, 0.0342, 0.3421, -0.0843, 0.0913, 0.3015]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = UpDecoderBlockaD # noqa F405
lowerCAmelCase__ = """up"""
@property
def UpperCAmelCase__ ( self : Optional[int] ):
return super().get_dummy_input(include_temb=A )
def UpperCAmelCase__ ( self : str ):
__snake_case: Union[str, Any] = {"""in_channels""": 32, """out_channels""": 32}
__snake_case: Dict = self.dummy_input
return init_dict, inputs_dict
def UpperCAmelCase__ ( self : Any ):
__snake_case: Dict = [0.4404, 0.1998, -0.9886, -0.3320, -0.3128, -0.7034, -0.6955, -0.2338, -0.3137]
super().test_output(A )
class __snake_case ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = AttnUpDecoderBlockaD # noqa F405
lowerCAmelCase__ = """up"""
@property
def UpperCAmelCase__ ( self : Optional[Any] ):
return super().get_dummy_input(include_temb=A )
def UpperCAmelCase__ ( self : Optional[Any] ):
__snake_case: Optional[Any] = {"""in_channels""": 32, """out_channels""": 32}
__snake_case: Any = self.dummy_input
return init_dict, inputs_dict
def UpperCAmelCase__ ( self : int ):
__snake_case: Any = [0.6738, 0.4491, 0.1055, 1.0710, 0.7316, 0.3339, 0.3352, 0.1023, 0.3568]
super().test_output(A )
| 293 | 0 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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 (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class A__ :
def __init__( self , __magic_name__ , ):
lowerCamelCase : Any = parent
lowerCamelCase : Any = 1_3
lowerCamelCase : Dict = 7
lowerCamelCase : List[str] = 3_0
lowerCamelCase : str = self.seq_length + self.mem_len
lowerCamelCase : int = 1_5
lowerCamelCase : List[Any] = True
lowerCamelCase : List[str] = True
lowerCamelCase : List[str] = 9_9
lowerCamelCase : Optional[int] = [1_0, 5_0, 8_0]
lowerCamelCase : Dict = 3_2
lowerCamelCase : List[Any] = 3_2
lowerCamelCase : Optional[int] = 4
lowerCamelCase : Optional[Any] = 8
lowerCamelCase : Union[str, Any] = 1_2_8
lowerCamelCase : Any = 2
lowerCamelCase : List[str] = 2
lowerCamelCase : Optional[int] = None
lowerCamelCase : Optional[int] = 1
lowerCamelCase : int = 0
lowerCamelCase : Tuple = 3
lowerCamelCase : List[str] = self.vocab_size - 1
lowerCamelCase : Tuple = 0.01
def UpperCamelCase__ ( self ):
lowerCamelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase : Union[str, Any] = None
if self.use_labels:
lowerCamelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase : Tuple = TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def UpperCamelCase__ ( self ):
random.seed(self.seed )
tf.random.set_seed(self.seed )
def UpperCamelCase__ ( self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
lowerCamelCase : Optional[Any] = TFTransfoXLModel(__a )
lowerCamelCase : Optional[Any] = model(__a ).to_tuple()
lowerCamelCase : Union[str, Any] = {'input_ids': input_ids_a, 'mems': mems_a}
lowerCamelCase : Optional[Any] = model(__a ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def UpperCamelCase__ ( self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
lowerCamelCase : Tuple = TFTransfoXLLMHeadModel(__a )
lowerCamelCase : Optional[int] = model(__a ).to_tuple()
lowerCamelCase : Any = {'input_ids': input_ids_a, 'labels': lm_labels}
lowerCamelCase : Any = model(__a ).to_tuple()
lowerCamelCase : List[Any] = model([input_ids_a, mems_a] ).to_tuple()
lowerCamelCase : Dict = {'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}
lowerCamelCase : Tuple = model(__a ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def UpperCamelCase__ ( self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
lowerCamelCase : int = TFTransfoXLForSequenceClassification(__a )
lowerCamelCase : str = model(__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase__ ( self ):
lowerCamelCase : int = self.prepare_config_and_inputs()
(lowerCamelCase) : str = config_and_inputs
lowerCamelCase : Optional[int] = {'input_ids': input_ids_a}
return config, inputs_dict
@require_tf
class A__ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase):
_UpperCAmelCase : Optional[int] = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
_UpperCAmelCase : int = () if is_tf_available() else ()
_UpperCAmelCase : Tuple = (
{
"""feature-extraction""": TFTransfoXLModel,
"""text-classification""": TFTransfoXLForSequenceClassification,
"""text-generation""": TFTransfoXLLMHeadModel,
"""zero-shot""": TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
_UpperCAmelCase : Union[str, Any] = False
_UpperCAmelCase : Optional[int] = False
_UpperCAmelCase : Any = False
_UpperCAmelCase : Optional[Any] = False
def UpperCamelCase__ ( self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def UpperCamelCase__ ( self ):
lowerCamelCase : List[Any] = TFTransfoXLModelTester(self )
lowerCamelCase : Optional[Any] = ConfigTester(self , config_class=__a , d_embed=3_7 )
def UpperCamelCase__ ( self ):
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
self.model_tester.set_seed()
lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*__a )
def UpperCamelCase__ ( self ):
self.model_tester.set_seed()
lowerCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*__a )
def UpperCamelCase__ ( self ):
lowerCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__a )
def UpperCamelCase__ ( self ):
lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase : str = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
lowerCamelCase : Tuple = model_class(__a )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
lowerCamelCase : Union[str, Any] = model.get_output_embeddings()
assert isinstance(__a , tf.keras.layers.Layer )
lowerCamelCase : List[str] = model.get_bias()
assert name is None
else:
lowerCamelCase : Any = model.get_output_embeddings()
assert x is None
lowerCamelCase : str = model.get_bias()
assert name is None
def UpperCamelCase__ ( self ):
pass
@slow
def UpperCamelCase__ ( self ):
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase : Tuple = TFTransfoXLModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@unittest.skip(reason="""This model doesn\'t play well with fit() due to not returning a single loss.""" )
def UpperCamelCase__ ( self ):
pass
@require_tf
class A__ ( unittest.TestCase):
@unittest.skip("""Skip test until #12651 is resolved.""" )
@slow
def UpperCamelCase__ ( self ):
lowerCamelCase : Dict = TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103""" )
# fmt: off
lowerCamelCase : List[Any] = tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
lowerCamelCase : List[Any] = [3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
lowerCamelCase : Optional[Any] = model.generate(__a , max_length=2_0_0 , do_sample=__a )
self.assertListEqual(output_ids[0].numpy().tolist() , __a )
| 287 |
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] ):
__a : Any = test_file.split(os.path.sep )
if components[0:2] != ["tests", "models"]:
raise ValueError(
'`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
F"""{test_file} instead.""" )
__a : Tuple = components[-1]
if not test_fn.endswith('py' ):
raise ValueError(F"""`test_file` should be a python file. Got {test_fn} instead.""" )
if not test_fn.startswith('test_modeling_' ):
raise ValueError(
F"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.""" )
__a : List[str] = components[:-1] + [test_fn.replace('.py' , '' )]
__a : Optional[Any] = '.'.join(_SCREAMING_SNAKE_CASE )
return test_module_path
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Tuple ):
__a : List[str] = get_module_path(_SCREAMING_SNAKE_CASE )
__a : Dict = importlib.import_module(_SCREAMING_SNAKE_CASE )
return test_module
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Tuple ):
__a : List[str] = []
__a : List[str] = get_test_module(_SCREAMING_SNAKE_CASE )
for attr in dir(_SCREAMING_SNAKE_CASE ):
if attr.endswith('ModelTester' ):
tester_classes.append(getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Tuple ):
__a : Any = []
__a : str = get_test_module(_SCREAMING_SNAKE_CASE )
for attr in dir(_SCREAMING_SNAKE_CASE ):
__a : int = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
# `all_model_classes` is not empty (which also excludes other special classes).
__a : Optional[Any] = getattr(_SCREAMING_SNAKE_CASE , 'all_model_classes' , [] )
if len(_SCREAMING_SNAKE_CASE ) > 0:
test_classes.append(_SCREAMING_SNAKE_CASE )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
__a : str = get_test_classes(_SCREAMING_SNAKE_CASE )
__a : Any = set()
for test_class in test_classes:
model_classes.update(test_class.all_model_classes )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
__a : Tuple = test_class()
if hasattr(_SCREAMING_SNAKE_CASE , 'setUp' ):
test.setUp()
__a : List[Any] = None
if hasattr(_SCREAMING_SNAKE_CASE , 'model_tester' ):
# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
if test.model_tester is not None:
__a : List[str] = test.model_tester.__class__
return model_tester
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Optional[Any] ):
__a : str = get_test_classes(_SCREAMING_SNAKE_CASE )
__a : int = []
for test_class in test_classes:
if model_class in test_class.all_model_classes:
target_test_classes.append(_SCREAMING_SNAKE_CASE )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : List[str] ):
__a : List[Any] = get_test_classes_for_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__a : Any = []
for test_class in test_classes:
__a : Any = get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE )
if tester_class is not None:
tester_classes.append(_SCREAMING_SNAKE_CASE )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[int] ):
__a : str = get_test_classes(_SCREAMING_SNAKE_CASE )
__a : int = {test_class: get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE ) for test_class in test_classes}
return test_tester_mapping
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[str] ):
__a : Optional[Any] = get_model_classes(_SCREAMING_SNAKE_CASE )
__a : Optional[int] = {
model_class: get_test_classes_for_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for model_class in model_classes
}
return model_test_mapping
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[str] ):
__a : Optional[Any] = get_model_classes(_SCREAMING_SNAKE_CASE )
__a : str = {
model_class: get_tester_classes_for_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for model_class in model_classes
}
return model_to_tester_mapping
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return o
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return o.__name__
elif isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ):
return [to_json(_SCREAMING_SNAKE_CASE ) for x in o]
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return {to_json(_SCREAMING_SNAKE_CASE ): to_json(_SCREAMING_SNAKE_CASE ) for k, v in o.items()}
else:
return o
| 27 | 0 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : List[Any] = [
['''attention''', '''attn'''],
['''encoder_attention''', '''encoder_attn'''],
['''q_lin''', '''q_proj'''],
['''k_lin''', '''k_proj'''],
['''v_lin''', '''v_proj'''],
['''out_lin''', '''out_proj'''],
['''norm_embeddings''', '''layernorm_embedding'''],
['''position_embeddings''', '''embed_positions'''],
['''embeddings''', '''embed_tokens'''],
['''ffn.lin''', '''fc'''],
]
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str ) -> List[str]:
"""simple docstring"""
if k == "embeddings.weight":
return "shared.weight"
for parlai_name, hf_name in PATTERNS:
UpperCamelCase :int = k.replace(lowercase__ , lowercase__ )
if k.startswith("""encoder""" ):
UpperCamelCase :Optional[int] = k.replace(""".attn""" , """.self_attn""" )
UpperCamelCase :List[str] = k.replace("""norm1""" , """self_attn_layer_norm""" )
UpperCamelCase :Tuple = k.replace("""norm2""" , """final_layer_norm""" )
elif k.startswith("""decoder""" ):
UpperCamelCase :int = k.replace("""norm1""" , """self_attn_layer_norm""" )
UpperCamelCase :List[str] = k.replace("""norm2""" , """encoder_attn_layer_norm""" )
UpperCamelCase :Optional[Any] = k.replace("""norm3""" , """final_layer_norm""" )
return k
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase :List[str] = [
"""model.encoder.layernorm_embedding.weight""",
"""model.encoder.layernorm_embedding.bias""",
"""model.decoder.layernorm_embedding.weight""",
"""model.decoder.layernorm_embedding.bias""",
]
for k in keys:
UpperCamelCase :str = sd.pop(lowercase__ )
UpperCamelCase :int = k.replace("""layernorm_embedding""" , """layer_norm""" )
assert new_k not in sd
UpperCamelCase :List[Any] = v
UpperCAmelCase_ : Any = ['''START''']
@torch.no_grad()
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Optional[int] , __magic_name__ : Tuple , __magic_name__ : Optional[int] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase :Dict = torch.load(lowercase__ , map_location="""cpu""" )
UpperCamelCase :str = model["""model"""]
UpperCamelCase :Optional[Any] = BlenderbotConfig.from_json_file(lowercase__ )
UpperCamelCase :Tuple = BlenderbotForConditionalGeneration(lowercase__ )
UpperCamelCase :Optional[Any] = m.model.state_dict().keys()
UpperCamelCase :Optional[int] = []
UpperCamelCase :Dict = {}
for k, v in sd.items():
if k in IGNORE_KEYS:
continue
UpperCamelCase :int = rename_state_dict_key(lowercase__ )
if new_k not in valid_keys:
failures.append([k, new_k] )
else:
UpperCamelCase :List[Any] = v
if cfg.normalize_before: # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
rename_layernorm_keys(lowercase__ )
m.model.load_state_dict(lowercase__ , strict=lowercase__ )
m.half()
m.save_pretrained(lowercase__ )
if __name__ == "__main__":
UpperCAmelCase_ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
parser.add_argument(
'''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
)
UpperCAmelCase_ : List[Any] = parser.parse_args()
convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)
| 356 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : tuple[int, int] , __magic_name__ : int ) -> list[tuple[int, int]]:
"""simple docstring"""
UpperCamelCase , UpperCamelCase :Union[str, Any] = position
UpperCamelCase :int = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
(y - 2, x + 1),
(y - 2, x - 1),
]
UpperCamelCase :Tuple = []
for position in positions:
UpperCamelCase , UpperCamelCase :int = position
if 0 <= y_test < n and 0 <= x_test < n:
permissible_positions.append(__magic_name__ )
return permissible_positions
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : list[list[int]] ) -> bool:
"""simple docstring"""
return not any(elem == 0 for row in board for elem in row )
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : list[list[int]] , __magic_name__ : tuple[int, int] , __magic_name__ : int ) -> bool:
"""simple docstring"""
if is_complete(__magic_name__ ):
return True
for position in get_valid_pos(__magic_name__ , len(__magic_name__ ) ):
UpperCamelCase , UpperCamelCase :Any = position
if board[y][x] == 0:
UpperCamelCase :Optional[int] = curr + 1
if open_knight_tour_helper(__magic_name__ , __magic_name__ , curr + 1 ):
return True
UpperCamelCase :List[Any] = 0
return False
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int ) -> list[list[int]]:
"""simple docstring"""
UpperCamelCase :Any = [[0 for i in range(__magic_name__ )] for j in range(__magic_name__ )]
for i in range(__magic_name__ ):
for j in range(__magic_name__ ):
UpperCamelCase :int = 1
if open_knight_tour_helper(__magic_name__ , (i, j) , 1 ):
return board
UpperCamelCase :Optional[int] = 0
UpperCamelCase :Union[str, Any] = f"""Open Kight Tour cannot be performed on a board of size {n}"""
raise ValueError(__magic_name__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 62 | 0 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Any = AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip''' )
snake_case_ : Tuple = AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip''' )
model.to(__magic_name__ )
from datasets import load_dataset
snake_case_ : Optional[Any] = load_dataset('''nielsr/rvlcdip-demo''' )
snake_case_ : Any = dataset['''train'''][0]['''image'''].convert('''RGB''' )
snake_case_ : Tuple = image_processor(__magic_name__ , return_tensors='''pt''' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
snake_case_ : Union[str, Any] = model(**__magic_name__ )
snake_case_ : int = outputs.logits
snake_case_ : Dict = torch.Size((1, 16) )
self.assertEqual(logits.shape , __magic_name__ )
snake_case_ : int = torch.tensor(
[-0.4_158, -0.4_092, -0.4_347] , device=__magic_name__ , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , __magic_name__ , atol=1e-4 ) )
| 279 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowerCAmelCase_ = (
'''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)
)
lowerCAmelCase_ = (
('''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'''),
)
lowerCAmelCase_ = (
('''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),
)
lowerCAmelCase_ = (
('''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),
)
lowerCAmelCase_ = (
('''2H 4D 3C AS 5S''', True, [5, 4, 3, 2, 1_4]),
('''2H 5D 3C AS 5S''', False, [1_4, 5, 5, 3, 2]),
('''JH QD KC AS TS''', False, [1_4, 1_3, 1_2, 1_1, 1_0]),
('''9D 3S 2C 7S 7C''', False, [9, 7, 7, 3, 2]),
)
lowerCAmelCase_ = (
('''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),
)
lowerCAmelCase_ = (
('''JH AH TH KH QH''', 2_3),
('''JH 9H TH KH QH''', 2_2),
('''JC KH JS JD JH''', 2_1),
('''KH KC 3S 3H 3D''', 2_0),
('''8C 9C 5C 3C TC''', 1_9),
('''JS QS 9H TS KH''', 1_8),
('''7C 7S KH 2H 7H''', 1_7),
('''3C KH 5D 5S KH''', 1_6),
('''QH 8H KD JH 8S''', 1_5),
('''2D 6D 9D TH 7D''', 1_4),
)
def lowerCamelCase_ ( ) -> Dict:
"""simple docstring"""
snake_case_ , snake_case_ : Any = randrange(len(_UpperCamelCase ) ), randrange(len(_UpperCamelCase ) )
snake_case_ : Any = ['''Loss''', '''Tie''', '''Win'''][(play >= oppo) + (play > oppo)]
snake_case_ , snake_case_ : Tuple = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def lowerCamelCase_ ( _UpperCamelCase = 100 ) -> str:
"""simple docstring"""
return (generate_random_hand() for _ in range(_UpperCamelCase ))
@pytest.mark.parametrize('''hand, expected''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
assert PokerHand(_UpperCamelCase )._is_flush() == expected
@pytest.mark.parametrize('''hand, expected''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
assert PokerHand(_UpperCamelCase )._is_straight() == expected
@pytest.mark.parametrize('''hand, expected, card_values''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
snake_case_ : str = PokerHand(_UpperCamelCase )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize('''hand, expected''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
assert PokerHand(_UpperCamelCase )._is_same_kind() == expected
@pytest.mark.parametrize('''hand, expected''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
assert PokerHand(_UpperCamelCase )._hand_type == expected
@pytest.mark.parametrize('''hand, other, expected''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
assert PokerHand(_UpperCamelCase ).compare_with(PokerHand(_UpperCamelCase ) ) == expected
@pytest.mark.parametrize('''hand, other, expected''' , generate_random_hands() )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
assert PokerHand(_UpperCamelCase ).compare_with(PokerHand(_UpperCamelCase ) ) == expected
def lowerCamelCase_ ( ) -> str:
"""simple docstring"""
snake_case_ : Dict = [PokerHand(_UpperCamelCase ) for hand in SORTED_HANDS]
snake_case_ : str = poker_hands.copy()
shuffle(_UpperCamelCase )
snake_case_ : List[str] = chain(sorted(_UpperCamelCase ) )
for index, hand in enumerate(_UpperCamelCase ):
assert hand == poker_hands[index]
def lowerCamelCase_ ( ) -> Dict:
"""simple docstring"""
snake_case_ : Union[str, Any] = [PokerHand('''2D AC 3H 4H 5S''' ), PokerHand('''2S 3H 4H 5S 6C''' )]
pokerhands.sort(reverse=_UpperCamelCase )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def lowerCamelCase_ ( ) -> str:
"""simple docstring"""
snake_case_ : Dict = PokerHand('''2C 4S AS 3D 5C''' )
snake_case_ : str = True
snake_case_ : Tuple = [5, 4, 3, 2, 14]
for _ in range(10 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def lowerCamelCase_ ( ) -> List[str]:
"""simple docstring"""
snake_case_ : List[str] = 0
snake_case_ : Union[str, Any] = os.path.abspath(os.path.dirname(_UpperCamelCase ) )
snake_case_ : Dict = os.path.join(_UpperCamelCase , '''poker_hands.txt''' )
with open(_UpperCamelCase ) as file_hand:
for line in file_hand:
snake_case_ : Dict = line[:14].strip()
snake_case_ : List[str] = line[15:].strip()
snake_case_ , snake_case_ : str = PokerHand(_UpperCamelCase ), PokerHand(_UpperCamelCase )
snake_case_ : int = player.compare_with(_UpperCamelCase )
if output == "Win":
answer += 1
assert answer == 376
| 279 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl": ["CTRLTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
"CTRLForSequenceClassification",
"CTRLLMHeadModel",
"CTRLModel",
"CTRLPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFCTRLForSequenceClassification",
"TFCTRLLMHeadModel",
"TFCTRLModel",
"TFCTRLPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .tokenization_ctrl import CTRLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ctrl import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
CTRLPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_ctrl import (
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCTRLForSequenceClassification,
TFCTRLLMHeadModel,
TFCTRLModel,
TFCTRLPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 108 |
"""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 snake_case ( __snake_case, unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Tuple = PriorTransformer
SCREAMING_SNAKE_CASE_ : List[str] = """hidden_states"""
@property
def lowercase_ ( self : Dict)-> str:
'''simple docstring'''
__lowerCAmelCase: str = 4
__lowerCAmelCase: int = 8
__lowerCAmelCase: int = 7
__lowerCAmelCase: str = floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: Optional[Any] = floats_tensor((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: Any = floats_tensor((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def lowercase_ ( self : Optional[int] , UpperCamelCase__ : str=0)-> str:
'''simple docstring'''
torch.manual_seed(UpperCamelCase__)
__lowerCAmelCase: List[Any] = 4
__lowerCAmelCase: Dict = 8
__lowerCAmelCase: int = 7
__lowerCAmelCase: List[str] = torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: Tuple = torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: List[Any] = torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
@property
def lowercase_ ( self : Dict)-> List[Any]:
'''simple docstring'''
return (4, 8)
@property
def lowercase_ ( self : Optional[int])-> int:
'''simple docstring'''
return (4, 8)
def lowercase_ ( self : Optional[int])-> Tuple:
'''simple docstring'''
__lowerCAmelCase: str = {
"num_attention_heads": 2,
"attention_head_dim": 4,
"num_layers": 2,
"embedding_dim": 8,
"num_embeddings": 7,
"additional_embeddings": 4,
}
__lowerCAmelCase: Any = self.dummy_input
return init_dict, inputs_dict
def lowercase_ ( self : List[Any])-> int:
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase: Optional[int] = PriorTransformer.from_pretrained(
"hf-internal-testing/prior-dummy" , output_loading_info=UpperCamelCase__)
self.assertIsNotNone(UpperCamelCase__)
self.assertEqual(len(loading_info["missing_keys"]) , 0)
model.to(UpperCamelCase__)
__lowerCAmelCase: Dict = model(**self.dummy_input)[0]
assert hidden_states is not None, "Make sure output is not None"
def lowercase_ ( self : List[str])-> Tuple:
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = self.prepare_init_args_and_inputs_for_common()
__lowerCAmelCase: Tuple = self.model_class(**UpperCamelCase__)
__lowerCAmelCase: List[str] = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCAmelCase: List[Any] = [*signature.parameters.keys()]
__lowerCAmelCase: Any = ["hidden_states", "timestep"]
self.assertListEqual(arg_names[:2] , UpperCamelCase__)
def lowercase_ ( self : Optional[int])-> List[str]:
'''simple docstring'''
__lowerCAmelCase: int = PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy")
__lowerCAmelCase: Union[str, Any] = model.to(UpperCamelCase__)
if hasattr(UpperCamelCase__ , "set_default_attn_processor"):
model.set_default_attn_processor()
__lowerCAmelCase: str = self.get_dummy_seed_input()
with torch.no_grad():
__lowerCAmelCase: Dict = model(**UpperCamelCase__)[0]
__lowerCAmelCase: Dict = output[0, :5].flatten().cpu()
print(UpperCamelCase__)
# 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(UpperCamelCase__ , UpperCamelCase__ , rtol=1e-2))
@slow
class snake_case ( unittest.TestCase ):
def lowercase_ ( self : int , UpperCamelCase__ : Dict=1 , UpperCamelCase__ : str=7_6_8 , UpperCamelCase__ : int=7_7 , UpperCamelCase__ : Any=0)-> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(UpperCamelCase__)
__lowerCAmelCase: List[Any] = batch_size
__lowerCAmelCase: Any = embedding_dim
__lowerCAmelCase: Dict = num_embeddings
__lowerCAmelCase: Dict = torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: str = torch.randn((batch_size, embedding_dim)).to(UpperCamelCase__)
__lowerCAmelCase: int = torch.randn((batch_size, num_embeddings, embedding_dim)).to(UpperCamelCase__)
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def lowercase_ ( self : List[Any])-> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@parameterized.expand(
[
# fmt: off
[1_3, [-0.5861, 0.1283, -0.0931, 0.0882, 0.4476, 0.1329, -0.0498, 0.0640]],
[3_7, [-0.4913, 0.0110, -0.0483, 0.0541, 0.4954, -0.0170, 0.0354, 0.1651]],
# fmt: on
])
def lowercase_ ( self : Optional[int] , UpperCamelCase__ : List[str] , UpperCamelCase__ : int)-> List[Any]:
'''simple docstring'''
__lowerCAmelCase: List[str] = PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior" , subfolder="prior")
model.to(UpperCamelCase__)
__lowerCAmelCase: Dict = self.get_dummy_seed_input(seed=UpperCamelCase__)
with torch.no_grad():
__lowerCAmelCase: Optional[Any] = model(**UpperCamelCase__)[0]
assert list(sample.shape) == [1, 7_6_8]
__lowerCAmelCase: Dict = sample[0, :8].flatten().cpu()
print(UpperCamelCase__)
__lowerCAmelCase: Union[str, Any] = torch.tensor(UpperCamelCase__)
assert torch_all_close(UpperCamelCase__ , UpperCamelCase__ , atol=1e-3)
| 108 | 1 |
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> Optional[int]:
"""simple docstring"""
_lowercase =len(__snake_case )
_lowercase =[[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
_lowercase =True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1 ):
_lowercase =False
for i in range(1 , arr_len + 1 ):
for j in range(1 , required_sum + 1 ):
if arr[i - 1] > j:
_lowercase =subset[i - 1][j]
if arr[i - 1] <= j:
_lowercase =subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 |
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__UpperCamelCase : Dict = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}
def _a ( SCREAMING_SNAKE_CASE : str = "dhaka" , SCREAMING_SNAKE_CASE : int = 5 ):
"""simple docstring"""
UpperCamelCase__ : Optional[int] = min(SCREAMING_SNAKE_CASE , 50 ) # Prevent abuse!
UpperCamelCase__ : str = {
'''q''': query,
'''tbm''': '''isch''',
'''hl''': '''en''',
'''ijn''': '''0''',
}
UpperCamelCase__ : List[str] = requests.get('''https://www.google.com/search''' , params=SCREAMING_SNAKE_CASE , headers=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Any = BeautifulSoup(html.text , '''html.parser''' )
UpperCamelCase__ : Union[str, Any] = ''''''.join(
re.findall(r'''AF_initDataCallback\(([^<]+)\);''' , str(soup.select('''script''' ) ) ) )
UpperCamelCase__ : Optional[Any] = json.dumps(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[Any] = json.loads(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[str] = re.findall(
r'''\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",''' , SCREAMING_SNAKE_CASE , )
if not matched_google_image_data:
return 0
UpperCamelCase__ : Optional[Any] = re.sub(
r'''\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]''' , '''''' , str(SCREAMING_SNAKE_CASE ) , )
UpperCamelCase__ : List[Any] = re.findall(
r'''(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]''' , SCREAMING_SNAKE_CASE , )
for index, fixed_full_res_image in enumerate(SCREAMING_SNAKE_CASE ):
if index >= max_images:
return index
UpperCamelCase__ : Optional[int] = bytes(SCREAMING_SNAKE_CASE , '''ascii''' ).decode(
'''unicode-escape''' )
UpperCamelCase__ : List[Any] = bytes(SCREAMING_SNAKE_CASE , '''ascii''' ).decode(
'''unicode-escape''' )
UpperCamelCase__ : List[Any] = urllib.request.build_opener()
UpperCamelCase__ : Optional[Any] = [
(
'''User-Agent''',
'''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582''',
)
]
urllib.request.install_opener(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Dict = F"query_{query.replace(' ' , '_' )}"
if not os.path.exists(SCREAMING_SNAKE_CASE ):
os.makedirs(SCREAMING_SNAKE_CASE )
urllib.request.urlretrieve( # noqa: S310
SCREAMING_SNAKE_CASE , F"{path_name}/original_size_img_{index}.jpg" )
return index
if __name__ == "__main__":
try:
__UpperCamelCase : List[Any] = download_images_from_google_query(sys.argv[1])
print(f"{image_count} images were downloaded to disk.")
except IndexError:
print("Please provide a search term.")
raise
| 146 | 0 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class A :
"""simple docstring"""
def __init__(self , lowerCAmelCase , lowerCAmelCase=3 , lowerCAmelCase=3_2 , lowerCAmelCase=3 , lowerCAmelCase=1_0 , lowerCAmelCase=[8, 1_6, 3_2, 6_4] , lowerCAmelCase=[1, 1, 2, 1] , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase="relu" , lowerCAmelCase=3 , lowerCAmelCase=None , lowerCAmelCase=["stage2", "stage3", "stage4"] , lowerCAmelCase=[2, 3, 4] , lowerCAmelCase=1 , ):
__lowercase= parent
__lowercase= batch_size
__lowercase= image_size
__lowercase= num_channels
__lowercase= embeddings_size
__lowercase= hidden_sizes
__lowercase= depths
__lowercase= is_training
__lowercase= use_labels
__lowercase= hidden_act
__lowercase= num_labels
__lowercase= scope
__lowercase= len(_a )
__lowercase= out_features
__lowercase= out_indices
__lowercase= num_groups
def _A (self ):
__lowercase= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase= None
if self.use_labels:
__lowercase= ids_tensor([self.batch_size] , self.num_labels )
__lowercase= self.get_config()
return config, pixel_values, labels
def _A (self ):
return BitConfig(
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 , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def _A (self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
__lowercase= BitModel(config=_a )
model.to(_a )
model.eval()
__lowercase= model(_a )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def _A (self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
__lowercase= self.num_labels
__lowercase= BitForImageClassification(_a )
model.to(_a )
model.eval()
__lowercase= model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _A (self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
__lowercase= BitBackbone(config=_a )
model.to(_a )
model.eval()
__lowercase= model(_a )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
__lowercase= None
__lowercase= BitBackbone(config=_a )
model.to(_a )
model.eval()
__lowercase= model(_a )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _A (self ):
__lowercase= self.prepare_config_and_inputs()
__lowercase= config_and_inputs
__lowercase= {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class A ( __lowercase , __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : List[str] =(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
UpperCamelCase_ : Optional[Any] =(
{"feature-extraction": BitModel, "image-classification": BitForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase_ : Any =False
UpperCamelCase_ : Any =False
UpperCamelCase_ : Any =False
UpperCamelCase_ : List[Any] =False
UpperCamelCase_ : str =False
def _A (self ):
__lowercase= BitModelTester(self )
__lowercase= ConfigTester(self , config_class=_a , has_text_modality=_a )
def _A (self ):
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 _A (self ):
return
@unittest.skip(reason='Bit does not output attentions' )
def _A (self ):
pass
@unittest.skip(reason='Bit does not use inputs_embeds' )
def _A (self ):
pass
@unittest.skip(reason='Bit does not support input and output embeddings' )
def _A (self ):
pass
def _A (self ):
__lowercase= self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase= model_class(_a )
__lowercase= inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase= [*signature.parameters.keys()]
__lowercase= ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _a )
def _A (self ):
__lowercase= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def _A (self ):
__lowercase= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_a )
def _A (self ):
__lowercase= self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase= model_class(config=_a )
for name, module in model.named_modules():
if isinstance(_a , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
def _A (self ):
def check_hidden_states_output(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
__lowercase= model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
__lowercase= model(**self._prepare_for_class(_a , _a ) )
__lowercase= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowercase= self.model_tester.num_stages
self.assertEqual(len(_a ) , expected_num_stages + 1 )
# Bit'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] , )
__lowercase= self.model_tester.prepare_config_and_inputs_for_common()
__lowercase= ['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
__lowercase= layer_type
__lowercase= True
check_hidden_states_output(_a , _a , _a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase= True
check_hidden_states_output(_a , _a , _a )
@unittest.skip(reason='Bit does not use feedforward chunking' )
def _A (self ):
pass
def _A (self ):
__lowercase= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
@slow
def _A (self ):
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase= BitModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def _lowerCamelCase( ) -> Dict:
'''simple docstring'''
__lowercase= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class A ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _A (self ):
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def _A (self ):
__lowercase= BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(_a )
__lowercase= self.default_image_processor
__lowercase= prepare_img()
__lowercase= image_processor(images=_a , return_tensors='pt' ).to(_a )
# forward pass
with torch.no_grad():
__lowercase= model(**_a )
# verify the logits
__lowercase= torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , _a )
__lowercase= torch.tensor([[-0.65_26, -0.52_63, -1.43_98]] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1E-4 ) )
@require_torch
class A ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : str =(BitBackbone,) if is_torch_available() else ()
UpperCamelCase_ : Union[str, Any] =BitConfig
UpperCamelCase_ : List[Any] =False
def _A (self ):
__lowercase= BitModelTester(self )
| 365 |
from math import factorial, radians
def _lowerCamelCase( lowercase__ , lowercase__ = 1_8 , lowercase__ = 1_0 ) -> float:
'''simple docstring'''
__lowercase= angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
__lowercase= radians(lowercase__ )
__lowercase= angle_in_radians
__lowercase= 3
__lowercase= -1
for _ in range(lowercase__ ):
result += (b * (angle_in_radians**a)) / factorial(lowercase__ )
__lowercase= -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(lowercase__ , lowercase__ )
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 304 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase : List[Any] = {
'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
'microsoft/deberta-v2-xlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
),
'microsoft/deberta-v2-xxlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
),
}
class __lowercase (UpperCamelCase__ ):
"""simple docstring"""
_snake_case = """deberta-v2"""
def __init__( self , A=1_2_8_1_0_0 , A=1_5_3_6 , A=2_4 , A=2_4 , A=6_1_4_4 , A="gelu" , A=0.1 , A=0.1 , A=5_1_2 , A=0 , A=0.02 , A=1e-7 , A=False , A=-1 , A=0 , A=True , A=None , A=0 , A="gelu" , **A , ) -> List[str]:
super().__init__(**A )
snake_case : int = hidden_size
snake_case : int = num_hidden_layers
snake_case : Optional[int] = num_attention_heads
snake_case : List[Any] = intermediate_size
snake_case : Dict = hidden_act
snake_case : List[Any] = hidden_dropout_prob
snake_case : Union[str, Any] = attention_probs_dropout_prob
snake_case : Tuple = max_position_embeddings
snake_case : str = type_vocab_size
snake_case : int = initializer_range
snake_case : int = relative_attention
snake_case : List[str] = max_relative_positions
snake_case : List[Any] = pad_token_id
snake_case : str = position_biased_input
# Backwards compatibility
if type(A ) == str:
snake_case : Tuple = [x.strip() for x in pos_att_type.lower().split("""|""" )]
snake_case : Optional[int] = pos_att_type
snake_case : str = vocab_size
snake_case : str = layer_norm_eps
snake_case : Tuple = kwargs.get("""pooler_hidden_size""" , A )
snake_case : Any = pooler_dropout
snake_case : str = pooler_hidden_act
class __lowercase (UpperCamelCase__ ):
"""simple docstring"""
@property
def UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
snake_case : str = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
snake_case : Any = {0: """batch""", 1: """sequence"""}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)] )
else:
return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)] )
@property
def UpperCAmelCase ( self ) -> int:
return 1_2
def UpperCAmelCase ( self , A , A = -1 , A = -1 , A = -1 , A = False , A = None , A = 3 , A = 4_0 , A = 4_0 , A = None , ) -> Mapping[str, Any]:
snake_case : Any = super().generate_dummy_inputs(preprocessor=A , framework=A )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 124 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> float:
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
snake_case : Optional[Any] = sum(lowercase ) / len(lowercase ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / len(lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 124 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCamelCase ( lowercase ):
UpperCAmelCase : List[Any] = ["""image_processor""", """tokenizer"""]
UpperCAmelCase : str = """BlipImageProcessor"""
UpperCAmelCase : List[str] = """AutoTokenizer"""
def __init__(self : str , _A : Any , _A : str) -> Union[str, Any]:
__snake_case : Dict = False
super().__init__(_A , _A)
__snake_case : int = self.image_processor
def __call__(self : Dict , _A : ImageInput = None , _A : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , _A : bool = True , _A : Union[bool, str, PaddingStrategy] = False , _A : Union[bool, str, TruncationStrategy] = None , _A : Optional[int] = None , _A : int = 0 , _A : Optional[int] = None , _A : Optional[bool] = None , _A : bool = False , _A : bool = False , _A : bool = False , _A : bool = False , _A : bool = False , _A : bool = True , _A : Optional[Union[str, TensorType]] = None , **_A : int , ) -> BatchEncoding:
if images is None and text is None:
raise ValueError('You have to specify either images or text.')
# Get only text
if images is None:
__snake_case : Optional[Any] = self.tokenizer
__snake_case : Optional[int] = self.tokenizer(
text=_A , add_special_tokens=_A , padding=_A , truncation=_A , max_length=_A , stride=_A , pad_to_multiple_of=_A , return_attention_mask=_A , return_overflowing_tokens=_A , return_special_tokens_mask=_A , return_offsets_mapping=_A , return_token_type_ids=_A , return_length=_A , verbose=_A , return_tensors=_A , **_A , )
return text_encoding
# add pixel_values
__snake_case : Dict = self.image_processor(_A , return_tensors=_A)
if text is not None:
__snake_case : List[str] = self.tokenizer(
text=_A , add_special_tokens=_A , padding=_A , truncation=_A , max_length=_A , stride=_A , pad_to_multiple_of=_A , return_attention_mask=_A , return_overflowing_tokens=_A , return_special_tokens_mask=_A , return_offsets_mapping=_A , return_token_type_ids=_A , return_length=_A , verbose=_A , return_tensors=_A , **_A , )
else:
__snake_case : Optional[int] = None
if text_encoding is not None:
encoding_image_processor.update(_A)
return encoding_image_processor
def _lowercase (self : Optional[Any] , *_A : Any , **_A : Tuple) -> str:
return self.tokenizer.batch_decode(*_A , **_A)
def _lowercase (self : Tuple , *_A : int , **_A : str) -> Tuple:
return self.tokenizer.decode(*_A , **_A)
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def _lowercase (self : str) -> List[Any]:
__snake_case : str = self.tokenizer.model_input_names
__snake_case : List[str] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
| 351 | """simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCamelCase ( lowercase ):
UpperCAmelCase : Optional[int] = """new-model"""
if is_tf_available():
class UpperCamelCase ( lowercase ):
UpperCAmelCase : List[str] = NewModelConfig
@require_tf
class UpperCamelCase ( unittest.TestCase ):
@slow
def _lowercase (self : List[str]) -> Dict:
__snake_case : Any = 'bert-base-cased'
__snake_case : Optional[Any] = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : Union[str, Any] = TFAutoModel.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
@slow
def _lowercase (self : List[Any]) -> str:
__snake_case : Optional[int] = 'bert-base-cased'
__snake_case : List[Any] = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : Dict = TFAutoModelForPreTraining.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
@slow
def _lowercase (self : Any) -> List[str]:
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : str = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : List[str] = TFAutoModelForCausalLM.from_pretrained(_A)
__snake_case , __snake_case : List[str] = TFAutoModelForCausalLM.from_pretrained(_A , output_loading_info=_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
@slow
def _lowercase (self : Tuple) -> Dict:
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : Union[str, Any] = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : Union[str, Any] = TFAutoModelWithLMHead.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
@slow
def _lowercase (self : Union[str, Any]) -> Optional[int]:
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : Union[str, Any] = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : Optional[Any] = TFAutoModelForMaskedLM.from_pretrained(_A)
__snake_case , __snake_case : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(_A , output_loading_info=_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
@slow
def _lowercase (self : str) -> Union[str, Any]:
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : Dict = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : str = TFAutoModelForSeqaSeqLM.from_pretrained(_A)
__snake_case , __snake_case : Union[str, Any] = TFAutoModelForSeqaSeqLM.from_pretrained(_A , output_loading_info=_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
@slow
def _lowercase (self : str) -> str:
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__snake_case : Tuple = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : Tuple = TFAutoModelForSequenceClassification.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
@slow
def _lowercase (self : Optional[Any]) -> Optional[int]:
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__snake_case : List[str] = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : Any = TFAutoModelForQuestionAnswering.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
@slow
@require_tensorflow_probability
def _lowercase (self : List[Any]) -> List[str]:
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
__snake_case : Optional[Any] = AutoConfig.from_pretrained(_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
__snake_case : int = TFAutoModelForTableQuestionAnswering.from_pretrained(_A)
__snake_case , __snake_case : Dict = TFAutoModelForTableQuestionAnswering.from_pretrained(
_A , output_loading_info=_A)
self.assertIsNotNone(_A)
self.assertIsInstance(_A , _A)
def _lowercase (self : Optional[Any]) -> Optional[Any]:
__snake_case : Optional[int] = TFAutoModelWithLMHead.from_pretrained(_A)
self.assertIsInstance(_A , _A)
self.assertEqual(model.num_parameters() , 1_44_10)
self.assertEqual(model.num_parameters(only_trainable=_A) , 1_44_10)
def _lowercase (self : Any) -> List[str]:
__snake_case : List[Any] = TFAutoModelWithLMHead.from_pretrained(_A)
self.assertIsInstance(_A , _A)
self.assertEqual(model.num_parameters() , 1_44_10)
self.assertEqual(model.num_parameters(only_trainable=_A) , 1_44_10)
def _lowercase (self : Optional[Any]) -> str:
# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
__snake_case : Optional[Any] = TFAutoModel.from_pretrained('sgugger/funnel-random-tiny')
self.assertIsInstance(_A , _A)
__snake_case : int = copy.deepcopy(model.config)
__snake_case : int = ['FunnelBaseModel']
__snake_case : int = TFAutoModel.from_config(_A)
self.assertIsInstance(_A , _A)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(_A)
__snake_case : List[Any] = TFAutoModel.from_pretrained(_A)
self.assertIsInstance(_A , _A)
def _lowercase (self : List[Any]) -> int:
try:
AutoConfig.register('new-model' , _A)
__snake_case : int = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__):
# Wrong config class will raise an error
with self.assertRaises(_A):
auto_class.register(_A , _A)
auto_class.register(_A , _A)
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_A):
auto_class.register(_A , _A)
# Now that the config is registered, it can be used as any other config with the auto-API
__snake_case : Union[str, Any] = BertModelTester(self).get_config()
__snake_case : Optional[int] = NewModelConfig(**tiny_config.to_dict())
__snake_case : List[str] = auto_class.from_config(_A)
self.assertIsInstance(_A , _A)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(_A)
__snake_case : Tuple = auto_class.from_pretrained(_A)
self.assertIsInstance(_A , _A)
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def _lowercase (self : Optional[int]) -> Union[str, Any]:
with self.assertRaisesRegex(
_A , 'bert-base is not a local folder and is not a valid model identifier'):
__snake_case : Any = TFAutoModel.from_pretrained('bert-base')
def _lowercase (self : str) -> str:
with self.assertRaisesRegex(
_A , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
__snake_case : Optional[Any] = TFAutoModel.from_pretrained(_A , revision='aaaaaa')
def _lowercase (self : int) -> Any:
with self.assertRaisesRegex(
_A , 'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin' , ):
__snake_case : List[str] = TFAutoModel.from_pretrained('hf-internal-testing/config-no-model')
def _lowercase (self : Optional[Any]) -> Any:
with self.assertRaisesRegex(_A , 'Use `from_pt=True` to load this model'):
__snake_case : List[str] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only')
def _lowercase (self : str) -> Any:
