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 |
|---|---|---|---|---|
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_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
lowercase : List[str] = logging.get_logger(__name__)
@add_end_docstrings(__lowercase)
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
def __init__( self :str , *a :Union[str, Any] , **a :Any ) -> List[str]:
super().__init__(*__lowercase , **__lowercase )
requires_backends(self , "vision" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def _lowerCamelCase ( self :Optional[Any] , a :Optional[Any]=None , a :int=None , a :List[str]=None ) -> int:
__UpperCamelCase : List[str] = {}
__UpperCamelCase : List[str] = {}
if prompt is not None:
__UpperCamelCase : str = prompt
if generate_kwargs is not None:
__UpperCamelCase : str = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
__UpperCamelCase : List[str] = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,"
" please use only one" )
__UpperCamelCase : Optional[Any] = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self :Union[str, Any] , a :Union[str, List[str], "Image.Image", List["Image.Image"]] , **a :Optional[Any] ) -> int:
return super().__call__(__lowercase , **__lowercase )
def _lowerCamelCase ( self :str , a :Tuple , a :Dict=None ) -> List[Any]:
__UpperCamelCase : str = load_image(__lowercase )
if prompt is not None:
if not isinstance(__lowercase , __lowercase ):
raise ValueError(
f'Received an invalid text input, got - {type(__lowercase )} - but expected a single string. '
"Note also that one single text can be provided for conditional image to text generation." )
__UpperCamelCase : List[str] = self.model.config.model_type
if model_type == "git":
__UpperCamelCase : List[Any] = self.image_processor(images=__lowercase , return_tensors=self.framework )
__UpperCamelCase : Optional[int] = self.tokenizer(text=__lowercase , add_special_tokens=__lowercase ).input_ids
__UpperCamelCase : Any = [self.tokenizer.cls_token_id] + input_ids
__UpperCamelCase : str = torch.tensor(__lowercase ).unsqueeze(0 )
model_inputs.update({"input_ids": input_ids} )
elif model_type == "pix2struct":
__UpperCamelCase : Dict = self.image_processor(images=__lowercase , header_text=__lowercase , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
__UpperCamelCase : List[str] = self.image_processor(images=__lowercase , return_tensors=self.framework )
__UpperCamelCase : Optional[Any] = self.tokenizer(__lowercase , return_tensors=self.framework )
model_inputs.update(__lowercase )
else:
raise ValueError(f'Model type {model_type} does not support conditional text generation' )
else:
__UpperCamelCase : Union[str, Any] = self.image_processor(images=__lowercase , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
__UpperCamelCase : Optional[Any] = None
return model_inputs
def _lowerCamelCase ( self :Optional[int] , a :Dict , a :Any=None ) -> Union[str, Any]:
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs["input_ids"] , __lowercase )
and all(x is None for x in model_inputs["input_ids"] )
):
__UpperCamelCase : List[str] = None
if generate_kwargs is None:
__UpperCamelCase : int = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
__UpperCamelCase : Optional[int] = model_inputs.pop(self.model.main_input_name )
__UpperCamelCase : Union[str, Any] = self.model.generate(__lowercase , **__lowercase , **__lowercase )
return model_outputs
def _lowerCamelCase ( self :List[str] , a :Optional[Any] ) -> Dict:
__UpperCamelCase : str = []
for output_ids in model_outputs:
__UpperCamelCase : int = {
'''generated_text''': self.tokenizer.decode(
__lowercase , skip_special_tokens=__lowercase , )
}
records.append(__lowercase )
return records | 232 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
a_ = re.compile(R'\s+')
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
return {"hash": hashlib.mda(re.sub(UpperCamelCase__, '''''', example['''content'''] ).encode('''utf-8''' ) ).hexdigest()}
def _a( UpperCamelCase__ : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =[len(UpperCamelCase__ ) for line in example['''content'''].splitlines()]
return {"line_mean": np.mean(UpperCamelCase__ ), "line_max": max(UpperCamelCase__ )}
def _a( UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple =np.mean([c.isalnum() for c in example['''content''']] )
return {"alpha_frac": alpha_frac}
def _a( UpperCamelCase__ : Any, UpperCamelCase__ : Any ):
'''simple docstring'''
if example["hash"] in uniques:
uniques.remove(example['''hash'''] )
return True
else:
return False
def _a( UpperCamelCase__ : Any, UpperCamelCase__ : Optional[Any]=5 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =['''auto-generated''', '''autogenerated''', '''automatically generated''']
SCREAMING_SNAKE_CASE__ : Dict =example['''content'''].splitlines()
for _, line in zip(range(UpperCamelCase__ ), UpperCamelCase__ ):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def _a( UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Tuple=5, UpperCamelCase__ : Optional[Any]=0.0_5 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple =['''unit tests''', '''test file''', '''configuration file''']
SCREAMING_SNAKE_CASE__ : List[Any] =example['''content'''].splitlines()
SCREAMING_SNAKE_CASE__ : List[str] =0
SCREAMING_SNAKE_CASE__ : Optional[Any] =0
# first test
for _, line in zip(range(UpperCamelCase__ ), UpperCamelCase__ ):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
SCREAMING_SNAKE_CASE__ : List[str] =example['''content'''].count('''\n''' )
SCREAMING_SNAKE_CASE__ : Optional[int] =int(coeff * nlines )
for line in lines:
count_config += line.lower().count('''config''' )
count_test += line.lower().count('''test''' )
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def _a( UpperCamelCase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] =['''def ''', '''class ''', '''for ''', '''while ''']
SCREAMING_SNAKE_CASE__ : List[Any] =example['''content'''].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def _a( UpperCamelCase__ : Any, UpperCamelCase__ : Dict=4 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] =example['''content'''].splitlines()
SCREAMING_SNAKE_CASE__ : Optional[Any] =0
for line in lines:
counter += line.lower().count('''=''' )
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def _a( UpperCamelCase__ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str =tokenizer(example['''content'''], truncation=UpperCamelCase__ )['''input_ids''']
SCREAMING_SNAKE_CASE__ : Optional[Any] =len(example['''content'''] ) / len(UpperCamelCase__ )
return {"ratio": ratio}
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict ={}
results.update(get_hash(UpperCamelCase__ ) )
results.update(line_stats(UpperCamelCase__ ) )
results.update(alpha_stats(UpperCamelCase__ ) )
results.update(char_token_ratio(UpperCamelCase__ ) )
results.update(is_autogenerated(UpperCamelCase__ ) )
results.update(is_config_or_test(UpperCamelCase__ ) )
results.update(has_no_keywords(UpperCamelCase__ ) )
results.update(has_few_assignments(UpperCamelCase__ ) )
return results
def _a( UpperCamelCase__ : Tuple, UpperCamelCase__ : List[Any], UpperCamelCase__ : str ):
'''simple docstring'''
if not check_uniques(UpperCamelCase__, UpperCamelCase__ ):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
with open(UpperCamelCase__, '''rb''' ) as f_in:
with gzip.open(str(UpperCamelCase__ ) + '''.gz''', '''wb''', compresslevel=6 ) as f_out:
shutil.copyfileobj(UpperCamelCase__, UpperCamelCase__ )
os.unlink(UpperCamelCase__ )
# Settings
a_ = HfArgumentParser(PreprocessingArguments)
a_ = parser.parse_args()
if args.num_workers is None:
a_ = multiprocessing.cpu_count()
a_ = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
a_ = time.time()
a_ = load_dataset(args.dataset_name, split='train')
print(F'''Time to load dataset: {time.time()-t_start:.2f}''')
# Run preprocessing
a_ = time.time()
a_ = ds.map(preprocess, num_proc=args.num_workers)
print(F'''Time to preprocess dataset: {time.time()-t_start:.2f}''')
# Deduplicate hashes
a_ = set(ds.unique('hash'))
a_ = len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')
# Deduplicate data and apply heuristics
a_ = time.time()
a_ = ds.filter(filter, fn_kwargs={'uniques': uniques, 'args': args})
print(F'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(F'''Size of filtered dataset: {len(ds_filter)}''')
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
a_ = time.time()
a_ , a_ = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(F'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
print(F'''Size of deduplicate dataset: {len(ds_filter)}''')
# Save data in batches of samples_per_file
a_ = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / 'duplicate_clusters.json', 'w') as f:
json.dump(duplicate_clusters, f)
a_ = output_dir / 'data'
data_dir.mkdir(exist_ok=True)
a_ = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
a_ = str(data_dir / F'''file-{file_number+1:012}.json''')
a_ = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(F'''Time to save dataset: {time.time()-t_start:.2f}''') | 152 | 0 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