# Make sure we have cached the model.
__snake_case : str = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert')
with RequestCounter() as counter:
__snake_case : List[str] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert')
self.assertEqual(counter.get_request_count , 0)
self.assertEqual(counter.head_request_count , 1)
self.assertEqual(counter.other_request_count , 0)
# With a sharded checkpoint
__snake_case : Optional[int] = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded')
with RequestCounter() as counter:
__snake_case : Any = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded')
self.assertEqual(counter.get_request_count , 0)
self.assertEqual(counter.head_request_count , 1)
self.assertEqual(counter.other_request_count , 0)
| 95 | 0 |
'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
lowerCamelCase : Any = True
except ImportError:
lowerCamelCase : Union[str, Any] = False
lowerCamelCase : Any = logging.get_logger(__name__) # pylint: disable=invalid-name
def _lowerCAmelCase ( _UpperCamelCase : Namespace ) -> Any:
"""simple docstring"""
return AddNewModelCommand(args.testing , args.testing_file , path=args.path )
class A__ ( A__ ):
@staticmethod
def A ( _a : ArgumentParser ) -> Dict:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =parser.add_parser('add-new-model' )
add_new_model_parser.add_argument('--testing' , action='store_true' , help='If in testing mode.' )
add_new_model_parser.add_argument('--testing_file' , type=_a , help='Configuration file on which to run.' )
add_new_model_parser.add_argument(
'--path' , type=_a , help='Path to cookiecutter. Should only be used for testing purposes.' )
add_new_model_parser.set_defaults(func=_a )
def __init__( self : Optional[Any] , _a : bool , _a : str , _a : Optional[Any]=None , *_a : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =testing
_SCREAMING_SNAKE_CASE =testing_file
_SCREAMING_SNAKE_CASE =path
def A ( self : str ) -> int:
'''simple docstring'''
warnings.warn(
'The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '
'It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '
'checks, you should use `transformers-cli add-new-model-like` instead.' )
if not _has_cookiecutter:
raise ImportError(
'Model creation dependencies are required to use the `add_new_model` command. Install them by running '
'the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n' )
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
_SCREAMING_SNAKE_CASE =[directory for directory in os.listdir() if 'cookiecutter-template-' == directory[:22]]
if len(_a ) > 0:
raise ValueError(
'Several directories starting with `cookiecutter-template-` in current working directory. '
'Please clean your directory by removing all folders starting with `cookiecutter-template-` or '
'change your working directory.' )
_SCREAMING_SNAKE_CASE =(
Path(_a ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
)
_SCREAMING_SNAKE_CASE =path_to_transformer_root / 'templates' / 'adding_a_new_model'
# Execute cookiecutter
if not self._testing:
cookiecutter(str(_a ) )
else:
with open(self._testing_file , 'r' ) as configuration_file:
_SCREAMING_SNAKE_CASE =json.load(_a )
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path ) , no_input=_a , extra_context=_a , )
_SCREAMING_SNAKE_CASE =[directory for directory in os.listdir() if 'cookiecutter-template-' in directory[:22]][0]
# Retrieve configuration
with open(directory + '/configuration.json' , 'r' ) as configuration_file:
_SCREAMING_SNAKE_CASE =json.load(_a )
_SCREAMING_SNAKE_CASE =configuration['lowercase_modelname']
_SCREAMING_SNAKE_CASE =configuration['generate_tensorflow_pytorch_and_flax']
os.remove(f"{directory}/configuration.json" )
_SCREAMING_SNAKE_CASE ='PyTorch' in generate_tensorflow_pytorch_and_flax
_SCREAMING_SNAKE_CASE ='TensorFlow' in generate_tensorflow_pytorch_and_flax
_SCREAMING_SNAKE_CASE ='Flax' in generate_tensorflow_pytorch_and_flax
_SCREAMING_SNAKE_CASE =f"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
os.makedirs(_a , exist_ok=_a )
os.makedirs(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}" , exist_ok=_a )
# Tests require submodules as they have parent imports
with open(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py" , 'w' ):
pass
shutil.move(
f"{directory}/__init__.py" , f"{model_dir}/__init__.py" , )
shutil.move(
f"{directory}/configuration_{lowercase_model_name}.py" , f"{model_dir}/configuration_{lowercase_model_name}.py" , )
def remove_copy_lines(_a : Any ):
with open(_a , 'r' ) as f:
_SCREAMING_SNAKE_CASE =f.readlines()
with open(_a , 'w' ) as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(_a )
if output_pytorch:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_{lowercase_model_name}.py" )
shutil.move(
f"{directory}/modeling_{lowercase_model_name}.py" , f"{model_dir}/modeling_{lowercase_model_name}.py" , )
shutil.move(
f"{directory}/test_modeling_{lowercase_model_name}.py" , f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py" , )
else:
os.remove(f"{directory}/modeling_{lowercase_model_name}.py" )
os.remove(f"{directory}/test_modeling_{lowercase_model_name}.py" )
if output_tensorflow:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_tf_{lowercase_model_name}.py" )
shutil.move(
f"{directory}/modeling_tf_{lowercase_model_name}.py" , f"{model_dir}/modeling_tf_{lowercase_model_name}.py" , )
shutil.move(
f"{directory}/test_modeling_tf_{lowercase_model_name}.py" , f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py" , )
else:
os.remove(f"{directory}/modeling_tf_{lowercase_model_name}.py" )
os.remove(f"{directory}/test_modeling_tf_{lowercase_model_name}.py" )
if output_flax:
if not self._testing:
remove_copy_lines(f"{directory}/modeling_flax_{lowercase_model_name}.py" )
shutil.move(
f"{directory}/modeling_flax_{lowercase_model_name}.py" , f"{model_dir}/modeling_flax_{lowercase_model_name}.py" , )
shutil.move(
f"{directory}/test_modeling_flax_{lowercase_model_name}.py" , f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py" , )
else:
os.remove(f"{directory}/modeling_flax_{lowercase_model_name}.py" )
os.remove(f"{directory}/test_modeling_flax_{lowercase_model_name}.py" )
shutil.move(
f"{directory}/{lowercase_model_name}.md" , f"{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md" , )
shutil.move(
f"{directory}/tokenization_{lowercase_model_name}.py" , f"{model_dir}/tokenization_{lowercase_model_name}.py" , )
shutil.move(
f"{directory}/tokenization_fast_{lowercase_model_name}.py" , f"{model_dir}/tokenization_{lowercase_model_name}_fast.py" , )
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(_a : str , _a : str , _a : List[str] ):
# Create temp file
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =mkstemp()
_SCREAMING_SNAKE_CASE =False
with fdopen(_a , 'w' ) as new_file:
with open(_a ) as old_file:
for line in old_file:
new_file.write(_a )
if line_to_copy_below in line:
_SCREAMING_SNAKE_CASE =True
for line_to_copy in lines_to_copy:
new_file.write(_a )
if not line_found:
raise ValueError(f"Line {line_to_copy_below} was not found in file." )
# Copy the file permissions from the old file to the new file
copymode(_a , _a )
# Remove original file
remove(_a )
# Move new file
move(_a , _a )
def skip_units(_a : str ):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(_a : str ):
with open(_a ) as datafile:
_SCREAMING_SNAKE_CASE =[]
_SCREAMING_SNAKE_CASE =False
_SCREAMING_SNAKE_CASE =False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
_SCREAMING_SNAKE_CASE =line.split('"' )[1]
_SCREAMING_SNAKE_CASE =skip_units(_a )
elif "# Below: " in line and "##" not in line:
_SCREAMING_SNAKE_CASE =line.split('"' )[1]
_SCREAMING_SNAKE_CASE =skip_units(_a )
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(_a , _a , _a )
_SCREAMING_SNAKE_CASE =[]
elif "# Replace with" in line and "##" not in line:
_SCREAMING_SNAKE_CASE =[]
elif "##" not in line:
lines_to_copy.append(_a )
remove(_a )
replace_in_files(f"{directory}/to_replace_{lowercase_model_name}.py" )
os.rmdir(_a )
| 47 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class A__ ( A__ , A__ ):
@register_to_config
def __init__( self : Dict , _a : int = 768 , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
_SCREAMING_SNAKE_CASE =nn.Parameter(torch.zeros(1 , _a ) )
_SCREAMING_SNAKE_CASE =nn.Parameter(torch.ones(1 , _a ) )
def A ( self : Tuple , _a : Optional[Union[str, torch.device]] = None , _a : Optional[torch.dtype] = None , ) -> List[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =nn.Parameter(self.mean.to(_a ).to(_a ) )
_SCREAMING_SNAKE_CASE =nn.Parameter(self.std.to(_a ).to(_a ) )
return self
def A ( self : Tuple , _a : str ) -> str:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =(embeds - self.mean) * 1.0 / self.std
return embeds
def A ( self : List[str] , _a : Optional[Any] ) -> Tuple:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =(embeds * self.std) + self.mean
return embeds
| 47 | 1 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'''
),
}
class a ( a_ ):
_lowercase = "xlm-prophetnet"
_lowercase = ["past_key_values"]
_lowercase = {
"num_attention_heads": "num_encoder_attention_heads",
}
def __init__( self , A_ = 0.1 , A_ = "gelu" , A_ = 30522 , A_ = 1024 , A_ = 4096 , A_ = 12 , A_ = 16 , A_ = 4096 , A_ = 12 , A_ = 16 , A_ = 0.1 , A_ = 0.1 , A_ = 512 , A_ = 0.02 , A_ = True , A_ = True , A_ = 0 , A_ = 2 , A_ = 32 , A_ = 128 , A_ = False , A_ = 0.0 , A_ = True , A_ = 0 , A_ = 1 , A_ = 2 , **A_ , ):
'''simple docstring'''
_UpperCAmelCase : Tuple = vocab_size
_UpperCAmelCase : Optional[Any] = hidden_size
_UpperCAmelCase : Dict = encoder_ffn_dim
_UpperCAmelCase : Dict = num_encoder_layers
_UpperCAmelCase : Any = num_encoder_attention_heads
_UpperCAmelCase : List[str] = decoder_ffn_dim
_UpperCAmelCase : Optional[Any] = num_decoder_layers
_UpperCAmelCase : Dict = num_decoder_attention_heads
_UpperCAmelCase : Optional[Any] = max_position_embeddings
_UpperCAmelCase : List[Any] = init_std # Normal(0, this parameter)
_UpperCAmelCase : List[str] = activation_function
# parameters for xlmprophetnet
_UpperCAmelCase : Dict = ngram
_UpperCAmelCase : List[Any] = num_buckets
_UpperCAmelCase : Dict = relative_max_distance
_UpperCAmelCase : Any = disable_ngram_loss
_UpperCAmelCase : str = eps
# 3 Types of Dropout
_UpperCAmelCase : Optional[int] = attention_dropout
_UpperCAmelCase : Any = activation_dropout
_UpperCAmelCase : Any = dropout
_UpperCAmelCase : str = use_cache
super().__init__(
pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , is_encoder_decoder=lowercase_ , add_cross_attention=lowercase_ , decoder_start_token_id=lowercase_ , **lowercase_ , )
@property
def _UpperCAmelCase ( self ):
'''simple docstring'''
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def _UpperCAmelCase ( self , A_ ):
'''simple docstring'''
raise NotImplementedError(
"This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
" `num_decoder_layers`." )
| 358 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class a ( UpperCAmelCase ):
_lowercase = ["image_processor", "tokenizer"]
_lowercase = "OwlViTImageProcessor"
_lowercase = ("CLIPTokenizer", "CLIPTokenizerFast")
def __init__( self , A_=None , A_=None , **A_ ):
'''simple docstring'''
_UpperCAmelCase : List[str] = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , A_ , )
_UpperCAmelCase : Union[str, Any] = kwargs.pop("feature_extractor" )
_UpperCAmelCase : Any = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(A_ , A_ )
def __call__( self , A_=None , A_=None , A_=None , A_="max_length" , A_="np" , **A_ ):
'''simple docstring'''
if text is None and query_images is None and images is None:
raise ValueError(
"You have to specify at least one text or query image or image. All three cannot be none." )
if text is not None:
if isinstance(A_ , A_ ) or (isinstance(A_ , A_ ) and not isinstance(text[0] , A_ )):
_UpperCAmelCase : Optional[int] = [self.tokenizer(A_ , padding=A_ , return_tensors=A_ , **A_ )]
elif isinstance(A_ , A_ ) and isinstance(text[0] , A_ ):
_UpperCAmelCase : Optional[int] = []
# Maximum number of queries across batch
_UpperCAmelCase : Optional[Any] = max([len(A_ ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(A_ ) != max_num_queries:
_UpperCAmelCase : Optional[int] = t + [" "] * (max_num_queries - len(A_ ))
_UpperCAmelCase : str = self.tokenizer(A_ , padding=A_ , return_tensors=A_ , **A_ )
encodings.append(A_ )
else:
raise TypeError("Input text should be a string, a list of strings or a nested list of strings" )
if return_tensors == "np":
_UpperCAmelCase : List[str] = np.concatenate([encoding["input_ids"] for encoding in encodings] , axis=0 )
_UpperCAmelCase : Tuple = np.concatenate([encoding["attention_mask"] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
_UpperCAmelCase : Optional[Any] = jnp.concatenate([encoding["input_ids"] for encoding in encodings] , axis=0 )
_UpperCAmelCase : str = jnp.concatenate([encoding["attention_mask"] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
_UpperCAmelCase : str = torch.cat([encoding["input_ids"] for encoding in encodings] , dim=0 )
_UpperCAmelCase : Dict = torch.cat([encoding["attention_mask"] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
_UpperCAmelCase : Union[str, Any] = tf.stack([encoding["input_ids"] for encoding in encodings] , axis=0 )
_UpperCAmelCase : Optional[int] = tf.stack([encoding["attention_mask"] for encoding in encodings] , axis=0 )
else:
raise ValueError("Target return tensor type could not be returned" )
_UpperCAmelCase : Optional[int] = BatchEncoding()
_UpperCAmelCase : str = input_ids
_UpperCAmelCase : Optional[Any] = attention_mask
if query_images is not None:
_UpperCAmelCase : int = BatchEncoding()
_UpperCAmelCase : str = self.image_processor(
A_ , return_tensors=A_ , **A_ ).pixel_values
_UpperCAmelCase : Optional[Any] = query_pixel_values
if images is not None:
_UpperCAmelCase : int = self.image_processor(A_ , return_tensors=A_ , **A_ )
if text is not None and images is not None:
_UpperCAmelCase : Optional[int] = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
_UpperCAmelCase : Any = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**A_ ) , tensor_type=A_ )
def _UpperCAmelCase ( self , *A_ , **A_ ):
'''simple docstring'''
return self.image_processor.post_process(*A_ , **A_ )
def _UpperCAmelCase ( self , *A_ , **A_ ):
'''simple docstring'''
return self.image_processor.post_process_object_detection(*A_ , **A_ )
def _UpperCAmelCase ( self , *A_ , **A_ ):
'''simple docstring'''
return self.image_processor.post_process_image_guided_detection(*A_ , **A_ )
def _UpperCAmelCase ( self , *A_ , **A_ ):
'''simple docstring'''
return self.tokenizer.batch_decode(*A_ , **A_ )
def _UpperCAmelCase ( self , *A_ , **A_ ):
'''simple docstring'''
return self.tokenizer.decode(*A_ , **A_ )
@property
def _UpperCAmelCase ( self ):
'''simple docstring'''
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , A_ , )
return self.image_processor_class
@property
def _UpperCAmelCase ( self ):
'''simple docstring'''
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , A_ , )
return self.image_processor
| 189 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] = {
"""s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] = 'open-llama'
def __init__(self , __lowercase=10_00_00 , __lowercase=40_96 , __lowercase=1_10_08 , __lowercase=32 , __lowercase=32 , __lowercase="silu" , __lowercase=20_48 , __lowercase=0.0_2 , __lowercase=1e-6 , __lowercase=True , __lowercase=0 , __lowercase=1 , __lowercase=2 , __lowercase=False , __lowercase=True , __lowercase=0.1 , __lowercase=0.1 , __lowercase=True , __lowercase=True , __lowercase=None , **__lowercase , ):
__lowerCAmelCase = vocab_size
__lowerCAmelCase = max_position_embeddings
__lowerCAmelCase = hidden_size
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = hidden_act
__lowerCAmelCase = initializer_range
__lowerCAmelCase = rms_norm_eps
__lowerCAmelCase = use_cache
__lowerCAmelCase = kwargs.pop(
'''use_memorry_efficient_attention''' , __lowerCAmelCase )
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_dropout_prob
__lowerCAmelCase = use_stable_embedding
__lowerCAmelCase = shared_input_output_embedding
__lowerCAmelCase = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , tie_word_embeddings=__lowerCAmelCase , **__lowerCAmelCase , )
def _snake_case (self ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __lowerCAmelCase ) or len(self.rope_scaling ) != 2:
raise ValueError(
'''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
F"""got {self.rope_scaling}""" )
__lowerCAmelCase = self.rope_scaling.get('''type''' , __lowerCAmelCase )
__lowerCAmelCase = self.rope_scaling.get('''factor''' , __lowerCAmelCase )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"""`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}""" )
if rope_scaling_factor is None or not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or rope_scaling_factor <= 1.0:
raise ValueError(F"""`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}""" )
| 174 | """simple docstring"""
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class _A ( lowerCAmelCase , lowerCAmelCase ):
snake_case__ : Tuple = 1
@register_to_config
def __init__( self , __lowerCAmelCase = 1000 , __lowerCAmelCase = None ):
"""simple docstring"""
self.set_timesteps(__lowerCAmelCase )
# standard deviation of the initial noise distribution
lowercase = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
lowercase = 4
# running values
lowercase = []
def A__ ( self , __lowerCAmelCase , __lowerCAmelCase = None ):
"""simple docstring"""
lowercase = num_inference_steps
lowercase = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
lowercase = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
lowercase = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
lowercase = torch.sin(steps * math.pi / 2 ) ** 2
lowercase = (1.0 - self.betas**2) ** 0.5
lowercase = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
lowercase = timesteps.to(__lowerCAmelCase )
lowercase = []
def A__ ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = True , ):
"""simple docstring"""
if self.num_inference_steps is None:
raise ValueError(
"""Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler""" )
lowercase = (self.timesteps == timestep).nonzero().item()
lowercase = timestep_index + 1
lowercase = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(__lowerCAmelCase )
if len(self.ets ) == 1:
lowercase = self.ets[-1]
elif len(self.ets ) == 2:
lowercase = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
lowercase = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
lowercase = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
lowercase = self._get_prev_sample(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__lowerCAmelCase )
def A__ ( self , __lowerCAmelCase , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
return sample
def A__ ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
"""simple docstring"""
lowercase = self.alphas[timestep_index]
lowercase = self.betas[timestep_index]
lowercase = self.alphas[prev_timestep_index]
lowercase = self.betas[prev_timestep_index]
lowercase = (sample - sigma * ets) / max(__lowerCAmelCase , 1E-8 )
lowercase = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self ):
"""simple docstring"""
return self.config.num_train_timesteps
| 197 | 0 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# 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.
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
lowerCamelCase = """Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"""
def _A ( ):
"""simple docstring"""
__lowercase =_ask_options(
'In which compute environment are you running?' , ['This machine', 'AWS (Amazon SageMaker)'] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
__lowercase =get_sagemaker_input()
else:
__lowercase =get_cluster_input()
return config
def _A ( _lowerCAmelCase=None ):
"""simple docstring"""
if subparsers is not None:
__lowercase =subparsers.add_parser('config' , description=_lowerCAmelCase )
else:
__lowercase =argparse.ArgumentParser('Accelerate config command' , description=_lowerCAmelCase )
parser.add_argument(
'--config_file' , default=_lowerCAmelCase , help=(
'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
'with \'huggingface\'.'
) , )
if subparsers is not None:
parser.set_defaults(func=_lowerCAmelCase )
return parser
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =get_user_input()
if args.config_file is not None:
__lowercase =args.config_file
else:
if not os.path.isdir(_lowerCAmelCase ):
os.makedirs(_lowerCAmelCase )
__lowercase =default_yaml_config_file
if config_file.endswith('.json' ):
config.to_json_file(_lowerCAmelCase )
else:
config.to_yaml_file(_lowerCAmelCase )
print(f"""accelerate configuration saved at {config_file}""" )
def _A ( ):
"""simple docstring"""
__lowercase =config_command_parser()
__lowercase =parser.parse_args()
config_command(_lowerCAmelCase )
if __name__ == "__main__":
main()
| 48 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , ):
"""simple docstring"""
if config_name_or_path is None:
__lowercase ='facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'
if generator_tokenizer_name_or_path is None:
__lowercase =generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
__lowercase =question_encoder_name_or_path
__lowercase =RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration
# Save model.
__lowercase =RagConfig.from_pretrained(_lowerCAmelCase )
__lowercase =AutoConfig.from_pretrained(_lowerCAmelCase )
__lowercase =AutoConfig.from_pretrained(_lowerCAmelCase )
__lowercase =gen_config
__lowercase =question_encoder_config
__lowercase =model_class.from_pretrained_question_encoder_generator(
_lowerCAmelCase , _lowerCAmelCase , config=_lowerCAmelCase )
rag_model.save_pretrained(_lowerCAmelCase )
# Sanity check.
model_class.from_pretrained(_lowerCAmelCase )
# Save tokenizers.
__lowercase =AutoTokenizer.from_pretrained(_lowerCAmelCase )
gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/' )
__lowercase =AutoTokenizer.from_pretrained(_lowerCAmelCase )
question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/' )
if __name__ == "__main__":
lowerCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""--model_type""",
choices=["""rag_sequence""", """rag_token"""],
required=True,
type=str,
help="""RAG model type: rag_sequence, rag_token""",
)
parser.add_argument("""--dest""", type=str, required=True, help="""Path to the output checkpoint directory.""")
parser.add_argument("""--generator_name_or_path""", type=str, required=True, help="""Generator model identifier""")
parser.add_argument(
"""--question_encoder_name_or_path""", type=str, required=True, help="""Question encoder model identifier"""
)
parser.add_argument(
"""--generator_tokenizer_name_or_path""",
type=str,
help="""Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``""",
)
parser.add_argument(
"""--question_encoder_tokenizer_name_or_path""",
type=str,
help="""Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``""",
)
parser.add_argument(
"""--config_name_or_path""",
type=str,
help=(
"""Identifier of the model config to use, if not provided, resolves to a base config for a given"""
""" ``model_type``"""
),
)
lowerCamelCase = parser.parse_args()
lowerCamelCase = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 48 | 1 |
"""simple docstring"""
import os
import re
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
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {"vocab_file": "spiece.model"}
SCREAMING_SNAKE_CASE = {
"vocab_file": {
"google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
"google/bigbird-roberta-large": (
"https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
),
"google/bigbird-base-trivia-itc": (
"https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
),
}
}
SCREAMING_SNAKE_CASE = {
"google/bigbird-roberta-base": 4096,
"google/bigbird-roberta-large": 4096,
"google/bigbird-base-trivia-itc": 4096,
}
class UpperCAmelCase_ ( A_ ):
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = ['''input_ids''', '''attention_mask''']
lowercase__ = []
def __init__( self : Union[str, Any] , snake_case_ : Dict , snake_case_ : List[str]="<unk>" , snake_case_ : Dict="<s>" , snake_case_ : List[str]="</s>" , snake_case_ : Tuple="<pad>" , snake_case_ : str="[SEP]" , snake_case_ : Any="[MASK]" , snake_case_ : Tuple="[CLS]" , snake_case_ : Optional[Dict[str, Any]] = None , **snake_case_ : Dict , ) -> None:
'''simple docstring'''
A__ = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else bos_token
A__ = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else eos_token
A__ = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else unk_token
A__ = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else pad_token
A__ = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else cls_token
A__ = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
A__ = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else mask_token
A__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=snake_case_ , eos_token=snake_case_ , unk_token=snake_case_ , pad_token=snake_case_ , sep_token=snake_case_ , mask_token=snake_case_ , cls_token=snake_case_ , sp_model_kwargs=self.sp_model_kwargs , **snake_case_ , )
A__ = vocab_file
A__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case_ )
@property
def __magic_name__ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
return self.sp_model.get_piece_size()
def __magic_name__ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
A__ = {self.convert_ids_to_tokens(snake_case_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Dict ) -> str:
'''simple docstring'''
A__ = self.__dict__.copy()
A__ = None
return state
def __setstate__( self : List[Any] , snake_case_ : Optional[Any] ) -> Tuple:
'''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 __magic_name__ ( self : Dict , snake_case_ : str ) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(snake_case_ , out_type=snake_case_ )
def __magic_name__ ( self : Dict , snake_case_ : Any ) -> str:
'''simple docstring'''
return self.sp_model.piece_to_id(snake_case_ )
def __magic_name__ ( self : Dict , snake_case_ : List[str] ) -> Union[str, Any]:
'''simple docstring'''
A__ = self.sp_model.IdToPiece(snake_case_ )
return token
def __magic_name__ ( self : List[Any] , snake_case_ : Optional[Any] ) -> List[str]:
'''simple docstring'''
A__ = []
A__ = ""
A__ = 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(snake_case_ ) + token
A__ = True
A__ = []
else:
current_sub_tokens.append(snake_case_ )
A__ = False
out_string += self.sp_model.decode(snake_case_ )
return out_string.strip()
def __magic_name__ ( self : Tuple , snake_case_ : List[int] , snake_case_ : bool = False , snake_case_ : bool = None , snake_case_ : bool = True , **snake_case_ : Optional[int] , ) -> str:
'''simple docstring'''
A__ = kwargs.pop("use_source_tokenizer" , snake_case_ )
A__ = self.convert_ids_to_tokens(snake_case_ , skip_special_tokens=snake_case_ )
# 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(snake_case_ ) )
A__ = []
sub_texts.append(snake_case_ )
else:
current_sub_text.append(snake_case_ )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(snake_case_ ) )
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
A__ = re.sub(R" (\[(MASK|SEP)\])" , R"\1" , " ".join(snake_case_ ) )
else:
A__ = "".join(snake_case_ )
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(snake_case_ )
return clean_text
else:
return text
def __magic_name__ ( self : Union[str, Any] , snake_case_ : str , snake_case_ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(snake_case_ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
A__ = os.path.join(
snake_case_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case_ )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case_ , "wb" ) as fi:
A__ = self.sp_model.serialized_model_proto()
fi.write(snake_case_ )
return (out_vocab_file,)
def __magic_name__ ( self : List[Any] , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
A__ = [self.cls_token_id]
A__ = [self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def __magic_name__ ( self : List[str] , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None , snake_case_ : bool = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case_ , token_ids_a=snake_case_ , already_has_special_tokens=snake_case_ )
if token_ids_a is None:
return [1] + ([0] * len(snake_case_ )) + [1]
return [1] + ([0] * len(snake_case_ )) + [1] + ([0] * len(snake_case_ )) + [1]
def __magic_name__ ( self : Dict , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
A__ = [self.sep_token_id]
A__ = [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]
| 247 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
A__ = _modexpt(lowercase_ , exponent // 2 , lowercase_ ) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modexpt(lowercase_ , exponent - 1 , lowercase_ )) % modulo_value
def _SCREAMING_SNAKE_CASE ( lowercase_ = 17_77 , lowercase_ = 18_55 , lowercase_ = 8 ) -> int:
A__ = base
for _ in range(1 , lowercase_ ):
A__ = _modexpt(lowercase_ , lowercase_ , 10**digits )
return result
if __name__ == "__main__":
print(f'{solution() = }')
| 247 | 1 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class _snake_case :
def __init__( self , a , a , a = True , a = False) -> Tuple:
SCREAMING_SNAKE_CASE = scheduler
SCREAMING_SNAKE_CASE = optimizers if isinstance(a , (list, tuple)) else [optimizers]
SCREAMING_SNAKE_CASE = split_batches
SCREAMING_SNAKE_CASE = step_with_optimizer
SCREAMING_SNAKE_CASE = GradientState()
def SCREAMING_SNAKE_CASE__ ( self , *a , **a) -> List[Any]:
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*a , **a)
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*a , **a)
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
SCREAMING_SNAKE_CASE = AcceleratorState().num_processes
for _ in range(a):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler , 'total_steps'):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*a , **a)
else:
self.scheduler.step(*a , **a)
def SCREAMING_SNAKE_CASE__ ( self) -> Tuple:
return self.scheduler.get_last_lr()
def SCREAMING_SNAKE_CASE__ ( self) -> Dict:
return self.scheduler.state_dict()
def SCREAMING_SNAKE_CASE__ ( self , a) -> List[Any]:
self.scheduler.load_state_dict(a)
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
return self.scheduler.get_lr()
def SCREAMING_SNAKE_CASE__ ( self , *a , **a) -> List[Any]:
return self.scheduler.print_lr(*a , **a)
| 327 |
from math import isqrt
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = False
return [i for i in range(2 , _UpperCAmelCase) if is_prime[i]]
def lowerCamelCase__ (_UpperCAmelCase = 10**8):
SCREAMING_SNAKE_CASE = calculate_prime_numbers(max_number // 2)
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = len(_UpperCAmelCase) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 327 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DonutImageProcessor
class lowercase ( unittest.TestCase ):
def __init__( self , _a , _a=7 , _a=3 , _a=18 , _a=30 , _a=400 , _a=True , _a=None , _a=True , _a=False , _a=True , _a=True , _a=[0.5, 0.5, 0.5] , _a=[0.5, 0.5, 0.5] , ) -> Dict:
_A : str = parent
_A : int = batch_size
_A : Optional[int] = num_channels
_A : List[Any] = image_size
_A : int = min_resolution
_A : Optional[int] = max_resolution
_A : Any = do_resize
_A : List[str] = size if size is not None else {"""height""": 18, """width""": 20}
_A : Optional[int] = do_thumbnail
_A : str = do_align_axis
_A : List[Any] = do_pad
_A : Optional[Any] = do_normalize
_A : Tuple = image_mean
_A : List[str] = image_std
def a__ ( self ) -> Optional[int]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class lowercase ( UpperCamelCase__,unittest.TestCase ):
_a = DonutImageProcessor if is_vision_available() else None
def a__ ( self ) -> Optional[int]:
_A : List[str] = DonutImageProcessingTester(self )
@property
def a__ ( self ) -> List[Any]:
return self.image_processor_tester.prepare_image_processor_dict()
def a__ ( self ) -> Optional[Any]:
_A : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_a , """do_resize""" ) )
self.assertTrue(hasattr(_a , """size""" ) )
self.assertTrue(hasattr(_a , """do_thumbnail""" ) )
self.assertTrue(hasattr(_a , """do_align_long_axis""" ) )
self.assertTrue(hasattr(_a , """do_pad""" ) )
self.assertTrue(hasattr(_a , """do_normalize""" ) )
self.assertTrue(hasattr(_a , """image_mean""" ) )
self.assertTrue(hasattr(_a , """image_std""" ) )
def a__ ( self ) -> List[Any]:
_A : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""height""": 18, """width""": 20} )
_A : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"""height""": 42, """width""": 42} )
# Previous config had dimensions in (width, height) order
_A : List[str] = self.image_processing_class.from_dict(self.image_processor_dict , size=(42, 84) )
self.assertEqual(image_processor.size , {"""height""": 84, """width""": 42} )
def a__ ( self ) -> Union[str, Any]:
pass
@is_flaky()
def a__ ( self ) -> Optional[int]:
# Initialize image_processing
_A : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_A : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a )
for image in image_inputs:
self.assertIsInstance(_a , Image.Image )
# Test not batched input
_A : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
_A : Any = image_processing(_a , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
@is_flaky()
def a__ ( self ) -> Dict:
# Initialize image_processing
_A : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_A : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , numpify=_a )
for image in image_inputs:
self.assertIsInstance(_a , np.ndarray )
# Test not batched input
_A : int = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
_A : List[str] = image_processing(_a , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
@is_flaky()
def a__ ( self ) -> Optional[int]:
# Initialize image_processing
_A : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_A : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , torchify=_a )
for image in image_inputs:
self.assertIsInstance(_a , torch.Tensor )
# Test not batched input
_A : Any = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
_A : str = image_processing(_a , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
| 26 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class UpperCAmelCase__ ( A_ ):
"""simple docstring"""
def _a ( self , A_ ) -> float:
return 0.0
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : int ):
__UpperCamelCase =min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
__UpperCamelCase =max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : FilterType , SCREAMING_SNAKE_CASE__ : int ):
__UpperCamelCase =5_12
__UpperCamelCase =[1] + [0] * (size - 1)
__UpperCamelCase =[filter_type.process(SCREAMING_SNAKE_CASE__ ) for item in inputs]
__UpperCamelCase =[0] * (samplerate - size) # zero-padding
outputs += filler
__UpperCamelCase =np.abs(np.fft.fft(SCREAMING_SNAKE_CASE__ ) )
__UpperCamelCase =20 * np.logaa(SCREAMING_SNAKE_CASE__ )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('Frequency (Hz)' )
plt.xscale('log' )
# Display within reasonable bounds
__UpperCamelCase =get_bounds(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel('Gain (dB)' )
plt.plot(SCREAMING_SNAKE_CASE__ )
plt.show()
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : FilterType , SCREAMING_SNAKE_CASE__ : int ):
__UpperCamelCase =5_12
__UpperCamelCase =[1] + [0] * (size - 1)
__UpperCamelCase =[filter_type.process(SCREAMING_SNAKE_CASE__ ) for item in inputs]
__UpperCamelCase =[0] * (samplerate - size) # zero-padding
outputs += filler
__UpperCamelCase =np.angle(np.fft.fft(SCREAMING_SNAKE_CASE__ ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('Frequency (Hz)' )
plt.xscale('log' )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel('Phase shift (Radians)' )
plt.plot(np.unwrap(SCREAMING_SNAKE_CASE__ , -2 * pi ) )
plt.show()
| 62 | 0 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class snake_case__ :
lowercase__ : int
lowercase__ : TreeNode | None = None
lowercase__ : TreeNode | None = None
__magic_name__: Dict = namedtuple("CoinsDistribResult", "moves excess")
def UpperCamelCase ( _A ):
"""simple docstring"""
if root is None:
return 0
# Validation
def count_nodes(_A ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(_A ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(_A ) != count_coins(_A ):
raise ValueError("""The nodes number should be same as the number of coins""" )
# Main calculation
def get_distrib(_A ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0, 1 )
__magic_name__ ,__magic_name__ : List[str] = get_distrib(node.left )
__magic_name__ ,__magic_name__ : Dict = get_distrib(node.right )
__magic_name__ : Tuple = 1 - left_distrib_excess
__magic_name__ : Optional[Any] = 1 - right_distrib_excess
__magic_name__ : Dict = (
left_distrib_moves
+ right_distrib_moves
+ abs(_A )
+ abs(_A )
)
__magic_name__ : Optional[Any] = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(_A, _A )
return get_distrib(_A )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 138 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
__magic_name__: int = "python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append("importlib_metadata")
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")
def UpperCamelCase ( _A, _A=None ):
"""simple docstring"""
require_version(deps[pkg], _A )
| 138 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : Optional[int] ="blenderbot-small"
a : str =["past_key_values"]
a : List[str] ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self , snake_case__=50_265 , snake_case__=512 , snake_case__=8 , snake_case__=2_048 , snake_case__=16 , snake_case__=8 , snake_case__=2_048 , snake_case__=16 , snake_case__=0.0 , snake_case__=0.0 , snake_case__=True , snake_case__=True , snake_case__="gelu" , snake_case__=512 , snake_case__=0.1 , snake_case__=0.0 , snake_case__=0.0 , snake_case__=0.02 , snake_case__=1 , snake_case__=False , snake_case__=0 , snake_case__=1 , snake_case__=2 , snake_case__=2 , **snake_case__ , ):
"""simple docstring"""
lowerCAmelCase : Dict = vocab_size
lowerCAmelCase : List[Any] = max_position_embeddings
lowerCAmelCase : Union[str, Any] = d_model
lowerCAmelCase : List[str] = encoder_ffn_dim
lowerCAmelCase : Optional[int] = encoder_layers
lowerCAmelCase : str = encoder_attention_heads
lowerCAmelCase : int = decoder_ffn_dim
lowerCAmelCase : int = decoder_layers
lowerCAmelCase : int = decoder_attention_heads
lowerCAmelCase : List[Any] = dropout
lowerCAmelCase : int = attention_dropout
lowerCAmelCase : List[str] = activation_dropout
lowerCAmelCase : int = activation_function
lowerCAmelCase : str = init_std
lowerCAmelCase : Optional[int] = encoder_layerdrop
lowerCAmelCase : Tuple = decoder_layerdrop
lowerCAmelCase : Dict = use_cache
lowerCAmelCase : Any = encoder_layers
lowerCAmelCase : Optional[Any] = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , is_encoder_decoder=snake_case__ , decoder_start_token_id=snake_case__ , forced_eos_token_id=snake_case__ , **snake_case__ , )
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
@property
def lowercase__ ( self ):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
lowerCAmelCase : List[str] = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
lowerCAmelCase : Optional[Any] = {0: "batch"}
lowerCAmelCase : int = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
lowerCAmelCase : str = {0: "batch", 1: "decoder_sequence"}
lowerCAmelCase : Any = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(snake_case__ , direction="inputs" )
elif self.task == "causal-lm":
# TODO: figure this case out.
lowerCAmelCase : Tuple = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
lowerCAmelCase , lowerCAmelCase : Union[str, Any] = self.num_layers
for i in range(snake_case__ ):
lowerCAmelCase : List[Any] = {0: "batch", 2: "past_sequence + sequence"}
lowerCAmelCase : Optional[int] = {0: "batch", 2: "past_sequence + sequence"}
else:
lowerCAmelCase : List[str] = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
] )
return common_inputs
@property
def lowercase__ ( self ):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
lowerCAmelCase : List[str] = super().outputs
else:
lowerCAmelCase : List[str] = super(snake_case__ , self ).outputs
if self.use_past:
lowerCAmelCase , lowerCAmelCase : Optional[int] = self.num_layers
for i in range(snake_case__ ):
lowerCAmelCase : Any = {0: "batch", 2: "past_sequence + sequence"}
lowerCAmelCase : Tuple = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def lowercase__ ( self , snake_case__ , snake_case__ = -1 , snake_case__ = -1 , snake_case__ = False , snake_case__ = None , ):
"""simple docstring"""
lowerCAmelCase : List[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
# Generate decoder inputs
lowerCAmelCase : str = seq_length if not self.use_past else 1
lowerCAmelCase : Tuple = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
lowerCAmelCase : Any = {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
lowerCAmelCase : str = dict(**snake_case__ , **snake_case__ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
lowerCAmelCase , lowerCAmelCase : Union[str, Any] = common_inputs["input_ids"].shape
lowerCAmelCase : int = common_inputs["decoder_input_ids"].shape[1]
lowerCAmelCase , lowerCAmelCase : str = self.num_attention_heads
lowerCAmelCase : Dict = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowerCAmelCase : Dict = decoder_seq_length + 3
lowerCAmelCase : Union[str, Any] = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
lowerCAmelCase : Dict = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(snake_case__ , snake_case__ )] , dim=1 )
lowerCAmelCase : Tuple = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
lowerCAmelCase , lowerCAmelCase : str = self.num_layers
lowerCAmelCase : Tuple = min(snake_case__ , snake_case__ )
lowerCAmelCase : Optional[int] = max(snake_case__ , snake_case__ ) - min_num_layers
lowerCAmelCase : Any = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(snake_case__ ):
common_inputs["past_key_values"].append(
(
torch.zeros(snake_case__ ),
torch.zeros(snake_case__ ),
torch.zeros(snake_case__ ),
torch.zeros(snake_case__ ),
) )
# TODO: test this.
lowerCAmelCase : Any = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(snake_case__ , snake_case__ ):
common_inputs["past_key_values"].append((torch.zeros(snake_case__ ), torch.zeros(snake_case__ )) )
return common_inputs
def lowercase__ ( self , snake_case__ , snake_case__ = -1 , snake_case__ = -1 , snake_case__ = False , snake_case__ = None , ):
"""simple docstring"""
lowerCAmelCase : Union[str, Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
lowerCAmelCase , lowerCAmelCase : Dict = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
lowerCAmelCase : List[Any] = seqlen + 2
lowerCAmelCase , lowerCAmelCase : Optional[int] = self.num_layers
lowerCAmelCase , lowerCAmelCase : Tuple = self.num_attention_heads
lowerCAmelCase : Optional[int] = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowerCAmelCase : List[Any] = common_inputs["attention_mask"].dtype
lowerCAmelCase : List[str] = torch.cat(
[common_inputs["attention_mask"], torch.ones(snake_case__ , snake_case__ , dtype=snake_case__ )] , dim=1 )
lowerCAmelCase : Union[str, Any] = [
(torch.zeros(snake_case__ ), torch.zeros(snake_case__ )) for _ in range(snake_case__ )
]
return common_inputs
def lowercase__ ( self , snake_case__ , snake_case__ = -1 , snake_case__ = -1 , snake_case__ = False , snake_case__ = None , ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = compute_effective_axis_dimension(
snake_case__ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowerCAmelCase : Any = tokenizer.num_special_tokens_to_add(snake_case__ )
lowerCAmelCase : str = compute_effective_axis_dimension(
snake_case__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case__ )
# Generate dummy inputs according to compute batch and sequence
lowerCAmelCase : List[Any] = [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
lowerCAmelCase : Tuple = dict(tokenizer(snake_case__ , return_tensors=snake_case__ ) )
return common_inputs
def lowercase__ ( self , snake_case__ , snake_case__ = -1 , snake_case__ = -1 , snake_case__ = False , snake_case__ = None , ):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
lowerCAmelCase : List[str] = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
snake_case__ , batch_size=snake_case__ , seq_length=snake_case__ , is_pair=snake_case__ , framework=snake_case__ )
elif self.task == "causal-lm":
lowerCAmelCase : Union[str, Any] = self._generate_dummy_inputs_for_causal_lm(
snake_case__ , batch_size=snake_case__ , seq_length=snake_case__ , is_pair=snake_case__ , framework=snake_case__ )
else:
lowerCAmelCase : List[str] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
snake_case__ , batch_size=snake_case__ , seq_length=snake_case__ , is_pair=snake_case__ , framework=snake_case__ )
return common_inputs
def lowercase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
lowerCAmelCase : Tuple = super()._flatten_past_key_values_(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
else:
lowerCAmelCase : Dict = super(snake_case__ , self )._flatten_past_key_values_(
snake_case__ , snake_case__ , snake_case__ , snake_case__ )
| 108 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
lowerCAmelCase__ = 100
lowerCAmelCase__ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCAmelCase__ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=1_0_0 )
def a__ ( SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
lowerCAmelCase : set[int] = set()
lowerCAmelCase : int
lowerCAmelCase : int
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def a__ ( SCREAMING_SNAKE_CASE : int = 5_0_0_0 ):
'''simple docstring'''
for number_to_partition in range(1 , SCREAMING_SNAKE_CASE ):
if len(partition(SCREAMING_SNAKE_CASE ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F"{solution() = }")
| 108 | 1 |
import qiskit
def __UpperCamelCase ( _A : int , _A : int ) ->qiskit.result.counts.Counts:
"""simple docstring"""
lowerCamelCase_ =qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
lowerCamelCase_ =qiskit.QuantumCircuit(_A , _A )
# Map the quantum measurement to the classical bits
circuit.measure([0] , [0] )
# Execute the circuit on the simulator
lowerCamelCase_ =qiskit.execute(_A , _A , shots=1000 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(_A )
if __name__ == "__main__":
print(F"""Total count for various states are: {single_qubit_measure(1, 1)}""")
| 49 |
__A : List[Any] = [
9_99,
8_00,
7_99,
6_00,
5_99,
5_00,
4_00,
3_99,
3_77,
3_55,
3_33,
3_11,
2_88,
2_66,
2_44,
2_22,
2_00,
1_99,
1_77,
1_55,
1_33,
1_11,
88,
66,
44,
22,
0,
]
__A : int = [
9_99,
9_76,
9_52,
9_28,
9_05,
8_82,
8_58,
8_57,
8_10,
7_62,
7_15,
7_14,
5_72,
4_29,
4_28,
2_86,
2_85,
2_38,
1_90,
1_43,
1_42,
1_18,
95,
71,
47,
24,
0,
]
__A : Any = [
9_99,
9_88,
9_77,
9_66,
9_55,
9_44,
9_33,
9_22,
9_11,
9_00,
8_99,
8_79,
8_59,
8_40,
8_20,
8_00,
7_99,
7_66,
7_33,
7_00,
6_99,
6_50,
6_00,
5_99,
5_00,
4_99,
4_00,
3_99,
3_50,
3_00,
2_99,
2_66,
2_33,
2_00,
1_99,
1_79,
1_59,
1_40,
1_20,
1_00,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
__A : Dict = [
9_99,
9_95,
9_92,
9_89,
9_85,
9_81,
9_78,
9_75,
9_71,
9_67,
9_64,
9_61,
9_57,
9_56,
9_51,
9_47,
9_42,
9_37,
9_33,
9_28,
9_23,
9_19,
9_14,
9_13,
9_08,
9_03,
8_97,
8_92,
8_87,
8_81,
8_76,
8_71,
8_70,
8_64,
8_58,
8_52,
8_46,
8_40,
8_34,
8_28,
8_27,
8_20,
8_13,
8_06,
7_99,
7_92,
7_85,
7_84,
7_77,
7_70,
7_63,
7_56,
7_49,
7_42,
7_41,
7_33,
7_24,
7_16,
7_07,
6_99,
6_98,
6_88,
6_77,
6_66,
6_56,
6_55,
6_45,
6_34,
6_23,
6_13,
6_12,
5_98,
5_84,
5_70,
5_69,
5_55,
5_41,
5_27,
5_26,
5_05,
4_84,
4_83,
4_62,
4_40,
4_39,
3_96,
3_95,
3_52,
3_51,
3_08,
3_07,
2_64,
2_63,
2_20,
2_19,
1_76,
1_32,
88,
44,
0,
]
__A : List[str] = [
9_99,
9_97,
9_95,
9_92,
9_90,
9_88,
9_86,
9_84,
9_81,
9_79,
9_77,
9_75,
9_72,
9_70,
9_68,
9_66,
9_64,
9_61,
9_59,
9_57,
9_56,
9_54,
9_51,
9_49,
9_46,
9_44,
9_41,
9_39,
9_36,
9_34,
9_31,
9_29,
9_26,
9_24,
9_21,
9_19,
9_16,
9_14,
9_13,
9_10,
9_07,
9_05,
9_02,
8_99,
8_96,
8_93,
8_91,
8_88,
8_85,
8_82,
8_79,
8_77,
8_74,
8_71,
8_70,
8_67,
8_64,
8_61,
8_58,
8_55,
8_52,
8_49,
8_46,
8_43,
8_40,
8_37,
8_34,
8_31,
8_28,
8_27,
8_24,
8_21,
8_17,
8_14,
8_11,
8_08,
8_04,
8_01,
7_98,
7_95,
7_91,
7_88,
7_85,
7_84,
7_80,
7_77,
7_74,
7_70,
7_66,
7_63,
7_60,
7_56,
7_52,
7_49,
7_46,
7_42,
7_41,
7_37,
7_33,
7_30,
7_26,
7_22,
7_18,
7_14,
7_10,
7_07,
7_03,
6_99,
6_98,
6_94,
6_90,
6_85,
6_81,
6_77,
6_73,
6_69,
6_64,
6_60,
6_56,
6_55,
6_50,
6_46,
6_41,
6_36,
6_32,
6_27,
6_22,
6_18,
6_13,
6_12,
6_07,
6_02,
5_96,
5_91,
5_86,
5_80,
5_75,
5_70,
5_69,
5_63,
5_57,
5_51,
5_45,
5_39,
5_33,
5_27,
5_26,
5_19,
5_12,
5_05,
4_98,
4_91,
4_84,
4_83,
4_74,
4_66,
4_57,
4_49,
4_40,
4_39,
4_28,
4_18,
4_07,
3_96,
3_95,
3_81,
3_66,
3_52,
3_51,
3_30,
3_08,
3_07,
2_86,
2_64,
2_63,
2_42,
2_20,
2_19,
1_76,
1_75,
1_32,
1_31,
88,
44,
0,
]
__A : List[str] = [
9_99,
9_91,
9_82,
9_74,
9_66,
9_58,
9_50,
9_41,
9_33,
9_25,
9_16,
9_08,
9_00,
8_99,
8_74,
8_50,
8_25,
8_00,
7_99,
7_00,
6_00,
5_00,
4_00,
3_00,
2_00,
1_00,
0,
]
__A : Dict = [
9_99,
9_92,
9_85,
9_78,
9_71,
9_64,
9_57,
9_49,
9_42,
9_35,
9_28,
9_21,
9_14,
9_07,
9_00,
8_99,
8_79,
8_59,
8_40,
8_20,
8_00,
7_99,
7_66,
7_33,
7_00,
6_99,
6_50,
6_00,
5_99,
5_00,
4_99,
4_00,
3_99,
3_00,
2_99,
2_00,
1_99,
1_00,
99,
0,
]
__A : str = [
9_99,
9_96,
9_92,
9_89,
9_85,
9_82,
9_79,
9_75,
9_72,
9_68,
9_65,
9_61,
9_58,
9_55,
9_51,
9_48,
9_44,
9_41,
9_38,
9_34,
9_31,
9_27,
9_24,
9_20,
9_17,
9_14,
9_10,
9_07,
9_03,
9_00,
8_99,
8_91,
8_84,
8_76,
8_69,
8_61,
8_53,
8_46,
8_38,
8_30,
8_23,
8_15,
8_08,
8_00,
7_99,
7_88,
7_77,
7_66,
7_55,
7_44,
7_33,
7_22,
7_11,
7_00,
6_99,
6_88,
6_77,
6_66,
6_55,
6_44,
6_33,
6_22,
6_11,
6_00,
5_99,
5_85,
5_71,
5_57,
5_42,
5_28,
5_14,
5_00,
4_99,
4_85,
4_71,
4_57,
4_42,
4_28,
4_14,
4_00,
3_99,
3_79,
3_59,
3_40,
3_20,
3_00,
2_99,
2_79,
2_59,
2_40,
2_20,
2_00,
1_99,
1_66,
1_33,
1_00,
99,
66,
33,
0,
]
| 49 | 1 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class a__ ( snake_case ):
"""simple docstring"""
def __get__( self , lowercase , lowercase=None ) -> Optional[Any]:
'''simple docstring'''
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute" )
A__ = "__cached_" + self.fget.__name__
A__ = getattr(lowercase , lowercase , lowercase )
if cached is None:
A__ = self.fget(lowercase )
setattr(lowercase , lowercase , lowercase )
return cached
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] ) -> Any:
'''simple docstring'''
A__ = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(F'invalid truth value {val!r}' )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] ) -> List[Any]:
'''simple docstring'''
if is_torch_fx_proxy(SCREAMING_SNAKE_CASE_ ):
return True
if is_torch_available():
import torch
if isinstance(SCREAMING_SNAKE_CASE_ , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(SCREAMING_SNAKE_CASE_ , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(SCREAMING_SNAKE_CASE_ , (jnp.ndarray, Tracer) ):
return True
return isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[Any] ) -> Tuple:
'''simple docstring'''
return isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> Optional[Any]:
'''simple docstring'''
return _is_numpy(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple ) -> Optional[Any]:
'''simple docstring'''
import torch
return isinstance(SCREAMING_SNAKE_CASE_ , torch.Tensor )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[Any] ) -> Any:
'''simple docstring'''
return False if not is_torch_available() else _is_torch(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] ) -> str:
'''simple docstring'''
import torch
return isinstance(SCREAMING_SNAKE_CASE_ , torch.device )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] ) -> Tuple:
'''simple docstring'''
return False if not is_torch_available() else _is_torch_device(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[Any] ) -> Tuple:
'''simple docstring'''
import torch
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
A__ = getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
else:
return False
return isinstance(SCREAMING_SNAKE_CASE_ , torch.dtype )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] ) -> Optional[Any]:
'''simple docstring'''
return False if not is_torch_available() else _is_torch_dtype(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
import tensorflow as tf
return isinstance(SCREAMING_SNAKE_CASE_ , tf.Tensor )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] ) -> Union[str, Any]:
'''simple docstring'''
return False if not is_tf_available() else _is_tensorflow(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> Union[str, Any]:
'''simple docstring'''
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(SCREAMING_SNAKE_CASE_ , "is_symbolic_tensor" ):
return tf.is_symbolic_tensor(SCREAMING_SNAKE_CASE_ )
return type(SCREAMING_SNAKE_CASE_ ) == tf.Tensor
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict ) -> int:
'''simple docstring'''
return False if not is_tf_available() else _is_tf_symbolic_tensor(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] ) -> str:
'''simple docstring'''
import jax.numpy as jnp # noqa: F811
return isinstance(SCREAMING_SNAKE_CASE_ , jnp.ndarray )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] ) -> Any:
'''simple docstring'''
return False if not is_flax_available() else _is_jax(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple ) -> str:
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE_ , (dict, UserDict) ):
return {k: to_py_obj(SCREAMING_SNAKE_CASE_ ) for k, v in obj.items()}
elif isinstance(SCREAMING_SNAKE_CASE_ , (list, tuple) ):
return [to_py_obj(SCREAMING_SNAKE_CASE_ ) for o in obj]
elif is_tf_tensor(SCREAMING_SNAKE_CASE_ ):
return obj.numpy().tolist()
elif is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(SCREAMING_SNAKE_CASE_ ):
return np.asarray(SCREAMING_SNAKE_CASE_ ).tolist()
elif isinstance(SCREAMING_SNAKE_CASE_ , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict ) -> Union[str, Any]:
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE_ , (dict, UserDict) ):
return {k: to_numpy(SCREAMING_SNAKE_CASE_ ) for k, v in obj.items()}
elif isinstance(SCREAMING_SNAKE_CASE_ , (list, tuple) ):
return np.array(SCREAMING_SNAKE_CASE_ )
elif is_tf_tensor(SCREAMING_SNAKE_CASE_ ):
return obj.numpy()
elif is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(SCREAMING_SNAKE_CASE_ ):
return np.asarray(SCREAMING_SNAKE_CASE_ )
else:
return obj
class a__ ( snake_case ):
"""simple docstring"""
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
A__ = fields(self )
# Safety and consistency checks
if not len(lowercase ):
raise ValueError(F'{self.__class__.__name__} has no fields.' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(F'{self.__class__.__name__} should not have more than one required field.' )
A__ = getattr(self , class_fields[0].name )
A__ = all(getattr(self , field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(lowercase ):
if isinstance(lowercase , lowercase ):
A__ = first_field.items()
A__ = True
else:
try:
A__ = iter(lowercase )
A__ = True
except TypeError:
A__ = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(lowercase ):
if (
not isinstance(lowercase , (list, tuple) )
or not len(lowercase ) == 2
or not isinstance(element[0] , lowercase )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
A__ = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
F'Cannot set key/value for {element}. It needs to be a tuple (key, value).' )
break
setattr(self , element[0] , element[1] )
if element[1] is not None:
A__ = element[1]
elif first_field is not None:
A__ = first_field
else:
for field in class_fields:
A__ = getattr(self , field.name )
if v is not None:
A__ = v
def __delitem__( self , *lowercase , **lowercase ) -> int:
'''simple docstring'''
raise Exception(F'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.' )
def UpperCamelCase ( self , *lowercase , **lowercase ) -> Optional[int]:
'''simple docstring'''
raise Exception(F'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.' )
def UpperCamelCase ( self , *lowercase , **lowercase ) -> Dict:
'''simple docstring'''
raise Exception(F'You cannot use ``pop`` on a {self.__class__.__name__} instance.' )
def UpperCamelCase ( self , *lowercase , **lowercase ) -> List[Any]:
'''simple docstring'''
raise Exception(F'You cannot use ``update`` on a {self.__class__.__name__} instance.' )
def __getitem__( self , lowercase ) -> Optional[Any]:
'''simple docstring'''
if isinstance(lowercase , lowercase ):
A__ = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self , lowercase , lowercase ) -> str:
'''simple docstring'''
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(lowercase , lowercase )
super().__setattr__(lowercase , lowercase )
def __setitem__( self , lowercase , lowercase ) -> str:
'''simple docstring'''
super().__setitem__(lowercase , lowercase )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(lowercase , lowercase )
def UpperCamelCase ( self ) -> Tuple[Any]:
'''simple docstring'''
return tuple(self[k] for k in self.keys() )
class a__ ( snake_case , snake_case ):
"""simple docstring"""
@classmethod
def UpperCamelCase ( cls , lowercase ) -> Optional[int]:
'''simple docstring'''
raise ValueError(
F'{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}' )
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = 'longest'
__lowerCamelCase = 'max_length'
__lowerCamelCase = 'do_not_pad'
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = 'pt'
__lowerCamelCase = 'tf'
__lowerCamelCase = 'np'
__lowerCamelCase = 'jax'
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> Union[str, Any]:
'''simple docstring'''
A__ = context_managers
A__ = ExitStack()
def __enter__( self ) -> Union[str, Any]:
'''simple docstring'''
for context_manager in self.context_managers:
self.stack.enter_context(lowercase )
def __exit__( self , *lowercase , **lowercase ) -> Union[str, Any]:
'''simple docstring'''
self.stack.__exit__(*lowercase , **lowercase )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] ) -> List[Any]:
'''simple docstring'''
A__ = infer_framework(SCREAMING_SNAKE_CASE_ )
if framework == "tf":
A__ = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
A__ = inspect.signature(model_class.forward ) # PyTorch models
else:
A__ = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] ) -> str:
'''simple docstring'''
A__ = model_class.__name__
A__ = infer_framework(SCREAMING_SNAKE_CASE_ )
if framework == "tf":
A__ = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
A__ = inspect.signature(model_class.forward ) # PyTorch models
else:
A__ = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: MutableMapping , SCREAMING_SNAKE_CASE_: str = "" , SCREAMING_SNAKE_CASE_: str = "." ) -> Union[str, Any]:
'''simple docstring'''
def _flatten_dict(SCREAMING_SNAKE_CASE_: Union[str, Any] , SCREAMING_SNAKE_CASE_: str="" , SCREAMING_SNAKE_CASE_: Any="." ):
for k, v in d.items():
A__ = str(SCREAMING_SNAKE_CASE_ ) + delimiter + str(SCREAMING_SNAKE_CASE_ ) if parent_key else k
if v and isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
yield from flatten_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , delimiter=SCREAMING_SNAKE_CASE_ ).items()
else:
yield key, v
return dict(_flatten_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
@contextmanager
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] , SCREAMING_SNAKE_CASE_: bool = False ) -> Dict:
'''simple docstring'''
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: List[str]=None ) -> List[Any]:
'''simple docstring'''
if is_numpy_array(SCREAMING_SNAKE_CASE_ ):
return np.transpose(SCREAMING_SNAKE_CASE_ , axes=SCREAMING_SNAKE_CASE_ )
elif is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
return array.T if axes is None else array.permute(*SCREAMING_SNAKE_CASE_ )
elif is_tf_tensor(SCREAMING_SNAKE_CASE_ ):
import tensorflow as tf
return tf.transpose(SCREAMING_SNAKE_CASE_ , perm=SCREAMING_SNAKE_CASE_ )
elif is_jax_tensor(SCREAMING_SNAKE_CASE_ ):
return jnp.transpose(SCREAMING_SNAKE_CASE_ , axes=SCREAMING_SNAKE_CASE_ )
else:
raise ValueError(F'Type not supported for transpose: {type(SCREAMING_SNAKE_CASE_ )}.' )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNAKE_CASE_: Optional[Any] ) -> int:
'''simple docstring'''
if is_numpy_array(SCREAMING_SNAKE_CASE_ ):
return np.reshape(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
elif is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
return array.reshape(*SCREAMING_SNAKE_CASE_ )
elif is_tf_tensor(SCREAMING_SNAKE_CASE_ ):
import tensorflow as tf
return tf.reshape(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
elif is_jax_tensor(SCREAMING_SNAKE_CASE_ ):
return jnp.reshape(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
else:
raise ValueError(F'Type not supported for reshape: {type(SCREAMING_SNAKE_CASE_ )}.' )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple , SCREAMING_SNAKE_CASE_: List[str]=None ) -> str:
'''simple docstring'''
if is_numpy_array(SCREAMING_SNAKE_CASE_ ):
return np.squeeze(SCREAMING_SNAKE_CASE_ , axis=SCREAMING_SNAKE_CASE_ )
elif is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
return array.squeeze() if axis is None else array.squeeze(dim=SCREAMING_SNAKE_CASE_ )
elif is_tf_tensor(SCREAMING_SNAKE_CASE_ ):
import tensorflow as tf
return tf.squeeze(SCREAMING_SNAKE_CASE_ , axis=SCREAMING_SNAKE_CASE_ )
elif is_jax_tensor(SCREAMING_SNAKE_CASE_ ):
return jnp.squeeze(SCREAMING_SNAKE_CASE_ , axis=SCREAMING_SNAKE_CASE_ )
else:
raise ValueError(F'Type not supported for squeeze: {type(SCREAMING_SNAKE_CASE_ )}.' )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: Any ) -> Union[str, Any]:
'''simple docstring'''
if is_numpy_array(SCREAMING_SNAKE_CASE_ ):
return np.expand_dims(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
elif is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
return array.unsqueeze(dim=SCREAMING_SNAKE_CASE_ )
elif is_tf_tensor(SCREAMING_SNAKE_CASE_ ):
import tensorflow as tf
return tf.expand_dims(SCREAMING_SNAKE_CASE_ , axis=SCREAMING_SNAKE_CASE_ )
elif is_jax_tensor(SCREAMING_SNAKE_CASE_ ):
return jnp.expand_dims(SCREAMING_SNAKE_CASE_ , axis=SCREAMING_SNAKE_CASE_ )
else:
raise ValueError(F'Type not supported for expand_dims: {type(SCREAMING_SNAKE_CASE_ )}.' )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> Tuple:
'''simple docstring'''
if is_numpy_array(SCREAMING_SNAKE_CASE_ ):
return np.size(SCREAMING_SNAKE_CASE_ )
elif is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
return array.numel()
elif is_tf_tensor(SCREAMING_SNAKE_CASE_ ):
import tensorflow as tf
return tf.size(SCREAMING_SNAKE_CASE_ )
elif is_jax_tensor(SCREAMING_SNAKE_CASE_ ):
return array.size
else:
raise ValueError(F'Type not supported for expand_dims: {type(SCREAMING_SNAKE_CASE_ )}.' )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: Optional[Any] ) -> Dict:
'''simple docstring'''
for key, value in auto_map.items():
if isinstance(SCREAMING_SNAKE_CASE_ , (tuple, list) ):
A__ = [F'{repo_id}--{v}' if (v is not None and "--" not in v) else v for v in value]
elif value is not None and "--" not in value:
A__ = F'{repo_id}--{value}'
return auto_map
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Any ) -> Union[str, Any]:
'''simple docstring'''
for base_class in inspect.getmro(SCREAMING_SNAKE_CASE_ ):
A__ = base_class.__module__
A__ = base_class.__name__
if module.startswith("tensorflow" ) or module.startswith("keras" ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith("torch" ) or name == "PreTrainedModel":
return "pt"
elif module.startswith("flax" ) or module.startswith("jax" ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(F'Could not infer framework from class {model_class}.' )
| 68 |
# Function to print upper half of diamond (pyramid)
def lowerCAmelCase__ ( lowerCamelCase_ : Optional[int]):
'''simple docstring'''
for i in range(0 ,lowerCamelCase_):
for _ in range(0 ,n - i - 1): # printing spaces
print(''' ''' ,end='''''')
for _ in range(0 ,i + 1): # printing stars
print('''* ''' ,end='''''')
print()
def lowerCAmelCase__ ( lowerCamelCase_ : str):
'''simple docstring'''
for i in range(lowerCamelCase_ ,0 ,-1):
for _ in range(lowerCamelCase_ ,0 ,-1): # printing stars
print('''* ''' ,end='''''')
print()
for _ in range(n - i + 1 ,0 ,-1): # printing spaces
print(''' ''' ,end='''''')
def lowerCAmelCase__ ( lowerCamelCase_ : Tuple):
'''simple docstring'''
if n <= 0:
print(''' ... .... nothing printing :(''')
return
floyd(lowerCamelCase_) # upper half
reverse_floyd(lowerCamelCase_) # lower half
if __name__ == "__main__":
print(R'| /\ | |- | |- |--| |\ /| |-')
print(R'|/ \| |- |_ |_ |__| | \/ | |_')
__snake_case : int =1
while K:
__snake_case : Optional[int] =int(input('enter the number and , and see the magic : '))
print()
pretty_print(user_number)
__snake_case : str =int(input('press 0 to exit... and 1 to continue...'))
print('Good Bye...')
| 129 | 0 |
from __future__ import annotations
import time
import numpy as np
lowerCAmelCase__ = [8, 5, 9, 7]
lowerCAmelCase__ = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
lowerCAmelCase__ = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class a_ :
'''simple docstring'''
def __init__( self : str , lowercase__ : list[int] , lowercase__ : list[list[int]] , lowercase__ : list[list[int]] , ):
'''simple docstring'''
lowerCAmelCase__ = claim_vector
lowerCAmelCase__ = allocated_resources_table
lowerCAmelCase__ = maximum_claim_table
def __snake_case ( self : Optional[int]):
'''simple docstring'''
return [
sum(p_item[i] for p_item in self.__allocated_resources_table)
for i in range(len(self.__allocated_resources_table[0]))
]
def __snake_case ( self : List[str]):
'''simple docstring'''
return np.array(self.__claim_vector) - np.array(
self.__processes_resource_summation())
def __snake_case ( self : Dict):
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i]) - np.array(lowercase__))
for i, allocated_resource in enumerate(self.__allocated_resources_table)
]
def __snake_case ( self : Dict):
'''simple docstring'''
return {self.__need().index(lowercase__): i for i in self.__need()}
def __snake_case ( self : List[Any] , **lowercase__ : int):
'''simple docstring'''
lowerCAmelCase__ = self.__need()
lowerCAmelCase__ = self.__allocated_resources_table
lowerCAmelCase__ = self.__available_resources()
lowerCAmelCase__ = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('_' * 50 + '\n')
while need_list:
lowerCAmelCase__ = False
for each_need in need_list:
lowerCAmelCase__ = True
for index, need in enumerate(lowercase__):
if need > available_resources[index]:
lowerCAmelCase__ = False
break
if execution:
lowerCAmelCase__ = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
lowerCAmelCase__ = original_need_index
print(F"""Process {process_number + 1} is executing.""")