A_ : List[Any] = get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
A_ : Union[str, Any] = json.load(f)
@require_torch
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ,a_ ) -> List[str]:
return FSMTTokenizer.from_pretrained(lowerCamelCase_ )
def _snake_case ( self ,a_ ) -> Dict:
_UpperCAmelCase : Tuple = FSMTForConditionalGeneration.from_pretrained(lowerCamelCase_ ).to(lowerCamelCase_ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["""en-ru""", 26.0],
["""ru-en""", 22.0],
["""en-de""", 22.0],
["""de-en""", 29.0],
] )
@slow
def _snake_case ( self ,a_ ,a_ ) -> int:
# note: this test is not testing the best performance since it only evals a small batch
# but it should be enough to detect a regression in the output quality
_UpperCAmelCase : str = f'''facebook/wmt19-{pair}'''
_UpperCAmelCase : Tuple = self.get_tokenizer(lowerCamelCase_ )
_UpperCAmelCase : str = self.get_model(lowerCamelCase_ )
_UpperCAmelCase : Union[str, Any] = bleu_data[pair]["""src"""]
_UpperCAmelCase : Union[str, Any] = bleu_data[pair]["""tgt"""]
_UpperCAmelCase : Optional[Any] = tokenizer(lowerCamelCase_ ,return_tensors="""pt""" ,truncation=lowerCamelCase_ ,padding="""longest""" ).to(lowerCamelCase_ )
_UpperCAmelCase : Tuple = model.generate(
input_ids=batch.input_ids ,num_beams=8 ,)
_UpperCAmelCase : Optional[Any] = tokenizer.batch_decode(
lowerCamelCase_ ,skip_special_tokens=lowerCamelCase_ ,clean_up_tokenization_spaces=lowerCamelCase_ )
_UpperCAmelCase : Optional[int] = calculate_bleu(lowerCamelCase_ ,lowerCamelCase_ )
print(lowerCamelCase_ )
self.assertGreaterEqual(scores["""bleu"""] ,lowerCamelCase_ )
| 365 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowercase :
"""simple docstring"""
UpperCAmelCase = 42
UpperCAmelCase = 42
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ) -> List[str]:
_UpperCAmelCase : list[list[Edge]] = [[] for _ in range(a_ )]
_UpperCAmelCase : int = size
def __getitem__( self ,a_ ) -> Iterator[Edge]:
return iter(self._graph[vertex] )
@property
def _snake_case ( self ) -> List[Any]:
return self._size
def _snake_case ( self ,a_ ,a_ ,a_ ) -> Tuple:
if weight not in (0, 1):
raise ValueError("""Edge weight must be either 0 or 1.""" )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("""Vertex indexes must be in [0; size).""" )
self._graph[from_vertex].append(Edge(a_ ,a_ ) )
def _snake_case ( self ,a_ ,a_ ) -> int | None:
_UpperCAmelCase : Union[str, Any] = deque([start_vertex] )
_UpperCAmelCase : list[int | None] = [None] * self.size
_UpperCAmelCase : Union[str, Any] = 0
while queue:
_UpperCAmelCase : Union[str, Any] = queue.popleft()
_UpperCAmelCase : Union[str, Any] = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
_UpperCAmelCase : List[Any] = current_distance + edge.weight
_UpperCAmelCase : List[Any] = distances[edge.destination_vertex]
if (
isinstance(a_ ,a_ )
and new_distance >= dest_vertex_distance
):
continue
_UpperCAmelCase : Tuple = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("""No path from start_vertex to finish_vertex.""" )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFeatureExtractor'''],
'''processing_mctct''': ['''MCTCTProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'''MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MCTCTForCTC''',
'''MCTCTModel''',
'''MCTCTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
from .feature_extraction_mctct import MCTCTFeatureExtractor
from .processing_mctct import MCTCTProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel
else:
import sys
a_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 340 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
'''tokenization_convbert''': ['''ConvBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['''ConvBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ConvBertForMaskedLM''',
'''ConvBertForMultipleChoice''',
'''ConvBertForQuestionAnswering''',
'''ConvBertForSequenceClassification''',
'''ConvBertForTokenClassification''',
'''ConvBertLayer''',
'''ConvBertModel''',
'''ConvBertPreTrainedModel''',
'''load_tf_weights_in_convbert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFConvBertForMaskedLM''',
'''TFConvBertForMultipleChoice''',
'''TFConvBertForQuestionAnswering''',
'''TFConvBertForSequenceClassification''',
'''TFConvBertForTokenClassification''',
'''TFConvBertLayer''',
'''TFConvBertModel''',
'''TFConvBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 340 | 1 |
import argparse
import os
import re
lowerCamelCase__ = '''src/transformers'''
# Pattern that looks at the indentation in a line.
lowerCamelCase__ = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCamelCase__ = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowerCamelCase__ = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
lowerCamelCase__ = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowerCamelCase__ = re.compile(R'''\[([^\]]+)\]''')
def lowerCAmelCase__ ( a__ ) ->List[str]:
'''simple docstring'''
_UpperCamelCase = _re_indent.search(a__ )
return "" if search is None else search.groups()[0]
def lowerCAmelCase__ ( a__ , a__="" , a__=None , a__=None ) ->Any:
'''simple docstring'''
_UpperCamelCase = 0
_UpperCamelCase = code.split("\n" )
if start_prompt is not None:
while not lines[index].startswith(a__ ):
index += 1
_UpperCamelCase = ["\n".join(lines[:index] )]
else:
_UpperCamelCase = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
_UpperCamelCase = [lines[index]]
index += 1
while index < len(a__ ) and (end_prompt is None or not lines[index].startswith(a__ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(a__ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + " " ):
current_block.append(lines[index] )
blocks.append("\n".join(a__ ) )
if index < len(a__ ) - 1:
_UpperCamelCase = [lines[index + 1]]
index += 1
else:
_UpperCamelCase = []
else:
blocks.append("\n".join(a__ ) )
_UpperCamelCase = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(a__ ) > 0:
blocks.append("\n".join(a__ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(a__ ):
blocks.append("\n".join(lines[index:] ) )
return blocks
def lowerCAmelCase__ ( a__ ) ->Union[str, Any]:
'''simple docstring'''
def _inner(a__ ):
return key(a__ ).lower().replace("_" , "" )
return _inner
def lowerCAmelCase__ ( a__ , a__=None ) ->Union[str, Any]:
'''simple docstring'''
def noop(a__ ):
return x
if key is None:
_UpperCamelCase = noop
# Constants are all uppercase, they go first.
_UpperCamelCase = [obj for obj in objects if key(a__ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
_UpperCamelCase = [obj for obj in objects if key(a__ )[0].isupper() and not key(a__ ).isupper()]
# Functions begin with a lowercase, they go last.
_UpperCamelCase = [obj for obj in objects if not key(a__ )[0].isupper()]
_UpperCamelCase = ignore_underscore(a__ )
return sorted(a__ , key=a__ ) + sorted(a__ , key=a__ ) + sorted(a__ , key=a__ )
def lowerCAmelCase__ ( a__ ) ->List[Any]:
'''simple docstring'''
def _replace(a__ ):
_UpperCamelCase = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
_UpperCamelCase = [part.strip().replace("\"" , "" ) for part in imports.split("," )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
_UpperCamelCase = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(a__ )] ) + "]"
_UpperCamelCase = import_statement.split("\n" )
if len(a__ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
_UpperCamelCase = 2 if lines[1].strip() == "[" else 1
_UpperCamelCase = [(i, _re_strip_line.search(a__ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
_UpperCamelCase = sort_objects(a__ , key=lambda a__ : x[1] )
_UpperCamelCase = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(a__ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
_UpperCamelCase = _re_bracket_content.sub(_replace , lines[1] )
else:
_UpperCamelCase = [part.strip().replace("\"" , "" ) for part in lines[1].split("," )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
_UpperCamelCase = keys[:-1]
_UpperCamelCase = get_indent(lines[1] ) + ", ".join([f'"{k}"' for k in sort_objects(a__ )] )
return "\n".join(a__ )
else:
# Finally we have to deal with imports fitting on one line
_UpperCamelCase = _re_bracket_content.sub(_replace , a__ )
return import_statement
def lowerCAmelCase__ ( a__ , a__=True ) ->Optional[int]:
'''simple docstring'''
with open(a__ , encoding="utf-8" ) as f:
_UpperCamelCase = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
_UpperCamelCase = split_code_in_indented_blocks(
a__ , start_prompt="_import_structure = {" , end_prompt="if TYPE_CHECKING:" )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(a__ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
_UpperCamelCase = main_blocks[block_idx]
_UpperCamelCase = block.split("\n" )