# remove the process run from stack
need_list.remove(lowercase__)
# update available/freed resources stack
lowerCAmelCase__ = np.array(lowercase__) + np.array(
alloc_resources_table[process_number])
print(
'Updated available resource stack for processes: '
+ ' '.join([str(lowercase__) for x in available_resources]))
break
if safe:
print('The process is in a safe state.\n')
else:
print('System in unsafe state. Aborting...\n')
break
def __snake_case ( self : List[str]):
'''simple docstring'''
print(' ' * 9 + 'Allocated Resource Table')
for item in self.__allocated_resources_table:
print(
F"""P{self.__allocated_resources_table.index(lowercase__) + 1}"""
+ ' '.join(F"""{it:>8}""" for it in item)
+ '\n')
print(' ' * 9 + 'System Resource Table')
for item in self.__maximum_claim_table:
print(
F"""P{self.__maximum_claim_table.index(lowercase__) + 1}"""
+ ' '.join(F"""{it:>8}""" for it in item)
+ '\n')
print(
'Current Usage by Active Processes: '
+ ' '.join(str(lowercase__) for x in self.__claim_vector))
print(
'Initial Available Resources: '
+ ' '.join(str(lowercase__) for x in self.__available_resources()))
time.sleep(1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 119 | lowerCAmelCase__ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def __lowerCamelCase ( lowerCAmelCase__ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
lowerCAmelCase__ = F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
raise TypeError(lowerCAmelCase__ )
lowerCAmelCase__ = ''.join(bin(lowerCAmelCase__ )[2:].zfill(8 ) for byte in data )
lowerCAmelCase__ = len(lowerCAmelCase__ ) % 6 != 0
if padding_needed:
# The padding that will be added later
lowerCAmelCase__ = b'=' * ((6 - len(lowerCAmelCase__ ) % 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(lowerCAmelCase__ ) % 6)
else:
lowerCAmelCase__ = 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(lowerCAmelCase__ ) , 6 ) ).encode()
+ padding
)
def __lowerCamelCase ( lowerCAmelCase__ ):
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
lowerCAmelCase__ = (
'argument should be a bytes-like object or ASCII string, '
F"""not '{encoded_data.__class__.__name__}'"""
)
raise TypeError(lowerCAmelCase__ )
# 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(lowerCAmelCase__ , lowerCAmelCase__ ):
try:
lowerCAmelCase__ = encoded_data.decode('utf-8' )
except UnicodeDecodeError:
raise ValueError('base64 encoded data should only contain ASCII characters' )
lowerCAmelCase__ = 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(lowerCAmelCase__ ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
lowerCAmelCase__ = encoded_data[:-padding]
lowerCAmelCase__ = ''.join(
bin(B64_CHARSET.index(lowerCAmelCase__ ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
lowerCAmelCase__ = ''.join(
bin(B64_CHARSET.index(lowerCAmelCase__ ) )[2:].zfill(6 ) for char in encoded_data )
lowerCAmelCase__ = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(lowerCAmelCase__ ) , 8 )
]
return bytes(lowerCAmelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 119 | 1 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@require_tf
class __A ( unittest.TestCase ):
def lowercase__ ( self : List[str] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Any ):
self.assertEqual(len(lowerCAmelCase__ ) , len(lowerCAmelCase__ ) )
for a, b in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
self.assertAlmostEqual(lowerCAmelCase__ , lowerCAmelCase__ , delta=lowerCAmelCase__ )
def lowercase__ ( self : int ):
lowerCAmelCase : Optional[Any] = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0] )] )
accumulator([tf.constant([-2.0, 1.0] )] )
accumulator([tf.constant([-1.0, 2.0] )] )
with self.assertRaises(lowerCAmelCase__ ):
accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
self.assertEqual(accumulator.step , 3 )
self.assertEqual(len(accumulator.gradients ) , 1 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1E-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1E-2 )
def lowercase__ ( self : str ):
lowerCAmelCase : Any = None
ops.enable_eager_execution_internal()
lowerCAmelCase : int = tf.config.list_physical_devices('CPU' )
if len(lowerCAmelCase__ ) == 1:
tf.config.set_logical_device_configuration(
physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
lowerCAmelCase : Optional[int] = tf.config.list_logical_devices(device_type='CPU' )
lowerCAmelCase : List[str] = tf.distribute.MirroredStrategy(devices=devices[:2] )
with strategy.scope():
lowerCAmelCase : int = GradientAccumulator()
lowerCAmelCase : Optional[Any] = tf.Variable([4.0, 3.0] )
lowerCAmelCase : Dict = create_optimizer(5E-5 , 10 , 5 )
lowerCAmelCase : Optional[int] = tf.Variable([0.0, 0.0] , trainable=lowerCAmelCase__ )
def accumulate_on_replica(UpperCAmelCase_ : str ):
accumulator([gradient] )
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) )
@tf.function
def accumulate(UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : int ):
with strategy.scope():
lowerCAmelCase : Optional[Any] = strategy.experimental_local_results(lowerCAmelCase__ )
local_variables[0].assign(lowerCAmelCase__ )
local_variables[1].assign(lowerCAmelCase__ )
strategy.run(lowerCAmelCase__ , args=(gradient_placeholder,) )
@tf.function
def apply_grad():
with strategy.scope():
strategy.run(lowerCAmelCase__ )
def _check_local_values(UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Union[str, Any] ):
lowerCAmelCase : Tuple = strategy.experimental_local_results(accumulator._gradients[0] )
self.assertListAlmostEqual(values[0].value() , lowerCAmelCase__ , tol=1E-2 )
self.assertListAlmostEqual(values[1].value() , lowerCAmelCase__ , tol=1E-2 )
accumulate([1.0, 2.0] , [-1.0, 1.0] )
accumulate([3.0, -1.0] , [-1.0, -1.0] )
accumulate([-2.0, 2.0] , [3.0, -2.0] )
self.assertEqual(accumulator.step , 3 )
_check_local_values([2.0, 3.0] , [1.0, -2.0] )
apply_grad()
self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1E-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
_check_local_values([0.0, 0.0] , [0.0, 0.0] )
| 138 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
UpperCAmelCase : int = False
class __lowerCAmelCase ( unittest.TestCase):
pass
@nightly
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase):
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self ) -> Any:
'''simple docstring'''
a__ : str =VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion" , torch_dtype=torch.floataa )
pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
a__ : int =load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
a__ : Optional[Any] =torch.manual_seed(0 )
a__ : Optional[Any] =pipe.dual_guided(
prompt="first prompt" , image=lowerCAmelCase__ , text_to_image_strength=0.75 , generator=lowerCAmelCase__ , guidance_scale=7.5 , num_inference_steps=2 , output_type="numpy" , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(lowerCAmelCase__ )
a__ : str =VersatileDiffusionPipeline.from_pretrained(lowerCAmelCase__ , torch_dtype=torch.floataa )
pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
a__ : Optional[Any] =generator.manual_seed(0 )
a__ : Tuple =pipe.dual_guided(
prompt="first prompt" , image=lowerCAmelCase__ , text_to_image_strength=0.75 , generator=lowerCAmelCase__ , guidance_scale=7.5 , num_inference_steps=2 , output_type="numpy" , ).images
assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"
def _lowercase ( self ) -> Any:
'''simple docstring'''
a__ : str =VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion" , torch_dtype=torch.floataa )
pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
a__ : Optional[Any] ="cyberpunk 2077"
a__ : int =load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
a__ : Union[str, Any] =torch.manual_seed(0 )
a__ : Tuple =pipe.dual_guided(
prompt=lowerCAmelCase__ , image=lowerCAmelCase__ , text_to_image_strength=0.75 , generator=lowerCAmelCase__ , guidance_scale=7.5 , num_inference_steps=5_0 , output_type="numpy" , ).images
a__ : int =image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
a__ : Any =np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
a__ : str ="A painting of a squirrel eating a burger "
a__ : Optional[int] =torch.manual_seed(0 )
a__ : str =pipe.text_to_image(
prompt=lowerCAmelCase__ , generator=lowerCAmelCase__ , guidance_scale=7.5 , num_inference_steps=5_0 , output_type="numpy" ).images
a__ : Any =image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
a__ : Optional[int] =np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
a__ : Optional[Any] =pipe.image_variation(lowerCAmelCase__ , generator=lowerCAmelCase__ , output_type="numpy" ).images
a__ : Union[str, Any] =image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
a__ : Any =np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
| 95 | 0 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class __SCREAMING_SNAKE_CASE (nn.Module ):
"""simple docstring"""
def __init__( self : Any , __a : int = 16 , __a : int = 88 , __a : Optional[int] = None , __a : int = 1 , __a : float = 0.0 , __a : int = 32 , __a : Optional[int] = None , __a : bool = False , __a : Optional[int] = None , __a : Optional[int] = None , __a : str = "geglu" , __a : Optional[int] = None , ):
super().__init__()
_a = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=__SCREAMING_SNAKE_CASE , attention_head_dim=__SCREAMING_SNAKE_CASE , in_channels=__SCREAMING_SNAKE_CASE , num_layers=__SCREAMING_SNAKE_CASE , dropout=__SCREAMING_SNAKE_CASE , norm_num_groups=__SCREAMING_SNAKE_CASE , cross_attention_dim=__SCREAMING_SNAKE_CASE , attention_bias=__SCREAMING_SNAKE_CASE , sample_size=__SCREAMING_SNAKE_CASE , num_vector_embeds=__SCREAMING_SNAKE_CASE , activation_fn=__SCREAMING_SNAKE_CASE , num_embeds_ada_norm=__SCREAMING_SNAKE_CASE , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
_a = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
_a = [77, 2_57]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
_a = [1, 0]
def UpperCamelCase__ ( self : int , __a : List[str] , __a : int , __a : Union[str, Any]=None , __a : Any=None , __a : Optional[Any]=None , __a : bool = True , ):
_a = hidden_states
_a = []
_a = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
_a = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
_a = self.transformer_index_for_condition[i]
_a = self.transformers[transformer_index](
__SCREAMING_SNAKE_CASE , encoder_hidden_states=__SCREAMING_SNAKE_CASE , timestep=__SCREAMING_SNAKE_CASE , cross_attention_kwargs=__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
_a = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
_a = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=__SCREAMING_SNAKE_CASE )
| 363 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase_ )
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : List[Any] , *__a : Optional[int] , **__a : List[str] ):
super().__init__(*__a , **__a )
self.check_model_type(__a )
def UpperCamelCase__ ( self : Optional[Any] , __a : Dict=None , __a : int=None , __a : Optional[Any]=None , **__a : List[Any] ):
_a , _a = {}, {}
if padding is not None:
_a = padding
if truncation is not None:
_a = truncation
if top_k is not None:
_a = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : Union[str, Any] , __a : Union["Image.Image", str] , __a : str = None , **__a : Any ):
if isinstance(__a , (Image.Image, str) ) and isinstance(__a , __a ):
_a = {"image": image, "question": question}
else:
_a = image
_a = super().__call__(__a , **__a )
return results
def UpperCamelCase__ ( self : Tuple , __a : Tuple , __a : Optional[Any]=False , __a : List[Any]=False ):
_a = load_image(inputs["image"] )
_a = self.tokenizer(
inputs["question"] , return_tensors=self.framework , padding=__a , truncation=__a )
_a = self.image_processor(images=__a , return_tensors=self.framework )
model_inputs.update(__a )
return model_inputs
def UpperCamelCase__ ( self : List[Any] , __a : List[str] ):
_a = self.model(**__a )
return model_outputs
def UpperCamelCase__ ( self : int , __a : Optional[int] , __a : Dict=5 ):
if top_k > self.model.config.num_labels:
_a = self.model.config.num_labels
if self.framework == "pt":
_a = model_outputs.logits.sigmoid()[0]
_a , _a = probs.topk(__a )
else:
raise ValueError(f'Unsupported framework: {self.framework}' )
_a = scores.tolist()
_a = ids.tolist()
return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__a , __a )]
| 346 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ : Dict ) -> List[Any]:
'''simple docstring'''
stooge(lowercase__ , 0 , len(lowercase__ ) - 1 )
return arr
def _snake_case ( lowercase__ : List[Any] , lowercase__ : str , lowercase__ : Optional[Any] ) -> int:
'''simple docstring'''
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
lowerCAmelCase_ , lowerCAmelCase_ :int = arr[h], arr[i]
# If there are more than 2 elements in the array
if h - i + 1 > 2:
lowerCAmelCase_ :Any = (int)((h - i + 1) / 3 )
# Recursively sort first 2/3 elements
stooge(lowercase__ , lowercase__ , (h - t) )
# Recursively sort last 2/3 elements
stooge(lowercase__ , i + t , (lowercase__) )
# Recursively sort first 2/3 elements
stooge(lowercase__ , lowercase__ , (h - t) )
if __name__ == "__main__":
__UpperCAmelCase = input('Enter numbers separated by a comma:\n').strip()
__UpperCAmelCase = [int(item) for item in user_input.split(',')]
print(stooge_sort(unsorted))
| 84 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCamelCase : Tuple =_symbol_database.Default()
lowerCamelCase : List[str] =_descriptor_pool.Default().AddSerializedFile(
b'''\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'''
)
lowerCamelCase : str =globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, '''sentencepiece_model_pb2''', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
lowerCamelCase : Optional[int] =None
lowerCamelCase : Tuple =b'''H\003'''
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
lowerCamelCase : List[str] =45
lowerCamelCase : List[Any] =1581
lowerCamelCase : Optional[int] =1517
lowerCamelCase : Tuple =1570
lowerCamelCase : Dict =1584
lowerCamelCase : Optional[Any] =1793
lowerCamelCase : Dict =1795
lowerCamelCase : Any =1916
lowerCamelCase : Dict =1864
lowerCamelCase : Dict =1905
lowerCamelCase : Dict =1919
lowerCamelCase : Union[str, Any] =2429
lowerCamelCase : List[Any] =2208
lowerCamelCase : List[Any] =2418
lowerCamelCase : List[str] =2323
lowerCamelCase : Dict =2407
# @@protoc_insertion_point(module_scope) | 189 | 0 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
_SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
_SCREAMING_SNAKE_CASE = """pytorch_model.bin"""
@dataclasses.dataclass
class SCREAMING_SNAKE_CASE_ :
__magic_name__: str = dataclasses.field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."} )
__magic_name__: Optional[str] = dataclasses.field(
default=snake_case_ , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."} , )
@dataclasses.dataclass
class SCREAMING_SNAKE_CASE_ :
__magic_name__: str = dataclasses.field(metadata={"help": "A csv or a json file containing the training data."} )
__magic_name__: str = dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."} )
__magic_name__: Optional[str] = dataclasses.field(
default=snake_case_ , metadata={"help": "A csv or a json file containing the validation data."} )
__magic_name__: Optional[str] = dataclasses.field(
default=snake_case_ , metadata={"help": "The name of the task to train on."} , )
__magic_name__: Optional[List[str]] = dataclasses.field(
default=snake_case_ , metadata={"help": "The list of labels for the task."} )
@dataclasses.dataclass
class SCREAMING_SNAKE_CASE_ :
__magic_name__: str = dataclasses.field(
metadata={"help": "The output directory where the model predictions and checkpoints will be written."} )
__magic_name__: Optional[str] = dataclasses.field(
default="accuracy" , metadata={"help": "The evaluation metric used for the task."} )
__magic_name__: Optional[str] = dataclasses.field(
default="no" , metadata={
"help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
} , )
__magic_name__: Optional[int] = dataclasses.field(
default=10 , metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} , )
__magic_name__: Optional[float] = dataclasses.field(
default=0.0 , metadata={
"help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
} , )
__magic_name__: Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."} , )
__magic_name__: Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."} , )
__magic_name__: Optional[bool] = dataclasses.field(
default=snake_case_ , metadata={"help": "Whether to fine-tune on labeled data after pseudo training."} , )
__magic_name__: Optional[float] = dataclasses.field(
default=0.0 , metadata={"help": "Confidence threshold for pseudo-labeled data filtering."} , )
__magic_name__: Optional[int] = dataclasses.field(
default=100 , metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} , )
__magic_name__: Optional[int] = dataclasses.field(
default=snake_case_ , metadata={"help": "Random seed for initialization."} , )
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a , __a , __a , __a ):
snake_case_ : Any = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
snake_case_ : Optional[int] = dataset.filter(lambda __a : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
snake_case_ : Optional[Any] = int(eval_result * len(__a ) )
print(__a )
snake_case_ : Optional[Any] = dataset.sort('probability' , reverse=__a )
snake_case_ : Dict = dataset.select(range(__a ) )
snake_case_ : Union[str, Any] = dataset.remove_columns(['label', 'probability'] )
snake_case_ : Optional[int] = dataset.rename_column('prediction' , 'label' )
snake_case_ : Any = dataset.map(lambda __a : {"label": idalabel[example["label"]]} )
snake_case_ : List[Any] = dataset.shuffle(seed=args.seed )
snake_case_ : List[Any] = os.path.join(__a , f"""train_pseudo.{args.data_file_extension}""" )
if args.data_file_extension == "csv":
dataset.to_csv(__a , index=__a )
else:
dataset.to_json(__a )
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a , __a , **__a ):
snake_case_ : List[str] = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
snake_case_ : List[Any] = STModelArguments(model_name_or_path=__a )
snake_case_ : str = STDataArguments(train_file=__a , infer_file=__a )
snake_case_ : str = STTrainingArguments(output_dir=__a )
snake_case_ : List[Any] = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(__a ).items():
setattr(__a , __a , __a )
for key, value in kwargs.items():
if hasattr(__a , __a ):
setattr(__a , __a , __a )
# Sanity checks
snake_case_ : str = {}
snake_case_ : Tuple = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
snake_case_ : int = args.train_file
snake_case_ : Tuple = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
snake_case_ : Union[str, Any] = args.eval_file
for key in data_files:
snake_case_ : Optional[Any] = data_files[key].split('.' )[-1]
assert extension in ["csv", "json"], f"""`{key}_file` should be a csv or a json file."""
if args.data_file_extension is None:
snake_case_ : Union[str, Any] = extension
else:
assert extension == args.data_file_extension, f"""`{key}_file` should be a {args.data_file_extension} file`."""
assert (
args.eval_metric in datasets.list_metrics()
), f"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info('Creating the initial data directory for self-training...' )
snake_case_ : List[Any] = f"""{args.output_dir}/self-train_iter-{{}}""".format
snake_case_ : Optional[Any] = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=__a )
os.makedirs(__a , exist_ok=__a )
accelerator.wait_for_everyone()
snake_case_ : Union[str, Any] = None
snake_case_ : List[str] = None
snake_case_ : List[Any] = 0
snake_case_ : Dict = False
# Show the progress bar
snake_case_ : Union[str, Any] = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
snake_case_ : Optional[int] = data_dir_format(__a )
assert os.path.exists(__a )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
snake_case_ : str = os.path.join(__a , 'stage-1' )
snake_case_ : Tuple = {
'accelerator': accelerator,
'model_name_or_path': args.model_name_or_path,
'cache_dir': args.cache_dir,
'do_train': True,
'train_file': data_files['train'] if iteration == 0 else data_files['train_pseudo'],
'do_eval': True if args.eval_file is not None else False,
'eval_file': data_files['eval'],
'do_predict': True,
'infer_file': data_files['infer'],
'task_name': args.task_name,
'label_list': args.label_list,
'output_dir': current_output_dir,
'eval_metric': args.eval_metric,
'evaluation_strategy': args.evaluation_strategy,
'early_stopping_patience': args.early_stopping_patience,
'early_stopping_threshold': args.early_stopping_threshold,
'seed': args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(__a , __a ):
arguments_dict.update({key: value} )
snake_case_ : List[Any] = os.path.join(__a , 'best-checkpoint' , __a )
if os.path.exists(__a ):
logger.info(
'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.' , __a , __a , )
else:
logger.info('***** Running self-training: iteration: %d, stage: 1 *****' , __a )
finetune(**__a )
accelerator.wait_for_everyone()
assert os.path.exists(__a )
logger.info('Self-training job completed: iteration: %d, stage: 1.' , __a )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
snake_case_ : str = os.path.join(__a , 'best-checkpoint' )
snake_case_ : str = os.path.join(__a , 'stage-2' )
# Update arguments_dict
snake_case_ : Any = model_path
snake_case_ : int = data_files['train']
snake_case_ : Dict = current_output_dir
snake_case_ : Any = os.path.join(__a , 'best-checkpoint' , __a )
if os.path.exists(__a ):
logger.info(
'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.' , __a , __a , )
else:
logger.info('***** Running self-training: iteration: %d, stage: 2 *****' , __a )
finetune(**__a )
accelerator.wait_for_everyone()
assert os.path.exists(__a )
logger.info('Self-training job completed: iteration: %d, stage: 2.' , __a )
snake_case_ : Optional[int] = iteration
snake_case_ : Tuple = data_dir_format(iteration + 1 )
snake_case_ : int = AutoConfig.from_pretrained(os.path.join(__a , 'best-checkpoint' ) )
snake_case_ : int = config.idalabel
snake_case_ : Optional[Any] = os.path.join(__a , 'eval_results_best-checkpoint.json' )
snake_case_ : Optional[Any] = os.path.join(__a , 'test_results_best-checkpoint.json' )
assert os.path.exists(__a )
with open(__a , 'r' ) as f:
snake_case_ : int = float(json.load(__a )[args.eval_metric] )
snake_case_ : List[Any] = os.path.join(__a , 'infer_output_best-checkpoint.csv' )
assert os.path.exists(__a )
# Loading the dataset from local csv or json files.
snake_case_ : Optional[int] = load_dataset(args.data_file_extension , data_files={'data': data_files['infer']} )['data']
snake_case_ : Union[str, Any] = load_dataset('csv' , data_files={'data': infer_output_file} )['data']
if accelerator.is_main_process:
os.makedirs(__a , exist_ok=__a )
shutil.copy(__a , os.path.join(__a , f"""eval_results_iter-{iteration}.json""" ) )
if os.path.exists(__a ):
shutil.copy(__a , os.path.join(__a , f"""test_results_iter-{iteration}.json""" ) )
create_pseudo_labeled_data(__a , __a , __a , __a , __a , __a )
accelerator.wait_for_everyone()
snake_case_ : Optional[int] = os.path.join(__a , f"""train_pseudo.{args.data_file_extension}""" )
if args.evaluation_strategy != IntervalStrategy.NO.value:
snake_case_ : int = eval_result
if best_iteration is None:
snake_case_ : Tuple = new_iteration
snake_case_ : Union[str, Any] = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
snake_case_ : List[str] = new_iteration
snake_case_ : Optional[int] = new_eval_result
snake_case_ : List[Any] = 0
else:
if new_eval_result == best_eval_result:
snake_case_ : Union[str, Any] = new_iteration
snake_case_ : List[Any] = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
snake_case_ : Optional[Any] = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info('Best iteration: %d' , __a )
logger.info('Best evaluation result: %s = %f' , args.eval_metric , __a )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(__a , f"""eval_results_iter-{iteration}.json""" ) , os.path.join(__a , 'eval_results_best-iteration.json' ) , )
else:
# Assume that the last iteration is the best
logger.info('Best iteration: %d' , args.max_selftrain_iterations - 1 )
logger.info('Best evaluation result: %s = %f' , args.eval_metric , __a )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(__a , f"""eval_results_iter-{args.max_selftrain_iterations - 1}.json""" ) , os.path.join(__a , 'eval_results_best-iteration.json' ) , )
| 88 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def SCREAMING_SNAKE_CASE__ ( __a , __a=None ):
snake_case_ : Optional[int] = None
if token is not None:
snake_case_ : List[str] = {'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}
snake_case_ : Union[str, Any] = f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
snake_case_ : Optional[int] = requests.get(__a , headers=__a ).json()
snake_case_ : List[str] = {}
try:
job_links.update({job['name']: job['html_url'] for job in result['jobs']} )
snake_case_ : Dict = math.ceil((result['total_count'] - 1_00) / 1_00 )
for i in range(__a ):
snake_case_ : Optional[Any] = requests.get(url + f"""&page={i + 2}""" , headers=__a ).json()
job_links.update({job['name']: job['html_url'] for job in result['jobs']} )
return job_links
except Exception:
print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
def SCREAMING_SNAKE_CASE__ ( __a , __a=None ):
snake_case_ : Union[str, Any] = None
if token is not None:
snake_case_ : List[Any] = {'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}
snake_case_ : Optional[Any] = f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
snake_case_ : Union[str, Any] = requests.get(__a , headers=__a ).json()
snake_case_ : Any = {}
try:
artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )
snake_case_ : str = math.ceil((result['total_count'] - 1_00) / 1_00 )
for i in range(__a ):
snake_case_ : int = requests.get(url + f"""&page={i + 2}""" , headers=__a ).json()
artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )
return artifacts
except Exception:
print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a , __a ):
snake_case_ : Dict = None
if token is not None:
snake_case_ : List[Any] = {'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}
snake_case_ : Optional[int] = requests.get(__a , headers=__a , allow_redirects=__a )
snake_case_ : str = result.headers['Location']
snake_case_ : List[str] = requests.get(__a , allow_redirects=__a )
snake_case_ : Optional[Any] = os.path.join(__a , f"""{artifact_name}.zip""" )
with open(__a , 'wb' ) as fp:
fp.write(response.content )
def SCREAMING_SNAKE_CASE__ ( __a , __a=None ):
snake_case_ : Any = []
snake_case_ : Any = []
snake_case_ : Tuple = None
with zipfile.ZipFile(__a ) as z:
for filename in z.namelist():
if not os.path.isdir(__a ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(__a ) as f:
for line in f:
snake_case_ : Tuple = line.decode('UTF-8' ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
snake_case_ : Tuple = line[: line.index(': ' )]
snake_case_ : Union[str, Any] = line[line.index(': ' ) + len(': ' ) :]
errors.append([error_line, error] )
except Exception:
# skip un-related lines
pass
elif filename == "summary_short.txt" and line.startswith('FAILED ' ):
# `test` is the test method that failed
snake_case_ : Any = line[len('FAILED ' ) :]
failed_tests.append(__a )
elif filename == "job_name.txt":
snake_case_ : Union[str, Any] = line
if len(__a ) != len(__a ):
raise ValueError(
f"""`errors` and `failed_tests` should have the same number of elements. Got {len(__a )} for `errors` """
f"""and {len(__a )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
' problem.' )
snake_case_ : List[str] = None
if job_name and job_links:
snake_case_ : Union[str, Any] = job_links.get(__a , __a )
# A list with elements of the form (line of error, error, failed test)
snake_case_ : Optional[Any] = [x + [y] + [job_link] for x, y in zip(__a , __a )]
return result
def SCREAMING_SNAKE_CASE__ ( __a , __a=None ):
snake_case_ : Any = []
snake_case_ : Any = [os.path.join(__a , __a ) for p in os.listdir(__a ) if p.endswith('.zip' )]
for p in paths:
errors.extend(get_errors_from_single_artifact(__a , job_links=__a ) )
return errors
def SCREAMING_SNAKE_CASE__ ( __a , __a=None ):
snake_case_ : Optional[int] = Counter()
counter.update([x[1] for x in logs] )
snake_case_ : str = counter.most_common()
snake_case_ : Tuple = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
snake_case_ : int = {'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}
snake_case_ : int = dict(sorted(r.items() , key=lambda __a : item[1]["count"] , reverse=__a ) )
return r
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : Tuple = test.split('::' )[0]
if test.startswith('tests/models/' ):
snake_case_ : List[str] = test.split('/' )[2]
else:
snake_case_ : Union[str, Any] = None
return test
def SCREAMING_SNAKE_CASE__ ( __a , __a=None ):
snake_case_ : Optional[int] = [(x[0], x[1], get_model(x[2] )) for x in logs]
snake_case_ : str = [x for x in logs if x[2] is not None]
snake_case_ : int = {x[2] for x in logs}
snake_case_ : Dict = {}
for test in tests:
snake_case_ : List[str] = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
snake_case_ : Any = counter.most_common()
snake_case_ : str = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
snake_case_ : Tuple = sum(error_counts.values() )
if n_errors > 0:
snake_case_ : List[Any] = {'count': n_errors, 'errors': error_counts}
snake_case_ : int = dict(sorted(r.items() , key=lambda __a : item[1]["count"] , reverse=__a ) )
return r
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : Optional[Any] = '| no. | error | status |'
snake_case_ : str = '|-:|:-|:-|'
snake_case_ : Tuple = [header, sep]
for error in reduced_by_error:
snake_case_ : Dict = reduced_by_error[error]['count']
snake_case_ : List[str] = f"""| {count} | {error[:1_00]} | |"""
lines.append(__a )
return "\n".join(__a )
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : Optional[Any] = '| model | no. of errors | major error | count |'
snake_case_ : Union[str, Any] = '|-:|-:|-:|-:|'
snake_case_ : Optional[int] = [header, sep]
for model in reduced_by_model:
snake_case_ : Any = reduced_by_model[model]['count']
snake_case_ ,snake_case_ : Dict = list(reduced_by_model[model]['errors'].items() )[0]
snake_case_ : Any = f"""| {model} | {count} | {error[:60]} | {_count} |"""
lines.append(__a )
return "\n".join(__a )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
parser.add_argument(
"""--output_dir""",
type=str,
required=True,
help="""Where to store the downloaded artifacts and other result files.""",
)
parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
_SCREAMING_SNAKE_CASE = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
_SCREAMING_SNAKE_CASE = get_job_links(args.workflow_run_id, token=args.token)
_SCREAMING_SNAKE_CASE = {}
# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
if _job_links:
for k, v in _job_links.items():
# This is how GitHub actions combine job names.
if " / " in k:
_SCREAMING_SNAKE_CASE = k.find(""" / """)
_SCREAMING_SNAKE_CASE = k[index + len(""" / """) :]
_SCREAMING_SNAKE_CASE = v
with open(os.path.join(args.output_dir, """job_links.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(job_links, fp, ensure_ascii=False, indent=4)
_SCREAMING_SNAKE_CASE = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
for idx, (name, url) in enumerate(artifacts.items()):
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
_SCREAMING_SNAKE_CASE = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
_SCREAMING_SNAKE_CASE = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
_SCREAMING_SNAKE_CASE = counter.most_common(30)
for item in most_common:
print(item)
with open(os.path.join(args.output_dir, """errors.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(errors, fp, ensure_ascii=False, indent=4)
_SCREAMING_SNAKE_CASE = reduce_by_error(errors)
_SCREAMING_SNAKE_CASE = reduce_by_model(errors)
_SCREAMING_SNAKE_CASE = make_github_table(reduced_by_error)
_SCREAMING_SNAKE_CASE = make_github_table_per_model(reduced_by_model)
with open(os.path.join(args.output_dir, """reduced_by_error.txt"""), """w""", encoding="""UTF-8""") as fp:
fp.write(sa)
with open(os.path.join(args.output_dir, """reduced_by_model.txt"""), """w""", encoding="""UTF-8""") as fp:
fp.write(sa)
| 88 | 1 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=() ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE="no" ,_SCREAMING_SNAKE_CASE="29500" ) -> Optional[int]:
lowerCamelCase : Optional[Any] = False
lowerCamelCase : int = False
if any(key.startswith("KAGGLE" ) for key in os.environ.keys() ):
lowerCamelCase : Dict = True
elif "IPython" in sys.modules:
lowerCamelCase : Dict = "google.colab" in str(sys.modules["IPython"].get_ipython() )
try:
lowerCamelCase : Tuple = PrecisionType(mixed_precision.lower() )
except ValueError:
raise ValueError(
f'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.''' )
if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME" ,_SCREAMING_SNAKE_CASE ) is not None):
# TPU launch
import torch_xla.distributed.xla_multiprocessing as xmp
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
"To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
"your training function. Restart your notebook and make sure no cells initializes an "
"`Accelerator`." )
if num_processes is None:
lowerCamelCase : Any = 8
lowerCamelCase : Tuple = PrepareForLaunch(_SCREAMING_SNAKE_CASE ,distributed_type="TPU" )
print(f'''Launching a training on {num_processes} TPU cores.''' )
xmp.spawn(_SCREAMING_SNAKE_CASE ,args=_SCREAMING_SNAKE_CASE ,nprocs=_SCREAMING_SNAKE_CASE ,start_method="fork" )
elif in_colab:
# No need for a distributed launch otherwise as it's either CPU or one GPU.
if torch.cuda.is_available():
print("Launching training on one GPU." )
else:
print("Launching training on one CPU." )
function(*_SCREAMING_SNAKE_CASE )
else:
if num_processes is None:
raise ValueError(
"You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call." )
if num_processes > 1:
# Multi-GPU launch
from torch.multiprocessing import start_processes
from torch.multiprocessing.spawn import ProcessRaisedException
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
"To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
"inside your training function. Restart your notebook and make sure no cells initializes an "
"`Accelerator`." )
if torch.cuda.is_initialized():
raise ValueError(
"To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
"using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
"function." )
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=_SCREAMING_SNAKE_CASE ,master_addr="127.0.01" ,master_port=_SCREAMING_SNAKE_CASE ,mixed_precision=_SCREAMING_SNAKE_CASE ):
lowerCamelCase : List[str] = PrepareForLaunch(_SCREAMING_SNAKE_CASE ,distributed_type="MULTI_GPU" )
print(f'''Launching training on {num_processes} GPUs.''' )
try:
start_processes(_SCREAMING_SNAKE_CASE ,args=_SCREAMING_SNAKE_CASE ,nprocs=_SCREAMING_SNAKE_CASE ,start_method="fork" )
except ProcessRaisedException as e:
if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
raise RuntimeError(
"CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
"This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
"Please review your imports and test them when running the `notebook_launcher()` to identify "
"which one is problematic." ) from e
else:
# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
if is_mps_available():
lowerCamelCase : List[str] = "1"
print("Launching training on MPS." )
elif torch.cuda.is_available():
print("Launching training on one GPU." )
else:
print("Launching training on CPU." )
function(*_SCREAMING_SNAKE_CASE )
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=() ,_SCREAMING_SNAKE_CASE=2 ) -> List[Any]:
from torch.multiprocessing import start_processes
with tempfile.NamedTemporaryFile() as tmp_file:
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=_SCREAMING_SNAKE_CASE ,master_addr="127.0.01" ,master_port="29500" ,accelerate_mixed_precision="no" ,accelerate_debug_rdv_file=tmp_file.name ,accelerate_use_cpu="yes" ,):
lowerCamelCase : List[Any] = PrepareForLaunch(_SCREAMING_SNAKE_CASE ,debug=_SCREAMING_SNAKE_CASE )
start_processes(_SCREAMING_SNAKE_CASE ,args=_SCREAMING_SNAKE_CASE ,nprocs=_SCREAMING_SNAKE_CASE ,start_method="fork" )
| 48 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = {
'b0': efficientnet.EfficientNetBa,
'b1': efficientnet.EfficientNetBa,
'b2': efficientnet.EfficientNetBa,
'b3': efficientnet.EfficientNetBa,
'b4': efficientnet.EfficientNetBa,
'b5': efficientnet.EfficientNetBa,
'b6': efficientnet.EfficientNetBa,
'b7': efficientnet.EfficientNetBa,
}
SCREAMING_SNAKE_CASE__ : Any = {
'b0': {
'hidden_dim': 1280,
'width_coef': 1.0,
'depth_coef': 1.0,
'image_size': 224,
'dropout_rate': 0.2,
'dw_padding': [],
},
'b1': {
'hidden_dim': 1280,
'width_coef': 1.0,
'depth_coef': 1.1,
'image_size': 240,
'dropout_rate': 0.2,
'dw_padding': [16],
},
'b2': {
'hidden_dim': 1408,
'width_coef': 1.1,
'depth_coef': 1.2,
'image_size': 260,
'dropout_rate': 0.3,
'dw_padding': [5, 8, 16],
},
'b3': {
'hidden_dim': 1536,
'width_coef': 1.2,
'depth_coef': 1.4,
'image_size': 300,
'dropout_rate': 0.3,
'dw_padding': [5, 18],
},
'b4': {
'hidden_dim': 1792,
'width_coef': 1.4,
'depth_coef': 1.8,
'image_size': 380,
'dropout_rate': 0.4,
'dw_padding': [6],
},
'b5': {
'hidden_dim': 2048,
'width_coef': 1.6,
'depth_coef': 2.2,
'image_size': 456,
'dropout_rate': 0.4,
'dw_padding': [13, 27],
},
'b6': {
'hidden_dim': 2304,
'width_coef': 1.8,
'depth_coef': 2.6,
'image_size': 528,
'dropout_rate': 0.5,
'dw_padding': [31],
},
'b7': {
'hidden_dim': 2560,
'width_coef': 2.0,
'depth_coef': 3.1,
'image_size': 600,
'dropout_rate': 0.5,
'dw_padding': [18],
},
}
def A ( _SCREAMING_SNAKE_CASE ) -> str:
lowerCamelCase : int = EfficientNetConfig()
lowerCamelCase : List[str] = CONFIG_MAP[model_name]["hidden_dim"]
lowerCamelCase : List[str] = CONFIG_MAP[model_name]["width_coef"]
lowerCamelCase : Any = CONFIG_MAP[model_name]["depth_coef"]
lowerCamelCase : Union[str, Any] = CONFIG_MAP[model_name]["image_size"]
lowerCamelCase : Optional[int] = CONFIG_MAP[model_name]["dropout_rate"]
lowerCamelCase : str = CONFIG_MAP[model_name]["dw_padding"]
lowerCamelCase : Tuple = "huggingface/label-files"
lowerCamelCase : List[str] = "imagenet-1k-id2label.json"
lowerCamelCase : Any = 1000
lowerCamelCase : Any = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,repo_type="dataset" ) ,"r" ) )
lowerCamelCase : List[str] = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
lowerCamelCase : Tuple = idalabel
lowerCamelCase : Any = {v: k for k, v in idalabel.items()}
return config
def A ( ) -> int:
lowerCamelCase : str = "http://images.cocodataset.org/val2017/000000039769.jpg"
lowerCamelCase : Tuple = Image.open(requests.get(_SCREAMING_SNAKE_CASE ,stream=_SCREAMING_SNAKE_CASE ).raw )
return im
def A ( _SCREAMING_SNAKE_CASE ) -> str:
lowerCamelCase : List[Any] = CONFIG_MAP[model_name]["image_size"]
lowerCamelCase : str = EfficientNetImageProcessor(
size={"height": size, "width": size} ,image_mean=[0.485, 0.456, 0.406] ,image_std=[0.47853944, 0.4732864, 0.47434163] ,do_center_crop=_SCREAMING_SNAKE_CASE ,)
return preprocessor
def A ( _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
lowerCamelCase : Any = [v.split("_" )[0].split("block" )[1] for v in original_param_names if v.startswith("block" )]
lowerCamelCase : Any = sorted(set(_SCREAMING_SNAKE_CASE ) )
lowerCamelCase : Dict = len(_SCREAMING_SNAKE_CASE )
lowerCamelCase : List[Any] = {b: str(_SCREAMING_SNAKE_CASE ) for b, i in zip(_SCREAMING_SNAKE_CASE ,range(_SCREAMING_SNAKE_CASE ) )}
lowerCamelCase : List[Any] = []
rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight") )
rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight") )
rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias") )
rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean") )
rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var") )
for b in block_names:
lowerCamelCase : Dict = block_name_mapping[b]
rename_keys.append((f'''block{b}_expand_conv/kernel:0''', f'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((f'''block{b}_expand_bn/gamma:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((f'''block{b}_expand_bn/beta:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(f'''block{b}_expand_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(f'''block{b}_expand_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(f'''block{b}_dwconv/depthwise_kernel:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((f'''block{b}_bn/gamma:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((f'''block{b}_bn/beta:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(f'''block{b}_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(f'''block{b}_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((f'''block{b}_se_reduce/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((f'''block{b}_se_reduce/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((f'''block{b}_se_expand/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((f'''block{b}_se_expand/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(f'''block{b}_project_conv/kernel:0''', f'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((f'''block{b}_project_bn/gamma:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((f'''block{b}_project_bn/beta:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(f'''block{b}_project_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(f'''block{b}_project_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight") )
rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight") )
rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias") )
rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean") )
rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var") )
lowerCamelCase : Optional[int] = {}
for item in rename_keys:
if item[0] in original_param_names:
lowerCamelCase : List[str] = "efficientnet." + item[1]
lowerCamelCase : int = "classifier.weight"
lowerCamelCase : Union[str, Any] = "classifier.bias"
return key_mapping
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Dict:
for key, value in tf_params.items():
if "normalization" in key:
continue
lowerCamelCase : Tuple = key_mapping[key]
if "_conv" in key and "kernel" in key:
lowerCamelCase : List[Any] = torch.from_numpy(_SCREAMING_SNAKE_CASE ).permute(3 ,2 ,0 ,1 )
elif "depthwise_kernel" in key:
lowerCamelCase : int = torch.from_numpy(_SCREAMING_SNAKE_CASE ).permute(2 ,3 ,0 ,1 )
elif "kernel" in key:
lowerCamelCase : List[str] = torch.from_numpy(np.transpose(_SCREAMING_SNAKE_CASE ) )
else:
lowerCamelCase : Optional[Any] = torch.from_numpy(_SCREAMING_SNAKE_CASE )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_SCREAMING_SNAKE_CASE )
@torch.no_grad()
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Optional[int]:
lowerCamelCase : Optional[int] = model_classes[model_name](
include_top=_SCREAMING_SNAKE_CASE ,weights="imagenet" ,input_tensor=_SCREAMING_SNAKE_CASE ,input_shape=_SCREAMING_SNAKE_CASE ,pooling=_SCREAMING_SNAKE_CASE ,classes=1000 ,classifier_activation="softmax" ,)
lowerCamelCase : List[Any] = original_model.trainable_variables
lowerCamelCase : Tuple = original_model.non_trainable_variables
lowerCamelCase : Union[str, Any] = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
lowerCamelCase : List[str] = param.numpy()
lowerCamelCase : int = list(tf_params.keys() )
# Load HuggingFace model
lowerCamelCase : Union[str, Any] = get_efficientnet_config(_SCREAMING_SNAKE_CASE )
lowerCamelCase : Optional[int] = EfficientNetForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
lowerCamelCase : Tuple = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("Converting parameters..." )
lowerCamelCase : Union[str, Any] = rename_keys(_SCREAMING_SNAKE_CASE )
replace_params(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
# Initialize preprocessor and preprocess input image
lowerCamelCase : int = convert_image_processor(_SCREAMING_SNAKE_CASE )
lowerCamelCase : int = preprocessor(images=prepare_img() ,return_tensors="pt" )
# HF model inference
hf_model.eval()
with torch.no_grad():
lowerCamelCase : Optional[Any] = hf_model(**_SCREAMING_SNAKE_CASE )
lowerCamelCase : str = outputs.logits.detach().numpy()
# Original model inference
lowerCamelCase : Optional[Any] = False
lowerCamelCase : Any = CONFIG_MAP[model_name]["image_size"]
lowerCamelCase : Optional[int] = prepare_img().resize((image_size, image_size) ,resample=PIL.Image.NEAREST )
lowerCamelCase : Union[str, Any] = image.img_to_array(_SCREAMING_SNAKE_CASE )
lowerCamelCase : str = np.expand_dims(_SCREAMING_SNAKE_CASE ,axis=0 )
lowerCamelCase : Dict = original_model.predict(_SCREAMING_SNAKE_CASE )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,atol=1e-3 ), "The predicted logits are not the same."
print("Model outputs match!" )
if save_model:
# Create folder to save model
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
os.mkdir(_SCREAMING_SNAKE_CASE )
# Save converted model and image processor
hf_model.save_pretrained(_SCREAMING_SNAKE_CASE )
preprocessor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
# Push model and image processor to hub
print(f'''Pushing converted {model_name} to the hub...''' )
lowerCamelCase : int = f'''efficientnet-{model_name}'''
preprocessor.push_to_hub(_SCREAMING_SNAKE_CASE )
hf_model.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='b0',
type=str,
help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='hf_model',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--save_model', action='store_true', help='Save model to local')
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
SCREAMING_SNAKE_CASE__ : Tuple = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 48 | 1 |
def UpperCAmelCase_ ( _A , _A , _A = 0 , _A = 0 ):
SCREAMING_SNAKE_CASE__ = right or len(_lowerCAmelCase ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(_lowerCAmelCase , _lowerCAmelCase , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 354 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_SCREAMING_SNAKE_CASE : Tuple = data_utils.TransfoXLTokenizer
_SCREAMING_SNAKE_CASE : Dict = data_utils.TransfoXLCorpus
_SCREAMING_SNAKE_CASE : Union[str, Any] = data_utils
_SCREAMING_SNAKE_CASE : Any = data_utils
def UpperCAmelCase_ ( _A , _A , _A , _A ):
'''simple docstring'''
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(_A , '''rb''' ) as fp:
SCREAMING_SNAKE_CASE__ = pickle.load(_A , encoding='''latin1''' )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
SCREAMING_SNAKE_CASE__ = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
print(F'''Save vocabulary to {pytorch_vocab_dump_path}''' )
SCREAMING_SNAKE_CASE__ = corpus.vocab.__dict__
torch.save(_A , _A )
SCREAMING_SNAKE_CASE__ = corpus.__dict__
corpus_dict_no_vocab.pop('''vocab''' , _A )
SCREAMING_SNAKE_CASE__ = pytorch_dump_folder_path + '''/''' + CORPUS_NAME
print(F'''Save dataset to {pytorch_dataset_dump_path}''' )
torch.save(_A , _A )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
SCREAMING_SNAKE_CASE__ = os.path.abspath(_A )
SCREAMING_SNAKE_CASE__ = os.path.abspath(_A )
print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.''' )
# Initialise PyTorch model
if transfo_xl_config_file == "":
SCREAMING_SNAKE_CASE__ = TransfoXLConfig()
else:
SCREAMING_SNAKE_CASE__ = TransfoXLConfig.from_json_file(_A )
print(F'''Building PyTorch model from configuration: {config}''' )
SCREAMING_SNAKE_CASE__ = TransfoXLLMHeadModel(_A )
SCREAMING_SNAKE_CASE__ = load_tf_weights_in_transfo_xl(_A , _A , _A )
# Save pytorch-model
SCREAMING_SNAKE_CASE__ = os.path.join(_A , _A )
SCREAMING_SNAKE_CASE__ = os.path.join(_A , _A )
print(F'''Save PyTorch model to {os.path.abspath(_A )}''' )
torch.save(model.state_dict() , _A )
print(F'''Save configuration file to {os.path.abspath(_A )}''' )
with open(_A , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Any = argparse.ArgumentParser()
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
)
parser.add_argument(
'''--tf_checkpoint_path''',
default='''''',
type=str,
help='''An optional path to a TensorFlow checkpoint path to be converted.''',
)
parser.add_argument(
'''--transfo_xl_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--transfo_xl_dataset_file''',
default='''''',
type=str,
help='''An optional dataset file to be converted in a vocabulary.''',
)
_SCREAMING_SNAKE_CASE : Union[str, Any] = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 218 | 0 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __A( unittest.TestCase ):
"""simple docstring"""
@property
def UpperCAmelCase_ (self ):
torch.manual_seed(0 )
UpperCamelCase__ = 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
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.dummy_uncond_unet
UpperCamelCase__ = KarrasVeScheduler()
UpperCamelCase__ = KarrasVePipeline(unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = torch.manual_seed(0 )
UpperCamelCase__ = pipe(num_inference_steps=2 , generator=SCREAMING_SNAKE_CASE_ , output_type="""numpy""" ).images
UpperCamelCase__ = torch.manual_seed(0 )
UpperCamelCase__ = pipe(num_inference_steps=2 , generator=SCREAMING_SNAKE_CASE_ , output_type="""numpy""" , return_dict=SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase__ = image[0, -3:, -3:, -1]
UpperCamelCase__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCamelCase__ = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class __A( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ (self ):
UpperCamelCase__ = """google/ncsnpp-celebahq-256"""
UpperCamelCase__ = UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = KarrasVeScheduler()
UpperCamelCase__ = KarrasVePipeline(unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = torch.manual_seed(0 )
UpperCamelCase__ = pipe(num_inference_steps=20 , generator=SCREAMING_SNAKE_CASE_ , output_type="""numpy""" ).images
UpperCamelCase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
UpperCamelCase__ = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 244 |
lowerCamelCase_ = frozenset(
[
'''prompt''',
'''height''',
'''width''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
'''cross_attention_kwargs''',
]
)
lowerCamelCase_ = frozenset(['''prompt''', '''negative_prompt'''])
lowerCamelCase_ = frozenset([])
lowerCamelCase_ = frozenset(['''image'''])
lowerCamelCase_ = frozenset(
[
'''image''',
'''height''',
'''width''',
'''guidance_scale''',
]
)
lowerCamelCase_ = frozenset(['''image'''])
lowerCamelCase_ = frozenset(
[
'''prompt''',
'''image''',
'''height''',
'''width''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
]
)
lowerCamelCase_ = frozenset(['''prompt''', '''image''', '''negative_prompt'''])
lowerCamelCase_ = frozenset(
[
# Text guided image variation with an image mask
'''prompt''',
'''image''',
'''mask_image''',
'''height''',
'''width''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
]
)
lowerCamelCase_ = frozenset(['''prompt''', '''image''', '''mask_image''', '''negative_prompt'''])
lowerCamelCase_ = frozenset(
[
# image variation with an image mask
'''image''',
'''mask_image''',
'''height''',
'''width''',
'''guidance_scale''',
]
)
lowerCamelCase_ = frozenset(['''image''', '''mask_image'''])
lowerCamelCase_ = frozenset(
[
'''example_image''',
'''image''',
'''mask_image''',
'''height''',
'''width''',
'''guidance_scale''',
]
)
lowerCamelCase_ = frozenset(['''example_image''', '''image''', '''mask_image'''])
lowerCamelCase_ = frozenset(['''class_labels'''])
lowerCamelCase_ = frozenset(['''class_labels'''])
lowerCamelCase_ = frozenset(['''batch_size'''])
lowerCamelCase_ = frozenset([])
lowerCamelCase_ = frozenset(['''batch_size'''])
lowerCamelCase_ = frozenset([])
lowerCamelCase_ = frozenset(
[
'''prompt''',
'''audio_length_in_s''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
'''cross_attention_kwargs''',
]
)
lowerCamelCase_ = frozenset(['''prompt''', '''negative_prompt'''])
lowerCamelCase_ = frozenset(['''input_tokens'''])
lowerCamelCase_ = frozenset(['''input_tokens'''])
| 244 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : Tuple = (UniPCMultistepScheduler,)
UpperCamelCase__ : Optional[Any] = (('''num_inference_steps''', 25),)
def _lowerCamelCase ( self : Optional[Any] , **__SCREAMING_SNAKE_CASE : Union[str, Any]):
'''simple docstring'''
__a = {
'''num_train_timesteps''': 1_000,
'''beta_start''': 0.00_01,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''solver_order''': 2,
'''solver_type''': '''bh2''',
}
config.update(**__SCREAMING_SNAKE_CASE)
return config
def _lowerCamelCase ( self : str , __SCREAMING_SNAKE_CASE : List[Any]=0 , **__SCREAMING_SNAKE_CASE : Any):
'''simple docstring'''
__a = dict(self.forward_default_kwargs)
__a = kwargs.pop('''num_inference_steps''' , __SCREAMING_SNAKE_CASE)
__a = self.dummy_sample
__a = 0.1 * sample
__a = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__a = self.get_scheduler_config(**__SCREAMING_SNAKE_CASE)
__a = scheduler_class(**__SCREAMING_SNAKE_CASE)
scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)
# copy over dummy past residuals
__a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__SCREAMING_SNAKE_CASE)
__a = scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE)
new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)
# copy over dummy past residuals
__a = dummy_past_residuals[: new_scheduler.config.solver_order]
__a , __a = sample, sample
for t in range(__SCREAMING_SNAKE_CASE , time_step + scheduler.config.solver_order + 1):
__a = scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE).prev_sample
__a = new_scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
def _lowerCamelCase ( self : int , __SCREAMING_SNAKE_CASE : Optional[Any]=0 , **__SCREAMING_SNAKE_CASE : Any):
'''simple docstring'''
__a = dict(self.forward_default_kwargs)
__a = kwargs.pop('''num_inference_steps''' , __SCREAMING_SNAKE_CASE)
__a = self.dummy_sample
__a = 0.1 * sample
__a = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__a = self.get_scheduler_config()
__a = scheduler_class(**__SCREAMING_SNAKE_CASE)
scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)
# copy over dummy past residuals (must be after setting timesteps)
__a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__SCREAMING_SNAKE_CASE)
__a = scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE)
# copy over dummy past residuals
new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)
# copy over dummy past residual (must be after setting timesteps)
__a = dummy_past_residuals[: new_scheduler.config.solver_order]
__a = scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE).prev_sample
__a = new_scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
def _lowerCamelCase ( self : Any , __SCREAMING_SNAKE_CASE : Any=None , **__SCREAMING_SNAKE_CASE : Tuple):
'''simple docstring'''
if scheduler is None:
__a = self.scheduler_classes[0]
__a = self.get_scheduler_config(**__SCREAMING_SNAKE_CASE)
__a = scheduler_class(**__SCREAMING_SNAKE_CASE)
__a = self.scheduler_classes[0]
__a = self.get_scheduler_config(**__SCREAMING_SNAKE_CASE)
__a = scheduler_class(**__SCREAMING_SNAKE_CASE)
__a = 10
__a = self.dummy_model()
__a = self.dummy_sample_deter
scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)
for i, t in enumerate(scheduler.timesteps):
__a = model(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
__a = scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE).prev_sample
return sample
def _lowerCamelCase ( self : int):
'''simple docstring'''
__a = dict(self.forward_default_kwargs)
__a = kwargs.pop('''num_inference_steps''' , __SCREAMING_SNAKE_CASE)
for scheduler_class in self.scheduler_classes:
__a = self.get_scheduler_config()
__a = scheduler_class(**__SCREAMING_SNAKE_CASE)
__a = self.dummy_sample
__a = 0.1 * sample
if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE , '''set_timesteps'''):
scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)
elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE , '''set_timesteps'''):
__a = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
__a = [residual + 0.2, residual + 0.15, residual + 0.10]
__a = dummy_past_residuals[: scheduler.config.solver_order]
__a = scheduler.timesteps[5]
__a = scheduler.timesteps[6]
__a = scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE).prev_sample
__a = scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE).prev_sample
self.assertEqual(output_a.shape , sample.shape)
self.assertEqual(output_a.shape , output_a.shape)
def _lowerCamelCase ( self : Dict):
'''simple docstring'''
__a = UniPCMultistepScheduler(**self.get_scheduler_config())
__a = self.full_loop(scheduler=__SCREAMING_SNAKE_CASE)
__a = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))
assert abs(result_mean.item() - 0.24_64) < 1E-3
__a = DPMSolverSinglestepScheduler.from_config(scheduler.config)
__a = DEISMultistepScheduler.from_config(scheduler.config)
__a = DPMSolverMultistepScheduler.from_config(scheduler.config)
__a = UniPCMultistepScheduler.from_config(scheduler.config)
__a = self.full_loop(scheduler=__SCREAMING_SNAKE_CASE)
__a = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))
assert abs(result_mean.item() - 0.24_64) < 1E-3
def _lowerCamelCase ( self : Any):
'''simple docstring'''
for timesteps in [25, 50, 100, 999, 1_000]:
self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : List[Any]):
'''simple docstring'''
self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE)
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__SCREAMING_SNAKE_CASE , prediction_type=__SCREAMING_SNAKE_CASE , sample_max_value=__SCREAMING_SNAKE_CASE , solver_order=__SCREAMING_SNAKE_CASE , solver_type=__SCREAMING_SNAKE_CASE , )
def _lowerCamelCase ( self : Dict):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : int):
'''simple docstring'''
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__SCREAMING_SNAKE_CASE , solver_type=__SCREAMING_SNAKE_CASE , prediction_type=__SCREAMING_SNAKE_CASE , )
__a = self.full_loop(
solver_order=__SCREAMING_SNAKE_CASE , solver_type=__SCREAMING_SNAKE_CASE , prediction_type=__SCREAMING_SNAKE_CASE , )
assert not torch.isnan(__SCREAMING_SNAKE_CASE).any(), "Samples have nan numbers"
def _lowerCamelCase ( self : int):
'''simple docstring'''
self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE)
self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE , time_step=0)
def _lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
__a = self.full_loop()
__a = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))
assert abs(result_mean.item() - 0.24_64) < 1E-3
def _lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__a = self.full_loop(prediction_type='''v_prediction''')
__a = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))
assert abs(result_mean.item() - 0.10_14) < 1E-3
def _lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__a = self.scheduler_classes[0]
__a = self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE , dynamic_thresholding_ratio=0)
__a = scheduler_class(**__SCREAMING_SNAKE_CASE)
__a = 10
__a = self.dummy_model()
__a = self.dummy_sample_deter.half()
scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)
for i, t in enumerate(scheduler.timesteps):
__a = model(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
__a = scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE).prev_sample
assert sample.dtype == torch.floataa
def _lowerCamelCase ( self : List[Any] , **__SCREAMING_SNAKE_CASE : int):
'''simple docstring'''
for scheduler_class in self.scheduler_classes:
__a = self.get_scheduler_config(**__SCREAMING_SNAKE_CASE)
__a = scheduler_class(**__SCREAMING_SNAKE_CASE)
scheduler.set_timesteps(scheduler.config.num_train_timesteps)
assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps
| 131 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case :Dict = logging.get_logger()
@dataclass
class _A :
UpperCamelCase__ : nn.Module
UpperCamelCase__ : List[nn.Module] = field(default_factory=__UpperCAmelCase )
UpperCamelCase__ : list = field(default_factory=__UpperCAmelCase )
def _lowerCamelCase ( self : int , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Tensor , __SCREAMING_SNAKE_CASE : Tensor):
'''simple docstring'''
__a = len(list(m.modules())) == 1 or isinstance(__SCREAMING_SNAKE_CASE , nn.Convad) or isinstance(__SCREAMING_SNAKE_CASE , nn.BatchNormad)
if has_not_submodules:
self.traced.append(__SCREAMING_SNAKE_CASE)
def __call__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Tensor):
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook))
self.module(__SCREAMING_SNAKE_CASE)
[x.remove() for x in self.handles]
return self
@property
def _lowerCamelCase ( self : List[Any]):
'''simple docstring'''
return list(filter(lambda __SCREAMING_SNAKE_CASE: len(list(x.state_dict().keys())) > 0 , self.traced))
@dataclass
class _A :
UpperCamelCase__ : nn.Module
UpperCamelCase__ : nn.Module
UpperCamelCase__ : int = 1
UpperCamelCase__ : List = field(default_factory=__UpperCAmelCase )
UpperCamelCase__ : List = field(default_factory=__UpperCAmelCase )
UpperCamelCase__ : bool = True
def __call__( self : Any , __SCREAMING_SNAKE_CASE : Tensor):
'''simple docstring'''
__a = Tracker(self.dest)(__SCREAMING_SNAKE_CASE).parametrized
__a = Tracker(self.src)(__SCREAMING_SNAKE_CASE).parametrized
__a = list(filter(lambda __SCREAMING_SNAKE_CASE: type(__SCREAMING_SNAKE_CASE) not in self.src_skip , __SCREAMING_SNAKE_CASE))
__a = list(filter(lambda __SCREAMING_SNAKE_CASE: type(__SCREAMING_SNAKE_CASE) not in self.dest_skip , __SCREAMING_SNAKE_CASE))
if len(__SCREAMING_SNAKE_CASE) != len(__SCREAMING_SNAKE_CASE) and self.raise_if_mismatch:
raise Exception(
F'Numbers of operations are different. Source module has {len(__SCREAMING_SNAKE_CASE)} operations while'
F' destination module has {len(__SCREAMING_SNAKE_CASE)}.')
for dest_m, src_m in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE):
dest_m.load_state_dict(src_m.state_dict())
if self.verbose == 1:
print(F'Transfered from={src_m} to={dest_m}')
class _A ( nn.Module ):
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : nn.Module):
'''simple docstring'''
super().__init__()
__a = []
# - get the stem
feature_blocks.append(('''conv1''', model.stem))
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('''block'''), F'Unexpected layer name {k}'
__a = len(__SCREAMING_SNAKE_CASE) + 1
feature_blocks.append((F'res{block_index}', v))
__a = nn.ModuleDict(__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Tensor):
'''simple docstring'''
return get_trunk_forward_outputs(
__SCREAMING_SNAKE_CASE , out_feat_keys=__SCREAMING_SNAKE_CASE , feature_blocks=self._feature_blocks , )
class _A ( __UpperCAmelCase ):
def _lowerCamelCase ( self : List[Any] , __SCREAMING_SNAKE_CASE : str):
'''simple docstring'''
__a = x.split('''-''')
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:])
def __getitem__( self : List[Any] , __SCREAMING_SNAKE_CASE : str):
'''simple docstring'''
if x not in self:
__a = self.convert_name_to_timm(__SCREAMING_SNAKE_CASE)
__a = partial(lambda: (timm.create_model(__SCREAMING_SNAKE_CASE , pretrained=__SCREAMING_SNAKE_CASE).eval(), None))
else:
__a = super().__getitem__(__SCREAMING_SNAKE_CASE)
return val
class _A ( __UpperCAmelCase ):
def __getitem__( self : Tuple , __SCREAMING_SNAKE_CASE : str):
'''simple docstring'''
if "seer" in x and "in1k" not in x:
__a = RegNetModel
else:
__a = RegNetForImageClassification
return val
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
for from_key, to_key in keys:
__a = from_state_dict[from_key].clone()
print(f'Copied key={from_key} to={to_key}' )
return to_state_dict
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = True , ):
print(f'Converting {name}...' )
with torch.no_grad():
__a , __a = from_model_func()
__a = our_model_func(_UpperCAmelCase ).eval()
__a = ModuleTransfer(src=_UpperCAmelCase , dest=_UpperCAmelCase , raise_if_mismatch=_UpperCAmelCase )
__a = torch.randn((1, 3, 224, 224) )
module_transfer(_UpperCAmelCase )
if from_state_dict is not None:
__a = []
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
__a = [('''0.clf.0.weight''', '''classifier.1.weight'''), ('''0.clf.0.bias''', '''classifier.1.bias''')]
__a = manually_copy_vissl_head(_UpperCAmelCase , our_model.state_dict() , _UpperCAmelCase )
our_model.load_state_dict(_UpperCAmelCase )
__a = our_model(_UpperCAmelCase , output_hidden_states=_UpperCAmelCase )
__a = (
our_outputs.logits if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else our_outputs.last_hidden_state
)
__a = from_model(_UpperCAmelCase )
__a = from_output[-1] if type(_UpperCAmelCase ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
__a = our_outputs.hidden_states[-1]
assert torch.allclose(_UpperCAmelCase , _UpperCAmelCase ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='''Add model''' , use_temp_dir=_UpperCAmelCase , )
__a = 224 if '''seer''' not in name else 384
# we can use the convnext one
__a = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' , size=_UpperCAmelCase )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='''Add image processor''' , use_temp_dir=_UpperCAmelCase , )
print(f'Pushed {name}' )
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase = None , _UpperCAmelCase = True ):
__a = '''imagenet-1k-id2label.json'''
__a = 1000
__a = (1, num_labels)
__a = '''huggingface/label-files'''
__a = num_labels
__a = json.load(open(cached_download(hf_hub_url(_UpperCAmelCase , _UpperCAmelCase , repo_type='''dataset''' ) ) , '''r''' ) )
__a = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
__a = idalabel
__a = {v: k for k, v in idalabel.items()}
__a = partial(_UpperCAmelCase , num_labels=_UpperCAmelCase , idalabel=_UpperCAmelCase , labelaid=_UpperCAmelCase )
__a = {
'''regnet-x-002''': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 152, 368] , groups_width=8 , layer_type='''x''' ),
'''regnet-x-004''': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12] , hidden_sizes=[32, 64, 160, 384] , groups_width=16 , layer_type='''x''' ),
'''regnet-x-006''': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7] , hidden_sizes=[48, 96, 240, 528] , groups_width=24 , layer_type='''x''' ),
'''regnet-x-008''': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5] , hidden_sizes=[64, 128, 288, 672] , groups_width=16 , layer_type='''x''' ),
'''regnet-x-016''': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2] , hidden_sizes=[72, 168, 408, 912] , groups_width=24 , layer_type='''x''' ),
'''regnet-x-032''': ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2] , hidden_sizes=[96, 192, 432, 1008] , groups_width=48 , layer_type='''x''' ),
'''regnet-x-040''': ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2] , hidden_sizes=[80, 240, 560, 1360] , groups_width=40 , layer_type='''x''' ),
'''regnet-x-064''': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[168, 392, 784, 1624] , groups_width=56 , layer_type='''x''' ),
'''regnet-x-080''': ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1] , hidden_sizes=[80, 240, 720, 1920] , groups_width=120 , layer_type='''x''' ),
'''regnet-x-120''': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[224, 448, 896, 2240] , groups_width=112 , layer_type='''x''' ),
'''regnet-x-160''': ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1] , hidden_sizes=[256, 512, 896, 2048] , groups_width=128 , layer_type='''x''' ),
'''regnet-x-320''': ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1] , hidden_sizes=[336, 672, 1344, 2520] , groups_width=168 , layer_type='''x''' ),
# y variant
'''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 152, 368] , groups_width=8 ),
'''regnet-y-004''': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6] , hidden_sizes=[48, 104, 208, 440] , groups_width=8 ),
'''regnet-y-006''': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4] , hidden_sizes=[48, 112, 256, 608] , groups_width=16 ),
'''regnet-y-008''': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2] , hidden_sizes=[64, 128, 320, 768] , groups_width=16 ),
'''regnet-y-016''': ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2] , hidden_sizes=[48, 120, 336, 888] , groups_width=24 ),
'''regnet-y-032''': ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1] , hidden_sizes=[72, 216, 576, 1512] , groups_width=24 ),
'''regnet-y-040''': ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2] , hidden_sizes=[128, 192, 512, 1088] , groups_width=64 ),
'''regnet-y-064''': ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2] , hidden_sizes=[144, 288, 576, 1296] , groups_width=72 ),
'''regnet-y-080''': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[168, 448, 896, 2016] , groups_width=56 ),
'''regnet-y-120''': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[224, 448, 896, 2240] , groups_width=112 ),
'''regnet-y-160''': ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1] , hidden_sizes=[224, 448, 1232, 3024] , groups_width=112 ),
'''regnet-y-320''': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1392, 3712] , groups_width=232 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1392, 3712] , groups_width=232 ),
'''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[328, 984, 1968, 4920] , groups_width=328 ),
'''regnet-y-1280-seer''': RegNetConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[528, 1056, 2904, 7392] , groups_width=264 ),
'''regnet-y-2560-seer''': RegNetConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[640, 1696, 2544, 5088] , groups_width=640 ),
'''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[2020, 4040, 11110, 28280] , groups_width=1010 ),
# finetuned on imagenet
'''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1392, 3712] , groups_width=232 ),
'''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[328, 984, 1968, 4920] , groups_width=328 ),
'''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[528, 1056, 2904, 7392] , groups_width=264 ),
'''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[640, 1696, 2544, 5088] , groups_width=640 ),
'''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[2020, 4040, 11110, 28280] , groups_width=1010 ),
}
__a = NameToOurModelFuncMap()
__a = NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(_UpperCAmelCase , _UpperCAmelCase ) -> Tuple[nn.Module, Dict]:
__a = torch.hub.load_state_dict_from_url(_UpperCAmelCase , model_dir=str(_UpperCAmelCase ) , map_location='''cpu''' )
__a = model_func()
# check if we have a head, if yes add it
__a = files['''classy_state_dict''']['''base_model''']['''model''']
__a = model_state_dict['''trunk''']
model.load_state_dict(_UpperCAmelCase )
return model.eval(), model_state_dict["heads"]
# pretrained
__a = partial(
_UpperCAmelCase , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
__a = partial(
_UpperCAmelCase , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
__a = partial(
_UpperCAmelCase , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
__a = partial(
_UpperCAmelCase , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch''' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=1010 , w_a=1744 , w_a=6_20.83 , w_m=2.52 ) ) ) , )
# IN1K finetuned
__a = partial(
_UpperCAmelCase , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
__a = partial(
_UpperCAmelCase , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
__a = partial(
_UpperCAmelCase , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
__a = partial(
_UpperCAmelCase , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch''' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=1010 , w_a=1744 , w_a=6_20.83 , w_m=2.52 ) ) ) , )
if model_name:
convert_weight_and_push(
_UpperCAmelCase , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , names_to_config[model_name] , _UpperCAmelCase , _UpperCAmelCase , )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
_UpperCAmelCase , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
return config, expected_shape
if __name__ == "__main__":
__snake_case :Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help=(
'''The name of the model you wish to convert, it must be one of the supported regnet* architecture,'''
''' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=Path,
required=True,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
default=True,
type=bool,
required=False,
help='''If True, push model and image processor to the hub.''',
)
__snake_case :Tuple = parser.parse_args()
__snake_case :Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 131 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
a = logging.get_logger(__name__)
a = {
"""allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
"""allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
"""allenai/longformer-large-4096-finetuned-triviaqa""": (
"""https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
),
"""allenai/longformer-base-4096-extra.pos.embd.only""": (
"""https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
),
"""allenai/longformer-large-4096-extra.pos.embd.only""": (
"""https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
),
}
class SCREAMING_SNAKE_CASE__ ( lowercase_ ):
_a = 'longformer'
def __init__( self : Union[str, Any] , lowerCAmelCase : Union[List[int], int] = 512 , lowerCAmelCase : int = 2 , lowerCAmelCase : int = 1 , lowerCAmelCase : int = 0 , lowerCAmelCase : int = 2 , lowerCAmelCase : int = 3_0522 , lowerCAmelCase : int = 768 , lowerCAmelCase : int = 12 , lowerCAmelCase : int = 12 , lowerCAmelCase : int = 3072 , lowerCAmelCase : str = "gelu" , lowerCAmelCase : float = 0.1 , lowerCAmelCase : float = 0.1 , lowerCAmelCase : int = 512 , lowerCAmelCase : int = 2 , lowerCAmelCase : float = 0.02 , lowerCAmelCase : float = 1e-12 , lowerCAmelCase : bool = False , **lowerCAmelCase : List[str] , ):
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = attention_window
lowerCAmelCase = sep_token_id
lowerCAmelCase = bos_token_id
lowerCAmelCase = eos_token_id
lowerCAmelCase = vocab_size
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = hidden_act
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = max_position_embeddings
lowerCAmelCase = type_vocab_size
lowerCAmelCase = initializer_range
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = onnx_export
class SCREAMING_SNAKE_CASE__ ( lowercase_ ):
def __init__( self : List[Any] , lowerCAmelCase : "PretrainedConfig" , lowerCAmelCase : str = "default" , lowerCAmelCase : "List[PatchingSpec]" = None ):
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = True
@property
def __lowercase ( self : Dict ):
if self.task == "multiple-choice":
lowerCAmelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
lowerCAmelCase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""global_attention_mask""", dynamic_axis),
] )
@property
def __lowercase ( self : int ):
lowerCAmelCase = super().outputs
if self.task == "default":
lowerCAmelCase = {0: 'batch'}
return outputs
@property
def __lowercase ( self : Dict ):
return 1e-4
@property
def __lowercase ( self : Union[str, Any] ):
# needs to be >= 14 to support tril operator
return max(super().default_onnx_opset , 14 )
def __lowercase ( self : Tuple , lowerCAmelCase : "PreTrainedTokenizerBase" , lowerCAmelCase : int = -1 , lowerCAmelCase : int = -1 , lowerCAmelCase : bool = False , lowerCAmelCase : Optional[TensorType] = None , ):
lowerCAmelCase = super().generate_dummy_inputs(
preprocessor=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ , seq_length=SCREAMING_SNAKE_CASE_ , is_pair=SCREAMING_SNAKE_CASE_ , framework=SCREAMING_SNAKE_CASE_ )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
lowerCAmelCase = torch.zeros_like(inputs["""input_ids"""] )
# make every second token global
lowerCAmelCase = 1
return inputs
| 155 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# 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.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (
"""This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."""
"""It takes two arguments named `image` which should be the original image, and `label` which should be a text """
"""describing the elements what should be identified in the segmentation mask. The tool returns the mask."""
)
_SCREAMING_SNAKE_CASE = """CIDAS/clipseg-rd64-refined"""
_SCREAMING_SNAKE_CASE = """image_segmenter"""
_SCREAMING_SNAKE_CASE = CLIPSegForImageSegmentation
_SCREAMING_SNAKE_CASE = ["""image""", """text"""]
_SCREAMING_SNAKE_CASE = ["""image"""]
def __init__( self : Dict , *SCREAMING_SNAKE_CASE_ : Tuple , **SCREAMING_SNAKE_CASE_ : Optional[int] ):
requires_backends(self , ['vision'] )
super().__init__(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : "Image" , SCREAMING_SNAKE_CASE_ : str ):
return self.pre_processor(text=[label] , images=[image] , padding=SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , SCREAMING_SNAKE_CASE_ : int ):
with torch.no_grad():
lowerCAmelCase_ : List[str] = self.model(**SCREAMING_SNAKE_CASE_ ).logits
return logits
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
lowerCAmelCase_ : Dict = outputs.cpu().detach().numpy()
lowerCAmelCase_ : Optional[Any] = 0
lowerCAmelCase_ : Optional[Any] = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta ) )
| 224 | 0 |
"""simple docstring"""
import os
import sys
import unittest
a__ : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
a__ : Any = os.path.join(git_repo_path, '''src''', '''diffusers''')
class UpperCamelCase_ ( unittest.TestCase):
"""simple docstring"""
def UpperCAmelCase_ ( self : int ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = find_backend(" if not is_torch_available():" )
self.assertEqual(UpperCAmelCase__ , "torch" )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
__SCREAMING_SNAKE_CASE = find_backend(" if not (is_torch_available() and is_transformers_available()):" )
self.assertEqual(UpperCAmelCase__ , "torch_and_transformers" )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
__SCREAMING_SNAKE_CASE = find_backend(
" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):" )
self.assertEqual(UpperCAmelCase__ , "torch_and_transformers_and_onnx" )
def UpperCAmelCase_ ( self : int ) -> int:
__SCREAMING_SNAKE_CASE = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("torch" , UpperCAmelCase__ )
self.assertIn("torch_and_transformers" , UpperCAmelCase__ )
self.assertIn("flax_and_transformers" , UpperCAmelCase__ )
self.assertIn("torch_and_transformers_and_onnx" , UpperCAmelCase__ )
# Likewise, we can't assert on the exact content of a key
self.assertIn("UNet2DModel" , objects["torch"] )
self.assertIn("FlaxUNet2DConditionModel" , objects["flax"] )
self.assertIn("StableDiffusionPipeline" , objects["torch_and_transformers"] )
self.assertIn("FlaxStableDiffusionPipeline" , objects["flax_and_transformers"] )
self.assertIn("LMSDiscreteScheduler" , objects["torch_and_scipy"] )
self.assertIn("OnnxStableDiffusionPipeline" , objects["torch_and_transformers_and_onnx"] )
def UpperCAmelCase_ ( self : List[Any] ) -> int:
__SCREAMING_SNAKE_CASE = create_dummy_object("CONSTANT" , "'torch'" )
self.assertEqual(UpperCAmelCase__ , "\nCONSTANT = None\n" )
__SCREAMING_SNAKE_CASE = create_dummy_object("function" , "'torch'" )
self.assertEqual(
UpperCAmelCase__ , "\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n" )
__SCREAMING_SNAKE_CASE = "\nclass FakeClass(metaclass=DummyObject):\n _backends = 'torch'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, 'torch')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n"
__SCREAMING_SNAKE_CASE = create_dummy_object("FakeClass" , "'torch'" )
self.assertEqual(UpperCAmelCase__ , UpperCAmelCase__ )
def UpperCAmelCase_ ( self : List[Any] ) -> int:
__SCREAMING_SNAKE_CASE = "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n"
__SCREAMING_SNAKE_CASE = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]} )
self.assertEqual(dummy_files["torch"] , UpperCAmelCase__ )
| 195 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(lowerCAmelCase_ , lowerCAmelCase_ ) ) )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
if dataset.ndim != value_array.ndim:
__SCREAMING_SNAKE_CASE = (
"Wrong input data's dimensions... "
f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
)
raise ValueError(lowerCAmelCase_ )
try:
if dataset.shape[1] != value_array.shape[1]:
__SCREAMING_SNAKE_CASE = (
"Wrong input data's shape... "
f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
)
raise ValueError(lowerCAmelCase_ )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError("Wrong shape" )
if dataset.dtype != value_array.dtype:
__SCREAMING_SNAKE_CASE = (
"Input data have different datatype... "
f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
)
raise TypeError(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = []
for value in value_array:
__SCREAMING_SNAKE_CASE = euclidean(lowerCAmelCase_ , dataset[0] )
__SCREAMING_SNAKE_CASE = dataset[0].tolist()
for dataset_value in dataset[1:]:
__SCREAMING_SNAKE_CASE = euclidean(lowerCAmelCase_ , lowerCAmelCase_ )
if dist > temp_dist:
__SCREAMING_SNAKE_CASE = temp_dist
__SCREAMING_SNAKE_CASE = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
return np.dot(lowerCAmelCase_ , lowerCAmelCase_ ) / (norm(lowerCAmelCase_ ) * norm(lowerCAmelCase_ ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 195 | 1 |
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
__snake_case :List[Any] = logging.get_logger(__name__)
__snake_case :Dict = {
'''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
'''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
'''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : Tuple = '''mobilenet_v2'''
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : Tuple=3 , __SCREAMING_SNAKE_CASE : int=224 , __SCREAMING_SNAKE_CASE : Optional[int]=1.0 , __SCREAMING_SNAKE_CASE : Union[str, Any]=8 , __SCREAMING_SNAKE_CASE : List[Any]=8 , __SCREAMING_SNAKE_CASE : int=6 , __SCREAMING_SNAKE_CASE : Dict=32 , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : Dict=True , __SCREAMING_SNAKE_CASE : Dict="relu6" , __SCREAMING_SNAKE_CASE : Dict=True , __SCREAMING_SNAKE_CASE : Dict=0.8 , __SCREAMING_SNAKE_CASE : Dict=0.02 , __SCREAMING_SNAKE_CASE : Dict=0.0_01 , __SCREAMING_SNAKE_CASE : Dict=255 , **__SCREAMING_SNAKE_CASE : Optional[int] , ):
'''simple docstring'''
super().__init__(**__SCREAMING_SNAKE_CASE)
if depth_multiplier <= 0:
raise ValueError('''depth_multiplier must be greater than zero.''')
__a = num_channels
__a = image_size
__a = depth_multiplier
__a = depth_divisible_by
__a = min_depth
__a = expand_ratio
__a = output_stride
__a = first_layer_is_expansion
__a = finegrained_output
__a = hidden_act
__a = tf_padding
__a = classifier_dropout_prob
__a = initializer_range
__a = layer_norm_eps
__a = semantic_loss_ignore_index
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : Any = version.parse('''1.11''' )
@property
def _lowerCamelCase ( self : Dict):
'''simple docstring'''
return OrderedDict([('''pixel_values''', {0: '''batch'''})])
@property
def _lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
if self.task == "image-classification":
return OrderedDict([('''logits''', {0: '''batch'''})])
else:
return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})])
@property
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
return 1E-4
| 49 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING
__snake_case :List[Any] = logging.get_logger(__name__)
@add_end_docstrings(__UpperCAmelCase )
class _A ( __UpperCAmelCase ):
def __init__( self : Dict , **__SCREAMING_SNAKE_CASE : Tuple):
'''simple docstring'''
super().__init__(**__SCREAMING_SNAKE_CASE)
requires_backends(self , '''vision''')
requires_backends(self , '''torch''')
if self.framework != "pt":
raise ValueError(F'The {self.__class__} is only available in PyTorch.')
self.check_model_type(__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Any , **__SCREAMING_SNAKE_CASE : Any):
'''simple docstring'''
__a = {}
__a = {}
__a = {}
# preprocess args
if "points_per_batch" in kwargs:
__a = kwargs['''points_per_batch''']
if "points_per_crop" in kwargs:
__a = kwargs['''points_per_crop''']
if "crops_n_layers" in kwargs:
__a = kwargs['''crops_n_layers''']
if "crop_overlap_ratio" in kwargs:
__a = kwargs['''crop_overlap_ratio''']
if "crop_n_points_downscale_factor" in kwargs:
__a = kwargs['''crop_n_points_downscale_factor''']
# postprocess args
if "pred_iou_thresh" in kwargs:
__a = kwargs['''pred_iou_thresh''']
if "stability_score_offset" in kwargs:
__a = kwargs['''stability_score_offset''']
if "mask_threshold" in kwargs:
__a = kwargs['''mask_threshold''']
if "stability_score_thresh" in kwargs:
__a = kwargs['''stability_score_thresh''']
if "crops_nms_thresh" in kwargs:
__a = kwargs['''crops_nms_thresh''']
if "output_rle_mask" in kwargs:
__a = kwargs['''output_rle_mask''']
if "output_bboxes_mask" in kwargs:
__a = kwargs['''output_bboxes_mask''']
return preprocess_kwargs, forward_params, postprocess_kwargs
def __call__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : int , *__SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Tuple=None , __SCREAMING_SNAKE_CASE : Any=None , **__SCREAMING_SNAKE_CASE : str):
'''simple docstring'''
return super().__call__(__SCREAMING_SNAKE_CASE , *__SCREAMING_SNAKE_CASE , num_workers=__SCREAMING_SNAKE_CASE , batch_size=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Any=64 , __SCREAMING_SNAKE_CASE : int = 0 , __SCREAMING_SNAKE_CASE : float = 512 / 1_500 , __SCREAMING_SNAKE_CASE : Optional[int] = 32 , __SCREAMING_SNAKE_CASE : Optional[int] = 1 , ):
'''simple docstring'''
__a = load_image(__SCREAMING_SNAKE_CASE)
__a = self.image_processor.size['''longest_edge''']
__a , __a , __a , __a = self.image_processor.generate_crop_boxes(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
__a = self.image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='''pt''')
with self.device_placement():
if self.framework == "pt":
__a = self.get_inference_context()
with inference_context():
__a = self._ensure_tensor_on_device(__SCREAMING_SNAKE_CASE , device=self.device)
__a = self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''))
__a = image_embeddings
__a = grid_points.shape[1]
__a = points_per_batch if points_per_batch is not None else n_points
if points_per_batch <= 0:
raise ValueError(
'''Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '''
'''To return all points at once, set points_per_batch to None''')
for i in range(0 , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE):
__a = grid_points[:, i : i + points_per_batch, :, :]
__a = input_labels[:, i : i + points_per_batch]
__a = i == n_points - points_per_batch
yield {
"input_points": batched_points,
"input_labels": labels,
"input_boxes": crop_boxes,
"is_last": is_last,
**model_inputs,
}
def _lowerCamelCase ( self : Any , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : int=0.88 , __SCREAMING_SNAKE_CASE : List[Any]=0.95 , __SCREAMING_SNAKE_CASE : List[Any]=0 , __SCREAMING_SNAKE_CASE : int=1 , ):
'''simple docstring'''
__a = model_inputs.pop('''input_boxes''')
__a = model_inputs.pop('''is_last''')
__a = model_inputs.pop('''original_sizes''').tolist()
__a = model_inputs.pop('''reshaped_input_sizes''').tolist()
__a = self.model(**__SCREAMING_SNAKE_CASE)
# post processing happens here in order to avoid CPU GPU copies of ALL the masks
__a = model_outputs['''pred_masks''']
__a = self.image_processor.post_process_masks(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , binarize=__SCREAMING_SNAKE_CASE)
__a = model_outputs['''iou_scores''']
__a , __a , __a = self.image_processor.filter_masks(
masks[0] , iou_scores[0] , original_sizes[0] , input_boxes[0] , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , )
return {
"masks": masks,
"is_last": is_last,
"boxes": boxes,
"iou_scores": iou_scores,
}
def _lowerCamelCase ( self : int , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Dict=False , __SCREAMING_SNAKE_CASE : Tuple=False , __SCREAMING_SNAKE_CASE : int=0.7 , ):
'''simple docstring'''
__a = []
__a = []
__a = []
for model_output in model_outputs:
all_scores.append(model_output.pop('''iou_scores'''))
all_masks.extend(model_output.pop('''masks'''))
all_boxes.append(model_output.pop('''boxes'''))
__a = torch.cat(__SCREAMING_SNAKE_CASE)
__a = torch.cat(__SCREAMING_SNAKE_CASE)
__a , __a , __a , __a = self.image_processor.post_process_for_mask_generation(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
__a = defaultdict(__SCREAMING_SNAKE_CASE)
for output in model_outputs:
for k, v in output.items():
extra[k].append(__SCREAMING_SNAKE_CASE)
__a = {}
if output_rle_mask:
__a = rle_mask
if output_bboxes_mask:
__a = bounding_boxes
return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
| 49 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__UpperCAmelCase :List[str] = logging.get_logger(__name__)
class a ( _a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = ["input_features", "is_longer"]
def __init__( self : Optional[Any] , snake_case : int=64 , snake_case : List[str]=4_8000 , snake_case : int=480 , snake_case : Optional[int]=10 , snake_case : List[str]=1024 , snake_case : List[str]=0.0 , snake_case : Union[str, Any]=False , snake_case : float = 0 , snake_case : float = 1_4000 , snake_case : int = None , snake_case : str = "fusion" , snake_case : str = "repeatpad" , **snake_case : Dict , ) -> Union[str, Any]:
super().__init__(
feature_size=snake_case , sampling_rate=snake_case , padding_value=snake_case , return_attention_mask=snake_case , **snake_case , )
__UpperCAmelCase : str = top_db
__UpperCAmelCase : List[str] = truncation
__UpperCAmelCase : Union[str, Any] = padding
__UpperCAmelCase : str = fft_window_size
__UpperCAmelCase : List[str] = (fft_window_size >> 1) + 1
__UpperCAmelCase : Any = hop_length
__UpperCAmelCase : Optional[Any] = max_length_s
__UpperCAmelCase : str = max_length_s * sampling_rate
__UpperCAmelCase : Any = sampling_rate
__UpperCAmelCase : Optional[int] = frequency_min
__UpperCAmelCase : Dict = frequency_max
__UpperCAmelCase : Optional[int] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=snake_case , min_frequency=snake_case , max_frequency=snake_case , sampling_rate=snake_case , norm=snake_case , mel_scale='''htk''' , )
__UpperCAmelCase : List[Any] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=snake_case , min_frequency=snake_case , max_frequency=snake_case , sampling_rate=snake_case , norm='''slaney''' , mel_scale='''slaney''' , )
def lowerCamelCase__ ( self : str ) -> Dict[str, Any]:
__UpperCAmelCase : int = copy.deepcopy(self.__dict__ )
__UpperCAmelCase : Optional[int] = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def lowerCamelCase__ ( self : str , snake_case : np.array , snake_case : Optional[np.array] = None ) -> np.ndarray:
__UpperCAmelCase : Dict = spectrogram(
snake_case , window_function(self.fft_window_size , '''hann''' ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=snake_case , log_mel='''dB''' , )
return log_mel_spectrogram.T
def lowerCamelCase__ ( self : Optional[int] , snake_case : List[str] , snake_case : List[Any] , snake_case : Tuple ) -> List[str]:
__UpperCAmelCase : int = np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
__UpperCAmelCase : List[Any] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
__UpperCAmelCase : str = [0]
# randomly choose index for each part
__UpperCAmelCase : Any = np.random.choice(ranges[0] )
__UpperCAmelCase : List[str] = np.random.choice(ranges[1] )
__UpperCAmelCase : Dict = np.random.choice(ranges[2] )
__UpperCAmelCase : Dict = mel[idx_front : idx_front + chunk_frames, :]
__UpperCAmelCase : Optional[int] = mel[idx_middle : idx_middle + chunk_frames, :]
__UpperCAmelCase : Any = mel[idx_back : idx_back + chunk_frames, :]
__UpperCAmelCase : Any = torch.tensor(mel[None, None, :] )
__UpperCAmelCase : List[str] = torch.nn.functional.interpolate(
snake_case , size=[chunk_frames, 64] , mode='''bilinear''' , align_corners=snake_case )
__UpperCAmelCase : List[Any] = mel_shrink[0][0].numpy()
__UpperCAmelCase : int = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def lowerCamelCase__ ( self : Dict , snake_case : np.array , snake_case : Optional[Any] , snake_case : List[Any] , snake_case : Optional[Any] ) -> np.array:
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
__UpperCAmelCase : Optional[Any] = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
__UpperCAmelCase : Dict = len(snake_case ) - max_length
__UpperCAmelCase : Optional[int] = np.random.randint(0 , overflow + 1 )
__UpperCAmelCase : Dict = waveform[idx : idx + max_length]
__UpperCAmelCase : List[str] = self._np_extract_fbank_features(snake_case , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
__UpperCAmelCase : Dict = self._np_extract_fbank_features(snake_case , self.mel_filters )
__UpperCAmelCase : Tuple = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
__UpperCAmelCase : int = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
__UpperCAmelCase : str = np.stack([mel, mel, mel, mel] , axis=0 )
__UpperCAmelCase : Union[str, Any] = False
else:
__UpperCAmelCase : List[Any] = self._random_mel_fusion(snake_case , snake_case , snake_case )
__UpperCAmelCase : Optional[Any] = True
else:
raise NotImplementedError(f'data_truncating {truncation} not implemented' )
else:
__UpperCAmelCase : Dict = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
__UpperCAmelCase : Tuple = int(max_length / len(snake_case ) )
__UpperCAmelCase : Tuple = np.stack(np.tile(snake_case , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
__UpperCAmelCase : Union[str, Any] = int(max_length / len(snake_case ) )
__UpperCAmelCase : Tuple = np.stack(np.tile(snake_case , snake_case ) )
__UpperCAmelCase : List[Any] = np.pad(snake_case , (0, max_length - waveform.shape[0]) , mode='''constant''' , constant_values=0 )
if truncation == "fusion":
__UpperCAmelCase : Optional[int] = self._np_extract_fbank_features(snake_case , self.mel_filters )
__UpperCAmelCase : Optional[int] = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
__UpperCAmelCase : List[str] = self._np_extract_fbank_features(snake_case , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : str , snake_case : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , snake_case : str = None , snake_case : Optional[str] = None , snake_case : Optional[int] = None , snake_case : Optional[int] = None , snake_case : Optional[Union[str, TensorType]] = None , **snake_case : Dict , ) -> BatchFeature:
__UpperCAmelCase : List[Any] = truncation if truncation is not None else self.truncation
__UpperCAmelCase : Union[str, Any] = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
f' was sampled with {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
__UpperCAmelCase : Optional[int] = isinstance(snake_case , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
__UpperCAmelCase : int = is_batched_numpy or (
isinstance(snake_case , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
__UpperCAmelCase : int = [np.asarray(snake_case , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(snake_case , np.ndarray ):
__UpperCAmelCase : Union[str, Any] = np.asarray(snake_case , dtype=np.floataa )
elif isinstance(snake_case , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
__UpperCAmelCase : Optional[Any] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
__UpperCAmelCase : Any = [np.asarray(snake_case )]
# convert to mel spectrogram, truncate and pad if needed.
__UpperCAmelCase : Optional[Any] = [
self._get_input_mel(snake_case , max_length if max_length else self.nb_max_samples , snake_case , snake_case )
for waveform in raw_speech
]
__UpperCAmelCase : Any = []
__UpperCAmelCase : Any = []
for mel, longer in padded_inputs:
input_mel.append(snake_case )
is_longer.append(snake_case )
if truncation == "fusion" and sum(snake_case ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
__UpperCAmelCase : int = np.random.randint(0 , len(snake_case ) )
__UpperCAmelCase : List[str] = True
if isinstance(input_mel[0] , snake_case ):
__UpperCAmelCase : str = [np.asarray(snake_case , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
__UpperCAmelCase : Optional[int] = [[longer] for longer in is_longer]
__UpperCAmelCase : List[str] = {'''input_features''': input_mel, '''is_longer''': is_longer}
__UpperCAmelCase : Optional[Any] = BatchFeature(snake_case )
if return_tensors is not None:
__UpperCAmelCase : List[Any] = input_features.convert_to_tensors(snake_case )
return input_features | 352 |
'''simple docstring'''
import os
import sys
import unittest
__UpperCAmelCase :Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase :Dict = os.path.join(git_repo_path, "src", "diffusers")
class a ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : Any ) -> int:
__UpperCAmelCase : Optional[Any] = find_backend(''' if not is_torch_available():''' )
self.assertEqual(snake_case , '''torch''' )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
__UpperCAmelCase : Union[str, Any] = find_backend(''' if not (is_torch_available() and is_transformers_available()):''' )
self.assertEqual(snake_case , '''torch_and_transformers''' )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
__UpperCAmelCase : List[str] = find_backend(
''' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):''' )
self.assertEqual(snake_case , '''torch_and_transformers_and_onnx''' )
def lowerCamelCase__ ( self : Optional[int] ) -> int:
__UpperCAmelCase : Tuple = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('''torch''' , snake_case )
self.assertIn('''torch_and_transformers''' , snake_case )
self.assertIn('''flax_and_transformers''' , snake_case )
self.assertIn('''torch_and_transformers_and_onnx''' , snake_case )
# Likewise, we can't assert on the exact content of a key
self.assertIn('''UNet2DModel''' , objects['''torch'''] )
self.assertIn('''FlaxUNet2DConditionModel''' , objects['''flax'''] )
self.assertIn('''StableDiffusionPipeline''' , objects['''torch_and_transformers'''] )
self.assertIn('''FlaxStableDiffusionPipeline''' , objects['''flax_and_transformers'''] )
self.assertIn('''LMSDiscreteScheduler''' , objects['''torch_and_scipy'''] )
self.assertIn('''OnnxStableDiffusionPipeline''' , objects['''torch_and_transformers_and_onnx'''] )
def lowerCamelCase__ ( self : Optional[int] ) -> List[Any]:
__UpperCAmelCase : str = create_dummy_object('''CONSTANT''' , '''\'torch\'''' )
self.assertEqual(snake_case , '''\nCONSTANT = None\n''' )
__UpperCAmelCase : Union[str, Any] = create_dummy_object('''function''' , '''\'torch\'''' )
self.assertEqual(
snake_case , '''\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n''' )
__UpperCAmelCase : Optional[int] = '''
class FakeClass(metaclass=DummyObject):
_backends = \'torch\'
def __init__(self, *args, **kwargs):
requires_backends(self, \'torch\')
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, \'torch\')
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, \'torch\')
'''
__UpperCAmelCase : Optional[Any] = create_dummy_object('''FakeClass''' , '''\'torch\'''' )
self.assertEqual(snake_case , snake_case )
def lowerCamelCase__ ( self : int ) -> List[Any]:
__UpperCAmelCase : List[str] = '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, ["torch"])
class FakeClass(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
'''
__UpperCAmelCase : Optional[int] = create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']} )
self.assertEqual(dummy_files['''torch'''] , snake_case ) | 240 | 0 |
'''simple docstring'''
import numpy as np
import qiskit
def _lowerCAmelCase ( __snake_case : int = 8 , __snake_case : int | None = None ) -> str:
__A : int = np.random.default_rng(seed=_UpperCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
__A : Dict = 6 * key_len
# Measurement basis for Alice's qubits.
__A : Optional[int] = rng.integers(2 , size=_UpperCAmelCase )
# The set of states Alice will prepare.
__A : Optional[int] = rng.integers(2 , size=_UpperCAmelCase )
# Measurement basis for Bob's qubits.
__A : List[str] = rng.integers(2 , size=_UpperCAmelCase )
# Quantum Circuit to simulate BB84
__A : Optional[Any] = qiskit.QuantumCircuit(_UpperCAmelCase , name='BB84' )
# Alice prepares her qubits according to rules above.
for index, _ in enumerate(_UpperCAmelCase ):
if alice_state[index] == 1:
bbaa_circ.x(_UpperCAmelCase )
if alice_basis[index] == 1:
bbaa_circ.h(_UpperCAmelCase )
bbaa_circ.barrier()
# Bob measures the received qubits according to rules above.
for index, _ in enumerate(_UpperCAmelCase ):
if bob_basis[index] == 1:
bbaa_circ.h(_UpperCAmelCase )
bbaa_circ.barrier()
bbaa_circ.measure_all()
# Simulate the quantum circuit.
__A : Dict = qiskit.Aer.get_backend('aer_simulator' )
# We only need to run one shot because the key is unique.
# Multiple shots will produce the same key.
__A : Union[str, Any] = qiskit.execute(_UpperCAmelCase , _UpperCAmelCase , shots=1 , seed_simulator=_UpperCAmelCase )
# Returns the result of measurement.
__A : Optional[Any] = job.result().get_counts(_UpperCAmelCase ).most_frequent()
# Extracting the generated key from the simulation results.
# Only keep measurement results where Alice and Bob chose the same basis.
__A : str = """""".join(
[
result_bit
for alice_basis_bit, bob_basis_bit, result_bit in zip(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if alice_basis_bit == bob_basis_bit
] )
# Get final key. Pad with 0 if too short, otherwise truncate.
__A : List[Any] = gen_key[:key_len] if len(_UpperCAmelCase ) >= key_len else gen_key.ljust(_UpperCAmelCase , '0' )
return key
if __name__ == "__main__":
print(f"""The generated key is : {bbaa(8, seed=0)}""")
from doctest import testmod
testmod() | 190 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class A__ ( UpperCAmelCase__ ):
__UpperCamelCase : str
__UpperCamelCase : int
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> list[str]:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
return [s[i:] + s[:i] for i in range(len(_UpperCAmelCase ) )]
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> BWTTransformDict:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
if not s:
raise ValueError("""The parameter s must not be empty.""" )
_a : List[Any] =all_rotations(_UpperCAmelCase )
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
_a : BWTTransformDict ={
"bwt_string": "".join([word[-1] for word in rotations] ),
"idx_original_string": rotations.index(_UpperCAmelCase ),
}
return response
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ,_UpperCAmelCase : int ) -> str:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
raise TypeError("""The parameter bwt_string type must be str.""" )
if not bwt_string:
raise ValueError("""The parameter bwt_string must not be empty.""" )
try:
_a : List[str] =int(_UpperCAmelCase )
except ValueError:
raise TypeError(
"""The parameter idx_original_string type must be int or passive"""
""" of cast to int.""" )
if idx_original_string < 0:
raise ValueError("""The parameter idx_original_string must not be lower than 0.""" )
if idx_original_string >= len(_UpperCAmelCase ):
raise ValueError(
"""The parameter idx_original_string must be lower than""" """ len(bwt_string).""" )
_a : Optional[int] =[""""""] * len(_UpperCAmelCase )
for _ in range(len(_UpperCAmelCase ) ):
for i in range(len(_UpperCAmelCase ) ):
_a : int =bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
A__: Any = '''Provide a string that I will generate its BWT transform: '''
A__: Union[str, Any] = input(entry_msg).strip()
A__: Optional[int] = bwt_transform(s)
print(
F"Burrows Wheeler transform for string '{s}' results "
F"in '{result['bwt_string']}'"
)
A__: Union[str, Any] = reverse_bwt(result['''bwt_string'''], result['''idx_original_string'''])
print(
F"Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' "
F"we get original string '{original_string}'"
)
| 276 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _UpperCAmelCase ( lowerCAmelCase_ , unittest.TestCase ):
a : Union[str, Any] =LEDTokenizer
a : Dict =LEDTokenizerFast
a : Union[str, Any] =True
def lowerCamelCase__ ( self ):
'''simple docstring'''
super().setUp()
__lowerCAmelCase = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
__lowerCAmelCase = dict(zip(__SCREAMING_SNAKE_CASE,range(len(__SCREAMING_SNAKE_CASE ) ) ) )
__lowerCAmelCase = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
__lowerCAmelCase = {"""unk_token""": """<unk>"""}
__lowerCAmelCase = os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["""vocab_file"""] )
__lowerCAmelCase = os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file,"""w""",encoding="""utf-8""" ) as fp:
fp.write(json.dumps(__SCREAMING_SNAKE_CASE ) + """\n""" )
with open(self.merges_file,"""w""",encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(__SCREAMING_SNAKE_CASE ) )
def lowerCamelCase__ ( self,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname,**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return "lower newer", "lower newer"
@cached_property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return LEDTokenizer.from_pretrained("""allenai/led-base-16384""" )
@cached_property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384""" )
@require_torch
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
__lowerCAmelCase = [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__lowerCAmelCase = tokenizer(__SCREAMING_SNAKE_CASE,max_length=len(__SCREAMING_SNAKE_CASE ),padding=__SCREAMING_SNAKE_CASE,return_tensors="""pt""" )
self.assertIsInstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
self.assertEqual((2, 9),batch.input_ids.shape )
self.assertEqual((2, 9),batch.attention_mask.shape )
__lowerCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
@require_torch
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__lowerCAmelCase = tokenizer(__SCREAMING_SNAKE_CASE,padding=__SCREAMING_SNAKE_CASE,return_tensors="""pt""" )
self.assertIn("""input_ids""",__SCREAMING_SNAKE_CASE )
self.assertIn("""attention_mask""",__SCREAMING_SNAKE_CASE )
self.assertNotIn("""labels""",__SCREAMING_SNAKE_CASE )
self.assertNotIn("""decoder_attention_mask""",__SCREAMING_SNAKE_CASE )
@require_torch
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = [
"""Summary of the text.""",
"""Another summary.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__lowerCAmelCase = tokenizer(text_target=__SCREAMING_SNAKE_CASE,max_length=32,padding="""max_length""",return_tensors="""pt""" )
self.assertEqual(32,targets["""input_ids"""].shape[1] )
@require_torch
def lowerCamelCase__ ( self ):
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__lowerCAmelCase = tokenizer(
["""I am a small frog""" * 10_24, """I am a small frog"""],padding=__SCREAMING_SNAKE_CASE,truncation=__SCREAMING_SNAKE_CASE,return_tensors="""pt""" )
self.assertIsInstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
self.assertEqual(batch.input_ids.shape,(2, 51_22) )
@require_torch
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = ["""A long paragraph for summarization."""]
__lowerCAmelCase = [
"""Summary of the text.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__lowerCAmelCase = tokenizer(__SCREAMING_SNAKE_CASE,return_tensors="""pt""" )
__lowerCAmelCase = tokenizer(text_target=__SCREAMING_SNAKE_CASE,return_tensors="""pt""" )
__lowerCAmelCase = inputs["""input_ids"""]
__lowerCAmelCase = targets["""input_ids"""]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
@require_torch
def lowerCamelCase__ ( self ):
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__lowerCAmelCase = ["""Summary of the text.""", """Another summary."""]
__lowerCAmelCase = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
__lowerCAmelCase = tokenizer(__SCREAMING_SNAKE_CASE,padding=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = [[0] * len(__SCREAMING_SNAKE_CASE ) for x in encoded_output["""input_ids"""]]
__lowerCAmelCase = tokenizer.pad(__SCREAMING_SNAKE_CASE )
self.assertSequenceEqual(outputs["""global_attention_mask"""],__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = """A, <mask> AllenNLP sentence."""
__lowerCAmelCase = tokenizer_r.encode_plus(__SCREAMING_SNAKE_CASE,add_special_tokens=__SCREAMING_SNAKE_CASE,return_token_type_ids=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = tokenizer_p.encode_plus(__SCREAMING_SNAKE_CASE,add_special_tokens=__SCREAMING_SNAKE_CASE,return_token_type_ids=__SCREAMING_SNAKE_CASE )
self.assertEqual(sum(tokens_r["""token_type_ids"""] ),sum(tokens_p["""token_type_ids"""] ) )
self.assertEqual(
sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] ),sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] ),)
__lowerCAmelCase = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
__lowerCAmelCase = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )
self.assertSequenceEqual(tokens_p["""input_ids"""],[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r["""input_ids"""],[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
__SCREAMING_SNAKE_CASE,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
self.assertSequenceEqual(
__SCREAMING_SNAKE_CASE,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
| 363 |
'''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 _UpperCAmelCase ( lowerCAmelCase_ , unittest.TestCase ):
a : str =KandinskyVaaInpaintPipeline
a : int =["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
a : str =[
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
a : Optional[int] =[
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
a : Dict =False
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return 32
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return 32
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self.time_input_dim
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return 1_00
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCAmelCase = {
"""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 = UNetaDConditionModel(**__SCREAMING_SNAKE_CASE )
return model
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
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 ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCAmelCase = VQModel(**self.dummy_movq_kwargs )
return model
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.dummy_unet
__lowerCAmelCase = self.dummy_movq
__lowerCAmelCase = 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 = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=0 ):
'''simple docstring'''
__lowerCAmelCase = floats_tensor((1, self.text_embedder_hidden_size),rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = floats_tensor((1, self.text_embedder_hidden_size),rng=random.Random(seed + 1 ) ).to(
__SCREAMING_SNAKE_CASE )
# create init_image
__lowerCAmelCase = floats_tensor((1, 3, 64, 64),rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = image.cpu().permute(0,2,3,1 )[0]
__lowerCAmelCase = Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE ) ).convert("""RGB""" ).resize((2_56, 2_56) )
# create mask
__lowerCAmelCase = np.ones((64, 64),dtype=np.floataa )
__lowerCAmelCase = 0
if str(__SCREAMING_SNAKE_CASE ).startswith("""mps""" ):
__lowerCAmelCase = torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = {
"""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 ):
'''simple docstring'''
__lowerCAmelCase = """cpu"""
__lowerCAmelCase = self.get_dummy_components()
__lowerCAmelCase = self.pipeline_class(**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase = output.images
__lowerCAmelCase = pipe(
**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE ),return_dict=__SCREAMING_SNAKE_CASE,)[0]
__lowerCAmelCase = image[0, -3:, -3:, -1]
__lowerCAmelCase = image_from_tuple[0, -3:, -3:, -1]
print(f'image.shape {image.shape}' )
assert image.shape == (1, 64, 64, 3)
__lowerCAmelCase = 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 ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy""" )
__lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
__lowerCAmelCase = np.ones((7_68, 7_68),dtype=np.floataa )
__lowerCAmelCase = 0
__lowerCAmelCase = """a hat"""
__lowerCAmelCase = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""",torch_dtype=torch.floataa )
pipe_prior.to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = KandinskyVaaInpaintPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-decoder-inpaint""",torch_dtype=torch.floataa )
__lowerCAmelCase = pipeline.to(__SCREAMING_SNAKE_CASE )
pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
__lowerCAmelCase , __lowerCAmelCase = pipe_prior(
__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE,num_inference_steps=5,negative_prompt="""""",).to_tuple()
__lowerCAmelCase = 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 = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
| 46 | 0 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
A : Optional[Any] = logging.get_logger(__name__)
def _lowerCamelCase ( ):
'''simple docstring'''
__lowerCAmelCase = os.getenv("SM_HP_MP_PARAMETERS" , "{}" )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
__lowerCAmelCase = json.loads(_UpperCamelCase )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
__lowerCAmelCase = os.getenv("SM_FRAMEWORK_PARAMS" , "{}" )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
__lowerCAmelCase = json.loads(_UpperCamelCase )
if not mpi_options.get("sagemaker_mpi_enabled" , _UpperCamelCase ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec("smdistributed" ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : str =field(
default="""""" ,metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""} ,)
def snake_case ( self ):
super().__post_init__()
warnings.warn(
"`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
"`TrainingArguments` instead." , __a , )
@cached_property
def snake_case ( self ):
logger.info("PyTorch: setting up devices" )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
"torch.distributed process group is initialized, but local_rank == -1. "
"In order to use Torch DDP, launch your script with `python -m torch.distributed.launch" )
if self.no_cuda:
__lowerCAmelCase = torch.device("cpu" )
__lowerCAmelCase = 0
elif is_sagemaker_model_parallel_available():
__lowerCAmelCase = smp.local_rank()
__lowerCAmelCase = torch.device("cuda" , __a )
__lowerCAmelCase = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend="smddp" , timeout=self.ddp_timeout_delta )
__lowerCAmelCase = int(os.getenv("SMDATAPARALLEL_LOCAL_RANK" ) )
__lowerCAmelCase = torch.device("cuda" , self.local_rank )
__lowerCAmelCase = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
__lowerCAmelCase = torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
__lowerCAmelCase = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend="nccl" , timeout=self.ddp_timeout_delta )
__lowerCAmelCase = torch.device("cuda" , self.local_rank )
__lowerCAmelCase = 1
if device.type == "cuda":
torch.cuda.set_device(__a )
return device
@property
def snake_case ( self ):
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def snake_case ( self ):
return not is_sagemaker_model_parallel_available()
@property
def snake_case ( self ):
return False
| 57 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class lowerCAmelCase_ ( lowerCamelCase_ ):
'''simple docstring'''
lowerCAmelCase_ : Union[List[np.ndarray], torch.FloatTensor]
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 .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 346 | 0 |
"""simple docstring"""
import logging
from transformers import PretrainedConfig
lowerCAmelCase_ : List[Any] = logging.getLogger(__name__)
lowerCAmelCase_ : int = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class UpperCamelCase_ ( lowerCamelCase_ ):
_A : Tuple = """bertabs"""
def __init__( self , snake_case__=3_05_22 , snake_case__=5_12 , snake_case__=6 , snake_case__=5_12 , snake_case__=8 , snake_case__=5_12 , snake_case__=0.2 , snake_case__=6 , snake_case__=7_68 , snake_case__=8 , snake_case__=20_48 , snake_case__=0.2 , **snake_case__ , ) -> Any:
"""simple docstring"""
super().__init__(**_UpperCAmelCase )
UpperCAmelCase = vocab_size
UpperCAmelCase = max_pos
UpperCAmelCase = enc_layers
UpperCAmelCase = enc_hidden_size
UpperCAmelCase = enc_heads
UpperCAmelCase = enc_ff_size
UpperCAmelCase = enc_dropout
UpperCAmelCase = dec_layers
UpperCAmelCase = dec_hidden_size
UpperCAmelCase = dec_heads
UpperCAmelCase = dec_ff_size
UpperCAmelCase = dec_dropout
| 367 |
"""simple docstring"""
import os
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = len(grid[0] )
UpperCAmelCase = len(lowerCAmelCase )
UpperCAmelCase = 0
UpperCAmelCase = 0
UpperCAmelCase = 0
# Check vertically, horizontally, diagonally at the same time (only works
# for nxn grid)
for i in range(lowerCAmelCase ):
for j in range(n_rows - 3 ):
UpperCAmelCase = grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
UpperCAmelCase = grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]
# Left-to-right diagonal (\) product
if i < n_columns - 3:
UpperCAmelCase = (
grid[i][j]
* grid[i + 1][j + 1]
* grid[i + 2][j + 2]
* grid[i + 3][j + 3]
)
# Right-to-left diagonal(/) product
if i > 2:
UpperCAmelCase = (
grid[i][j]
* grid[i - 1][j + 1]
* grid[i - 2][j + 2]
* grid[i - 3][j + 3]
)
UpperCAmelCase = max(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
if max_product > largest:
UpperCAmelCase = max_product
return largest
def _lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase = []
with open(os.path.dirname(lowerCAmelCase ) + """/grid.txt""" ) as file:
for line in file:
grid.append(line.strip("""\n""" ).split(""" """ ) )
UpperCAmelCase = [[int(lowerCAmelCase ) for i in grid[j]] for j in range(len(lowerCAmelCase ) )]
return largest_product(lowerCAmelCase )
if __name__ == "__main__":
print(solution())
| 248 | 0 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class UpperCAmelCase_ ( _A ):
'''simple docstring'''
def __init__( self : List[Any] , UpperCamelCase__ : NestedDataStructureLike[PathLike] , UpperCamelCase__ : Optional[NamedSplit] = None , UpperCamelCase__ : Optional[Features] = None , UpperCamelCase__ : str = None , UpperCamelCase__ : bool = False , UpperCamelCase__ : bool = False , UpperCamelCase__ : Optional[int] = None , **UpperCamelCase__ : List[str] , ) -> List[Any]:
"""simple docstring"""
super().__init__(
UpperCamelCase__ , split=UpperCamelCase__ , features=UpperCamelCase__ , cache_dir=UpperCamelCase__ , keep_in_memory=UpperCamelCase__ , streaming=UpperCamelCase__ , num_proc=UpperCamelCase__ , **UpperCamelCase__ , )
__magic_name__ = path_or_paths if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else {self.split: path_or_paths}
__magic_name__ = Text(
cache_dir=UpperCamelCase__ , data_files=UpperCamelCase__ , features=UpperCamelCase__ , **UpperCamelCase__ , )
def _lowercase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
if self.streaming:
__magic_name__ = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
__magic_name__ = None
__magic_name__ = None
__magic_name__ = None
__magic_name__ = None
self.builder.download_and_prepare(
download_config=UpperCamelCase__ , download_mode=UpperCamelCase__ , verification_mode=UpperCamelCase__ , base_path=UpperCamelCase__ , num_proc=self.num_proc , )
__magic_name__ = self.builder.as_dataset(
split=self.split , verification_mode=UpperCamelCase__ , in_memory=self.keep_in_memory )
return dataset
| 88 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
__lowerCAmelCase : int = subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
__lowerCAmelCase : Any = (
subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode('utf-8').split()
)
__lowerCAmelCase : str = '|'.join(sys.argv[1:])
__lowerCAmelCase : Tuple = re.compile(RF'''^({joined_dirs}).*?\.py$''')
__lowerCAmelCase : Union[str, Any] = [x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')
| 88 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase ( _UpperCamelCase : float , _UpperCamelCase : float , _UpperCamelCase : float , ) -> tuple[str, float]:
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif stress < 0:
raise ValueError("""Stress cannot be negative""" )
elif tangential_force < 0:
raise ValueError("""Tangential Force cannot be negative""" )
elif area < 0:
raise ValueError("""Area cannot be negative""" )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 320 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase : List[str] = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Tuple = [
'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'AdaptiveEmbedding',
'TransfoXLForSequenceClassification',
'TransfoXLLMHeadModel',
'TransfoXLModel',
'TransfoXLPreTrainedModel',
'load_tf_weights_in_transfo_xl',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Dict = [
'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFAdaptiveEmbedding',
'TFTransfoXLForSequenceClassification',
'TFTransfoXLLMHeadModel',
'TFTransfoXLMainLayer',
'TFTransfoXLModel',
'TFTransfoXLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
UpperCAmelCase : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 320 | 1 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _UpperCamelCase ( lowerCAmelCase__ ,unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase : List[str] ="""ssube/stable-diffusion-x4-upscaler-onnx"""
def snake_case ( self , __a=0 ):
__lowerCAmelCase = floats_tensor((1, 3, 1_28, 1_28) , rng=random.Random(__a ) )
__lowerCAmelCase = torch.manual_seed(__a )
__lowerCAmelCase = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def snake_case ( self ):
__lowerCAmelCase = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
pipe.set_progress_bar_config(disable=__a )
__lowerCAmelCase = self.get_dummy_inputs()
__lowerCAmelCase = pipe(**__a ).images
__lowerCAmelCase = image[0, -3:, -3:, -1].flatten()
# started as 128, should now be 512
assert image.shape == (1, 5_12, 5_12, 3)
__lowerCAmelCase = np.array(
[0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def snake_case ( self ):
__lowerCAmelCase = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
__lowerCAmelCase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=__a )
pipe.set_progress_bar_config(disable=__a )
__lowerCAmelCase = self.get_dummy_inputs()
__lowerCAmelCase = pipe(**__a ).images
__lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
__lowerCAmelCase = np.array(
[0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def snake_case ( self ):
__lowerCAmelCase = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
__lowerCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__a )
__lowerCAmelCase = self.get_dummy_inputs()
__lowerCAmelCase = pipe(**__a ).images
__lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
__lowerCAmelCase = np.array(
[0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def snake_case ( self ):
__lowerCAmelCase = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
__lowerCAmelCase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__a )
__lowerCAmelCase = self.get_dummy_inputs()
__lowerCAmelCase = pipe(**__a ).images
__lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
__lowerCAmelCase = np.array(
[0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def snake_case ( self ):
__lowerCAmelCase = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
__lowerCAmelCase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__a )
__lowerCAmelCase = self.get_dummy_inputs()
__lowerCAmelCase = pipe(**__a ).images
__lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
__lowerCAmelCase = np.array(
[0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def snake_case ( self ):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def snake_case ( self ):
__lowerCAmelCase = ort.SessionOptions()
__lowerCAmelCase = False
return options
def snake_case ( self ):
__lowerCAmelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg" )
__lowerCAmelCase = init_image.resize((1_28, 1_28) )
# using the PNDM scheduler by default
__lowerCAmelCase = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"ssube/stable-diffusion-x4-upscaler-onnx" , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__a )
__lowerCAmelCase = "A fantasy landscape, trending on artstation"
__lowerCAmelCase = torch.manual_seed(0 )
__lowerCAmelCase = pipe(
prompt=__a , image=__a , guidance_scale=7.5 , num_inference_steps=10 , generator=__a , output_type="np" , )
__lowerCAmelCase = output.images
__lowerCAmelCase = images[0, 2_55:2_58, 3_83:3_86, -1]
assert images.shape == (1, 5_12, 5_12, 3)
__lowerCAmelCase = np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def snake_case ( self ):
__lowerCAmelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg" )
__lowerCAmelCase = init_image.resize((1_28, 1_28) )
__lowerCAmelCase = LMSDiscreteScheduler.from_pretrained(
"ssube/stable-diffusion-x4-upscaler-onnx" , subfolder="scheduler" )
__lowerCAmelCase = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"ssube/stable-diffusion-x4-upscaler-onnx" , scheduler=__a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__a )
__lowerCAmelCase = "A fantasy landscape, trending on artstation"
__lowerCAmelCase = torch.manual_seed(0 )
__lowerCAmelCase = pipe(
prompt=__a , image=__a , guidance_scale=7.5 , num_inference_steps=20 , generator=__a , output_type="np" , )
__lowerCAmelCase = output.images
__lowerCAmelCase = images[0, 2_55:2_58, 3_83:3_86, -1]
assert images.shape == (1, 5_12, 5_12, 3)
__lowerCAmelCase = np.array(
[0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 57 |
_lowerCAmelCase : Optional[int] = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
_lowerCAmelCase : Tuple = [{"type": "code", "content": INSTALL_CONTENT}]
_lowerCAmelCase : Optional[int] = {
"{processor_class}": "FakeProcessorClass",
"{model_class}": "FakeModelClass",
"{object_class}": "FakeObjectClass",
}
| 218 | 0 |
"""simple docstring"""
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
lowerCAmelCase_ = '\\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'
lowerCAmelCase_ = '\\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'
lowerCAmelCase_ = '\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 __A ( datasets.Metric ):
'''simple docstring'''
def UpperCAmelCase ( self : Dict ) -> Optional[int]:
"""simple docstring"""
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 UpperCAmelCase ( self : List[str] ,_snake_case : Optional[Any] ,_snake_case : Optional[int] ,_snake_case : int=None ,_snake_case : Optional[Any]=True ,_snake_case : Tuple=False ) -> List[str]:
"""simple docstring"""
if rouge_types is None:
lowercase__ : Tuple = ['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']
lowercase__ : str = rouge_scorer.RougeScorer(rouge_types=_snake_case ,use_stemmer=_snake_case )
if use_aggregator:
lowercase__ : Optional[int] = scoring.BootstrapAggregator()
else:
lowercase__ : Any = []
for ref, pred in zip(_snake_case ,_snake_case ):
lowercase__ : str = scorer.score(_snake_case ,_snake_case )
if use_aggregator:
aggregator.add_scores(_snake_case )
else:
scores.append(_snake_case )
if use_aggregator:
lowercase__ : Optional[int] = aggregator.aggregate()
else:
lowercase__ : Dict = {}
for key in scores[0]:
lowercase__ : Optional[int] = [score[key] for score in scores]
return result
| 302 |
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
lowerCAmelCase_ = logging.get_logger(__name__)
# General docstring
lowerCAmelCase_ = 'RegNetConfig'
# Base docstring
lowerCAmelCase_ = 'facebook/regnet-y-040'
lowerCAmelCase_ = [1, 1_088, 7, 7]
# Image classification docstring
lowerCAmelCase_ = 'facebook/regnet-y-040'
lowerCAmelCase_ = 'tabby, tabby cat'
lowerCAmelCase_ = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : int ,_snake_case : int ,_snake_case : int ,_snake_case : int = 3 ,_snake_case : int = 1 ,_snake_case : int = 1 ,_snake_case : Optional[str] = "relu" ,) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
lowercase__ : Tuple = nn.Convad(
_snake_case ,_snake_case ,kernel_size=_snake_case ,stride=_snake_case ,padding=kernel_size // 2 ,groups=_snake_case ,bias=_snake_case ,)
lowercase__ : List[Any] = nn.BatchNormad(_snake_case )
lowercase__ : Optional[int] = ACTaFN[activation] if activation is not None else nn.Identity()
def UpperCAmelCase ( self : List[str] ,_snake_case : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.convolution(_snake_case )
lowercase__ : Tuple = self.normalization(_snake_case )
lowercase__ : Tuple = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] ,_snake_case : RegNetConfig ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = RegNetConvLayer(
config.num_channels ,config.embedding_size ,kernel_size=3 ,stride=2 ,activation=config.hidden_act )
lowercase__ : str = config.num_channels
def UpperCAmelCase ( self : int ,_snake_case : Dict ) -> str:
"""simple docstring"""
lowercase__ : Union[str, Any] = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
'''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''' )
lowercase__ : Optional[int] = self.embedder(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : str ,_snake_case : int ,_snake_case : int ,_snake_case : int = 2 ) -> Any:
"""simple docstring"""
super().__init__()
lowercase__ : List[str] = nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ,stride=_snake_case ,bias=_snake_case )
lowercase__ : Any = nn.BatchNormad(_snake_case )
def UpperCAmelCase ( self : List[str] ,_snake_case : Tensor ) -> Tensor:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.convolution(_snake_case )
lowercase__ : Optional[int] = self.normalization(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : int ,_snake_case : int ) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ : Any = nn.AdaptiveAvgPoolad((1, 1) )
lowercase__ : Dict = nn.Sequential(
nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ) ,nn.ReLU() ,nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ) ,nn.Sigmoid() ,)
def UpperCAmelCase ( self : int ,_snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.pooler(_snake_case )
lowercase__ : Union[str, Any] = self.attention(_snake_case )
lowercase__ : List[str] = hidden_state * attention
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 1 ) -> List[str]:
"""simple docstring"""
super().__init__()
lowercase__ : Tuple = in_channels != out_channels or stride != 1
lowercase__ : Optional[int] = max(1 ,out_channels // config.groups_width )
lowercase__ : str = (
RegNetShortCut(_snake_case ,_snake_case ,stride=_snake_case ) if should_apply_shortcut else nn.Identity()
)
lowercase__ : Optional[int] = nn.Sequential(
RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,stride=_snake_case ,groups=_snake_case ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=_snake_case ) ,)
lowercase__ : str = ACTaFN[config.hidden_act]
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : List[Any] ) -> List[str]:
"""simple docstring"""
lowercase__ : Tuple = hidden_state
lowercase__ : Union[str, Any] = self.layer(_snake_case )
lowercase__ : List[Any] = self.shortcut(_snake_case )
hidden_state += residual
lowercase__ : Optional[int] = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 1 ) -> Optional[int]:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = in_channels != out_channels or stride != 1
lowercase__ : List[str] = max(1 ,out_channels // config.groups_width )
lowercase__ : Tuple = (
RegNetShortCut(_snake_case ,_snake_case ,stride=_snake_case ) if should_apply_shortcut else nn.Identity()
)
lowercase__ : str = nn.Sequential(
RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,stride=_snake_case ,groups=_snake_case ,activation=config.hidden_act ) ,RegNetSELayer(_snake_case ,reduced_channels=int(round(in_channels / 4 ) ) ) ,RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=_snake_case ) ,)
lowercase__ : Optional[Any] = ACTaFN[config.hidden_act]
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ : str = hidden_state
lowercase__ : Optional[Any] = self.layer(_snake_case )
lowercase__ : int = self.shortcut(_snake_case )
hidden_state += residual
lowercase__ : str = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 2 ,_snake_case : int = 2 ,) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[Any] = RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer
lowercase__ : Optional[Any] = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
_snake_case ,_snake_case ,_snake_case ,stride=_snake_case ,) ,*[layer(_snake_case ,_snake_case ,_snake_case ) for _ in range(depth - 1 )] ,)
def UpperCAmelCase ( self : Tuple ,_snake_case : int ) -> List[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.layers(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_snake_case : RegNetConfig ) -> List[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : str = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
_snake_case ,config.embedding_size ,config.hidden_sizes[0] ,stride=2 if config.downsample_in_first_stage else 1 ,depth=config.depths[0] ,) )
lowercase__ : str = zip(config.hidden_sizes ,config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(_snake_case ,config.depths[1:] ):
self.stages.append(RegNetStage(_snake_case ,_snake_case ,_snake_case ,depth=_snake_case ) )
def UpperCAmelCase ( self : List[str] ,_snake_case : Tensor ,_snake_case : bool = False ,_snake_case : bool = True ) -> BaseModelOutputWithNoAttention:
"""simple docstring"""
lowercase__ : Dict = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowercase__ : int = hidden_states + (hidden_state,)
lowercase__ : Any = stage_module(_snake_case )
if output_hidden_states:
lowercase__ : Optional[int] = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=_snake_case ,hidden_states=_snake_case )
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : int = RegNetConfig
lowerCAmelCase : List[Any] = "regnet"
lowerCAmelCase : Optional[int] = "pixel_values"
lowerCAmelCase : Union[str, Any] = True
def UpperCAmelCase ( self : Any ,_snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
if isinstance(_snake_case ,nn.Convad ):
nn.init.kaiming_normal_(module.weight ,mode='''fan_out''' ,nonlinearity='''relu''' )
elif isinstance(_snake_case ,(nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight ,1 )
nn.init.constant_(module.bias ,0 )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : Dict ,_snake_case : Any=False ) -> Optional[int]:
"""simple docstring"""
if isinstance(_snake_case ,_snake_case ):
lowercase__ : str = value
lowerCAmelCase_ = R'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
lowerCAmelCase_ = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConvNextImageProcessor.__call__`] for details.\n\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." ,A_ ,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class __A ( A_ ):
'''simple docstring'''
def __init__( self : Optional[Any] ,_snake_case : Any ) -> Tuple:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : Any = config
lowercase__ : List[str] = RegNetEmbeddings(_snake_case )
lowercase__ : Any = RegNetEncoder(_snake_case )
lowercase__ : Dict = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_snake_case )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=_snake_case ,config_class=_CONFIG_FOR_DOC ,modality='''vision''' ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def UpperCAmelCase ( self : Dict ,_snake_case : Tensor ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ) -> BaseModelOutputWithPoolingAndNoAttention:
"""simple docstring"""
lowercase__ : List[Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase__ : Dict = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : Union[str, Any] = self.embedder(_snake_case )
lowercase__ : List[Any] = self.encoder(
_snake_case ,output_hidden_states=_snake_case ,return_dict=_snake_case )
lowercase__ : str = encoder_outputs[0]
lowercase__ : Optional[int] = self.pooler(_snake_case )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_snake_case ,pooler_output=_snake_case ,hidden_states=encoder_outputs.hidden_states ,)
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " ,A_ ,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class __A ( A_ ):
'''simple docstring'''
def __init__( self : int ,_snake_case : Tuple ) -> Any:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : Optional[Any] = config.num_labels
lowercase__ : int = RegNetModel(_snake_case )
# classification head
lowercase__ : str = nn.Sequential(
nn.Flatten() ,nn.Linear(config.hidden_sizes[-1] ,config.num_labels ) if config.num_labels > 0 else nn.Identity() ,)
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_snake_case )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=_snake_case ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def UpperCAmelCase ( self : List[Any] ,_snake_case : Optional[torch.FloatTensor] = None ,_snake_case : Optional[torch.LongTensor] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ,) -> ImageClassifierOutputWithNoAttention:
"""simple docstring"""
lowercase__ : Any = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : List[Any] = self.regnet(_snake_case ,output_hidden_states=_snake_case ,return_dict=_snake_case )
lowercase__ : List[str] = outputs.pooler_output if return_dict else outputs[1]
lowercase__ : Union[str, Any] = self.classifier(_snake_case )
lowercase__ : Optional[int] = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase__ : List[Any] = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase__ : Dict = '''single_label_classification'''
else:
lowercase__ : Optional[int] = '''multi_label_classification'''
if self.config.problem_type == "regression":
lowercase__ : Union[str, Any] = MSELoss()
if self.num_labels == 1:
lowercase__ : List[Any] = loss_fct(logits.squeeze() ,labels.squeeze() )
else:
lowercase__ : Tuple = loss_fct(_snake_case ,_snake_case )
elif self.config.problem_type == "single_label_classification":
lowercase__ : Tuple = CrossEntropyLoss()
lowercase__ : str = loss_fct(logits.view(-1 ,self.num_labels ) ,labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase__ : Any = BCEWithLogitsLoss()
lowercase__ : Union[str, Any] = loss_fct(_snake_case ,_snake_case )
if not return_dict:
lowercase__ : Tuple = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=_snake_case ,logits=_snake_case ,hidden_states=outputs.hidden_states )
| 302 | 1 |
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Tuple, lowerCamelCase : Optional[int], lowerCamelCase : Any, lowerCamelCase : str ):
'''simple docstring'''
lowercase__ = name
lowercase__ = value
lowercase__ = weight
def __repr__( self : Union[str, Any] ):
'''simple docstring'''
return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""
def lowercase__ ( self : Tuple ):
'''simple docstring'''
return self.value
def lowercase__ ( self : str ):
'''simple docstring'''
return self.name
def lowercase__ ( self : Optional[Any] ):
'''simple docstring'''
return self.weight
def lowercase__ ( self : Optional[int] ):
'''simple docstring'''
return self.value / self.weight
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = []
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = sorted(SCREAMING_SNAKE_CASE_ , key=SCREAMING_SNAKE_CASE_ , reverse=SCREAMING_SNAKE_CASE_ )
lowercase__ = []
lowercase__ , lowercase__ = 0.0, 0.0
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def a ( ):
'''simple docstring'''
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 207 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
UpperCamelCase__ = {
"""configuration_audio_spectrogram_transformer""": [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ASTConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ASTForAudioClassification""",
"""ASTModel""",
"""ASTPreTrainedModel""",
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = ["""ASTFeatureExtractor"""]
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase ):
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
UpperCAmelCase = str(bin(lowerCAmelCase ) )
binary_number += "0" * shift_amount
return binary_number
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase ):
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
UpperCAmelCase = str(bin(lowerCAmelCase ) )[2:]
if shift_amount >= len(lowerCAmelCase ):
return "0b0"
UpperCAmelCase = binary_number[: len(lowerCAmelCase ) - shift_amount]
return "0b" + shifted_binary_number
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase ):
'''simple docstring'''
if number >= 0: # Get binary representation of positive number
UpperCAmelCase = """0""" + str(bin(lowerCAmelCase ) ).strip("""-""" )[2:]
else: # Get binary (2's complement) representation of negative number
UpperCAmelCase = len(bin(lowerCAmelCase )[3:] ) # Find 2's complement of number
UpperCAmelCase = bin(abs(lowerCAmelCase ) - (1 << binary_number_length) )[3:]
UpperCAmelCase = (
"""1""" + """0""" * (binary_number_length - len(lowerCAmelCase )) + binary_number
)
if shift_amount >= len(lowerCAmelCase ):
return "0b" + binary_number[0] * len(lowerCAmelCase )
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(lowerCAmelCase ) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 248 |
"""simple docstring"""
import os
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = len(grid[0] )
UpperCAmelCase = len(lowerCAmelCase )
UpperCAmelCase = 0
UpperCAmelCase = 0
UpperCAmelCase = 0
# Check vertically, horizontally, diagonally at the same time (only works
# for nxn grid)
for i in range(lowerCAmelCase ):
for j in range(n_rows - 3 ):
UpperCAmelCase = grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
UpperCAmelCase = grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]
# Left-to-right diagonal (\) product
if i < n_columns - 3:
UpperCAmelCase = (
grid[i][j]
* grid[i + 1][j + 1]
* grid[i + 2][j + 2]
* grid[i + 3][j + 3]
)
# Right-to-left diagonal(/) product
if i > 2:
UpperCAmelCase = (
grid[i][j]
* grid[i - 1][j + 1]
* grid[i - 2][j + 2]
* grid[i - 3][j + 3]
)
UpperCAmelCase = max(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
if max_product > largest:
UpperCAmelCase = max_product
return largest
def _lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase = []
with open(os.path.dirname(lowerCAmelCase ) + """/grid.txt""" ) as file:
for line in file:
grid.append(line.strip("""\n""" ).split(""" """ ) )
UpperCAmelCase = [[int(lowerCAmelCase ) for i in grid[j]] for j in range(len(lowerCAmelCase ) )]
return largest_product(lowerCAmelCase )
if __name__ == "__main__":
print(solution())
| 248 | 1 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = [0] * len(__UpperCamelCase )
SCREAMING_SNAKE_CASE : Union[str, Any] = []
SCREAMING_SNAKE_CASE : Dict = [1] * len(__UpperCamelCase )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__UpperCamelCase ) ):
if indegree[i] == 0:
queue.append(__UpperCamelCase )
while queue:
SCREAMING_SNAKE_CASE : Dict = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
SCREAMING_SNAKE_CASE : List[Any] = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__UpperCamelCase )
print(max(__UpperCamelCase ) )
# Adjacency list of Graph
UpperCamelCase_ = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 251 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCamelCase_ = logging.get_logger(__name__)
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : str = ['''input_values''', '''padding_mask''']
def __init__( self, A = 1, A = 24_000, A = 0.0, A = None, A = None, **A, ):
'''simple docstring'''
super().__init__(feature_size=A, sampling_rate=A, padding_value=A, **A )
SCREAMING_SNAKE_CASE : Any = chunk_length_s
SCREAMING_SNAKE_CASE : Dict = overlap
@property
def UpperCamelCase_ ( self ):
'''simple docstring'''
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def UpperCamelCase_ ( self ):
'''simple docstring'''
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1, int((1.0 - self.overlap) * self.chunk_length ) )
def __call__( self, A, A = None, A = False, A = None, A = None, A = None, ):
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
F" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
F" {self.sampling_rate} and not {sampling_rate}." )
else:
logger.warning(
'It is strongly recommended to pass the `sampling_rate` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
if padding and truncation:
raise ValueError('Both padding and truncation were set. Make sure you only set one.' )
elif padding is None:
# by default let's pad the inputs
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : Optional[int] = bool(
isinstance(A, (list, tuple) ) and (isinstance(raw_audio[0], (np.ndarray, tuple, list) )) )
if is_batched:
SCREAMING_SNAKE_CASE : Union[str, Any] = [np.asarray(A, dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(A, np.ndarray ):
SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(A, dtype=np.floataa )
elif isinstance(A, np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
SCREAMING_SNAKE_CASE : Optional[Any] = raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
SCREAMING_SNAKE_CASE : str = [np.asarray(A ).T]
# verify inputs are valid
for idx, example in enumerate(A ):
if example.ndim > 2:
raise ValueError(F"Expected input shape (channels, length) but got shape {example.shape}" )
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(F"Expected mono audio but example has {example.shape[-1]} channels" )
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(F"Expected stereo audio but example has {example.shape[-1]} channels" )
SCREAMING_SNAKE_CASE : List[str] = None
SCREAMING_SNAKE_CASE : List[str] = BatchFeature({'input_values': raw_audio} )
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
SCREAMING_SNAKE_CASE : Optional[int] = min(array.shape[0] for array in raw_audio )
SCREAMING_SNAKE_CASE : Tuple = int(np.floor(max_length / self.chunk_stride ) )
SCREAMING_SNAKE_CASE : int = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
SCREAMING_SNAKE_CASE : str = max(array.shape[0] for array in raw_audio )
SCREAMING_SNAKE_CASE : Tuple = int(np.ceil(max_length / self.chunk_stride ) )
SCREAMING_SNAKE_CASE : Dict = (nb_step - 1) * self.chunk_stride + self.chunk_length
SCREAMING_SNAKE_CASE : List[str] = 'max_length'
else:
SCREAMING_SNAKE_CASE : List[Any] = input_values
# normal padding on batch
if padded_inputs is None:
SCREAMING_SNAKE_CASE : int = self.pad(
A, max_length=A, truncation=A, padding=A, return_attention_mask=A, )
if padding:
SCREAMING_SNAKE_CASE : Dict = padded_inputs.pop('attention_mask' )
SCREAMING_SNAKE_CASE : Optional[int] = []
for example in padded_inputs.pop('input_values' ):
if self.feature_size == 1:
SCREAMING_SNAKE_CASE : List[str] = example[..., None]
input_values.append(example.T )
SCREAMING_SNAKE_CASE : Dict = input_values
if return_tensors is not None:
SCREAMING_SNAKE_CASE : int = padded_inputs.convert_to_tensors(A )
return padded_inputs
| 251 | 1 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
# Throw an error if user passes both BYO and on-demand cluster args
# Otherwise, use default values
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''--user''', type=str, default='''ubuntu''')
parser.add_argument('''--host''', type=str, default='''localhost''')
parser.add_argument('''--key_path''', type=str, default=None)
parser.add_argument('''--instance''', type=str, default='''V100:1''')
parser.add_argument('''--provider''', type=str, default='''cheapest''')
parser.add_argument('''--use_spot''', type=bool, default=False)
parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
__UpperCAmelCase , __UpperCAmelCase = parser.parse_known_args()
if args.host != "localhost":
if args.instance != "V100:1" or args.provider != "cheapest":
raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
__UpperCAmelCase = rh.cluster(
name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
)
else:
__UpperCAmelCase = rh.cluster(
name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
)
__UpperCAmelCase = args.example.rsplit('''/''', 1)[0]
# Set up remote environment
cluster.install_packages(['''pip:./''']) # Installs transformers from local source
# Note transformers is copied into the home directory on the remote machine, so we can install from there
cluster.run([F"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])
# Run example. You can bypass the CLI wrapper and paste your own code here.
cluster.run([F"""python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}"""])
# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
# from my_script... import train
# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
# launch_train_gpu = rh.function(fn=train,
# system=gpu,
# reqs=reqs,
# name='train_bert_glue')
#
# We can pass in arguments just like we would to a function:
# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
# stream_logs=True)
| 103 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class lowerCAmelCase_ ( a__ , unittest.TestCase ):
UpperCAmelCase__ : Union[str, Any] = ShapEPipeline
UpperCAmelCase__ : List[Any] = ["prompt"]
UpperCAmelCase__ : List[str] = ["prompt"]
UpperCAmelCase__ : int = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
UpperCAmelCase__ : Union[str, Any] = False
@property
def snake_case_ ( self ) -> Union[str, Any]:
return 32
@property
def snake_case_ ( self ) -> List[str]:
return 32
@property
def snake_case_ ( self ) -> int:
return self.time_input_dim * 4
@property
def snake_case_ ( self ) -> Optional[int]:
return 8
@property
def snake_case_ ( self ) -> str:
UpperCamelCase : Union[str, Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def snake_case_ ( self ) -> Optional[Any]:
torch.manual_seed(0 )
UpperCamelCase : Dict = CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=self.text_embedder_hidden_size, projection_dim=self.text_embedder_hidden_size, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1000, )
return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE_ )
@property
def snake_case_ ( self ) -> Dict:
torch.manual_seed(0 )
UpperCamelCase : int = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
UpperCamelCase : Any = PriorTransformer(**SCREAMING_SNAKE_CASE_ )
return model
@property
def snake_case_ ( self ) -> Tuple:
torch.manual_seed(0 )
UpperCamelCase : Any = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
UpperCamelCase : Dict = ShapERenderer(**SCREAMING_SNAKE_CASE_ )
return model
def snake_case_ ( self ) -> Tuple:
UpperCamelCase : List[Any] = self.dummy_prior
UpperCamelCase : int = self.dummy_text_encoder
UpperCamelCase : Dict = self.dummy_tokenizer
UpperCamelCase : List[str] = self.dummy_renderer
UpperCamelCase : str = HeunDiscreteScheduler(
beta_schedule='exp', num_train_timesteps=1024, prediction_type='sample', use_karras_sigmas=SCREAMING_SNAKE_CASE_, clip_sample=SCREAMING_SNAKE_CASE_, clip_sample_range=1.0, )
UpperCamelCase : List[str] = {
'prior': prior,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=0 ) -> Any:
if str(SCREAMING_SNAKE_CASE_ ).startswith('mps' ):
UpperCamelCase : Optional[int] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Any = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = {
'prompt': 'horse',
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def snake_case_ ( self ) -> int:
UpperCamelCase : str = 'cpu'
UpperCamelCase : Optional[int] = self.get_dummy_components()
UpperCamelCase : int = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Optional[int] = output.images[0]
UpperCamelCase : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
UpperCamelCase : List[str] = np.array(
[
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def snake_case_ ( self ) -> int:
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def snake_case_ ( self ) -> str:
UpperCamelCase : str = torch_device == 'cpu'
UpperCamelCase : Optional[int] = True
self._test_inference_batch_single_identical(
batch_size=2, test_max_difference=SCREAMING_SNAKE_CASE_, relax_max_difference=SCREAMING_SNAKE_CASE_, )
def snake_case_ ( self ) -> List[Any]:
UpperCamelCase : List[Any] = self.get_dummy_components()
UpperCamelCase : str = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = 1
UpperCamelCase : Union[str, Any] = 2
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
for key in inputs.keys():
if key in self.batch_params:
UpperCamelCase : List[Any] = batch_size * [inputs[key]]
UpperCamelCase : int = pipe(**SCREAMING_SNAKE_CASE_, num_images_per_prompt=SCREAMING_SNAKE_CASE_ )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
def snake_case_ ( self ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self ) -> Tuple:
UpperCamelCase : Optional[int] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_np_out.npy' )
UpperCamelCase : Optional[int] = ShapEPipeline.from_pretrained('openai/shap-e' )
UpperCamelCase : Optional[int] = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(0 )
UpperCamelCase : Dict = pipe(
'a shark', generator=SCREAMING_SNAKE_CASE_, guidance_scale=15.0, num_inference_steps=64, frame_size=64, output_type='np', ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
| 103 | 1 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def lowerCAmelCase_ ( __lowerCamelCase ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4e_00 and cp <= 0X9f_ff)
or (cp >= 0X34_00 and cp <= 0X4d_bf) #
or (cp >= 0X2_00_00 and cp <= 0X2_a6_df) #
or (cp >= 0X2_a7_00 and cp <= 0X2_b7_3f) #
or (cp >= 0X2_b7_40 and cp <= 0X2_b8_1f) #
or (cp >= 0X2_b8_20 and cp <= 0X2_ce_af) #
or (cp >= 0Xf9_00 and cp <= 0Xfa_ff)
or (cp >= 0X2_f8_00 and cp <= 0X2_fa_1f) #
): #
return True
return False
def lowerCAmelCase_ ( __lowerCamelCase ):
# word like '180' or '身高' or '神'
for char in word:
__snake_case : List[Any] = ord(__lowerCamelCase )
if not _is_chinese_char(__lowerCamelCase ):
return 0
return 1
def lowerCAmelCase_ ( __lowerCamelCase ):
__snake_case : List[Any] = set()
for token in tokens:
__snake_case : Tuple = len(__lowerCamelCase ) > 1 and is_chinese(__lowerCamelCase )
if chinese_word:
word_set.add(__lowerCamelCase )
__snake_case : List[Any] = list(__lowerCamelCase )
return word_list
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
if not chinese_word_set:
return bert_tokens
__snake_case : Optional[int] = max([len(__lowerCamelCase ) for w in chinese_word_set] )
__snake_case : Any = bert_tokens
__snake_case , __snake_case : Optional[Any] = 0, len(__lowerCamelCase )
while start < end:
__snake_case : Union[str, Any] = True
if is_chinese(bert_word[start] ):
__snake_case : Any = min(end - start , __lowerCamelCase )
for i in range(__lowerCamelCase , 1 , -1 ):
__snake_case : str = "".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
__snake_case : Optional[Any] = "##" + bert_word[j]
__snake_case : Optional[int] = start + i
__snake_case : Tuple = False
break
if single_word:
start += 1
return bert_word
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
__snake_case : str = []
for i in range(0 , len(__lowerCamelCase ) , 1_0_0 ):
__snake_case : Tuple = ltp_tokenizer.seg(lines[i : i + 1_0_0] )[0]
__snake_case : Union[str, Any] = [get_chinese_word(__lowerCamelCase ) for r in res]
ltp_res.extend(__lowerCamelCase )
assert len(__lowerCamelCase ) == len(__lowerCamelCase )
__snake_case : Any = []
for i in range(0 , len(__lowerCamelCase ) , 1_0_0 ):
__snake_case : str = bert_tokenizer(lines[i : i + 1_0_0] , add_special_tokens=__lowerCamelCase , truncation=__lowerCamelCase , max_length=5_1_2 )
bert_res.extend(res["input_ids"] )
assert len(__lowerCamelCase ) == len(__lowerCamelCase )
__snake_case : List[str] = []
for input_ids, chinese_word in zip(__lowerCamelCase , __lowerCamelCase ):
__snake_case : Tuple = []
for id in input_ids:
__snake_case : Optional[int] = bert_tokenizer._convert_id_to_token(__lowerCamelCase )
input_tokens.append(__lowerCamelCase )
__snake_case : Union[str, Any] = add_sub_symbol(__lowerCamelCase , __lowerCamelCase )
__snake_case : Any = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__lowerCamelCase ):
if token[:2] == "##":
__snake_case : Optional[Any] = token[2:]
# save chinese tokens' pos
if len(__lowerCamelCase ) == 1 and _is_chinese_char(ord(__lowerCamelCase ) ):
ref_id.append(__lowerCamelCase )
ref_ids.append(__lowerCamelCase )
assert len(__lowerCamelCase ) == len(__lowerCamelCase )
return ref_ids
def lowerCAmelCase_ ( __lowerCamelCase ):
# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
with open(args.file_name , "r" , encoding="utf-8" ) as f:
__snake_case : Any = f.readlines()
__snake_case : Tuple = [line.strip() for line in data if len(__lowerCamelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
__snake_case : Dict = LTP(args.ltp ) # faster in GPU device
__snake_case : Optional[Any] = BertTokenizer.from_pretrained(args.bert )
__snake_case : List[Any] = prepare_ref(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
with open(args.save_path , "w" , encoding="utf-8" ) as f:
__snake_case : Dict = [json.dumps(__lowerCamelCase ) + "\n" for ref in ref_ids]
f.writelines(__lowerCamelCase )
if __name__ == "__main__":
_snake_case : int = argparse.ArgumentParser(description="prepare_chinese_ref")
parser.add_argument(
"--file_name",
type=str,
default="./resources/chinese-demo.txt",
help="file need process, same as training data in lm",
)
parser.add_argument(
"--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
)
parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")
_snake_case : Tuple = parser.parse_args()
main(args)
| 123 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class a (_lowerCAmelCase ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase : List[str] , lowerCamelCase : Union[str, Any] , lowerCamelCase : Optional[int] ) -> Any:
__snake_case : List[Any] = dataset
__snake_case : Optional[int] = process
__snake_case : str = params
def __len__( self : Optional[Any] ) -> Any:
return len(self.dataset )
def __getitem__( self : Dict , lowerCamelCase : List[Any] ) -> List[str]:
__snake_case : List[Any] = self.dataset[i]
__snake_case : Tuple = self.process(lowerCamelCase , **self.params )
return processed
class a (_lowerCAmelCase ):
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase : Optional[int] , lowerCamelCase : Optional[int] , lowerCamelCase : Optional[Any] , lowerCamelCase : Dict=None ) -> int:
__snake_case : List[Any] = loader
__snake_case : Dict = infer
__snake_case : Tuple = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
__snake_case : Union[str, Any] = None
__snake_case : Optional[Any] = loader_batch_size
# Internal bookkeeping
__snake_case : int = None
__snake_case : Optional[int] = None
def __len__( self : Optional[Any] ) -> Tuple:
return len(self.loader )
def __iter__( self : str ) -> Tuple:
__snake_case : int = iter(self.loader )
return self
def __snake_case ( self : int ) -> Any:
if isinstance(self._loader_batch_data , torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
__snake_case : Union[str, Any] = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
__snake_case : int = {}
for k, element in self._loader_batch_data.items():
if isinstance(lowerCamelCase , lowerCamelCase ):
# Convert ModelOutput to tuple first
__snake_case : Dict = element.to_tuple()
if isinstance(element[0] , torch.Tensor ):
__snake_case : Any = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
__snake_case : Optional[Any] = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(lowerCamelCase , lowerCamelCase ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] , torch.Tensor ):
__snake_case : Dict = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
__snake_case : str = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if element is None:
# This can happen for optional data that get passed around
__snake_case : Union[str, Any] = None
elif isinstance(element[self._loader_batch_index] , torch.Tensor ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
__snake_case : List[Any] = element[self._loader_batch_index].unsqueeze(0 )
elif isinstance(element[self._loader_batch_index] , np.ndarray ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
__snake_case : Optional[Any] = np.expand_dims(element[self._loader_batch_index] , 0 )
else:
# This is typically a list, so no need to `unsqueeze`.
__snake_case : Tuple = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
__snake_case : str = self._loader_batch_data.__class__(lowerCamelCase )
self._loader_batch_index += 1
return result
def __snake_case ( self : Dict ) -> Union[str, Any]:
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
__snake_case : List[str] = next(self.iterator )
__snake_case : int = self.infer(lowerCamelCase , **self.params )
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(lowerCamelCase , torch.Tensor ):
__snake_case : List[Any] = processed
else:
__snake_case : Optional[Any] = list(processed.keys() )[0]
__snake_case : List[Any] = processed[key]
if isinstance(lowerCamelCase , lowerCamelCase ):
__snake_case : List[str] = len(lowerCamelCase )
else:
__snake_case : Tuple = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
__snake_case : Optional[Any] = observed_batch_size
# Setting internal index to unwrap the batch
__snake_case : Union[str, Any] = processed
__snake_case : str = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class a (_lowerCAmelCase ):
"""simple docstring"""
def __init__( self : int , lowerCamelCase : Any , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[Any] , lowerCamelCase : Optional[int]=None ) -> Any:
super().__init__(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def __iter__( self : Optional[int] ) -> Optional[int]:
__snake_case : Union[str, Any] = iter(self.loader )
__snake_case : int = None
return self
def __snake_case ( self : List[Any] ) -> List[Any]:
if self.subiterator is None:
__snake_case : Optional[int] = self.infer(next(self.iterator ) , **self.params )
try:
# Try to return next item
__snake_case : int = next(self.subiterator )
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
__snake_case : Union[str, Any] = self.infer(next(self.iterator ) , **self.params )
__snake_case : int = next(self.subiterator )
return processed
class a (_lowerCAmelCase ):
"""simple docstring"""
def __iter__( self : Any ) -> Optional[Any]:
__snake_case : str = iter(self.loader )
return self
def __snake_case ( self : Tuple ) -> str:
# Extremely similar to PipelineIterator in its unpacking mechanism
# BUT, we have an extra required item which is the presence of `is_last`
# That is because everything is flattened by `PipelineChunkIterator` we
# need to keep track of how to regroup here in the original `process`
# boundaries so that `process` and `postprocess` see the same data.
# This iterator accumulates items (possibly while unbatching) until it
# its a `is_last` and then just passes it on to the caller.
__snake_case : Dict = False
__snake_case : Dict = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
__snake_case : Union[str, Any] = self.loader_batch_item()
__snake_case : Any = item.pop("is_last" )
accumulator.append(lowerCamelCase )
if is_last:
return accumulator
while not is_last:
__snake_case : str = self.infer(next(self.iterator ) , **self.params )
if self.loader_batch_size is not None:
if isinstance(lowerCamelCase , torch.Tensor ):
__snake_case : Optional[int] = processed
else:
__snake_case : Union[str, Any] = list(processed.keys() )[0]
__snake_case : Optional[Any] = processed[key]
if isinstance(lowerCamelCase , lowerCamelCase ):
__snake_case : int = len(lowerCamelCase )
else:
__snake_case : int = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
__snake_case : Dict = observed_batch_size
__snake_case : Union[str, Any] = processed
__snake_case : List[str] = 0
while self._loader_batch_index < self.loader_batch_size:
__snake_case : str = self.loader_batch_item()
__snake_case : str = item.pop("is_last" )
accumulator.append(lowerCamelCase )
if is_last:
return accumulator
else:
__snake_case : List[str] = processed
__snake_case : Tuple = item.pop("is_last" )
accumulator.append(lowerCamelCase )
return accumulator
class a (_lowerCAmelCase ):
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase : Dataset , lowerCamelCase : str ) -> Optional[Any]:
__snake_case : int = dataset
__snake_case : Union[str, Any] = key
def __len__( self : Tuple ) -> Union[str, Any]:
return len(self.dataset )
def __getitem__( self : Optional[Any] , lowerCamelCase : str ) -> Optional[int]:
return self.dataset[i][self.key]
class a (_lowerCAmelCase ):
"""simple docstring"""
def __init__( self : List[Any] , lowerCamelCase : Dataset , lowerCamelCase : str , lowerCamelCase : str ) -> List[str]:
__snake_case : Any = dataset
__snake_case : Any = keya
__snake_case : Union[str, Any] = keya
def __len__( self : Optional[int] ) -> Tuple:
return len(self.dataset )
def __getitem__( self : Tuple , lowerCamelCase : List[str] ) -> Optional[Any]:
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 123 | 1 |
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
A_ = 50_00_00
A_ , A_ = os.path.split(__file__)
A_ = os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def UpperCAmelCase__ (snake_case__ : datasets.Dataset , **snake_case__ : Union[str, Any] ):
"""simple docstring"""
_snake_case : List[Any] = dataset.map(**snake_case__ )
@get_duration
def UpperCAmelCase__ (snake_case__ : datasets.Dataset , **snake_case__ : Union[str, Any] ):
"""simple docstring"""
_snake_case : int = dataset.filter(**snake_case__ )
def UpperCAmelCase__ ():
"""simple docstring"""
_snake_case : List[str] = {"""num examples""": SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
_snake_case : Tuple = datasets.Features({"""text""": datasets.Value("""string""" ), """numbers""": datasets.Value("""float32""" )} )
_snake_case : Any = generate_example_dataset(
os.path.join(snake_case__ , """dataset.arrow""" ) , snake_case__ , num_examples=snake_case__ )
_snake_case : List[Any] = transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" , use_fast=snake_case__ )
def tokenize(snake_case__ : Tuple ):
return tokenizer(examples["""text"""] )
_snake_case : List[Any] = map(snake_case__ )
_snake_case : List[Any] = map(snake_case__ , batched=snake_case__ )
_snake_case : Any = map(snake_case__ , function=lambda snake_case__ : None , batched=snake_case__ )
with dataset.formatted_as(type="""numpy""" ):
_snake_case : Dict = map(snake_case__ , function=lambda snake_case__ : None , batched=snake_case__ )
with dataset.formatted_as(type="""pandas""" ):
_snake_case : Any = map(snake_case__ , function=lambda snake_case__ : None , batched=snake_case__ )
with dataset.formatted_as(type="""torch""" , columns="""numbers""" ):
_snake_case : Any = map(snake_case__ , function=lambda snake_case__ : None , batched=snake_case__ )
with dataset.formatted_as(type="""tensorflow""" , columns="""numbers""" ):
_snake_case : Optional[Any] = map(snake_case__ , function=lambda snake_case__ : None , batched=snake_case__ )
_snake_case : str = map(snake_case__ , function=snake_case__ , batched=snake_case__ )
_snake_case : List[Any] = filter(snake_case__ )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(snake_case__ , """wb""" ) as f:
f.write(json.dumps(snake_case__ ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 350 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : Optional[Any] ):
"""simple docstring"""
_snake_case : Union[str, Any] = []
_snake_case : Dict = set({"""(""", """[""", """{"""} )
_snake_case : Union[str, Any] = set({""")""", """]""", """}"""} )
_snake_case : Tuple = {"""{""": """}""", """[""": """]""", """(""": """)"""}
for i in range(len(snake_case__ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(snake_case__ ) == 0 or (len(snake_case__ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(snake_case__ ) == 0
def UpperCAmelCase__ ():
"""simple docstring"""
_snake_case : Any = input("""Enter sequence of brackets: """ )
if is_balanced(snake_case__ ):
print(snake_case__ , """is balanced""" )
else:
print(snake_case__ , """is not balanced""" )
if __name__ == "__main__":
main()
| 132 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
a_ : Optional[Any] = StableDiffusionSAGPipeline
a_ : Dict = TEXT_TO_IMAGE_PARAMS
a_ : Optional[Any] = TEXT_TO_IMAGE_BATCH_PARAMS
a_ : Tuple = TEXT_TO_IMAGE_IMAGE_PARAMS
a_ : Any = TEXT_TO_IMAGE_IMAGE_PARAMS
a_ : List[str] = False
def lowerCamelCase ( self : int ):
torch.manual_seed(0 )
lowerCAmelCase_ : List[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 , )
lowerCAmelCase_ : Tuple = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=a_ , set_alpha_to_one=a_ , )
torch.manual_seed(0 )
lowerCAmelCase_ : Optional[int] = 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 , )
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-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
lowerCAmelCase_ : List[Any] = CLIPTextModel(a_ )
lowerCAmelCase_ : int = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowerCAmelCase_ : str = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def lowerCamelCase ( self : Union[str, Any] , a_ : Optional[int] , a_ : Dict=0 ):
if str(a_ ).startswith("mps" ):
lowerCAmelCase_ : str = torch.manual_seed(a_ )
else:
lowerCAmelCase_ : Optional[Any] = torch.Generator(device=a_ ).manual_seed(a_ )
lowerCAmelCase_ : Tuple = {
"prompt": ".",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 1.0,
"sag_scale": 1.0,
"output_type": "numpy",
}
return inputs
def lowerCamelCase ( self : List[Any] ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase ( self : Tuple ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self : Tuple ):
lowerCAmelCase_ : List[Any] = StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
lowerCAmelCase_ : Optional[Any] = sag_pipe.to(a_ )
sag_pipe.set_progress_bar_config(disable=a_ )
lowerCAmelCase_ : List[Any] = "."
lowerCAmelCase_ : Optional[int] = torch.manual_seed(0 )
lowerCAmelCase_ : Optional[int] = sag_pipe(
[prompt] , generator=a_ , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="np" )
lowerCAmelCase_ : List[Any] = output.images
lowerCAmelCase_ : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
lowerCAmelCase_ : List[Any] = np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2
def lowerCamelCase ( self : Optional[Any] ):
lowerCAmelCase_ : Dict = StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
lowerCAmelCase_ : Any = sag_pipe.to(a_ )
sag_pipe.set_progress_bar_config(disable=a_ )
lowerCAmelCase_ : List[Any] = "."
lowerCAmelCase_ : Tuple = torch.manual_seed(0 )
lowerCAmelCase_ : List[str] = sag_pipe(
[prompt] , generator=a_ , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="np" )
lowerCAmelCase_ : Optional[int] = output.images
lowerCAmelCase_ : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
lowerCAmelCase_ : int = np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2
def lowerCamelCase ( self : Tuple ):
lowerCAmelCase_ : Any = StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
lowerCAmelCase_ : Optional[int] = sag_pipe.to(a_ )
sag_pipe.set_progress_bar_config(disable=a_ )
lowerCAmelCase_ : Dict = "."
lowerCAmelCase_ : Tuple = torch.manual_seed(0 )
lowerCAmelCase_ : str = sag_pipe(
[prompt] , width=7_68 , height=5_12 , generator=a_ , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="np" , )
lowerCAmelCase_ : Union[str, Any] = output.images
assert image.shape == (1, 5_12, 7_68, 3)
| 241 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader
from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
ROUGE_KEYS,
LegacySeqaSeqDataset,
SeqaSeqDataset,
assert_all_frozen,
calculate_bleu,
calculate_rouge,
check_output_dir,
flatten_list,
freeze_embeds,
freeze_params,
get_git_info,
label_smoothed_nll_loss,
lmap,
pickle_save,
save_git_info,
save_json,
use_task_specific_params,
)
# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
class lowercase ( _UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = 'summarization'
_SCREAMING_SNAKE_CASE = ['loss']
_SCREAMING_SNAKE_CASE = ROUGE_KEYS
_SCREAMING_SNAKE_CASE = 'rouge2'
def __init__( self , lowercase , **lowercase ) -> str:
if hparams.sortish_sampler and hparams.gpus > 1:
lowerCAmelCase = False
elif hparams.max_tokens_per_batch is not None:
if hparams.gpus > 1:
raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training""" )
if hparams.sortish_sampler:
raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously""" )
super().__init__(lowercase , num_labels=lowercase , mode=self.mode , **lowercase )
use_task_specific_params(self.model , """summarization""" )
save_git_info(self.hparams.output_dir )
lowerCAmelCase = Path(self.output_dir ) / """metrics.json"""
lowerCAmelCase = Path(self.output_dir ) / """hparams.pkl"""
pickle_save(self.hparams , self.hparams_save_path )
lowerCAmelCase = 0
lowerCAmelCase = defaultdict(lowercase )
lowerCAmelCase = self.config.model_type
lowerCAmelCase = self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size
lowerCAmelCase = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
lowerCAmelCase = {
"""train""": self.hparams.n_train,
"""val""": self.hparams.n_val,
"""test""": self.hparams.n_test,
}
lowerCAmelCase = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
lowerCAmelCase = {
"""train""": self.hparams.max_target_length,
"""val""": self.hparams.val_max_target_length,
"""test""": self.hparams.test_max_target_length,
}
assert self.target_lens["train"] <= self.target_lens["val"], f'target_lens: {self.target_lens}'
assert self.target_lens["train"] <= self.target_lens["test"], f'target_lens: {self.target_lens}'
if self.hparams.freeze_embeds:
freeze_embeds(self.model )
if self.hparams.freeze_encoder:
freeze_params(self.model.get_encoder() )
assert_all_frozen(self.model.get_encoder() )
lowerCAmelCase = get_git_info()["""repo_sha"""]
lowerCAmelCase = hparams.num_workers
lowerCAmelCase = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , lowercase ):
lowerCAmelCase = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
lowerCAmelCase = self.decoder_start_token_id
lowerCAmelCase = (
SeqaSeqDataset if hasattr(self.tokenizer , """prepare_seq2seq_batch""" ) else LegacySeqaSeqDataset
)
lowerCAmelCase = False
lowerCAmelCase = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
if self.hparams.eval_max_gen_length is not None:
lowerCAmelCase = self.hparams.eval_max_gen_length
else:
lowerCAmelCase = self.model.config.max_length
lowerCAmelCase = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def _snake_case ( self , lowercase ) -> Dict[str, List[str]]:
lowerCAmelCase = {
k: self.tokenizer.batch_decode(v.tolist() ) if """mask""" not in k else v.shape for k, v in batch.items()
}
save_json(lowercase , Path(self.output_dir ) / """text_batch.json""" )
save_json({k: v.tolist() for k, v in batch.items()} , Path(self.output_dir ) / """tok_batch.json""" )
lowerCAmelCase = True
return readable_batch
def _snake_case ( self , lowercase , **lowercase ) -> Union[str, Any]:
return self.model(lowercase , **lowercase )
def _snake_case ( self , lowercase ) -> Union[str, Any]:
lowerCAmelCase = self.tokenizer.batch_decode(
lowercase , skip_special_tokens=lowercase , clean_up_tokenization_spaces=lowercase )
return lmap(str.strip , lowercase )
def _snake_case ( self , lowercase ) -> Tuple:
lowerCAmelCase = self.tokenizer.pad_token_id
lowerCAmelCase , lowerCAmelCase = batch["""input_ids"""], batch["""attention_mask"""]
lowerCAmelCase = batch["""labels"""]
if isinstance(self.model , lowercase ):
lowerCAmelCase = self.model._shift_right(lowercase )
else:
lowerCAmelCase = shift_tokens_right(lowercase , lowercase )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
lowerCAmelCase = decoder_input_ids
self.save_readable_batch(lowercase )
lowerCAmelCase = self(lowercase , attention_mask=lowercase , decoder_input_ids=lowercase , use_cache=lowercase )
lowerCAmelCase = outputs["""logits"""]
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
lowerCAmelCase = nn.CrossEntropyLoss(ignore_index=lowercase )
assert lm_logits.shape[-1] == self.vocab_size
lowerCAmelCase = ce_loss_fct(lm_logits.view(-1 , lm_logits.shape[-1] ) , tgt_ids.view(-1 ) )
else:
lowerCAmelCase = nn.functional.log_softmax(lowercase , dim=-1 )
lowerCAmelCase , lowerCAmelCase = label_smoothed_nll_loss(
lowercase , lowercase , self.hparams.label_smoothing , ignore_index=lowercase )
return (loss,)
@property
def _snake_case ( self ) -> int:
return self.tokenizer.pad_token_id
def _snake_case ( self , lowercase , lowercase ) -> Dict:
lowerCAmelCase = self._step(lowercase )
lowerCAmelCase = dict(zip(self.loss_names , lowercase ) )
# tokens per batch
lowerCAmelCase = batch["""input_ids"""].ne(self.pad ).sum() + batch["""labels"""].ne(self.pad ).sum()
lowerCAmelCase = batch["""input_ids"""].shape[0]
lowerCAmelCase = batch["""input_ids"""].eq(self.pad ).sum()
lowerCAmelCase = batch["""input_ids"""].eq(self.pad ).float().mean()
# TODO(SS): make a wandb summary metric for this
return {"loss": loss_tensors[0], "log": logs}
def _snake_case ( self , lowercase , lowercase ) -> Dict:
return self._generative_step(lowercase )
def _snake_case ( self , lowercase , lowercase="val" ) -> Dict:
self.step_count += 1
lowerCAmelCase = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
lowerCAmelCase = losses["""loss"""]
lowerCAmelCase = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
}
lowerCAmelCase = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
lowerCAmelCase = torch.tensor(lowercase ).type_as(lowercase )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(lowercase )
lowerCAmelCase = {f'{prefix}_avg_{k}': x for k, x in losses.items()}
lowerCAmelCase = self.step_count
self.metrics[prefix].append(lowercase ) # callback writes this to self.metrics_save_path
lowerCAmelCase = flatten_list([x["""preds"""] for x in outputs] )
return {
"log": all_metrics,
"preds": preds,
f'{prefix}_loss': loss,
f'{prefix}_{self.val_metric}': metric_tensor,
}
def _snake_case ( self , lowercase , lowercase ) -> Dict:
return calculate_rouge(lowercase , lowercase )
def _snake_case ( self , lowercase ) -> dict:
lowerCAmelCase = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
lowerCAmelCase = self.model.generate(
batch["""input_ids"""] , attention_mask=batch["""attention_mask"""] , use_cache=lowercase , decoder_start_token_id=self.decoder_start_token_id , num_beams=self.eval_beams , max_length=self.eval_max_length , )
lowerCAmelCase = (time.time() - ta) / batch["""input_ids"""].shape[0]
lowerCAmelCase = self.ids_to_clean_text(lowercase )
lowerCAmelCase = self.ids_to_clean_text(batch["""labels"""] )
lowerCAmelCase = self._step(lowercase )
lowerCAmelCase = dict(zip(self.loss_names , lowercase ) )
lowerCAmelCase = self.calc_generative_metrics(lowercase , lowercase )
lowerCAmelCase = np.mean(lmap(lowercase , lowercase ) )
base_metrics.update(gen_time=lowercase , gen_len=lowercase , preds=lowercase , target=lowercase , **lowercase )
return base_metrics
def _snake_case ( self , lowercase , lowercase ) -> Dict:
return self._generative_step(lowercase )
def _snake_case ( self , lowercase ) -> int:
return self.validation_epoch_end(lowercase , prefix="""test""" )
def _snake_case ( self , lowercase ) -> SeqaSeqDataset:
lowerCAmelCase = self.n_obs[type_path]
lowerCAmelCase = self.target_lens[type_path]
lowerCAmelCase = self.dataset_class(
self.tokenizer , type_path=lowercase , n_obs=lowercase , max_target_length=lowercase , **self.dataset_kwargs , )
return dataset
def _snake_case ( self , lowercase , lowercase , lowercase = False ) -> DataLoader:
lowerCAmelCase = self.get_dataset(lowercase )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
lowerCAmelCase = dataset.make_sortish_sampler(lowercase , distributed=self.hparams.gpus > 1 )
return DataLoader(
lowercase , batch_size=lowercase , collate_fn=dataset.collate_fn , shuffle=lowercase , num_workers=self.num_workers , sampler=lowercase , )
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
lowerCAmelCase = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch , distributed=self.hparams.gpus > 1 )
return DataLoader(
lowercase , batch_sampler=lowercase , collate_fn=dataset.collate_fn , num_workers=self.num_workers , )
else:
return DataLoader(
lowercase , batch_size=lowercase , collate_fn=dataset.collate_fn , shuffle=lowercase , num_workers=self.num_workers , sampler=lowercase , )
def _snake_case ( self ) -> DataLoader:
lowerCAmelCase = self.get_dataloader("""train""" , batch_size=self.hparams.train_batch_size , shuffle=lowercase )
return dataloader
def _snake_case ( self ) -> DataLoader:
return self.get_dataloader("""val""" , batch_size=self.hparams.eval_batch_size )
def _snake_case ( self ) -> DataLoader:
return self.get_dataloader("""test""" , batch_size=self.hparams.eval_batch_size )
@staticmethod
def _snake_case ( lowercase , lowercase ) -> Optional[int]:
BaseTransformer.add_model_specific_args(lowercase , lowercase )
add_generic_args(lowercase , lowercase )
parser.add_argument(
"""--max_source_length""" , default=1_024 , type=lowercase , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--max_target_length""" , default=56 , type=lowercase , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--val_max_target_length""" , default=142 , type=lowercase , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--test_max_target_length""" , default=142 , type=lowercase , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument("""--freeze_encoder""" , action="""store_true""" )
parser.add_argument("""--freeze_embeds""" , action="""store_true""" )
parser.add_argument("""--sortish_sampler""" , action="""store_true""" , default=lowercase )
parser.add_argument("""--overwrite_output_dir""" , action="""store_true""" , default=lowercase )
parser.add_argument("""--max_tokens_per_batch""" , type=lowercase , default=lowercase )
parser.add_argument("""--logger_name""" , type=lowercase , choices=["""default""", """wandb""", """wandb_shared"""] , default="""default""" )
parser.add_argument("""--n_train""" , type=lowercase , default=-1 , required=lowercase , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_val""" , type=lowercase , default=500 , required=lowercase , help="""# examples. -1 means use all.""" )
parser.add_argument("""--n_test""" , type=lowercase , default=-1 , required=lowercase , help="""# examples. -1 means use all.""" )
parser.add_argument(
"""--task""" , type=lowercase , default="""summarization""" , required=lowercase , help="""# examples. -1 means use all.""" )
parser.add_argument("""--label_smoothing""" , type=lowercase , default=0.0 , required=lowercase )
parser.add_argument("""--src_lang""" , type=lowercase , default="""""" , required=lowercase )
parser.add_argument("""--tgt_lang""" , type=lowercase , default="""""" , required=lowercase )
parser.add_argument("""--eval_beams""" , type=lowercase , default=lowercase , required=lowercase )
parser.add_argument(
"""--val_metric""" , type=lowercase , default=lowercase , required=lowercase , choices=["""bleu""", """rouge2""", """loss""", None] )
parser.add_argument("""--eval_max_gen_length""" , type=lowercase , default=lowercase , help="""never generate more than n tokens""" )
parser.add_argument("""--save_top_k""" , type=lowercase , default=1 , required=lowercase , help="""How many checkpoints to save""" )
parser.add_argument(
"""--early_stopping_patience""" , type=lowercase , default=-1 , required=lowercase , help=(
"""-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
""" val_check_interval will effect it."""
) , )
return parser
class lowercase ( _UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = 'translation'
_SCREAMING_SNAKE_CASE = ['loss']
_SCREAMING_SNAKE_CASE = ['bleu']
_SCREAMING_SNAKE_CASE = 'bleu'
def __init__( self , lowercase , **lowercase ) -> Union[str, Any]:
super().__init__(lowercase , **lowercase )
lowerCAmelCase = hparams.src_lang
lowerCAmelCase = hparams.tgt_lang
def _snake_case ( self , lowercase , lowercase ) -> dict:
return calculate_bleu(lowercase , lowercase )
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : int=None ):
'''simple docstring'''
Path(args.output_dir ).mkdir(exist_ok=SCREAMING_SNAKE_CASE )
check_output_dir(SCREAMING_SNAKE_CASE , expected_items=3 )
if model is None:
if "summarization" in args.task:
lowerCAmelCase = SummarizationModule(SCREAMING_SNAKE_CASE )
else:
lowerCAmelCase = TranslationModule(SCREAMING_SNAKE_CASE )
lowerCAmelCase = Path(args.data_dir ).name
if (
args.logger_name == "default"
or args.fast_dev_run
or str(args.output_dir ).startswith("""/tmp""" )
or str(args.output_dir ).startswith("""/var""" )
):
lowerCAmelCase = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
lowerCAmelCase = os.environ.get("""WANDB_PROJECT""" , SCREAMING_SNAKE_CASE )
lowerCAmelCase = WandbLogger(name=model.output_dir.name , project=SCREAMING_SNAKE_CASE )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
lowerCAmelCase = WandbLogger(name=model.output_dir.name , project=F'hf_{dataset}' )
if args.early_stopping_patience >= 0:
lowerCAmelCase = get_early_stopping_callback(model.val_metric , args.early_stopping_patience )
else:
lowerCAmelCase = False
lowerCAmelCase = args.val_metric == """loss"""
lowerCAmelCase = generic_train(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , logging_callback=SeqaSeqLoggingCallback() , checkpoint_callback=get_checkpoint_callback(
args.output_dir , model.val_metric , args.save_top_k , SCREAMING_SNAKE_CASE ) , early_stopping_callback=SCREAMING_SNAKE_CASE , logger=SCREAMING_SNAKE_CASE , )
pickle_save(model.hparams , model.output_dir / """hparams.pkl""" )
if not args.do_predict:
return model
lowerCAmelCase = """"""
lowerCAmelCase = sorted(glob.glob(os.path.join(args.output_dir , """*.ckpt""" ) , recursive=SCREAMING_SNAKE_CASE ) )
if checkpoints:
lowerCAmelCase = checkpoints[-1]
lowerCAmelCase = checkpoints[-1]
trainer.logger.log_hyperparams(model.hparams )
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
SCREAMING_SNAKE_CASE__ = pl.Trainer.add_argparse_args(parser)
SCREAMING_SNAKE_CASE__ = SummarizationModule.add_model_specific_args(parser, os.getcwd())
SCREAMING_SNAKE_CASE__ = parser.parse_args()
main(args)
| 46 | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class lowercase__ ( __lowerCamelCase , unittest.TestCase ):
A__ : Any =TextToVideoSDPipeline
A__ : Union[str, Any] =TEXT_TO_IMAGE_PARAMS
A__ : Any =TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
A__ : List[str] =frozenset(
[
"""num_inference_steps""",
"""generator""",
"""latents""",
"""return_dict""",
"""callback""",
"""callback_steps""",
] )
def A_ ( self : Union[str, Any] ):
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D') , up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D') , cross_attention_dim=32 , attention_head_dim=4 , )
SCREAMING_SNAKE_CASE__ = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=__lowercase , set_alpha_to_one=__lowercase , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ = 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 )
SCREAMING_SNAKE_CASE__ = 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 , )
SCREAMING_SNAKE_CASE__ = CLIPTextModel(__lowercase )
SCREAMING_SNAKE_CASE__ = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
SCREAMING_SNAKE_CASE__ = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
}
return components
def A_ ( self : Tuple , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Optional[int]=0 ):
if str(__lowercase ).startswith('mps' ):
SCREAMING_SNAKE_CASE__ = torch.manual_seed(__lowercase )
else:
SCREAMING_SNAKE_CASE__ = torch.Generator(device=__lowercase ).manual_seed(__lowercase )
SCREAMING_SNAKE_CASE__ = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''pt''',
}
return inputs
def A_ ( self : int ):
SCREAMING_SNAKE_CASE__ = '''cpu''' # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE__ = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ = TextToVideoSDPipeline(**__lowercase )
SCREAMING_SNAKE_CASE__ = sd_pipe.to(__lowercase )
sd_pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ = self.get_dummy_inputs(__lowercase )
SCREAMING_SNAKE_CASE__ = '''np'''
SCREAMING_SNAKE_CASE__ = sd_pipe(**__lowercase ).frames
SCREAMING_SNAKE_CASE__ = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
SCREAMING_SNAKE_CASE__ = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def A_ ( self : int ):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__lowercase , expected_max_diff=3e-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def A_ ( self : Optional[Any] ):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__lowercase , expected_max_diff=1e-2 )
@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.' )
def A_ ( self : int ):
pass
@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.' )
def A_ ( self : Tuple ):
pass
@unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.' )
def A_ ( self : List[Any] ):
pass
def A_ ( self : Tuple ):
return super().test_progress_bar()
@slow
@skip_mps
class lowercase__ ( unittest.TestCase ):
def A_ ( self : Tuple ):
SCREAMING_SNAKE_CASE__ = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy' )
SCREAMING_SNAKE_CASE__ = TextToVideoSDPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b' )
SCREAMING_SNAKE_CASE__ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
SCREAMING_SNAKE_CASE__ = pipe.to('cuda' )
SCREAMING_SNAKE_CASE__ = '''Spiderman is surfing'''
SCREAMING_SNAKE_CASE__ = torch.Generator(device='cpu' ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ = pipe(__lowercase , generator=__lowercase , num_inference_steps=25 , output_type='pt' ).frames
SCREAMING_SNAKE_CASE__ = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
def A_ ( self : Dict ):
SCREAMING_SNAKE_CASE__ = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy' )
SCREAMING_SNAKE_CASE__ = TextToVideoSDPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b' )
SCREAMING_SNAKE_CASE__ = pipe.to('cuda' )
SCREAMING_SNAKE_CASE__ = '''Spiderman is surfing'''
SCREAMING_SNAKE_CASE__ = torch.Generator(device='cpu' ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ = pipe(__lowercase , generator=__lowercase , num_inference_steps=2 , output_type='pt' ).frames
SCREAMING_SNAKE_CASE__ = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
| 350 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase__ ( _UpperCAmelCase ):
A__ : Any =(CMStochasticIterativeScheduler,)
A__ : Optional[int] =1_0
def A_ ( self : Dict , **UpperCAmelCase_ : Union[str, Any] ):
SCREAMING_SNAKE_CASE__ = {
'num_train_timesteps': 201,
'sigma_min': 0.002,
'sigma_max': 80.0,
}
config.update(**UpperCAmelCase_ )
return config
def A_ ( self : Tuple ):
SCREAMING_SNAKE_CASE__ = 10
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0](**UpperCAmelCase_ )
scheduler.set_timesteps(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = scheduler.timesteps[0]
SCREAMING_SNAKE_CASE__ = scheduler.timesteps[1]
SCREAMING_SNAKE_CASE__ = self.dummy_sample
SCREAMING_SNAKE_CASE__ = 0.1 * sample
SCREAMING_SNAKE_CASE__ = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ).prev_sample
SCREAMING_SNAKE_CASE__ = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A_ ( self : List[str] ):
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase_ )
def A_ ( self : Any ):
for clip_denoised in [True, False]:
self.check_over_configs(clip_denoised=UpperCAmelCase_ )
def A_ ( self : List[Any] ):
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ = scheduler_class(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = 1
scheduler.set_timesteps(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = scheduler.timesteps
SCREAMING_SNAKE_CASE__ = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ = self.dummy_model()
SCREAMING_SNAKE_CASE__ = self.dummy_sample_deter * scheduler.init_noise_sigma
for i, t in enumerate(UpperCAmelCase_ ):
# 1. scale model input
SCREAMING_SNAKE_CASE__ = scheduler.scale_model_input(UpperCAmelCase_ , UpperCAmelCase_ )
# 2. predict noise residual
SCREAMING_SNAKE_CASE__ = model(UpperCAmelCase_ , UpperCAmelCase_ )
# 3. predict previous sample x_t-1
SCREAMING_SNAKE_CASE__ = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_ ).prev_sample
SCREAMING_SNAKE_CASE__ = pred_prev_sample
SCREAMING_SNAKE_CASE__ = torch.sum(torch.abs(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE__ = torch.mean(torch.abs(UpperCAmelCase_ ) )
assert abs(result_sum.item() - 192.7_614 ) < 1e-2
assert abs(result_mean.item() - 0.2_510 ) < 1e-3
def A_ ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ = scheduler_class(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = [106, 0]
scheduler.set_timesteps(timesteps=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = scheduler.timesteps
SCREAMING_SNAKE_CASE__ = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ = self.dummy_model()
SCREAMING_SNAKE_CASE__ = self.dummy_sample_deter * scheduler.init_noise_sigma
for t in timesteps:
# 1. scale model input
SCREAMING_SNAKE_CASE__ = scheduler.scale_model_input(UpperCAmelCase_ , UpperCAmelCase_ )
# 2. predict noise residual
SCREAMING_SNAKE_CASE__ = model(UpperCAmelCase_ , UpperCAmelCase_ )
# 3. predict previous sample x_t-1
SCREAMING_SNAKE_CASE__ = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_ ).prev_sample
SCREAMING_SNAKE_CASE__ = pred_prev_sample
SCREAMING_SNAKE_CASE__ = torch.sum(torch.abs(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE__ = torch.mean(torch.abs(UpperCAmelCase_ ) )
assert abs(result_sum.item() - 347.6_357 ) < 1e-2
assert abs(result_mean.item() - 0.4_527 ) < 1e-3
def A_ ( self : Tuple ):
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ = scheduler_class(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = [39, 30, 12, 15, 0]
with self.assertRaises(UpperCAmelCase_ , msg='`timesteps` must be in descending order.' ):
scheduler.set_timesteps(timesteps=UpperCAmelCase_ )
def A_ ( self : List[Any] ):
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ = scheduler_class(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = [39, 30, 12, 1, 0]
SCREAMING_SNAKE_CASE__ = len(UpperCAmelCase_ )
with self.assertRaises(UpperCAmelCase_ , msg='Can only pass one of `num_inference_steps` or `timesteps`.' ):
scheduler.set_timesteps(num_inference_steps=UpperCAmelCase_ , timesteps=UpperCAmelCase_ )
def A_ ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ = scheduler_class(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = [scheduler.config.num_train_timesteps]
with self.assertRaises(
UpperCAmelCase_ , msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}' , ):
scheduler.set_timesteps(timesteps=UpperCAmelCase_ )
| 169 | 0 |
"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers import AutoModelForCausalLM
@require_torch
class __A ( unittest.TestCase ):
def __A ( self ):
_lowerCAmelCase : Tuple = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
_lowerCAmelCase : int = AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(a__ )
_lowerCAmelCase : Dict = -1
_lowerCAmelCase : Union[str, Any] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(a__ )
_lowerCAmelCase : List[Any] = model.generate(a__ , max_new_tokens=10 , do_sample=a__ )
_lowerCAmelCase : Any = tokenizer.decode(greedy_ids[0] )
with CaptureStdout() as cs:
_lowerCAmelCase : Tuple = TextStreamer(a__ )
model.generate(a__ , max_new_tokens=10 , do_sample=a__ , streamer=a__ )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
_lowerCAmelCase : Any = cs.out[:-1]
self.assertEqual(a__ , a__ )
def __A ( self ):
_lowerCAmelCase : Optional[int] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
_lowerCAmelCase : Tuple = AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(a__ )
_lowerCAmelCase : Optional[int] = -1
_lowerCAmelCase : Tuple = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(a__ )
_lowerCAmelCase : Any = model.generate(a__ , max_new_tokens=10 , do_sample=a__ )
_lowerCAmelCase : List[Any] = tokenizer.decode(greedy_ids[0] )
_lowerCAmelCase : int = TextIteratorStreamer(a__ )
_lowerCAmelCase : Optional[int] = {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
_lowerCAmelCase : Optional[int] = Thread(target=model.generate , kwargs=a__ )
thread.start()
_lowerCAmelCase : int = """"""
for new_text in streamer:
streamer_text += new_text
self.assertEqual(a__ , a__ )
def __A ( self ):
_lowerCAmelCase : Union[str, Any] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
_lowerCAmelCase : Tuple = AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(a__ )
_lowerCAmelCase : str = -1
_lowerCAmelCase : Optional[int] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(a__ )
_lowerCAmelCase : Any = model.generate(a__ , max_new_tokens=10 , do_sample=a__ )
_lowerCAmelCase : Optional[Any] = greedy_ids[:, input_ids.shape[1] :]
_lowerCAmelCase : List[Any] = tokenizer.decode(new_greedy_ids[0] )
with CaptureStdout() as cs:
_lowerCAmelCase : List[Any] = TextStreamer(a__ , skip_prompt=a__ )
model.generate(a__ , max_new_tokens=10 , do_sample=a__ , streamer=a__ )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
_lowerCAmelCase : Union[str, Any] = cs.out[:-1]
self.assertEqual(a__ , a__ )
def __A ( self ):
_lowerCAmelCase : List[Any] = AutoTokenizer.from_pretrained("""distilgpt2""" )
_lowerCAmelCase : Optional[int] = AutoModelForCausalLM.from_pretrained("""distilgpt2""" ).to(a__ )
_lowerCAmelCase : Optional[Any] = -1
_lowerCAmelCase : List[Any] = torch.ones((1, 5) , device=a__ ).long() * model.config.bos_token_id
with CaptureStdout() as cs:
_lowerCAmelCase : Tuple = TextStreamer(a__ , skip_special_tokens=a__ )
model.generate(a__ , max_new_tokens=1 , do_sample=a__ , streamer=a__ )
# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
# re-tokenized, must only contain one token
_lowerCAmelCase : Optional[int] = cs.out[:-1] # Remove the final "\n"
_lowerCAmelCase : Union[str, Any] = tokenizer(a__ , return_tensors="""pt""" )
self.assertEqual(streamer_text_tokenized.input_ids.shape , (1, 1) )
def __A ( self ):
_lowerCAmelCase : int = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
_lowerCAmelCase : Optional[int] = AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(a__ )
_lowerCAmelCase : Dict = -1
_lowerCAmelCase : Any = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(a__ )
_lowerCAmelCase : Any = TextIteratorStreamer(a__ , timeout=0.0_0_1 )
_lowerCAmelCase : Union[str, Any] = {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
_lowerCAmelCase : List[str] = Thread(target=model.generate , kwargs=a__ )
thread.start()
# The streamer will timeout after 0.001 seconds, so an exception will be raised
with self.assertRaises(a__ ):
_lowerCAmelCase : List[Any] = """"""
for new_text in streamer:
streamer_text += new_text
| 44 |
import argparse
import gc
import json
import os
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.deepspeed import DummyOptim, DummyScheduler
UpperCAmelCase : Optional[Any] = 16
UpperCAmelCase : Optional[Any] = 32
def __lowerCamelCase ( lowerCamelCase__ : List[str] ):
'''simple docstring'''
return int(x / 2**20 )
class __lowercase :
"""simple docstring"""
def __enter__( self ) -> Optional[Any]:
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero
lowerCamelCase = torch.cuda.memory_allocated()
return self
def __exit__( self , *A ) -> int:
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
lowerCamelCase = torch.cuda.memory_allocated()
lowerCamelCase = torch.cuda.max_memory_allocated()
lowerCamelCase = bamb(self.end - self.begin )
lowerCamelCase = bamb(self.peak - self.begin )
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
def __lowerCamelCase ( lowerCamelCase__ : Accelerator , lowerCamelCase__ : int = 16 , lowerCamelCase__ : str = "bert-base-cased" , lowerCamelCase__ : int = 320 , lowerCamelCase__ : int = 160 , ):
'''simple docstring'''
lowerCamelCase = AutoTokenizer.from_pretrained(lowerCamelCase__ )
lowerCamelCase = load_dataset(
"""glue""" , """mrpc""" , split={"""train""": f'train[:{n_train}]', """validation""": f'validation[:{n_val}]'} )
def tokenize_function(lowerCamelCase__ : str ):
# max_length=None => use the model max length (it's actually the default)
lowerCamelCase = 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
lowerCamelCase = datasets.map(
lowerCamelCase__ , batched=lowerCamelCase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=lowerCamelCase__ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCamelCase = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCamelCase__ : Union[str, Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowerCamelCase__ , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return tokenizer.pad(lowerCamelCase__ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
lowerCamelCase = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=lowerCamelCase__ )
lowerCamelCase = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=lowerCamelCase__ )
return train_dataloader, eval_dataloader
def __lowerCamelCase ( lowerCamelCase__ : Tuple , lowerCamelCase__ : Tuple ):
'''simple docstring'''
lowerCamelCase = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCamelCase = config["""lr"""]
lowerCamelCase = int(config["""num_epochs"""] )
lowerCamelCase = int(config["""seed"""] )
lowerCamelCase = int(config["""batch_size"""] )
lowerCamelCase = args.model_name_or_path
set_seed(lowerCamelCase__ )
lowerCamelCase , lowerCamelCase = get_dataloaders(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , args.n_train , args.n_val )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(lowerCamelCase__ , return_dict=lowerCamelCase__ )
# Instantiate optimizer
lowerCamelCase = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowerCamelCase = optimizer_cls(params=model.parameters() , lr=lowerCamelCase__ )
if accelerator.state.deepspeed_plugin is not None:
lowerCamelCase = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
lowerCamelCase = 1
lowerCamelCase = (len(lowerCamelCase__ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowerCamelCase = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase__ , num_warmup_steps=0 , num_training_steps=lowerCamelCase__ , )
else:
lowerCamelCase = DummyScheduler(lowerCamelCase__ , total_num_steps=lowerCamelCase__ , warmup_num_steps=0 )
# 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 = accelerator.prepare(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# We need to keep track of how many total steps we have iterated over
lowerCamelCase = 0
# We also need to keep track of the stating epoch so files are named properly
lowerCamelCase = 0
# Now we train the model
lowerCamelCase = {}
for epoch in range(lowerCamelCase__ , lowerCamelCase__ ):
with TorchTracemalloc() as tracemalloc:
model.train()
for step, batch in enumerate(lowerCamelCase__ ):
lowerCamelCase = model(**lowerCamelCase__ )
lowerCamelCase = outputs.loss
lowerCamelCase = loss / gradient_accumulation_steps
accelerator.backward(lowerCamelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin ) ) )
accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used ) )
accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked ) )
accelerator.print(
"""Total Peak Memory consumed during the train (max): {}""".format(
tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
lowerCamelCase = tracemalloc.peaked + bamb(tracemalloc.begin )
if args.peak_memory_upper_bound is not None:
assert (
train_total_peak_memory[f'epoch-{epoch}'] <= args.peak_memory_upper_bound
), "Peak memory usage exceeded the upper bound"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , """peak_memory_utilization.json""" ) , """w""" ) as f:
json.dump(lowerCamelCase__ , lowerCamelCase__ )
def __lowerCamelCase ( ):
'''simple docstring'''
lowerCamelCase = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=lowerCamelCase__ , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=lowerCamelCase__ , )
parser.add_argument(
"""--output_dir""" , type=lowerCamelCase__ , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--peak_memory_upper_bound""" , type=lowerCamelCase__ , default=lowerCamelCase__ , help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.""" , )
parser.add_argument(
"""--n_train""" , type=lowerCamelCase__ , default=320 , help="""Number of training examples to use.""" , )
parser.add_argument(
"""--n_val""" , type=lowerCamelCase__ , default=160 , help="""Number of validation examples to use.""" , )
parser.add_argument(
"""--num_epochs""" , type=lowerCamelCase__ , default=1 , help="""Number of train epochs.""" , )
lowerCamelCase = parser.parse_args()
lowerCamelCase = {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(lowerCamelCase__ , lowerCamelCase__ )
if __name__ == "__main__":
main()
| 252 | 0 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def __snake_case ( _UpperCAmelCase ):
monkeypatch.setattr('''datasets.utils.deprecation_utils._emitted_deprecation_warnings''' , set() )
@pytest.fixture
def __snake_case ( _UpperCAmelCase ):
class _A :
def __init__( self : List[Any] , __SCREAMING_SNAKE_CASE : Dict):
'''simple docstring'''
__a = metric_id
class _A :
UpperCamelCase__ : Optional[int] = [MetricMock(__UpperCAmelCase ) for metric_id in ['''accuracy''', '''mse''', '''precision''', '''codeparrot/apps_metric''']]
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
return self._metrics
monkeypatch.setattr('''datasets.inspect.huggingface_hub''' , HfhMock() )
@pytest.mark.parametrize(
'''func, args''' , [(load_metric, ('''metrics/mse''',)), (list_metrics, ()), (inspect_metric, ('''metrics/mse''', '''tmp_path'''))] )
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
if "tmp_path" in args:
__a = tuple(arg if arg != '''tmp_path''' else tmp_path for arg in args )
with pytest.warns(a__ , match='''https://huggingface.co/docs/evaluate''' ):
func(*a__ )
| 361 |
def __snake_case ( _UpperCAmelCase = 1000000 ):
__a = limit + 1
__a = [0] * limit
for first_term in range(1 , _UpperCAmelCase ):
for n in range(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
__a = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f'{solution() = }')
| 131 | 0 |
"""simple docstring"""
from __future__ import annotations
def A_ ( _lowerCAmelCase : float, _lowerCAmelCase : float, _lowerCAmelCase : float, ):
"""simple docstring"""
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif stress < 0:
raise ValueError('''Stress cannot be negative''' )
elif tangential_force < 0:
raise ValueError('''Tangential Force cannot be negative''' )
elif area < 0:
raise ValueError('''Area cannot be negative''' )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod() | 320 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Optional[Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> int:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> List[Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> str:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : str = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> str:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Union[str, Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[int]:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Any = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> List[str]:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> List[Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Union[str, Any]:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Union[str, Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> List[str]:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Dict:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Dict = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> Tuple:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[Any]:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Optional[Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> List[Any]:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Tuple:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> int:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Union[str, Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> Any:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Any:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Any:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Union[str, Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[Any]:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Tuple:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[int]:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Union[str, Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[int]:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> List[Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Union[str, Any]:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Union[str, Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> str:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> List[Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[int]:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Tuple = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> Dict:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> str:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[int]:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Optional[Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> Union[str, Any]:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Any:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> str:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Any = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> List[Any]:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Any:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Dict:
requires_backends(cls , ['''flax'''] ) | 320 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Dict = logging.get_logger(__name__)
_lowercase : Any = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class __SCREAMING_SNAKE_CASE ( a__ ):
'''simple docstring'''
_a = 'speech_to_text_2'
_a = ['past_key_values']
_a = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : List[Any], lowerCamelCase : Dict=1_0000, lowerCamelCase : Optional[Any]=6, lowerCamelCase : Any=2048, lowerCamelCase : Optional[Any]=4, lowerCamelCase : List[str]=0.0, lowerCamelCase : str=True, lowerCamelCase : str="relu", lowerCamelCase : Any=256, lowerCamelCase : Optional[Any]=0.1, lowerCamelCase : Optional[Any]=0.0, lowerCamelCase : List[Any]=0.0, lowerCamelCase : Tuple=0.02, lowerCamelCase : Dict=2, lowerCamelCase : Optional[int]=True, lowerCamelCase : str=1, lowerCamelCase : int=0, lowerCamelCase : Optional[int]=2, lowerCamelCase : Union[str, Any]=1024, **lowerCamelCase : int, )-> Union[str, Any]:
lowerCamelCase__ : int =vocab_size
lowerCamelCase__ : List[Any] =d_model
lowerCamelCase__ : Union[str, Any] =decoder_ffn_dim
lowerCamelCase__ : Tuple =decoder_layers
lowerCamelCase__ : Dict =decoder_attention_heads
lowerCamelCase__ : Any =dropout
lowerCamelCase__ : List[Any] =attention_dropout
lowerCamelCase__ : Any =activation_dropout
lowerCamelCase__ : Dict =activation_function
lowerCamelCase__ : Optional[Any] =init_std
lowerCamelCase__ : Optional[Any] =decoder_layerdrop
lowerCamelCase__ : int =use_cache
lowerCamelCase__ : List[Any] =decoder_layers
lowerCamelCase__ : str =scale_embedding # scale factor will be sqrt(d_model) if True
lowerCamelCase__ : Any =max_target_positions
super().__init__(
pad_token_id=lowerCamelCase, bos_token_id=lowerCamelCase, eos_token_id=lowerCamelCase, decoder_start_token_id=lowerCamelCase, **lowerCamelCase, )
| 370 |
"""simple docstring"""
from collections import defaultdict
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Union[str, Any], lowerCamelCase : List[Any], lowerCamelCase : List[str] )-> Optional[int]:
lowerCamelCase__ : List[Any] =total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
lowerCamelCase__ : Optional[Any] =[
[-1 for i in range(total + 1 )] for j in range(2 ** len(lowerCamelCase ) )
]
lowerCamelCase__ : Any =defaultdict(lowerCamelCase ) # 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
lowerCamelCase__ : List[Any] =(1 << len(lowerCamelCase )) - 1
def snake_case ( self : int, lowerCamelCase : str, lowerCamelCase : Any )-> Any:
# if mask == self.finalmask all persons are distributed tasks, return 1
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
lowerCamelCase__ : Optional[int] =self.count_ways_until(lowerCamelCase, 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.
lowerCamelCase__ : int =total_ways_util
return self.dp[mask][task_no]
def snake_case ( self : Dict, lowerCamelCase : Dict )-> int:
# Store the list of persons for each task
for i in range(len(lowerCamelCase ) ):
for j in task_performed[i]:
self.task[j].append(lowerCamelCase )
# 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 : Tuple = 5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
_lowercase : Dict = [[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
)
| 272 | 0 |
from functools import lru_cache
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
__a = 2
__a = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(_SCREAMING_SNAKE_CASE )
if n > 1:
factors.add(_SCREAMING_SNAKE_CASE )
return factors
@lru_cache
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
return len(unique_prime_factors(_SCREAMING_SNAKE_CASE ) )
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : list ):
"""simple docstring"""
return len(set(_SCREAMING_SNAKE_CASE ) ) in (0, 1)
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
__a = 2
while True:
# Increment each value of a generated range
__a = [base + i for i in range(_SCREAMING_SNAKE_CASE )]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
__a = [upf_len(_SCREAMING_SNAKE_CASE ) for x in group]
checker.append(_SCREAMING_SNAKE_CASE )
# If all numbers in the list are equal, return the group variable.
if equality(_SCREAMING_SNAKE_CASE ):
return group
# Increment our base variable by 1
base += 1
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int = 4 ):
"""simple docstring"""
__a = run(_SCREAMING_SNAKE_CASE )
return results[0] if len(_SCREAMING_SNAKE_CASE ) else None
if __name__ == "__main__":
print(solution())
| 302 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
__a = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
__a = 128
elif "12-12" in model_name:
__a = 12
__a = 12
elif "14-14" in model_name:
__a = 14
__a = 14
elif "16-16" in model_name:
__a = 16
__a = 16
else:
raise ValueError("""Model not supported""" )
__a = """huggingface/label-files"""
if "speech-commands" in model_name:
__a = 35
__a = """speech-commands-v2-id2label.json"""
else:
__a = 527
__a = """audioset-id2label.json"""
__a = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
__a = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__a = idalabel
__a = {v: k for k, v in idalabel.items()}
return config
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
if "module.v" in name:
__a = name.replace("""module.v""" , """audio_spectrogram_transformer""" )
if "cls_token" in name:
__a = name.replace("""cls_token""" , """embeddings.cls_token""" )
if "dist_token" in name:
__a = name.replace("""dist_token""" , """embeddings.distillation_token""" )
if "pos_embed" in name:
__a = name.replace("""pos_embed""" , """embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
__a = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
# transformer blocks
if "blocks" in name:
__a = name.replace("""blocks""" , """encoder.layer""" )
if "attn.proj" in name:
__a = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
__a = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
__a = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
__a = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
__a = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
__a = name.replace("""mlp.fc2""" , """output.dense""" )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
__a = name.replace("""audio_spectrogram_transformer.norm""" , """audio_spectrogram_transformer.layernorm""" )
# classifier head
if "module.mlp_head.0" in name:
__a = name.replace("""module.mlp_head.0""" , """classifier.layernorm""" )
if "module.mlp_head.1" in name:
__a = name.replace("""module.mlp_head.1""" , """classifier.dense""" )
return name
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
__a = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "qkv" in key:
__a = key.split(""".""" )
__a = int(key_split[3] )
__a = config.hidden_size
if "weight" in key:
__a = val[:dim, :]
__a = val[dim : dim * 2, :]
__a = val[-dim:, :]
else:
__a = val[:dim]
__a = val[dim : dim * 2]
__a = val[-dim:]
else:
__a = val
return orig_state_dict
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
__a = [
"""module.v.head.weight""",
"""module.v.head.bias""",
"""module.v.head_dist.weight""",
"""module.v.head_dist.bias""",
]
for k in ignore_keys:
state_dict.pop(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@torch.no_grad()
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : List[str]=False ):
"""simple docstring"""
__a = get_audio_spectrogram_transformer_config(_SCREAMING_SNAKE_CASE )
__a = {
"""ast-finetuned-audioset-10-10-0.4593""": (
"""https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.450""": (
"""https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.448""": (
"""https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.448-v2""": (
"""https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"""
),
"""ast-finetuned-audioset-12-12-0.447""": (
"""https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"""
),
"""ast-finetuned-audioset-14-14-0.443""": (
"""https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"""
),
"""ast-finetuned-audioset-16-16-0.442""": (
"""https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"""
),
"""ast-finetuned-speech-commands-v2""": (
"""https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"""
),
}
# load original state_dict
__a = model_name_to_url[model_name]
__a = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE , map_location="""cpu""" )
# remove some keys
remove_keys(_SCREAMING_SNAKE_CASE )
# rename some keys
__a = convert_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# load 🤗 model
__a = ASTForAudioClassification(_SCREAMING_SNAKE_CASE )
model.eval()
model.load_state_dict(_SCREAMING_SNAKE_CASE )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
__a = -4.267_7393 if """speech-commands""" not in model_name else -6.84_5978
__a = 4.568_9974 if """speech-commands""" not in model_name else 5.565_4526
__a = 1024 if """speech-commands""" not in model_name else 128
__a = ASTFeatureExtractor(mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE )
if "speech-commands" in model_name:
__a = load_dataset("""speech_commands""" , """v0.02""" , split="""validation""" )
__a = dataset[0]["""audio"""]["""array"""]
else:
__a = hf_hub_download(
repo_id="""nielsr/audio-spectogram-transformer-checkpoint""" , filename="""sample_audio.flac""" , repo_type="""dataset""" , )
__a , __a = torchaudio.load(_SCREAMING_SNAKE_CASE )
__a = waveform.squeeze().numpy()
__a = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=1_6000 , return_tensors="""pt""" )
# forward pass
__a = model(**_SCREAMING_SNAKE_CASE )
__a = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
__a = torch.tensor([-0.8760, -7.0042, -8.6602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
__a = torch.tensor([-1.1986, -7.0903, -8.2718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
__a = torch.tensor([-2.6128, -8.0080, -9.4344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
__a = torch.tensor([-1.5080, -7.4534, -8.8917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
__a = torch.tensor([-0.5050, -6.5833, -8.0843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
__a = torch.tensor([-0.3826, -7.0336, -8.2413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
__a = torch.tensor([-1.2113, -6.9101, -8.3470] )
elif model_name == "ast-finetuned-speech-commands-v2":
__a = torch.tensor([6.1589, -8.0566, -8.7984] )
else:
raise ValueError("""Unknown model name""" )
if not torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 ):
raise ValueError("""Logits don't match""" )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(f"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(f"Saving feature extractor to {pytorch_dump_folder_path}" )
feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
print("""Pushing model and feature extractor to the hub...""" )
model.push_to_hub(f"MIT/{model_name}" )
feature_extractor.push_to_hub(f"MIT/{model_name}" )
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""ast-finetuned-audioset-10-10-0.4593""",
type=str,
help="""Name of the Audio Spectrogram Transformer 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 or not to push the converted model to the 🤗 hub."""
)
lowerCamelCase__ = parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 302 | 1 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
_lowerCAmelCase :int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
_lowerCAmelCase :Optional[Any] = [file for file in filepaths if file != file.lower()]
if upper_files:
print(f"{len(upper_files)} files contain uppercase characters:")
print('\n'.join(upper_files) + '\n')
_lowerCAmelCase :Union[str, Any] = [file for file in filepaths if ' ' in file]
if space_files:
print(f"{len(space_files)} files contain space characters:")
print('\n'.join(space_files) + '\n')
_lowerCAmelCase :str = [file for file in filepaths if '-' in file]
if hyphen_files:
print(f"{len(hyphen_files)} files contain hyphen characters:")
print('\n'.join(hyphen_files) + '\n')
_lowerCAmelCase :int = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(f"{len(nodir_files)} files are not in a directory:")
print('\n'.join(nodir_files) + '\n')
_lowerCAmelCase :int = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 364 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class _UpperCAmelCase ( a ):
'''simple docstring'''
def __init__( self , A , A=1_3 , A=7 , A=True , A=True , A=False , A=True , A=9_9 , A=3_2 , A=5 , A=4 , A=3_7 , A="gelu" , A=0.1 , A=0.1 , A=5_1_2 , A=1_6 , A=2 , A=0.02 , A=3 , A=4 , A=None , ) -> Dict:
_UpperCAmelCase : Dict = parent
_UpperCAmelCase : int = batch_size
_UpperCAmelCase : Union[str, Any] = seq_length
_UpperCAmelCase : str = is_training
_UpperCAmelCase : Optional[Any] = use_input_mask
_UpperCAmelCase : List[Any] = use_token_type_ids
_UpperCAmelCase : str = use_labels
_UpperCAmelCase : List[str] = vocab_size
_UpperCAmelCase : str = hidden_size
_UpperCAmelCase : List[str] = num_hidden_layers
_UpperCAmelCase : List[str] = num_attention_heads
_UpperCAmelCase : Union[str, Any] = intermediate_size
_UpperCAmelCase : str = hidden_act
_UpperCAmelCase : Optional[int] = hidden_dropout_prob
_UpperCAmelCase : Union[str, Any] = attention_probs_dropout_prob
_UpperCAmelCase : str = max_position_embeddings
_UpperCAmelCase : Optional[int] = type_vocab_size
_UpperCAmelCase : List[str] = type_sequence_label_size
_UpperCAmelCase : Optional[Any] = initializer_range
_UpperCAmelCase : Optional[int] = num_labels
_UpperCAmelCase : int = num_choices
_UpperCAmelCase : Dict = scope
def __lowerCAmelCase ( self ) -> int:
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : int = None
if self.use_input_mask:
_UpperCAmelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : int = None
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Optional[Any] = None
if self.use_labels:
_UpperCAmelCase : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase : Union[str, Any] = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase : List[str] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self ) -> Union[str, Any]:
return 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 , )
def __lowerCAmelCase ( self , A , A , A , A , A , A ) -> Tuple:
_UpperCAmelCase : int = DistilBertModel(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = model(A , A )
_UpperCAmelCase : List[str] = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , A , A , A , A , A , A ) -> Any:
_UpperCAmelCase : int = DistilBertForMaskedLM(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Any = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self , A , A , A , A , A , A ) -> List[Any]:
_UpperCAmelCase : Tuple = DistilBertForQuestionAnswering(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = model(
A , attention_mask=A , start_positions=A , end_positions=A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowerCAmelCase ( self , A , A , A , A , A , A ) -> str:
_UpperCAmelCase : List[Any] = self.num_labels
_UpperCAmelCase : Union[str, Any] = DistilBertForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : int = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , A , A , A , A , A , A ) -> List[Any]:
_UpperCAmelCase : Any = self.num_labels
_UpperCAmelCase : Optional[int] = DistilBertForTokenClassification(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[Any] = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCAmelCase ( self , A , A , A , A , A , A ) -> List[Any]:
_UpperCAmelCase : str = self.num_choices
_UpperCAmelCase : Dict = DistilBertForMultipleChoice(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase : Optional[int] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase : Optional[Any] = model(
A , attention_mask=A , labels=A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCAmelCase ( self ) -> Optional[Any]:
_UpperCAmelCase : Dict = self.prepare_config_and_inputs()
((_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase)) : int = config_and_inputs
_UpperCAmelCase : Union[str, Any] = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( a ,a ,unittest.TestCase ):
'''simple docstring'''
a__ =(
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
a__ =(
{
'''feature-extraction''': DistilBertModel,
'''fill-mask''': DistilBertForMaskedLM,
'''question-answering''': DistilBertForQuestionAnswering,
'''text-classification''': DistilBertForSequenceClassification,
'''token-classification''': DistilBertForTokenClassification,
'''zero-shot''': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
a__ =True
a__ =True
a__ =True
a__ =True
def __lowerCAmelCase ( self ) -> Optional[Any]:
_UpperCAmelCase : Union[str, Any] = DistilBertModelTester(self )
_UpperCAmelCase : List[Any] = ConfigTester(self , config_class=A , dim=3_7 )
def __lowerCAmelCase ( self ) -> Tuple:
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ) -> Optional[Any]:
_UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*A )
def __lowerCAmelCase ( self ) -> Tuple:
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*A )
def __lowerCAmelCase ( self ) -> Dict:
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*A )
def __lowerCAmelCase ( self ) -> Any:
_UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*A )
def __lowerCAmelCase ( self ) -> Tuple:
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*A )
def __lowerCAmelCase ( self ) -> Optional[int]:
_UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*A )
@slow
def __lowerCAmelCase ( self ) -> Optional[Any]:
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Optional[int] = DistilBertModel.from_pretrained(A )
self.assertIsNotNone(A )
@slow
@require_torch_gpu
def __lowerCAmelCase ( self ) -> Optional[Any]:
_UpperCAmelCase , _UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
_UpperCAmelCase : Tuple = True
_UpperCAmelCase : Union[str, Any] = model_class(config=A )
_UpperCAmelCase : List[Any] = self._prepare_for_class(A , A )
_UpperCAmelCase : int = torch.jit.trace(
A , (inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(A , os.path.join(A , '''traced_model.pt''' ) )
_UpperCAmelCase : Optional[int] = torch.jit.load(os.path.join(A , '''traced_model.pt''' ) , map_location=A )
loaded(inputs_dict['''input_ids'''].to(A ) , inputs_dict['''attention_mask'''].to(A ) )
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __lowerCAmelCase ( self ) -> List[str]:
_UpperCAmelCase : Dict = DistilBertModel.from_pretrained('''distilbert-base-uncased''' )
_UpperCAmelCase : List[str] = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
_UpperCAmelCase : Optional[Any] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
_UpperCAmelCase : str = model(A , attention_mask=A )[0]
_UpperCAmelCase : Dict = torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , A )
_UpperCAmelCase : Any = torch.tensor(
[[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , A , atol=1E-4 ) )
| 68 | 0 |
"""simple docstring"""
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 A_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self :Optional[int] , lowerCamelCase_ :Any , lowerCamelCase_ :Optional[Any]=100 , lowerCamelCase_ :List[str]=13 , lowerCamelCase_ :List[Any]=30 , lowerCamelCase_ :int=2 , lowerCamelCase_ :int=3 , lowerCamelCase_ :Union[str, Any]=True , lowerCamelCase_ :List[str]=True , lowerCamelCase_ :Any=32 , lowerCamelCase_ :List[str]=5 , lowerCamelCase_ :Tuple=4 , lowerCamelCase_ :Any=37 , lowerCamelCase_ :List[Any]="gelu" , lowerCamelCase_ :int=0.1 , lowerCamelCase_ :Dict=0.1 , lowerCamelCase_ :int=10 , lowerCamelCase_ :Dict=0.02 , lowerCamelCase_ :Dict=3 , ):
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] =parent
lowerCamelCase__ : str =vocab_size
lowerCamelCase__ : int =batch_size
lowerCamelCase__ : Any =image_size
lowerCamelCase__ : Any =patch_size
lowerCamelCase__ : Optional[int] =num_channels
lowerCamelCase__ : Any =is_training
lowerCamelCase__ : List[str] =use_labels
lowerCamelCase__ : Tuple =hidden_size
lowerCamelCase__ : Dict =num_hidden_layers
lowerCamelCase__ : str =num_attention_heads
lowerCamelCase__ : List[Any] =intermediate_size
lowerCamelCase__ : Any =hidden_act
lowerCamelCase__ : Optional[int] =hidden_dropout_prob
lowerCamelCase__ : Optional[Any] =attention_probs_dropout_prob
lowerCamelCase__ : List[str] =type_sequence_label_size
lowerCamelCase__ : List[Any] =initializer_range
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
lowerCamelCase__ : List[str] =(image_size // patch_size) ** 2
lowerCamelCase__ : Union[str, Any] =num_patches + 1
def UpperCAmelCase__ ( self :Union[str, Any] ):
"""simple docstring"""
lowerCamelCase__ : int =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase__ : List[Any] =None
if self.use_labels:
lowerCamelCase__ : List[Any] =ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase__ : Union[str, Any] =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=SCREAMING_SNAKE_CASE__ , initializer_range=self.initializer_range , )
return config, pixel_values, labels
def UpperCAmelCase__ ( self :List[Any] , lowerCamelCase_ :List[str] , lowerCamelCase_ :Optional[Any] , lowerCamelCase_ :Union[str, Any] ):
"""simple docstring"""
lowerCamelCase__ : List[Any] =FlaxBeitModel(config=SCREAMING_SNAKE_CASE__ )
lowerCamelCase__ : int =model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase__ ( self :Any , lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :str , lowerCamelCase_ :List[str] ):
"""simple docstring"""
lowerCamelCase__ : List[Any] =FlaxBeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__ )
lowerCamelCase__ : Optional[Any] =model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def UpperCAmelCase__ ( self :int , lowerCamelCase_ :Optional[int] , lowerCamelCase_ :int , lowerCamelCase_ :Optional[int] ):
"""simple docstring"""
lowerCamelCase__ : List[Any] =self.type_sequence_label_size
lowerCamelCase__ : List[str] =FlaxBeitForImageClassification(config=SCREAMING_SNAKE_CASE__ )
lowerCamelCase__ : Any =model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCamelCase__ : Optional[int] =1
lowerCamelCase__ : str =FlaxBeitForImageClassification(SCREAMING_SNAKE_CASE__ )
lowerCamelCase__ : Optional[int] =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCamelCase__ : List[Any] =model(SCREAMING_SNAKE_CASE__ )
def UpperCAmelCase__ ( self :Tuple ):
"""simple docstring"""
lowerCamelCase__ : int =self.prepare_config_and_inputs()
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) : List[Any] =config_and_inputs
lowerCamelCase__ : Optional[Any] ={'pixel_values': pixel_values}
return config, inputs_dict
@require_flax
class A_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = (
(FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
)
def UpperCAmelCase__ ( self :int ):
"""simple docstring"""
lowerCamelCase__ : Optional[Any] =FlaxBeitModelTester(self )
lowerCamelCase__ : int =ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ , has_text_modality=SCREAMING_SNAKE_CASE__ , hidden_size=37 )
def UpperCAmelCase__ ( self :Optional[int] ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self :List[Any] ):
"""simple docstring"""
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__ : Optional[int] =model_class(SCREAMING_SNAKE_CASE__ )
lowerCamelCase__ : Tuple =inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase__ : Optional[Any] =[*signature.parameters.keys()]
lowerCamelCase__ : Dict =['pixel_values']
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE__ )
def UpperCAmelCase__ ( self :Union[str, Any] ):
"""simple docstring"""
lowerCamelCase__ , lowerCamelCase__ : int =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowerCamelCase__ : List[Any] =self._prepare_for_class(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowerCamelCase__ : int =model_class(SCREAMING_SNAKE_CASE__ )
@jax.jit
def model_jitted(lowerCamelCase_ :str , **lowerCamelCase_ :Dict ):
return model(pixel_values=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
with self.subTest('JIT Enabled' ):
lowerCamelCase__ : Optional[Any] =model_jitted(**SCREAMING_SNAKE_CASE__ ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
lowerCamelCase__ : Any =model_jitted(**SCREAMING_SNAKE_CASE__ ).to_tuple()
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , len(SCREAMING_SNAKE_CASE__ ) )
for jitted_output, output in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
self.assertEqual(jitted_output.shape , output.shape )
def UpperCAmelCase__ ( self :Any ):
"""simple docstring"""
lowerCamelCase__ : List[str] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__ )
def UpperCAmelCase__ ( self :Any ):
"""simple docstring"""
lowerCamelCase__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__ )
def UpperCAmelCase__ ( self :Optional[int] ):
"""simple docstring"""
lowerCamelCase__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__ )
@slow
def UpperCAmelCase__ ( self :List[Any] ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowerCamelCase__ : int =model_class_name.from_pretrained('microsoft/beit-base-patch16-224' )
lowerCamelCase__ : int =model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
def lowerCAmelCase_ ( ) ->List[str]:
lowerCamelCase__ : Dict =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_vision
@require_flax
class A_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCAmelCase__ ( self :List[str] ):
"""simple docstring"""
return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224' ) if is_vision_available() else None
@slow
def UpperCAmelCase__ ( self :int ):
"""simple docstring"""
lowerCamelCase__ : List[str] =FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k' )
lowerCamelCase__ : List[str] =self.default_image_processor
lowerCamelCase__ : List[str] =prepare_img()
lowerCamelCase__ : Tuple =image_processor(images=SCREAMING_SNAKE_CASE__ , return_tensors='np' ).pixel_values
# prepare bool_masked_pos
lowerCamelCase__ : Optional[int] =np.ones((1, 196) , dtype=SCREAMING_SNAKE_CASE__ )
# forward pass
lowerCamelCase__ : int =model(pixel_values=SCREAMING_SNAKE_CASE__ , bool_masked_pos=SCREAMING_SNAKE_CASE__ )
lowerCamelCase__ : Union[str, Any] =outputs.logits
# verify the logits
lowerCamelCase__ : int =(1, 196, 8_192)
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE__ )
lowerCamelCase__ : str =np.array(
[[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]] )
self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] , SCREAMING_SNAKE_CASE__ , atol=1e-2 ) )
@slow
def UpperCAmelCase__ ( self :int ):
"""simple docstring"""
lowerCamelCase__ : Dict =FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224' )
lowerCamelCase__ : int =self.default_image_processor
lowerCamelCase__ : Any =prepare_img()
lowerCamelCase__ : Union[str, Any] =image_processor(images=SCREAMING_SNAKE_CASE__ , return_tensors='np' )
# forward pass
lowerCamelCase__ : Dict =model(**SCREAMING_SNAKE_CASE__ )
lowerCamelCase__ : Tuple =outputs.logits
# verify the logits
lowerCamelCase__ : Dict =(1, 1_000)
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE__ )
lowerCamelCase__ : int =np.array([-1.23_85, -1.09_87, -1.01_08] )
self.assertTrue(np.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE__ , atol=1e-4 ) )
lowerCamelCase__ : Any =281
self.assertEqual(logits.argmax(-1 ).item() , SCREAMING_SNAKE_CASE__ )
@slow
def UpperCAmelCase__ ( self :Dict ):
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] =FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k' )
lowerCamelCase__ : List[str] =self.default_image_processor
lowerCamelCase__ : int =prepare_img()
lowerCamelCase__ : Tuple =image_processor(images=SCREAMING_SNAKE_CASE__ , return_tensors='np' )
# forward pass
lowerCamelCase__ : List[str] =model(**SCREAMING_SNAKE_CASE__ )
lowerCamelCase__ : List[Any] =outputs.logits
# verify the logits
lowerCamelCase__ : str =(1, 21_841)
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE__ )
lowerCamelCase__ : Dict =np.array([1.68_81, -0.27_87, 0.59_01] )
self.assertTrue(np.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE__ , atol=1e-4 ) )
lowerCamelCase__ : Optional[int] =2_396
self.assertEqual(logits.argmax(-1 ).item() , SCREAMING_SNAKE_CASE__ ) | 126 |
import pprint
import requests
SCREAMING_SNAKE_CASE__ : str = "https://zenquotes.io/api"
def __magic_name__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def __magic_name__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/random''' ).json()
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : int = random_quotes()
pprint.pprint(response)
| 270 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if num <= 0:
_a = f'{num}: Invalid input, please enter a positive integer.'
raise ValueError(lowerCAmelCase__ )
_a = [True] * (num + 1)
_a = []
_a = 2
_a = int(math.sqrt(lowerCAmelCase__ ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(lowerCAmelCase__ )
# Set multiples of start be False
for i in range(start * start, num + 1, lowerCAmelCase__ ):
if sieve[i] is True:
_a = False
start += 1
for j in range(end + 1, num + 1 ):
if sieve[j] is True:
prime.append(lowerCAmelCase__ )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input('''Enter a positive integer: ''').strip()))) | 360 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if p < 2:
raise ValueError('''p should not be less than 2!''' )
elif p == 2:
return True
_a = 4
_a = (1 << p) - 1
for _ in range(p - 2 ):
_a = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(11)) | 153 | 0 |
'''simple docstring'''
import os
import sys
_lowerCAmelCase = 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 = [
'''torch''',
'''numpy''',
'''tokenizers''',
'''filelock''',
'''requests''',
'''tqdm''',
'''regex''',
'''sentencepiece''',
'''sacremoses''',
'''importlib_metadata''',
'''huggingface_hub''',
]
@add_start_docstrings(AutoConfig.__doc__ )
def __lowerCAmelCase ( *snake_case__ , **snake_case__ ):
return AutoConfig.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoTokenizer.__doc__ )
def __lowerCAmelCase ( *snake_case__ , **snake_case__ ):
return AutoTokenizer.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoModel.__doc__ )
def __lowerCAmelCase ( *snake_case__ , **snake_case__ ):
return AutoModel.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def __lowerCAmelCase ( *snake_case__ , **snake_case__ ):
return AutoModelForCausalLM.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def __lowerCAmelCase ( *snake_case__ , **snake_case__ ):
return AutoModelForMaskedLM.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def __lowerCAmelCase ( *snake_case__ , **snake_case__ ):
return AutoModelForSequenceClassification.from_pretrained(*snake_case__ , **snake_case__ )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def __lowerCAmelCase ( *snake_case__ , **snake_case__ ):
return AutoModelForQuestionAnswering.from_pretrained(*snake_case__ , **snake_case__ )
| 298 |
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class A :
'''simple docstring'''
def __init__(self , _UpperCAmelCase , _UpperCAmelCase=1_3 , _UpperCAmelCase=7 , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=9_9 , _UpperCAmelCase=3_2 , _UpperCAmelCase=2 , _UpperCAmelCase=4 , _UpperCAmelCase=3_7 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=5_1_2 , _UpperCAmelCase=1_6 , _UpperCAmelCase=2 , _UpperCAmelCase=0.02 , _UpperCAmelCase=3 , _UpperCAmelCase=4 , _UpperCAmelCase=None , ) -> Dict:
__UpperCamelCase : Optional[Any] = parent
__UpperCamelCase : List[str] = 1_3
__UpperCamelCase : List[Any] = 7
__UpperCamelCase : List[str] = True
__UpperCamelCase : Optional[Any] = True
__UpperCamelCase : Tuple = True
__UpperCamelCase : str = True
__UpperCamelCase : List[Any] = 9_9
__UpperCamelCase : Union[str, Any] = 3_8_4
__UpperCamelCase : str = 2
__UpperCamelCase : Optional[Any] = 4
__UpperCamelCase : Any = 3_7
__UpperCamelCase : str = "gelu"
__UpperCamelCase : Optional[Any] = 0.1
__UpperCamelCase : str = 0.1
__UpperCamelCase : str = 5_1_2
__UpperCamelCase : Optional[Any] = 1_6
__UpperCamelCase : Dict = 2
__UpperCamelCase : Optional[int] = 0.02
__UpperCamelCase : List[Any] = 3
__UpperCamelCase : Optional[Any] = 4
__UpperCamelCase : int = 1_2_8
__UpperCamelCase : Tuple = 2
__UpperCamelCase : str = 9
__UpperCamelCase : List[Any] = 1
__UpperCamelCase : Any = None
def a_ (self ) -> int:
__UpperCamelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase : str = None
if self.use_input_mask:
__UpperCamelCase : str = random_attention_mask([self.batch_size, self.seq_length] )
__UpperCamelCase : int = None
if self.use_token_type_ids:
__UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCamelCase : List[Any] = None
__UpperCamelCase : Union[str, Any] = None
__UpperCamelCase : Optional[Any] = None
if self.use_labels:
__UpperCamelCase : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCamelCase : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.num_choices )
__UpperCamelCase : str = ConvBertConfig(
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 , initializer_range=self.initializer_range , return_dict=_UpperCAmelCase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Dict:
__UpperCamelCase : Tuple = TFConvBertModel(config=_UpperCAmelCase )
__UpperCamelCase : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
__UpperCamelCase : Optional[Any] = [input_ids, input_mask]
__UpperCamelCase : str = model(_UpperCAmelCase )
__UpperCamelCase : int = model(_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Optional[int]:
__UpperCamelCase : int = TFConvBertForMaskedLM(config=_UpperCAmelCase )
__UpperCamelCase : Dict = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
__UpperCamelCase : List[str] = model(_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Optional[int]:
__UpperCamelCase : Union[str, Any] = self.num_labels
__UpperCamelCase : Optional[Any] = TFConvBertForSequenceClassification(config=_UpperCAmelCase )
__UpperCamelCase : List[str] = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
__UpperCamelCase : Optional[Any] = model(_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> List[str]:
__UpperCamelCase : Optional[int] = self.num_choices
__UpperCamelCase : List[Any] = TFConvBertForMultipleChoice(config=_UpperCAmelCase )
__UpperCamelCase : Optional[int] = tf.tile(tf.expand_dims(_UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__UpperCamelCase : Optional[Any] = tf.tile(tf.expand_dims(_UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__UpperCamelCase : str = tf.tile(tf.expand_dims(_UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
__UpperCamelCase : List[str] = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
__UpperCamelCase : int = model(_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Any:
__UpperCamelCase : List[str] = self.num_labels
__UpperCamelCase : Tuple = TFConvBertForTokenClassification(config=_UpperCAmelCase )
__UpperCamelCase : Dict = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
__UpperCamelCase : Union[str, Any] = model(_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Union[str, Any]:
__UpperCamelCase : int = TFConvBertForQuestionAnswering(config=_UpperCAmelCase )
__UpperCamelCase : Dict = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
__UpperCamelCase : Any = model(_UpperCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ (self ) -> str:
__UpperCamelCase : str = self.prepare_config_and_inputs()
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) : Any = config_and_inputs
__UpperCamelCase : int = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class A ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
A = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
A = (
{
"feature-extraction": TFConvBertModel,
"fill-mask": TFConvBertForMaskedLM,
"question-answering": TFConvBertForQuestionAnswering,
"text-classification": TFConvBertForSequenceClassification,
"token-classification": TFConvBertForTokenClassification,
"zero-shot": TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
A = False
A = False
A = False
def a_ (self ) -> Optional[int]:
__UpperCamelCase : Tuple = TFConvBertModelTester(self )
__UpperCamelCase : Optional[Any] = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=3_7 )
def a_ (self ) -> Dict:
self.config_tester.run_common_tests()
def a_ (self ) -> Dict:
__UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def a_ (self ) -> Tuple:
__UpperCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase )
def a_ (self ) -> Tuple:
__UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase )
def a_ (self ) -> Dict:
__UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase )
def a_ (self ) -> Dict:
__UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase )
def a_ (self ) -> Optional[int]:
__UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase )
@slow
def a_ (self ) -> Any:
__UpperCamelCase , __UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase : str = True
__UpperCamelCase : int = True
if hasattr(_UpperCAmelCase , "use_cache" ):
__UpperCamelCase : List[Any] = True
__UpperCamelCase : List[str] = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
__UpperCamelCase : Optional[Any] = getattr(self.model_tester , "key_length" , _UpperCAmelCase )
for model_class in self.all_model_classes:
__UpperCamelCase : Any = self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase )
__UpperCamelCase : int = model_class(_UpperCAmelCase )
__UpperCamelCase : Any = len(model(_UpperCAmelCase ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_UpperCAmelCase , saved_model=_UpperCAmelCase )
__UpperCamelCase : List[str] = os.path.join(_UpperCAmelCase , "saved_model" , "1" )
__UpperCamelCase : List[str] = tf.keras.models.load_model(_UpperCAmelCase )
__UpperCamelCase : Dict = model(_UpperCAmelCase )
if self.is_encoder_decoder:
__UpperCamelCase : Any = outputs["encoder_hidden_states"]
__UpperCamelCase : Tuple = outputs["encoder_attentions"]
else:
__UpperCamelCase : Tuple = outputs["hidden_states"]
__UpperCamelCase : Optional[int] = outputs["attentions"]
self.assertEqual(len(_UpperCAmelCase ) , _UpperCAmelCase )
__UpperCamelCase : Any = getattr(
self.model_tester , "expected_num_hidden_layers" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(_UpperCAmelCase ) , _UpperCAmelCase )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(_UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def a_ (self ) -> Optional[Any]:
__UpperCamelCase : Tuple = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
self.assertIsNotNone(_UpperCAmelCase )
def a_ (self ) -> Tuple:
__UpperCamelCase , __UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase : str = True
__UpperCamelCase : Tuple = getattr(self.model_tester , "decoder_seq_length" , self.model_tester.seq_length )
__UpperCamelCase : Optional[int] = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
__UpperCamelCase : Any = getattr(self.model_tester , "key_length" , _UpperCAmelCase )
__UpperCamelCase : List[Any] = getattr(self.model_tester , "key_length" , _UpperCAmelCase )
def check_decoder_attentions_output(_UpperCAmelCase ):
__UpperCamelCase : Dict = len(_UpperCAmelCase )
self.assertEqual(out_len % 2 , 0 )
__UpperCamelCase : List[str] = outputs.decoder_attentions
self.assertEqual(len(_UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(_UpperCAmelCase ):
__UpperCamelCase : Any = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(_UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
__UpperCamelCase : Any = True
__UpperCamelCase : Dict = False
__UpperCamelCase : str = model_class(_UpperCAmelCase )
__UpperCamelCase : Tuple = model(self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
__UpperCamelCase : List[Any] = len(_UpperCAmelCase )
self.assertEqual(config.output_hidden_states , _UpperCAmelCase )
check_encoder_attentions_output(_UpperCAmelCase )
if self.is_encoder_decoder:
__UpperCamelCase : str = model_class(_UpperCAmelCase )
__UpperCamelCase : Dict = model(self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
self.assertEqual(config.output_hidden_states , _UpperCAmelCase )
check_decoder_attentions_output(_UpperCAmelCase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
__UpperCamelCase : Optional[Any] = True
__UpperCamelCase : Tuple = model_class(_UpperCAmelCase )
__UpperCamelCase : int = model(self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
self.assertEqual(config.output_hidden_states , _UpperCAmelCase )
check_encoder_attentions_output(_UpperCAmelCase )
# Check attention is always last and order is fine
__UpperCamelCase : int = True
__UpperCamelCase : str = True
__UpperCamelCase : Optional[Any] = model_class(_UpperCAmelCase )
__UpperCamelCase : Optional[int] = model(self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_UpperCAmelCase ) )
self.assertEqual(model.config.output_hidden_states , _UpperCAmelCase )
check_encoder_attentions_output(_UpperCAmelCase )
@require_tf
class A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a_ (self ) -> str:
__UpperCamelCase : Dict = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
__UpperCamelCase : str = tf.constant([[0, 1, 2, 3, 4, 5]] )
__UpperCamelCase : Optional[int] = model(_UpperCAmelCase )[0]
__UpperCamelCase : Tuple = [1, 6, 7_6_8]
self.assertEqual(output.shape , _UpperCAmelCase )
__UpperCamelCase : Any = tf.constant(
[
[
[-0.03_475_493, -0.4_686_034, -0.30_638_832],
[0.22_637_248, -0.26_988_646, -0.7_423_424],
[0.10_324_868, -0.45_013_508, -0.58_280_784],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , _UpperCAmelCase , atol=1E-4 )
| 298 | 1 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
a__ = 299792458
# Symbols
a__ , a__ , a__ , a__ = symbols('''ct x y z''')
def __UpperCAmelCase ( __a : float ) -> float:
"""simple docstring"""
if velocity > c:
raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!''' )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError('''Speed must be greater than or equal to 1!''' )
return velocity / c
def __UpperCAmelCase ( __a : float ) -> float:
"""simple docstring"""
return 1 / sqrt(1 - beta(__a ) ** 2 )
def __UpperCAmelCase ( __a : float ) -> np.ndarray:
"""simple docstring"""
return np.array(
[
[gamma(__a ), -gamma(__a ) * beta(__a ), 0, 0],
[-gamma(__a ) * beta(__a ), gamma(__a ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def __UpperCAmelCase ( __a : float ,__a : np.ndarray | None = None ) -> np.ndarray:
"""simple docstring"""
if event is None:
_a : List[Any] = np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(__a ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
a__ = transform(29979245)
print('''Example of four vector: ''')
print(f'''ct\' = {four_vector[0]}''')
print(f'''x\' = {four_vector[1]}''')
print(f'''y\' = {four_vector[2]}''')
print(f'''z\' = {four_vector[3]}''')
# Substitute symbols with numerical values
a__ = {ct: c, x: 1, y: 1, z: 1}
a__ = [four_vector[i].subs(sub_dict) for i in range(4)]
print(f'''\n{numerical_vector}''')
| 15 |
from math import ceil
def __UpperCAmelCase ( __a : int = 1_001 ) -> int:
"""simple docstring"""
_a : Dict = 1
for i in range(1 ,int(ceil(n / 2.0 ) ) ):
_a : int = 2 * i + 1
_a : str = 2 * i
_a : Any = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
a__ = int(sys.argv[1])
print(solution(n))
except ValueError:
print('''Invalid entry - please enter a number''')
| 15 | 1 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : List[str] = """char"""
lowerCamelCase_ : Union[str, Any] = """bpe"""
lowerCamelCase_ : Optional[Any] = """wp"""
SCREAMING_SNAKE_CASE__ : Tuple = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : Optional[Any] = ["""image_processor""", """char_tokenizer"""]
lowerCamelCase_ : Optional[int] = """ViTImageProcessor"""
lowerCamelCase_ : List[Any] = """MgpstrTokenizer"""
def __init__( self , UpperCamelCase__=None , UpperCamelCase__=None , **UpperCamelCase__ ) -> Optional[int]:
lowerCamelCase : int = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , UpperCamelCase__ , )
lowerCamelCase : List[str] = kwargs.pop("feature_extractor" )
lowerCamelCase : List[Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
lowerCamelCase : Any = tokenizer
lowerCamelCase : str = AutoTokenizer.from_pretrained("gpt2" )
lowerCamelCase : Tuple = AutoTokenizer.from_pretrained("bert-base-uncased" )
super().__init__(UpperCamelCase__ , UpperCamelCase__ )
def __call__( self , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , **UpperCamelCase__ ) -> Optional[int]:
if images is None and text is None:
raise ValueError("You need to specify either an `images` or `text` input to process." )
if images is not None:
lowerCamelCase : Optional[int] = self.image_processor(UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ )
if text is not None:
lowerCamelCase : Tuple = self.char_tokenizer(UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ )
if text is None:
return inputs
elif images is None:
return encodings
else:
lowerCamelCase : Dict = encodings["input_ids"]
return inputs
def _lowercase ( self , UpperCamelCase__ ) -> str:
lowerCamelCase , lowerCamelCase , lowerCamelCase : Optional[Any] = sequences
lowerCamelCase : Any = char_preds.size(0 )
lowerCamelCase , lowerCamelCase : Any = self._decode_helper(UpperCamelCase__ , "char" )
lowerCamelCase , lowerCamelCase : List[Any] = self._decode_helper(UpperCamelCase__ , "bpe" )
lowerCamelCase , lowerCamelCase : Tuple = self._decode_helper(UpperCamelCase__ , "wp" )
lowerCamelCase : Union[str, Any] = []
lowerCamelCase : str = []
for i in range(UpperCamelCase__ ):
lowerCamelCase : List[str] = [char_scores[i], bpe_scores[i], wp_scores[i]]
lowerCamelCase : str = [char_strs[i], bpe_strs[i], wp_strs[i]]
lowerCamelCase : Optional[int] = scores.index(max(UpperCamelCase__ ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
lowerCamelCase : Optional[Any] = {}
lowerCamelCase : List[str] = final_strs
lowerCamelCase : str = final_scores
lowerCamelCase : Optional[int] = char_strs
lowerCamelCase : Union[str, Any] = bpe_strs
lowerCamelCase : Tuple = wp_strs
return out
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
if format == DecodeType.CHARACTER:
lowerCamelCase : Union[str, Any] = self.char_decode
lowerCamelCase : List[Any] = 1
lowerCamelCase : Union[str, Any] = "[s]"
elif format == DecodeType.BPE:
lowerCamelCase : Dict = self.bpe_decode
lowerCamelCase : List[str] = 2
lowerCamelCase : List[Any] = "#"
elif format == DecodeType.WORDPIECE:
lowerCamelCase : Any = self.wp_decode
lowerCamelCase : List[str] = 102
lowerCamelCase : Any = "[SEP]"
else:
raise ValueError(F'''Format {format} is not supported.''' )
lowerCamelCase , lowerCamelCase : List[str] = [], []
lowerCamelCase : List[Any] = pred_logits.size(0 )
lowerCamelCase : Union[str, Any] = pred_logits.size(1 )
lowerCamelCase , lowerCamelCase : Optional[int] = pred_logits.topk(1 , dim=-1 , largest=UpperCamelCase__ , sorted=UpperCamelCase__ )
lowerCamelCase : Tuple = preds_index.view(-1 , UpperCamelCase__ )[:, 1:]
lowerCamelCase : str = decoder(UpperCamelCase__ )
lowerCamelCase , lowerCamelCase : str = torch.nn.functional.softmax(UpperCamelCase__ , dim=2 ).max(dim=2 )
lowerCamelCase : Optional[int] = preds_max_prob[:, 1:]
for index in range(UpperCamelCase__ ):
lowerCamelCase : List[str] = preds_str[index].find(UpperCamelCase__ )
lowerCamelCase : int = preds_str[index][:pred_eos]
lowerCamelCase : Optional[Any] = preds_index[index].cpu().tolist()
lowerCamelCase : List[Any] = pred_index.index(UpperCamelCase__ ) if eos_token in pred_index else -1
lowerCamelCase : Optional[int] = preds_max_prob[index][: pred_eos_index + 1]
lowerCamelCase : Union[str, Any] = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(UpperCamelCase__ )
conf_scores.append(UpperCamelCase__ )
return dec_strs, conf_scores
def _lowercase ( self , UpperCamelCase__ ) -> Tuple:
lowerCamelCase : Optional[int] = [seq.replace(" " , "" ) for seq in self.char_tokenizer.batch_decode(UpperCamelCase__ )]
return decode_strs
def _lowercase ( self , UpperCamelCase__ ) -> str:
return self.bpe_tokenizer.batch_decode(UpperCamelCase__ )
def _lowercase ( self , UpperCamelCase__ ) -> List[Any]:
lowerCamelCase : Tuple = [seq.replace(" " , "" ) for seq in self.wp_tokenizer.batch_decode(UpperCamelCase__ )]
return decode_strs
| 48 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Optional[Any] = set()
# edges = list of graph's edges
UpperCAmelCase : str = get_edges(UpperCAmelCase_ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = edges.pop()
chosen_vertices.add(UpperCAmelCase_ )
chosen_vertices.add(UpperCAmelCase_ )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(UpperCAmelCase_ )
return chosen_vertices
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : List[str] = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 151 | 0 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
UpperCamelCase__ : List[Any] = None
try:
import msvcrt
except ImportError:
UpperCamelCase__ : Optional[Any] = None
try:
import fcntl
except ImportError:
UpperCamelCase__ : Optional[Any] = None
# Backward compatibility
# ------------------------------------------------
try:
TimeoutError
except NameError:
UpperCamelCase__ : List[str] = OSError
# Data
# ------------------------------------------------
UpperCamelCase__ : str = [
"""Timeout""",
"""BaseFileLock""",
"""WindowsFileLock""",
"""UnixFileLock""",
"""SoftFileLock""",
"""FileLock""",
]
UpperCamelCase__ : str = """3.0.12"""
UpperCamelCase__ : Optional[Any] = None
def SCREAMING_SNAKE_CASE__ ( ) -> int:
"""simple docstring"""
global _logger
a = _logger or logging.getLogger(__name__ )
return _logger
class lowerCamelCase_ ( a_ ):
def __init__( self : Optional[Any] ,__lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
a = lock_file
return None
def __str__( self : List[Any] ):
'''simple docstring'''
a = F"""The file lock '{self.lock_file}' could not be acquired."""
return temp
class lowerCamelCase_ :
def __init__( self : Dict ,__lowerCamelCase : Tuple ):
'''simple docstring'''
a = lock
return None
def __enter__( self : int ):
'''simple docstring'''
return self.lock
def __exit__( self : Dict ,__lowerCamelCase : Union[str, Any] ,__lowerCamelCase : Optional[int] ,__lowerCamelCase : List[Any] ):
'''simple docstring'''
self.lock.release()
return None
class lowerCamelCase_ :
def __init__( self : str ,__lowerCamelCase : List[str] ,__lowerCamelCase : Union[str, Any]=-1 ,__lowerCamelCase : Optional[int]=None ):
'''simple docstring'''
a = max_filename_length if max_filename_length is not None else 2_55
# Hash the filename if it's too long
a = self.hash_filename_if_too_long(__lowerCamelCase ,__lowerCamelCase )
# The path to the lock file.
a = lock_file
# The file descriptor for the *_lock_file* as it is returned by the
# os.open() function.
# This file lock is only NOT None, if the object currently holds the
# lock.
a = None
# The default timeout value.
a = timeout
# We use this lock primarily for the lock counter.
a = threading.Lock()
# The lock counter is used for implementing the nested locking
# mechanism. Whenever the lock is acquired, the counter is increased and
# the lock is only released, when this value is 0 again.
a = 0
return None
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
return self._lock_file
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
'''simple docstring'''
return self._timeout
@timeout.setter
def SCREAMING_SNAKE_CASE_ ( self : int ,__lowerCamelCase : Optional[Any] ):
'''simple docstring'''
a = float(__lowerCamelCase )
return None
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
'''simple docstring'''
raise NotImplementedError()
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
'''simple docstring'''
raise NotImplementedError()
@property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
'''simple docstring'''
return self._lock_file_fd is not None
def SCREAMING_SNAKE_CASE_ ( self : Tuple ,__lowerCamelCase : Dict=None ,__lowerCamelCase : str=0.05 ):
'''simple docstring'''
if timeout is None:
a = self.timeout
# Increment the number right at the beginning.
# We can still undo it, if something fails.
with self._thread_lock:
self._lock_counter += 1
a = id(self )
a = self._lock_file
a = time.time()
try:
while True:
with self._thread_lock:
if not self.is_locked:
logger().debug(F"""Attempting to acquire lock {lock_id} on {lock_filename}""" )
self._acquire()
if self.is_locked:
logger().debug(F"""Lock {lock_id} acquired on {lock_filename}""" )
break
elif timeout >= 0 and time.time() - start_time > timeout:
logger().debug(F"""Timeout on acquiring lock {lock_id} on {lock_filename}""" )
raise Timeout(self._lock_file )
else:
logger().debug(
F"""Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...""" )
time.sleep(__lowerCamelCase )
except: # noqa
# Something did go wrong, so decrement the counter.
with self._thread_lock:
a = max(0 ,self._lock_counter - 1 )
raise
return _Acquire_ReturnProxy(lock=self )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ,__lowerCamelCase : int=False ):
'''simple docstring'''
with self._thread_lock:
if self.is_locked:
self._lock_counter -= 1
if self._lock_counter == 0 or force:
a = id(self )
a = self._lock_file
logger().debug(F"""Attempting to release lock {lock_id} on {lock_filename}""" )
self._release()
a = 0
logger().debug(F"""Lock {lock_id} released on {lock_filename}""" )
return None
def __enter__( self : Union[str, Any] ):
'''simple docstring'''
self.acquire()
return self
def __exit__( self : Optional[int] ,__lowerCamelCase : Union[str, Any] ,__lowerCamelCase : Union[str, Any] ,__lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
self.release()
return None
def __del__( self : Dict ):
'''simple docstring'''
self.release(force=__lowerCamelCase )
return None
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ,__lowerCamelCase : str ,__lowerCamelCase : int ):
'''simple docstring'''
a = os.path.basename(__lowerCamelCase )
if len(__lowerCamelCase ) > max_length and max_length > 0:
a = os.path.dirname(__lowerCamelCase )
a = str(hash(__lowerCamelCase ) )
a = filename[: max_length - len(__lowerCamelCase ) - 8] + '''...''' + hashed_filename + '''.lock'''
return os.path.join(__lowerCamelCase ,__lowerCamelCase )
else:
return path
class lowerCamelCase_ ( a_ ):
def __init__( self : Dict ,__lowerCamelCase : str ,__lowerCamelCase : Any=-1 ,__lowerCamelCase : Tuple=None ):
'''simple docstring'''
from .file_utils import relative_to_absolute_path
super().__init__(__lowerCamelCase ,timeout=__lowerCamelCase ,max_filename_length=__lowerCamelCase )
a = '''\\\\?\\''' + relative_to_absolute_path(self.lock_file )
def SCREAMING_SNAKE_CASE_ ( self : str ):
'''simple docstring'''
a = os.O_RDWR | os.O_CREAT | os.O_TRUNC
try:
a = os.open(self._lock_file ,__lowerCamelCase )
except OSError:
pass
else:
try:
msvcrt.locking(__lowerCamelCase ,msvcrt.LK_NBLCK ,1 )
except OSError:
os.close(__lowerCamelCase )
else:
a = fd
return None
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
'''simple docstring'''
a = self._lock_file_fd
a = None
msvcrt.locking(__lowerCamelCase ,msvcrt.LK_UNLCK ,1 )
os.close(__lowerCamelCase )
try:
os.remove(self._lock_file )
# Probably another instance of the application
# that acquired the file lock.
except OSError:
pass
return None
class lowerCamelCase_ ( a_ ):
def __init__( self : Dict ,__lowerCamelCase : str ,__lowerCamelCase : Tuple=-1 ,__lowerCamelCase : Tuple=None ):
'''simple docstring'''
a = os.statvfs(os.path.dirname(__lowerCamelCase ) ).f_namemax
super().__init__(__lowerCamelCase ,timeout=__lowerCamelCase ,max_filename_length=__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
'''simple docstring'''
a = os.O_RDWR | os.O_CREAT | os.O_TRUNC
a = os.open(self._lock_file ,__lowerCamelCase )
try:
fcntl.flock(__lowerCamelCase ,fcntl.LOCK_EX | fcntl.LOCK_NB )
except OSError:
os.close(__lowerCamelCase )
else:
a = fd
return None
def SCREAMING_SNAKE_CASE_ ( self : str ):
'''simple docstring'''
a = self._lock_file_fd
a = None
fcntl.flock(__lowerCamelCase ,fcntl.LOCK_UN )
os.close(__lowerCamelCase )
return None
class lowerCamelCase_ ( a_ ):
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
'''simple docstring'''
a = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
try:
a = os.open(self._lock_file ,__lowerCamelCase )
except OSError:
pass
else:
a = fd
return None
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
'''simple docstring'''
os.close(self._lock_file_fd )
a = None
try:
os.remove(self._lock_file )
# The file is already deleted and that's what we want.
except OSError:
pass
return None
UpperCamelCase__ : Any = None
if msvcrt:
UpperCamelCase__ : Tuple = WindowsFileLock
elif fcntl:
UpperCamelCase__ : Any = UnixFileLock
else:
UpperCamelCase__ : Union[str, Any] = SoftFileLock
if warnings is not None:
warnings.warn("""only soft file lock is available""")
| 330 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
UpperCamelCase__ : Any = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""),
("""kernel""", """weight"""),
("""beta""", """bias"""),
("""gamma""", """weight"""),
("""pegasus""", """model"""),
]
UpperCamelCase__ : Optional[Any] = [
(""".output.dense""", """.fc2"""),
("""intermediate.LayerNorm""", """final_layer_norm"""),
("""intermediate.dense""", """fc1"""),
]
UpperCamelCase__ : Optional[Any] = (
INIT_COMMON
+ [
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.out_proj"""),
("""attention.self""", """self_attn"""),
("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
("""attention.encdec""", """encoder_attn"""),
("""key""", """k_proj"""),
("""value""", """v_proj"""),
("""query""", """q_proj"""),
("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
]
+ END_COMMON
)
UpperCamelCase__ : List[str] = (
INIT_COMMON
+ [
("""embeddings.word_embeddings""", """shared.weight"""),
("""embeddings.position_embeddings""", """embed_positions.weight"""),
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.output"""),
("""attention.self""", """self_attn.self"""),
("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
]
+ END_COMMON
)
UpperCamelCase__ : Optional[int] = [
"""encdec/key/bias""",
"""encdec/query/bias""",
"""encdec/value/bias""",
"""self/key/bias""",
"""self/query/bias""",
"""self/value/bias""",
"""encdec_output/dense/bias""",
"""attention/output/dense/bias""",
]
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> List[Any]:
"""simple docstring"""
for tf_name, hf_name in patterns:
a = k.replace(snake_case_, snake_case_ )
return k
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> BigBirdPegasusForConditionalGeneration:
"""simple docstring"""
a = BigBirdPegasusConfig(**snake_case_ )
a = BigBirdPegasusForConditionalGeneration(snake_case_ )
a = torch_model.state_dict()
a = {}
# separating decoder weights
a = {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder''' )}
a = {k: tf_weights[k] for k in tf_weights if not k.startswith('''pegasus/decoder''' )}
for k, v in tqdm(decoder_weights.items(), '''tf -> hf conversion''' ):
a = [k.endswith(snake_case_ ) for ending in KEYS_TO_IGNORE]
if any(snake_case_ ):
continue
a = DECODER_PATTERNS
a = rename_state_dict_key(snake_case_, snake_case_ )
if new_k not in state_dict:
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
a = v.T
a = torch.from_numpy(snake_case_ )
assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
for k, v in tqdm(remaining_weights.items(), '''tf -> hf conversion''' ):
a = [k.endswith(snake_case_ ) for ending in KEYS_TO_IGNORE]
if any(snake_case_ ):
continue
a = REMAINING_PATTERNS
a = rename_state_dict_key(snake_case_, snake_case_ )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
a = v.T
a = torch.from_numpy(snake_case_ )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
a = mapping['''model.embed_positions.weight''']
a = mapping.pop('''model.embed_positions.weight''' )
a , a = torch_model.load_state_dict(snake_case_, strict=snake_case_ )
a = [
k
for k in missing
if k
not in [
'''final_logits_bias''',
'''model.encoder.embed_tokens.weight''',
'''model.decoder.embed_tokens.weight''',
'''lm_head.weight''',
]
]
assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], f"""no matches found for the following tf keys {extra}"""
return torch_model
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> Dict:
"""simple docstring"""
a = tf.train.list_variables(snake_case_ )
a = {}
a = ['''global_step''']
for name, shape in tqdm(snake_case_, desc='''converting tf checkpoint to dict''' ):
a = any(pat in name for pat in ignore_name )
if skip_key:
continue
a = tf.train.load_variable(snake_case_, snake_case_ )
a = array
return tf_weights
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_, snake_case_ ) -> int:
"""simple docstring"""
a = get_tf_weights_as_numpy(snake_case_ )
a = convert_bigbird_pegasus(snake_case_, snake_case_ )
torch_model.save_pretrained(snake_case_ )
if __name__ == "__main__":
UpperCamelCase__ : str = argparse.ArgumentParser()
parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
UpperCamelCase__ : int = parser.parse_args()
UpperCamelCase__ : Tuple = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 330 | 1 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_=None , ) -> int:
'''simple docstring'''
if attention_mask is None:
SCREAMING_SNAKE_CASE__ = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE__ = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
SCREAMING_SNAKE_CASE__ = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=__lowerCamelCase )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE__ = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=__lowerCamelCase )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE__ = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=__lowerCamelCase )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class lowercase__ :
def __init__( self : Optional[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : int=13 , UpperCAmelCase_ : Optional[int]=7 , UpperCAmelCase_ : Tuple=True , UpperCAmelCase_ : Optional[int]=False , UpperCAmelCase_ : List[Any]=99 , UpperCAmelCase_ : List[str]=16 , UpperCAmelCase_ : Dict=2 , UpperCAmelCase_ : List[Any]=4 , UpperCAmelCase_ : Dict=4 , UpperCAmelCase_ : Optional[Any]="relu" , UpperCAmelCase_ : int=0.1 , UpperCAmelCase_ : str=0.1 , UpperCAmelCase_ : Any=0.0 , UpperCAmelCase_ : Optional[int]=0.0 , UpperCAmelCase_ : Any=20 , UpperCAmelCase_ : Dict=2 , UpperCAmelCase_ : Dict=1 , UpperCAmelCase_ : Any=0 , ):
SCREAMING_SNAKE_CASE__ = parent
SCREAMING_SNAKE_CASE__ = batch_size
SCREAMING_SNAKE_CASE__ = seq_length
SCREAMING_SNAKE_CASE__ = is_training
SCREAMING_SNAKE_CASE__ = use_labels
SCREAMING_SNAKE_CASE__ = vocab_size
SCREAMING_SNAKE_CASE__ = hidden_size
SCREAMING_SNAKE_CASE__ = num_hidden_layers
SCREAMING_SNAKE_CASE__ = num_attention_heads
SCREAMING_SNAKE_CASE__ = intermediate_size
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = encoder_layerdrop
SCREAMING_SNAKE_CASE__ = decoder_layerdrop
SCREAMING_SNAKE_CASE__ = max_position_embeddings
SCREAMING_SNAKE_CASE__ = eos_token_id
SCREAMING_SNAKE_CASE__ = pad_token_id
SCREAMING_SNAKE_CASE__ = bos_token_id
def A_ ( self : Any ):
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ = self.eos_token_id # Eos Token
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
SCREAMING_SNAKE_CASE__ = input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ = decoder_input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ = self.get_config()
SCREAMING_SNAKE_CASE__ = prepare_mam_aaa_inputs_dict(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
return config, inputs_dict
def A_ ( self : Dict ):
return MaMaaaConfig(
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 , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def A_ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.prepare_config_and_inputs()
return config, inputs_dict
def A_ ( self : Tuple , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Any ):
SCREAMING_SNAKE_CASE__ = MaMaaaModel(config=UpperCAmelCase_ ).get_decoder().to(UpperCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE__ = inputs_dict['input_ids']
SCREAMING_SNAKE_CASE__ = inputs_dict['attention_mask']
SCREAMING_SNAKE_CASE__ = inputs_dict['head_mask']
# first forward pass
SCREAMING_SNAKE_CASE__ = model(UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , head_mask=UpperCAmelCase_ , use_cache=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ = ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE__ = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
SCREAMING_SNAKE_CASE__ = model(UpperCAmelCase_ , attention_mask=UpperCAmelCase_ )['last_hidden_state']
SCREAMING_SNAKE_CASE__ = model(UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , past_key_values=UpperCAmelCase_ )[
'last_hidden_state'
]
# select random slice
SCREAMING_SNAKE_CASE__ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ = output_from_no_past[:, -3:, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(UpperCAmelCase_ , UpperCAmelCase_ , atol=1e-2 ) )
def A_ ( self : Dict , UpperCAmelCase_ : str , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE__ = MaMaaaModel(config=UpperCAmelCase_ ).to(UpperCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE__ = model(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = outputs.encoder_last_hidden_state
SCREAMING_SNAKE_CASE__ = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE__ = model.get_encoder()
encoder.save_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = MaMaaaEncoder.from_pretrained(UpperCAmelCase_ ).to(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = encoder(inputs_dict['input_ids'] , attention_mask=inputs_dict['attention_mask'] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE__ = model.get_decoder()
decoder.save_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = MaMaaaDecoder.from_pretrained(UpperCAmelCase_ ).to(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = decoder(
input_ids=inputs_dict['decoder_input_ids'] , attention_mask=inputs_dict['decoder_attention_mask'] , encoder_hidden_states=UpperCAmelCase_ , encoder_attention_mask=inputs_dict['attention_mask'] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class lowercase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
A__ : List[str] =(
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
A__ : int =(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
A__ : Union[str, Any] =(
{
"""conversational""": MaMaaaForConditionalGeneration,
"""feature-extraction""": MaMaaaModel,
"""summarization""": MaMaaaForConditionalGeneration,
"""text2text-generation""": MaMaaaForConditionalGeneration,
"""translation""": MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
A__ : Union[str, Any] =True
A__ : Tuple =True
A__ : Optional[int] =False
A__ : Optional[int] =False
def A_ ( self : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : str ):
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def A_ ( self : Any ):
SCREAMING_SNAKE_CASE__ = MaMaaaModelTester(self )
SCREAMING_SNAKE_CASE__ = ConfigTester(self , config_class=UpperCAmelCase_ )
def A_ ( self : List[str] ):
self.config_tester.run_common_tests()
def A_ ( self : str ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ = model_class(UpperCAmelCase_ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = model_class.from_pretrained(UpperCAmelCase_ , output_loading_info=UpperCAmelCase_ )
self.assertEqual(info['missing_keys'] , [] )
def A_ ( self : str ):
SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCAmelCase_ )
def A_ ( self : str ):
SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*UpperCAmelCase_ )
def A_ ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
SCREAMING_SNAKE_CASE__ = model_class(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
SCREAMING_SNAKE_CASE__ = copy.deepcopy(self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_ ) )
if not self.is_encoder_decoder:
SCREAMING_SNAKE_CASE__ = inputs['input_ids']
del inputs["input_ids"]
else:
SCREAMING_SNAKE_CASE__ = inputs['input_ids']
SCREAMING_SNAKE_CASE__ = inputs.get('decoder_input_ids' , UpperCAmelCase_ )
del inputs["input_ids"]
inputs.pop('decoder_input_ids' , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = model.get_input_embeddings()
if not self.is_encoder_decoder:
SCREAMING_SNAKE_CASE__ = wte(UpperCAmelCase_ )
else:
SCREAMING_SNAKE_CASE__ = wte(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = wte(UpperCAmelCase_ )
with torch.no_grad():
model(**UpperCAmelCase_ )[0]
def A_ ( self : Any ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ = input_dict['input_ids']
SCREAMING_SNAKE_CASE__ = input_ids.ne(1 ).to(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = MaMaaaForConditionalGeneration(UpperCAmelCase_ ).eval().to(UpperCAmelCase_ )
if torch_device == "cuda":
model.half()
model.generate(UpperCAmelCase_ , attention_mask=UpperCAmelCase_ )
model.generate(num_beams=4 , do_sample=UpperCAmelCase_ , early_stopping=UpperCAmelCase_ , num_return_sequences=3 )
def _lowercase ( UpperCamelCase_ ) -> List[Any]:
'''simple docstring'''
return torch.tensor(__lowerCamelCase , dtype=torch.long , device=__lowerCamelCase )
__snake_case = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class lowercase__ ( unittest.TestCase ):
@cached_property
def A_ ( self : List[str] ):
return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' )
def A_ ( self : int ):
SCREAMING_SNAKE_CASE__ = MaMaaaModel.from_pretrained('facebook/m2m100_418M' ).to(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
SCREAMING_SNAKE_CASE__ = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
SCREAMING_SNAKE_CASE__ = prepare_mam_aaa_inputs_dict(model.config , UpperCAmelCase_ , UpperCAmelCase_ )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ = model(**UpperCAmelCase_ )[0]
SCREAMING_SNAKE_CASE__ = torch.Size((1, 11, 1024) )
self.assertEqual(output.shape , UpperCAmelCase_ )
# change to expected output here
SCREAMING_SNAKE_CASE__ = torch.tensor(
[[-0.7_780, -0.1_676, 0.1_038], [-6.7_556, -1.3_992, 0.0_567], [-7.5_383, -0.5_920, -0.2_779]] , device=UpperCAmelCase_ )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCAmelCase_ , atol=UpperCAmelCase_ ) )
def A_ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE__ = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(UpperCAmelCase_ )
# change to intended input
SCREAMING_SNAKE_CASE__ = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
SCREAMING_SNAKE_CASE__ = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
SCREAMING_SNAKE_CASE__ = prepare_mam_aaa_inputs_dict(model.config , UpperCAmelCase_ , UpperCAmelCase_ )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ = model(**UpperCAmelCase_ )[0]
SCREAMING_SNAKE_CASE__ = torch.Size((1, 11, model.config.vocab_size) )
self.assertEqual(output.shape , UpperCAmelCase_ )
# change to expected output here
SCREAMING_SNAKE_CASE__ = torch.tensor(
[[-1.0_448, -1.0_411, 3.7_992], [-3.2_191, -3.2_386, -1.3_451], [-3.6_210, -3.5_993, 0.4_925]] , device=UpperCAmelCase_ )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCAmelCase_ , atol=UpperCAmelCase_ ) )
def A_ ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE__ = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' , src_lang='fr' , tgt_lang='en' )
SCREAMING_SNAKE_CASE__ = [
'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
SCREAMING_SNAKE_CASE__ = tokenizer(UpperCAmelCase_ , padding=UpperCAmelCase_ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ = model.generate(
input_ids=dct['input_ids'].to(UpperCAmelCase_ ) , attention_mask=dct['attention_mask'].to(UpperCAmelCase_ ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id('en' ) , )
SCREAMING_SNAKE_CASE__ = [
'The NSA case highlights the total absence of intelligence debate',
'I think there are two levels of response from the French government.',
'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
' communications in France.',
]
SCREAMING_SNAKE_CASE__ = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=UpperCAmelCase_ , skip_special_tokens=UpperCAmelCase_ )
assert generated == expected_en
| 176 |
__UpperCamelCase : Optional[int] = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggingface-hub>=0.13.2",
"requests-mock": "requests-mock==1.10.0",
"importlib_metadata": "importlib_metadata",
"invisible-watermark": "invisible-watermark",
"isort": "isort>=5.5.4",
"jax": "jax>=0.2.8,!=0.3.2",
"jaxlib": "jaxlib>=0.1.65",
"Jinja2": "Jinja2",
"k-diffusion": "k-diffusion>=0.0.12",
"torchsde": "torchsde",
"note_seq": "note_seq",
"librosa": "librosa",
"numpy": "numpy",
"omegaconf": "omegaconf",
"parameterized": "parameterized",
"protobuf": "protobuf>=3.20.3,<4",
"pytest": "pytest",
"pytest-timeout": "pytest-timeout",
"pytest-xdist": "pytest-xdist",
"ruff": "ruff>=0.0.241",
"safetensors": "safetensors",
"sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
"scipy": "scipy",
"onnx": "onnx",
"regex": "regex!=2019.12.17",
"requests": "requests",
"tensorboard": "tensorboard",
"torch": "torch>=1.4",
"torchvision": "torchvision",
"transformers": "transformers>=4.25.1",
"urllib3": "urllib3<=2.0.0",
}
| 228 | 0 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
def __init__( self : str , _A : str , _A : List[str]=13 , _A : Union[str, Any]=30 , _A : Dict=2 , _A : str=3 , _A : List[Any]=True , _A : Any=True , _A : Tuple=32 , _A : List[Any]=5 , _A : Any=4 , _A : Optional[int]=37 , _A : Tuple="gelu" , _A : Optional[int]=0.1 , _A : Optional[Any]=0.1 , _A : Dict=10 , _A : int=0.0_2 , ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Tuple = parent
snake_case_ : Dict = batch_size
snake_case_ : int = image_size
snake_case_ : Optional[int] = patch_size
snake_case_ : Optional[int] = num_channels
snake_case_ : Tuple = is_training
snake_case_ : Union[str, Any] = use_labels
snake_case_ : Any = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : List[str] = num_attention_heads
snake_case_ : Dict = intermediate_size
snake_case_ : str = hidden_act
snake_case_ : Any = hidden_dropout_prob
snake_case_ : int = attention_probs_dropout_prob
snake_case_ : List[Any] = type_sequence_label_size
snake_case_ : Tuple = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case_ : Optional[Any] = (image_size // patch_size) ** 2
snake_case_ : Optional[int] = num_patches + 1
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
snake_case_ : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : Dict = ViTConfig(
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 , )
return config, pixel_values
def UpperCAmelCase_ ( self : str , _A : Any , _A : Tuple ) -> Any:
"""simple docstring"""
snake_case_ : str = FlaxViTModel(config=_A )
snake_case_ : Dict = model(_A )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
snake_case_ : Any = (self.image_size, self.image_size)
snake_case_ : List[str] = (self.patch_size, self.patch_size)
snake_case_ : List[str] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, num_patches + 1, self.hidden_size) )
def UpperCAmelCase_ ( self : Union[str, Any] , _A : Tuple , _A : Optional[Any] ) -> str:
"""simple docstring"""
snake_case_ : str = self.type_sequence_label_size
snake_case_ : List[Any] = FlaxViTForImageClassification(config=_A )
snake_case_ : Dict = model(_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case_ : List[Any] = 1
snake_case_ : Dict = FlaxViTForImageClassification(_A )
snake_case_ : List[str] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ : Optional[int] = model(_A )
def UpperCAmelCase_ ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : Optional[int] = self.prepare_config_and_inputs()
(
(
snake_case_
) ,(
snake_case_
) ,
) : Optional[int] = config_and_inputs
snake_case_ : List[str] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_flax
class SCREAMING_SNAKE_CASE_ ( snake_case_ , unittest.TestCase ):
__magic_name__: int = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def UpperCAmelCase_ ( self : Dict ) -> None:
"""simple docstring"""
snake_case_ : int = FlaxViTModelTester(self )
snake_case_ : Dict = ConfigTester(self , config_class=_A , has_text_modality=_A , hidden_size=37 )
def UpperCAmelCase_ ( self : Optional[Any] ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self : Dict ) -> List[Any]:
"""simple docstring"""
snake_case_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def UpperCAmelCase_ ( self : Tuple ) -> str:
"""simple docstring"""
snake_case_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_A )
def UpperCAmelCase_ ( self : str ) -> List[str]:
"""simple docstring"""
snake_case_ ,snake_case_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : List[str] = model_class(_A )
snake_case_ : str = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : List[Any] = [*signature.parameters.keys()]
snake_case_ : int = ['pixel_values']
self.assertListEqual(arg_names[:1] , _A )
def UpperCAmelCase_ ( self : Dict ) -> List[Any]:
"""simple docstring"""
snake_case_ ,snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
snake_case_ : Optional[Any] = self._prepare_for_class(_A , _A )
snake_case_ : List[str] = model_class(_A )
@jax.jit
def model_jitted(_A : int , **_A : Optional[Any] ):
return model(pixel_values=_A , **_A )
with self.subTest('JIT Enabled' ):
snake_case_ : List[Any] = model_jitted(**_A ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
snake_case_ : Any = model_jitted(**_A ).to_tuple()
self.assertEqual(len(_A ) , len(_A ) )
for jitted_output, output in zip(_A , _A ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
snake_case_ : Optional[Any] = model_class_name.from_pretrained('google/vit-base-patch16-224' )
snake_case_ : Optional[Any] = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(_A )
| 88 |
_SCREAMING_SNAKE_CASE = {
"""Pillow""": """Pillow""",
"""accelerate""": """accelerate>=0.11.0""",
"""compel""": """compel==0.1.8""",
"""black""": """black~=23.1""",
"""datasets""": """datasets""",
"""filelock""": """filelock""",
"""flax""": """flax>=0.4.1""",
"""hf-doc-builder""": """hf-doc-builder>=0.3.0""",
"""huggingface-hub""": """huggingface-hub>=0.13.2""",
"""requests-mock""": """requests-mock==1.10.0""",
"""importlib_metadata""": """importlib_metadata""",
"""invisible-watermark""": """invisible-watermark""",
"""isort""": """isort>=5.5.4""",
"""jax""": """jax>=0.2.8,!=0.3.2""",
"""jaxlib""": """jaxlib>=0.1.65""",
"""Jinja2""": """Jinja2""",
"""k-diffusion""": """k-diffusion>=0.0.12""",
"""torchsde""": """torchsde""",
"""note_seq""": """note_seq""",
"""librosa""": """librosa""",
"""numpy""": """numpy""",
"""omegaconf""": """omegaconf""",
"""parameterized""": """parameterized""",
"""protobuf""": """protobuf>=3.20.3,<4""",
"""pytest""": """pytest""",
"""pytest-timeout""": """pytest-timeout""",
"""pytest-xdist""": """pytest-xdist""",
"""ruff""": """ruff>=0.0.241""",
"""safetensors""": """safetensors""",
"""sentencepiece""": """sentencepiece>=0.1.91,!=0.1.92""",
"""scipy""": """scipy""",
"""onnx""": """onnx""",
"""regex""": """regex!=2019.12.17""",
"""requests""": """requests""",
"""tensorboard""": """tensorboard""",
"""torch""": """torch>=1.4""",
"""torchvision""": """torchvision""",
"""transformers""": """transformers>=4.25.1""",
"""urllib3""": """urllib3<=2.0.0""",
}
| 88 | 1 |
from __future__ import annotations
class __snake_case :
def __init__( self : Tuple , _snake_case : Dict=None):
"""simple docstring"""
UpperCAmelCase_ = data
UpperCAmelCase_ = None
def __repr__( self : str):
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = self
while temp:
string_rep.append(F"""{temp.data}""")
UpperCAmelCase_ = temp.next
return "->".join(_snake_case)
def A (__A : list ) -> List[str]:
"""simple docstring"""
if not elements_list:
raise Exception('''The Elements List is empty''' )
UpperCAmelCase_ = UpperCAmelCase_ = Node(elements_list[0] )
for i in range(1 , len(__A ) ):
UpperCAmelCase_ = Node(elements_list[i] )
UpperCAmelCase_ = current.next
return head
def A (__A : Node ) -> None:
"""simple docstring"""
if head_node is not None and isinstance(__A , __A ):
print_reverse(head_node.next )
print(head_node.data )
def A () -> List[str]:
"""simple docstring"""
from doctest import testmod
testmod()
UpperCAmelCase_ = make_linked_list([14, 52, 14, 12, 43] )
print('''Linked List:''' )
print(__A )
print('''Elements in Reverse:''' )
print_reverse(__A )
if __name__ == "__main__":
main()
| 51 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( _lowercase):
_a : Union[str, Any] = ['''image_processor''', '''tokenizer''']
_a : List[Any] = '''ChineseCLIPImageProcessor'''
_a : List[Any] = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self : Union[str, Any] , _SCREAMING_SNAKE_CASE : List[str]=None , _SCREAMING_SNAKE_CASE : Dict=None , **_SCREAMING_SNAKE_CASE : Optional[Any] )-> List[str]:
lowerCAmelCase__ : Dict = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , _SCREAMING_SNAKE_CASE , )
lowerCAmelCase__ : Union[str, Any] = kwargs.pop('''feature_extractor''' )
lowerCAmelCase__ : Optional[int] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Dict = self.image_processor
def __call__( self : Tuple , _SCREAMING_SNAKE_CASE : Optional[Any]=None , _SCREAMING_SNAKE_CASE : Optional[int]=None , _SCREAMING_SNAKE_CASE : List[Any]=None , **_SCREAMING_SNAKE_CASE : Dict )-> List[Any]:
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
lowerCAmelCase__ : List[Any] = self.tokenizer(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if images is not None:
lowerCAmelCase__ : Dict = self.image_processor(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if text is not None and images is not None:
lowerCAmelCase__ : Optional[Any] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE ) , tensor_type=_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__( self : Dict , *_SCREAMING_SNAKE_CASE : int , **_SCREAMING_SNAKE_CASE : Optional[int] )-> Any:
return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__( self : str , *_SCREAMING_SNAKE_CASE : Tuple , **_SCREAMING_SNAKE_CASE : Any )-> int:
return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@property
def UpperCAmelCase__( self : Union[str, Any] )-> Union[str, Any]:
lowerCAmelCase__ : Any = self.tokenizer.model_input_names
lowerCAmelCase__ : Dict = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCAmelCase__( self : str )-> List[str]:
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , _SCREAMING_SNAKE_CASE , )
return self.image_processor_class
| 131 | 0 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
_snake_case = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n year={2020},\n booktitle={Findings of EMNLP},\n}\n'
_snake_case = '\\n IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n'
_snake_case = '\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n predictions: list of predictions to score (as int64),\n except for \'cvit-mkb-clsr\' where each prediction is a vector (of float32).\n references: list of ground truth labels corresponding to the predictions (as int64),\n except for \'cvit-mkb-clsr\' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n "accuracy": Accuracy\n "f1": F1 score\n "precision": Precision@10\nExamples:\n\n >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wnli\') # \'wnli\' or any of ["copa", "sna", "csqa", "wstp", "inltkh", "bbca", "iitp-mr", "iitp-pr", "actsa-sc", "md"]\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0}\n\n >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wiki-ner\')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0, \'f1\': 1.0}\n\n >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'cvit-mkb-clsr\')\n >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'precision@10\': 1.0}\n\n'
def _A ( snake_case , snake_case ) -> int:
return float((preds == labels).mean() )
def _A ( snake_case , snake_case ) -> Union[str, Any]:
_lowercase : Any = simple_accuracy(snake_case , snake_case )
_lowercase : List[Any] = float(fa_score(y_true=snake_case , y_pred=snake_case ) )
return {
"accuracy": acc,
"f1": fa,
}
def _A ( snake_case , snake_case ) -> List[str]:
_lowercase : Any = np.array(snake_case )
_lowercase : Any = np.array(snake_case )
_lowercase : str = en_sentvecs.shape[0]
# mean centering
_lowercase : List[Any] = en_sentvecs - np.mean(snake_case , axis=0 )
_lowercase : Tuple = in_sentvecs - np.mean(snake_case , axis=0 )
_lowercase : Any = cdist(snake_case , snake_case , "cosine" )
_lowercase : Any = np.array(range(snake_case ) )
_lowercase : Dict = sim.argsort(axis=1 )[:, :10]
_lowercase : List[str] = np.any(preds == actual[:, None] , axis=1 )
return float(matches.mean() )
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
def _lowerCamelCase ( self ):
"""simple docstring"""
if self.config_name not in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"cvit-mkb-clsr",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
"wiki-ner",
]:
raise KeyError(
"You should supply a configuration name selected in "
"[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", "
"\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", "
"\"wiki-ner\"]" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("int64" )
if self.config_name != "cvit-mkb-clsr"
else datasets.Sequence(datasets.Value("float32" ) ),
"references": datasets.Value("int64" )
if self.config_name != "cvit-mkb-clsr"
else datasets.Sequence(datasets.Value("float32" ) ),
} ) , codebase_urls=[] , reference_urls=[] , format="numpy" if self.config_name != "cvit-mkb-clsr" else None , )
def _lowerCamelCase ( self , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
if self.config_name == "cvit-mkb-clsr":
return {"precision@10": precision_at_aa(_UpperCamelCase , _UpperCamelCase )}
elif self.config_name in ["wiki-ner"]:
return acc_and_fa(_UpperCamelCase , _UpperCamelCase )
elif self.config_name in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
]:
return {"accuracy": simple_accuracy(_UpperCamelCase , _UpperCamelCase )}
else:
raise KeyError(
"You should supply a configuration name selected in "
"[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", "
"\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", "
"\"wiki-ner\"]" )
| 369 |
'''simple docstring'''
def _A ( snake_case , snake_case ) -> int:
return int((input_a, input_a).count(0 ) != 0 )
def _A ( ) -> None:
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 199 | 0 |
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
__SCREAMING_SNAKE_CASE ='scheduler_config.json'
class UpperCamelCase ( lowerCamelCase__ ):
lowercase = 1
lowercase = 2
lowercase = 3
lowercase = 4
lowercase = 5
lowercase = 6
lowercase = 7
lowercase = 8
lowercase = 9
lowercase = 1_0
lowercase = 1_1
lowercase = 1_2
lowercase = 1_3
lowercase = 1_4
@dataclass
class UpperCamelCase ( lowerCamelCase__ ):
lowercase = 4_2
class UpperCamelCase :
lowercase = SCHEDULER_CONFIG_NAME
lowercase = []
lowercase = True
@classmethod
def _UpperCAmelCase ( cls ,__UpperCamelCase = None ,__UpperCamelCase = None ,__UpperCamelCase=False ,**__UpperCamelCase ,) -> List[Any]:
'''simple docstring'''
lowercase_ : Tuple = cls.load_config(
pretrained_model_name_or_path=__lowerCamelCase ,subfolder=__lowerCamelCase ,return_unused_kwargs=__lowerCamelCase ,return_commit_hash=__lowerCamelCase ,**__lowerCamelCase ,)
return cls.from_config(__lowerCamelCase ,return_unused_kwargs=__lowerCamelCase ,**__lowerCamelCase )
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase = False ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
self.save_config(save_directory=__lowerCamelCase ,push_to_hub=__lowerCamelCase ,**__lowerCamelCase )
@property
def _UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
return self._get_compatibles()
@classmethod
def _UpperCAmelCase ( cls ) -> Optional[Any]:
'''simple docstring'''
lowercase_ : List[Any] = list(set([cls.__name__] + cls._compatibles ) )
lowercase_ : Tuple = importlib.import_module(__name__.split('.' )[0] )
lowercase_ : Union[str, Any] = [
getattr(__lowerCamelCase ,__lowerCamelCase ) for c in compatible_classes_str if hasattr(__lowerCamelCase ,__lowerCamelCase )
]
return compatible_classes
| 213 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCamelCase__ ( lowerCamelCase__):
'''simple docstring'''
snake_case_ =(DPMSolverSDEScheduler,)
snake_case_ =10
def lowerCAmelCase__ (self ,**__lowerCamelCase ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ : List[str] = {
'''num_train_timesteps''': 11_00,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''noise_sampler_seed''': 0,
}
config.update(**__lowerCamelCase )
return config
def lowerCAmelCase__ (self ) -> int:
"""simple docstring"""
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__lowerCamelCase )
def lowerCAmelCase__ (self ) -> Optional[Any]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] ,[0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=__lowerCamelCase ,beta_end=__lowerCamelCase )
def lowerCAmelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__lowerCamelCase )
def lowerCAmelCase__ (self ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__lowerCamelCase )
def lowerCAmelCase__ (self ) -> int:
"""simple docstring"""
lowerCAmelCase__ : List[str] = self.scheduler_classes[0]
lowerCAmelCase__ : str = self.get_scheduler_config()
lowerCAmelCase__ : Optional[Any] = scheduler_class(**__lowerCamelCase )
scheduler.set_timesteps(self.num_inference_steps )
lowerCAmelCase__ : Union[str, Any] = self.dummy_model()
lowerCAmelCase__ : Tuple = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCAmelCase__ : Union[str, Any] = sample.to(__lowerCamelCase )
for i, t in enumerate(scheduler.timesteps ):
lowerCAmelCase__ : Dict = scheduler.scale_model_input(__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : Any = model(__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : List[Any] = scheduler.step(__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : Optional[int] = output.prev_sample
lowerCAmelCase__ : List[Any] = torch.sum(torch.abs(__lowerCamelCase ) )
lowerCAmelCase__ : Dict = torch.mean(torch.abs(__lowerCamelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47_8210_4492_1875 ) < 1e-2
assert abs(result_mean.item() - 0.2178_7059_6456_5277 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3521_1181_6406 ) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9068_9229_9652 ) < 1e-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1e-3
def lowerCAmelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ : Dict = self.scheduler_classes[0]
lowerCAmelCase__ : Any = self.get_scheduler_config(prediction_type='''v_prediction''' )
lowerCAmelCase__ : List[Any] = scheduler_class(**__lowerCamelCase )
scheduler.set_timesteps(self.num_inference_steps )
lowerCAmelCase__ : Optional[int] = self.dummy_model()
lowerCAmelCase__ : Optional[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCAmelCase__ : Tuple = sample.to(__lowerCamelCase )
for i, t in enumerate(scheduler.timesteps ):
lowerCAmelCase__ : Optional[Any] = scheduler.scale_model_input(__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : Optional[Any] = model(__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : Optional[int] = scheduler.step(__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : Union[str, Any] = output.prev_sample
lowerCAmelCase__ : Any = torch.sum(torch.abs(__lowerCamelCase ) )
lowerCAmelCase__ : Union[str, Any] = torch.mean(torch.abs(__lowerCamelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77_1492_0043_9453 ) < 1e-2
assert abs(result_mean.item() - 0.1_6226_2890_1481_6284 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1_6633_6059_5703 ) < 1e-2
assert abs(result_mean.item() - 0.1_6688_3260_0116_7297 ) < 1e-3
else:
assert abs(result_sum.item() - 119.8_4875_4882_8125 ) < 1e-2
assert abs(result_mean.item() - 0.1560_5306_6253_6621 ) < 1e-3
def lowerCAmelCase__ (self ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase__ : Any = self.scheduler_classes[0]
lowerCAmelCase__ : Tuple = self.get_scheduler_config()
lowerCAmelCase__ : str = scheduler_class(**__lowerCamelCase )
scheduler.set_timesteps(self.num_inference_steps ,device=__lowerCamelCase )
lowerCAmelCase__ : Optional[Any] = self.dummy_model()
lowerCAmelCase__ : List[Any] = self.dummy_sample_deter.to(__lowerCamelCase ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
lowerCAmelCase__ : List[Any] = scheduler.scale_model_input(__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : Any = model(__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : List[Any] = scheduler.step(__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : List[Any] = output.prev_sample
lowerCAmelCase__ : List[str] = torch.sum(torch.abs(__lowerCamelCase ) )
lowerCAmelCase__ : Dict = torch.mean(torch.abs(__lowerCamelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46_9573_9746_0938 ) < 1e-2
assert abs(result_mean.item() - 0.2_1805_9346_0798_2635 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3536_3769_5312 ) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9083_8241_5771 ) < 1e-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1e-3
def lowerCAmelCase__ (self ) -> int:
"""simple docstring"""
lowerCAmelCase__ : str = self.scheduler_classes[0]
lowerCAmelCase__ : List[Any] = self.get_scheduler_config()
lowerCAmelCase__ : Union[str, Any] = scheduler_class(**__lowerCamelCase ,use_karras_sigmas=__lowerCamelCase )
scheduler.set_timesteps(self.num_inference_steps ,device=__lowerCamelCase )
lowerCAmelCase__ : str = self.dummy_model()
lowerCAmelCase__ : Union[str, Any] = self.dummy_sample_deter.to(__lowerCamelCase ) * scheduler.init_noise_sigma
lowerCAmelCase__ : Union[str, Any] = sample.to(__lowerCamelCase )
for t in scheduler.timesteps:
lowerCAmelCase__ : List[Any] = scheduler.scale_model_input(__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : str = model(__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : Tuple = scheduler.step(__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : str = output.prev_sample
lowerCAmelCase__ : Tuple = torch.sum(torch.abs(__lowerCamelCase ) )
lowerCAmelCase__ : List[Any] = torch.mean(torch.abs(__lowerCamelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66_9741_3574_2188 ) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1e-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63_6535_6445_3125 ) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1e-2
else:
assert abs(result_sum.item() - 170.3_1352_2338_8672 ) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1e-2
| 129 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Dict = logging.get_logger(__name__)
lowercase : Dict = {
'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 A ( __snake_case ):
__magic_name__ = '''canine'''
def __init__( self , SCREAMING_SNAKE_CASE=768 , SCREAMING_SNAKE_CASE=12 , SCREAMING_SNAKE_CASE=12 , SCREAMING_SNAKE_CASE=3072 , SCREAMING_SNAKE_CASE="gelu" , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=16384 , SCREAMING_SNAKE_CASE=16 , SCREAMING_SNAKE_CASE=0.02 , SCREAMING_SNAKE_CASE=1e-12 , SCREAMING_SNAKE_CASE=0 , SCREAMING_SNAKE_CASE=0XE_000 , SCREAMING_SNAKE_CASE=0XE_001 , SCREAMING_SNAKE_CASE=4 , SCREAMING_SNAKE_CASE=4 , SCREAMING_SNAKE_CASE=8 , SCREAMING_SNAKE_CASE=16384 , SCREAMING_SNAKE_CASE=128 , **SCREAMING_SNAKE_CASE , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE , bos_token_id=SCREAMING_SNAKE_CASE , eos_token_id=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
A : List[str] = max_position_embeddings
A : Any = hidden_size
A : List[str] = num_hidden_layers
A : Any = num_attention_heads
A : Dict = intermediate_size
A : str = hidden_act
A : Optional[int] = hidden_dropout_prob
A : Tuple = attention_probs_dropout_prob
A : str = initializer_range
A : List[str] = type_vocab_size
A : List[Any] = layer_norm_eps
# Character config:
A : Optional[int] = downsampling_rate
A : int = upsampling_kernel_size
A : Any = num_hash_functions
A : List[str] = num_hash_buckets
A : int = local_transformer_stride
| 367 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A ( __snake_case ):
__magic_name__ = (UniPCMultistepScheduler,)
__magic_name__ = (('''num_inference_steps''', 25),)
def __lowerCAmelCase ( self , **SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
A : str = {
'''num_train_timesteps''': 1000,
'''beta_start''': 0.0_001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''solver_order''': 2,
'''solver_type''': '''bh2''',
}
config.update(**SCREAMING_SNAKE_CASE )
return config
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE=0 , **SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
A : List[Any] = dict(self.forward_default_kwargs )
A : Union[str, Any] = kwargs.pop('''num_inference_steps''' , SCREAMING_SNAKE_CASE )
A : Optional[Any] = self.dummy_sample
A : int = 0.1 * sample
A : Union[str, Any] = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
A : Optional[Any] = self.get_scheduler_config(**SCREAMING_SNAKE_CASE )
A : Optional[int] = scheduler_class(**SCREAMING_SNAKE_CASE )
scheduler.set_timesteps(SCREAMING_SNAKE_CASE )
# copy over dummy past residuals
A : List[Any] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(SCREAMING_SNAKE_CASE )
A : List[Any] = scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE )
new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE )
# copy over dummy past residuals
A : Dict = dummy_past_residuals[: new_scheduler.config.solver_order]
A, A : Tuple = sample, sample
for t in range(SCREAMING_SNAKE_CASE , time_step + scheduler.config.solver_order + 1 ):
A : Any = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
A : Optional[Any] = new_scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE=0 , **SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
A : Optional[Any] = dict(self.forward_default_kwargs )
A : Tuple = kwargs.pop('''num_inference_steps''' , SCREAMING_SNAKE_CASE )
A : List[Any] = self.dummy_sample
A : int = 0.1 * sample
A : Optional[Any] = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
A : Optional[int] = self.get_scheduler_config()
A : Any = scheduler_class(**SCREAMING_SNAKE_CASE )
scheduler.set_timesteps(SCREAMING_SNAKE_CASE )
# copy over dummy past residuals (must be after setting timesteps)
A : int = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(SCREAMING_SNAKE_CASE )
A : int = scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE )
# copy over dummy past residuals
new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE )
# copy over dummy past residual (must be after setting timesteps)
A : Optional[Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
A : List[Any] = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
A : List[Any] = new_scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE=None , **SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
if scheduler is None:
A : Dict = self.scheduler_classes[0]
A : Union[str, Any] = self.get_scheduler_config(**SCREAMING_SNAKE_CASE )
A : List[Any] = scheduler_class(**SCREAMING_SNAKE_CASE )
A : Tuple = self.scheduler_classes[0]
A : Union[str, Any] = self.get_scheduler_config(**SCREAMING_SNAKE_CASE )
A : List[str] = scheduler_class(**SCREAMING_SNAKE_CASE )
A : int = 10
A : Tuple = self.dummy_model()
A : Any = self.dummy_sample_deter
scheduler.set_timesteps(SCREAMING_SNAKE_CASE )
for i, t in enumerate(scheduler.timesteps ):
A : int = model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
A : Optional[Any] = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).prev_sample
return sample
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : Tuple = dict(self.forward_default_kwargs )
A : List[Any] = kwargs.pop('''num_inference_steps''' , SCREAMING_SNAKE_CASE )
for scheduler_class in self.scheduler_classes:
A : Dict = self.get_scheduler_config()
A : Dict = scheduler_class(**SCREAMING_SNAKE_CASE )
A : Optional[Any] = self.dummy_sample
A : Optional[int] = 0.1 * sample
if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE , '''set_timesteps''' ):
scheduler.set_timesteps(SCREAMING_SNAKE_CASE )
elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE , '''set_timesteps''' ):
A : Tuple = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
A : Dict = [residual + 0.2, residual + 0.15, residual + 0.10]
A : List[str] = dummy_past_residuals[: scheduler.config.solver_order]
A : List[Any] = scheduler.timesteps[5]
A : Dict = scheduler.timesteps[6]
A : List[Any] = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
A : List[Any] = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : Union[str, Any] = UniPCMultistepScheduler(**self.get_scheduler_config() )
A : List[Any] = self.full_loop(scheduler=SCREAMING_SNAKE_CASE )
A : List[str] = torch.mean(torch.abs(SCREAMING_SNAKE_CASE ) )
assert abs(result_mean.item() - 0.2_464 ) < 1e-3
A : Dict = DPMSolverSinglestepScheduler.from_config(scheduler.config )
A : Optional[int] = DEISMultistepScheduler.from_config(scheduler.config )
A : List[Any] = DPMSolverMultistepScheduler.from_config(scheduler.config )
A : List[Any] = UniPCMultistepScheduler.from_config(scheduler.config )
A : Optional[Any] = self.full_loop(scheduler=SCREAMING_SNAKE_CASE )
A : Optional[Any] = torch.mean(torch.abs(SCREAMING_SNAKE_CASE ) )
assert abs(result_mean.item() - 0.2_464 ) < 1e-3
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=SCREAMING_SNAKE_CASE , prediction_type=SCREAMING_SNAKE_CASE , sample_max_value=SCREAMING_SNAKE_CASE , solver_order=SCREAMING_SNAKE_CASE , solver_type=SCREAMING_SNAKE_CASE , )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=SCREAMING_SNAKE_CASE , solver_type=SCREAMING_SNAKE_CASE , prediction_type=SCREAMING_SNAKE_CASE , )
A : Dict = self.full_loop(
solver_order=SCREAMING_SNAKE_CASE , solver_type=SCREAMING_SNAKE_CASE , prediction_type=SCREAMING_SNAKE_CASE , )
assert not torch.isnan(SCREAMING_SNAKE_CASE ).any(), "Samples have nan numbers"
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE )
self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE , time_step=0 )
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
A : int = self.full_loop()
A : int = torch.mean(torch.abs(SCREAMING_SNAKE_CASE ) )
assert abs(result_mean.item() - 0.2_464 ) < 1e-3
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
A : List[Any] = self.full_loop(prediction_type='''v_prediction''' )
A : Any = torch.mean(torch.abs(SCREAMING_SNAKE_CASE ) )
assert abs(result_mean.item() - 0.1_014 ) < 1e-3
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
A : Dict = self.scheduler_classes[0]
A : List[Any] = self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE , dynamic_thresholding_ratio=0 )
A : List[str] = scheduler_class(**SCREAMING_SNAKE_CASE )
A : Tuple = 10
A : Union[str, Any] = self.dummy_model()
A : Dict = self.dummy_sample_deter.half()
scheduler.set_timesteps(SCREAMING_SNAKE_CASE )
for i, t in enumerate(scheduler.timesteps ):
A : Dict = model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
A : Optional[Any] = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).prev_sample
assert sample.dtype == torch.floataa
def __lowerCAmelCase ( self , **SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
for scheduler_class in self.scheduler_classes:
A : Dict = self.get_scheduler_config(**SCREAMING_SNAKE_CASE )
A : Tuple = scheduler_class(**SCREAMING_SNAKE_CASE )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 311 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Tuple , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Tuple=13 , __UpperCAmelCase : Optional[int]=7 , __UpperCAmelCase : int=True , __UpperCAmelCase : str=True , __UpperCAmelCase : Union[str, Any]=True , __UpperCAmelCase : str=True , __UpperCAmelCase : List[str]=99 , __UpperCAmelCase : List[str]=32 , __UpperCAmelCase : Union[str, Any]=2 , __UpperCAmelCase : List[str]=4 , __UpperCAmelCase : Optional[Any]=37 , __UpperCAmelCase : Any="gelu" , __UpperCAmelCase : Optional[Any]=0.1 , __UpperCAmelCase : int=0.1 , __UpperCAmelCase : Dict=512 , __UpperCAmelCase : List[Any]=16 , __UpperCAmelCase : List[str]=2 , __UpperCAmelCase : Optional[Any]=0.02 , __UpperCAmelCase : int=3 , __UpperCAmelCase : Dict=4 , __UpperCAmelCase : str=None , ):
'''simple docstring'''
_A = parent
_A = 13
_A = 7
_A = True
_A = True
_A = True
_A = True
_A = 99
_A = 32
_A = 2
_A = 4
_A = 37
_A = "gelu"
_A = 0.1
_A = 0.1
_A = 512
_A = 16
_A = 2
_A = 0.02
_A = 3
_A = 4
_A = None
def lowerCAmelCase ( self : Dict ):
'''simple docstring'''
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = None
if self.use_input_mask:
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_A = None
_A = None
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = RoFormerConfig(
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 , initializer_range=self.initializer_range , return_dict=__UpperCAmelCase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : str , __UpperCAmelCase : Dict , __UpperCAmelCase : Any , __UpperCAmelCase : str , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Union[str, Any] ):
'''simple docstring'''
_A = TFRoFormerModel(config=__UpperCAmelCase )
_A = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_A = [input_ids, input_mask]
_A = model(__UpperCAmelCase )
_A = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase ( self : int , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : int , __UpperCAmelCase : Tuple , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Any , __UpperCAmelCase : List[Any] ):
'''simple docstring'''
_A = True
_A = TFRoFormerForCausalLM(config=__UpperCAmelCase )
_A = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
_A = model(__UpperCAmelCase )["logits"]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def lowerCAmelCase ( self : int , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Tuple , __UpperCAmelCase : str , __UpperCAmelCase : Tuple , __UpperCAmelCase : Dict , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : str ):
'''simple docstring'''
_A = TFRoFormerForMaskedLM(config=__UpperCAmelCase )
_A = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
_A = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Union[str, Any] ):
'''simple docstring'''
_A = self.num_labels
_A = TFRoFormerForSequenceClassification(config=__UpperCAmelCase )
_A = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
_A = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : str , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : List[Any] ):
'''simple docstring'''
_A = self.num_choices
_A = TFRoFormerForMultipleChoice(config=__UpperCAmelCase )
_A = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
_A = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
_A = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
_A = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
_A = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCAmelCase ( self : Dict , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : int , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[int] ):
'''simple docstring'''
_A = self.num_labels
_A = TFRoFormerForTokenClassification(config=__UpperCAmelCase )
_A = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
_A = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Dict , __UpperCAmelCase : int , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Any , __UpperCAmelCase : int , __UpperCAmelCase : int ):
'''simple docstring'''
_A = TFRoFormerForQuestionAnswering(config=__UpperCAmelCase )
_A = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
_A = model(__UpperCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
_A = self.prepare_config_and_inputs()
(
(
_A
) , (
_A
) , (
_A
) , (
_A
) , (
_A
) , (
_A
) , (
_A
) ,
) = config_and_inputs
_A = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class _UpperCAmelCase ( snake_case_ , snake_case_ , unittest.TestCase ):
"""simple docstring"""
snake_case = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
snake_case = (
{
'''feature-extraction''': TFRoFormerModel,
'''fill-mask''': TFRoFormerForMaskedLM,
'''question-answering''': TFRoFormerForQuestionAnswering,
'''text-classification''': TFRoFormerForSequenceClassification,
'''text-generation''': TFRoFormerForCausalLM,
'''token-classification''': TFRoFormerForTokenClassification,
'''zero-shot''': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
snake_case = False
snake_case = False
def lowerCAmelCase ( self : int , __UpperCAmelCase : Dict , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : Optional[int] ):
'''simple docstring'''
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def lowerCAmelCase ( self : Optional[int] ):
'''simple docstring'''
_A = TFRoFormerModelTester(self )
_A = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def lowerCAmelCase ( self : Optional[int] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCAmelCase ( self : Optional[int] ):
'''simple docstring'''
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCAmelCase ( self : Any ):
'''simple docstring'''
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )
def lowerCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*__UpperCAmelCase )
def lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase )
def lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )
def lowerCAmelCase ( self : str ):
'''simple docstring'''
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )
def lowerCAmelCase ( self : Any ):
'''simple docstring'''
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )
@slow
def lowerCAmelCase ( self : Dict ):
'''simple docstring'''
_A = TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base" )
self.assertIsNotNone(__UpperCAmelCase )
@require_tf
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowerCAmelCase ( self : Optional[int] ):
'''simple docstring'''
_A = TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base" )
_A = tf.constant([[0, 1, 2, 3, 4, 5]] )
_A = model(__UpperCAmelCase )[0]
# TODO Replace vocab size
_A = 50000
_A = [1, 6, vocab_size]
self.assertEqual(output.shape , __UpperCAmelCase )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
_A = tf.constant(
[
[
[-0.12053341, -1.0264901, 0.29221946],
[-1.5133783, 0.197433, 0.15190607],
[-5.0135403, -3.900256, -0.84038764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 )
@require_tf
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
snake_case = 1E-4
def lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
_A = tf.constant([[4, 10]] )
_A = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
_A = emba(input_ids.shape )
_A = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(__UpperCAmelCase , __UpperCAmelCase , atol=self.tolerance )
def lowerCAmelCase ( self : Tuple ):
'''simple docstring'''
_A = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
_A = TFRoFormerSinusoidalPositionalEmbedding(num_positions=512 , embedding_dim=512 )
emba([2, 16, 512] )
_A = emba.weight[:3, :5]
tf.debugging.assert_near(__UpperCAmelCase , __UpperCAmelCase , atol=self.tolerance )
@require_tf
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
snake_case = 1E-4
def lowerCAmelCase ( self : str ):
'''simple docstring'''
_A = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 100
_A = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 100
_A = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 )
_A = embed_positions([2, 16, 768] )[None, None, :, :]
_A , _A = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
_A = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
_A = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , __UpperCAmelCase , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , __UpperCAmelCase , atol=self.tolerance )
| 79 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase__ = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConfig""",
"""GroupViTOnnxConfig""",
"""GroupViTTextConfig""",
"""GroupViTVisionConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GroupViTModel""",
"""GroupViTPreTrainedModel""",
"""GroupViTTextModel""",
"""GroupViTVisionModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFGroupViTModel""",
"""TFGroupViTPreTrainedModel""",
"""TFGroupViTTextModel""",
"""TFGroupViTVisionModel""",
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 68 | 0 |
def __lowerCamelCase (UpperCAmelCase__ : int = 1_0_0 ):
SCREAMING_SNAKE_CASE = (n * (n + 1) // 2) ** 2
SCREAMING_SNAKE_CASE = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"""{solution() = }""")
| 355 | import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileNetVaForImageClassification, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class lowercase ( a ):
def __snake_case( self : Optional[int] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_UpperCamelCase , "tf_padding" ) )
self.parent.assertTrue(hasattr(_UpperCamelCase , "depth_multiplier" ) )
class lowercase :
def __init__( self : Tuple , _UpperCamelCase : Optional[Any] , _UpperCamelCase : Any=13 , _UpperCamelCase : Any=3 , _UpperCamelCase : Union[str, Any]=32 , _UpperCamelCase : Optional[Any]=0.2_5 , _UpperCamelCase : int=8 , _UpperCamelCase : str=True , _UpperCamelCase : Any=1_024 , _UpperCamelCase : Tuple=32 , _UpperCamelCase : List[str]="relu6" , _UpperCamelCase : Tuple=0.1 , _UpperCamelCase : List[str]=0.0_2 , _UpperCamelCase : int=True , _UpperCamelCase : int=True , _UpperCamelCase : Optional[Any]=10 , _UpperCamelCase : List[str]=None , ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = depth_multiplier
SCREAMING_SNAKE_CASE = min_depth
SCREAMING_SNAKE_CASE = tf_padding
SCREAMING_SNAKE_CASE = int(last_hidden_size * depth_multiplier )
SCREAMING_SNAKE_CASE = output_stride
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = classifier_dropout_prob
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = num_labels
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = scope
def __snake_case( self : Dict ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
if self.use_labels:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels, pixel_labels
def __snake_case( self : Optional[Any] ) -> str:
'''simple docstring'''
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , min_depth=self.min_depth , tf_padding=self.tf_padding , hidden_act=self.hidden_act , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def __snake_case( self : int , _UpperCamelCase : Optional[int] , _UpperCamelCase : Any , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : int ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MobileNetVaModel(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
SCREAMING_SNAKE_CASE = model(_UpperCamelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __snake_case( self : Dict , _UpperCamelCase : Tuple , _UpperCamelCase : Any , _UpperCamelCase : str , _UpperCamelCase : List[Any] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.num_labels
SCREAMING_SNAKE_CASE = MobileNetVaForImageClassification(_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
SCREAMING_SNAKE_CASE = model(_UpperCamelCase , labels=_UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __snake_case( self : Optional[Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = config_and_inputs
SCREAMING_SNAKE_CASE = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class lowercase ( a , a , unittest.TestCase ):
lowercase__ : Dict = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
lowercase__ : Tuple = (
{"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
if is_torch_available()
else {}
)
lowercase__ : Union[str, Any] = False
lowercase__ : Union[str, Any] = False
lowercase__ : Tuple = False
lowercase__ : List[str] = False
def __snake_case( self : List[str] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MobileNetVaModelTester(self )
SCREAMING_SNAKE_CASE = MobileNetVaConfigTester(self , config_class=_UpperCamelCase , has_text_modality=_UpperCamelCase )
def __snake_case( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileNetV1 does not use inputs_embeds" )
def __snake_case( self : Tuple ) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason="MobileNetV1 does not support input and output embeddings" )
def __snake_case( self : Optional[int] ) -> Dict:
'''simple docstring'''
pass
@unittest.skip(reason="MobileNetV1 does not output attentions" )
def __snake_case( self : Any ) -> List[str]:
'''simple docstring'''
pass
def __snake_case( self : List[Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(_UpperCamelCase )
SCREAMING_SNAKE_CASE = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _UpperCamelCase )
def __snake_case( self : List[Any] ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCamelCase )
def __snake_case( self : List[str] ) -> Optional[int]:
'''simple docstring'''
def check_hidden_states_output(_UpperCamelCase : Any , _UpperCamelCase : Dict , _UpperCamelCase : Tuple ):
SCREAMING_SNAKE_CASE = model_class(_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(_UpperCamelCase , _UpperCamelCase ) )
SCREAMING_SNAKE_CASE = outputs.hidden_states
SCREAMING_SNAKE_CASE = 26
self.assertEqual(len(_UpperCamelCase ) , _UpperCamelCase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
def __snake_case( self : Any ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase )
@slow
def __snake_case( self : int ) -> str:
'''simple docstring'''
for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE = MobileNetVaModel.from_pretrained(_UpperCamelCase )
self.assertIsNotNone(_UpperCamelCase )
def __lowerCamelCase ():
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class lowercase ( unittest.TestCase ):
@cached_property
def __snake_case( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
return (
MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224" ) if is_vision_available() else None
)
@slow
def __snake_case( self : str ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224" ).to(_UpperCamelCase )
SCREAMING_SNAKE_CASE = self.default_image_processor
SCREAMING_SNAKE_CASE = prepare_img()
SCREAMING_SNAKE_CASE = image_processor(images=_UpperCamelCase , return_tensors="pt" ).to(_UpperCamelCase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**_UpperCamelCase )
# verify the logits
SCREAMING_SNAKE_CASE = torch.Size((1, 1_001) )
self.assertEqual(outputs.logits.shape , _UpperCamelCase )
SCREAMING_SNAKE_CASE = torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5] ).to(_UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _UpperCamelCase , atol=1e-4 ) )
| 206 | 0 |
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