# Get to the start of the imports.
_UpperCamelCase = 0
while line_idx < len(a__ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
_UpperCamelCase = len(a__ )
else:
line_idx += 1
if line_idx >= len(a__ ):
continue
# Ignore beginning and last line: they don't contain anything.
_UpperCamelCase = "\n".join(block_lines[line_idx:-1] )
_UpperCamelCase = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
_UpperCamelCase = split_code_in_indented_blocks(a__ , indent_level=a__ )
# We have two categories of import key: list or _import_structure[key].append/extend
_UpperCamelCase = _re_direct_key if "_import_structure = {" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
_UpperCamelCase = [(pattern.search(a__ ).groups()[0] if pattern.search(a__ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
_UpperCamelCase = [(i, key) for i, key in enumerate(a__ ) if key is not None]
_UpperCamelCase = [x[0] for x in sorted(a__ , key=lambda a__ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
_UpperCamelCase = 0
_UpperCamelCase = []
for i in range(len(a__ ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
_UpperCamelCase = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(a__ )
count += 1
# And we put our main block back together with its first and last line.
_UpperCamelCase = "\n".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(a__ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(a__ , "w" , encoding="utf-8" ) as f:
f.write("\n".join(a__ ) )
def lowerCAmelCase__ ( a__=True ) ->int:
'''simple docstring'''
_UpperCamelCase = []
for root, _, files in os.walk(a__ ):
if "__init__.py" in files:
_UpperCamelCase = sort_imports(os.path.join(a__ , "__init__.py" ) , check_only=a__ )
if result:
_UpperCamelCase = [os.path.join(a__ , "__init__.py" )]
if len(a__ ) > 0:
raise ValueError(f'Would overwrite {len(a__ )} files, run `make style`.' )
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
lowerCamelCase__ = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 63 | import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __UpperCAmelCase ( self : List[str]) -> List[str]:
"""simple docstring"""
_UpperCamelCase = FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
_UpperCamelCase = AutoTokenizer.from_pretrained("google/mt5-small")
_UpperCamelCase = tokenizer("Hello there" , return_tensors="np").input_ids
_UpperCamelCase = tokenizer("Hi I am" , return_tensors="np").input_ids
_UpperCamelCase = shift_tokens_right(lowercase_ , model.config.pad_token_id , model.config.decoder_start_token_id)
_UpperCamelCase = model(lowercase_ , decoder_input_ids=lowercase_).logits
_UpperCamelCase = optax.softmax_cross_entropy(lowercase_ , onehot(lowercase_ , logits.shape[-1])).mean()
_UpperCamelCase = -(labels.shape[-1] * loss.item())
_UpperCamelCase = -84.91_27
self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)
| 63 | 1 |
'''simple docstring'''
import math
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase = 0 , lowerCAmelCase = 0 ):
"""simple docstring"""
_lowerCAmelCase = end or len(lowerCAmelCase )
for i in range(lowerCAmelCase , lowerCAmelCase ):
_lowerCAmelCase = i
_lowerCAmelCase = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
_lowerCAmelCase = array[temp_index - 1]
temp_index -= 1
_lowerCAmelCase = temp_index_value
return array
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ): # Max Heap
"""simple docstring"""
_lowerCAmelCase = index
_lowerCAmelCase = 2 * index + 1 # Left Node
_lowerCAmelCase = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
_lowerCAmelCase = left_index
if right_index < heap_size and array[largest] < array[right_index]:
_lowerCAmelCase = right_index
if largest != index:
_lowerCAmelCase , _lowerCAmelCase = array[largest], array[index]
heapify(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = len(lowerCAmelCase )
for i in range(n // 2 , -1 , -1 ):
heapify(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
for i in range(n - 1 , 0 , -1 ):
_lowerCAmelCase , _lowerCAmelCase = array[0], array[i]
heapify(lowerCAmelCase , 0 , lowerCAmelCase )
return array
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = low
_lowerCAmelCase = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
_lowerCAmelCase , _lowerCAmelCase = array[j], array[i]
i += 1
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
if len(lowerCAmelCase ) == 0:
return array
_lowerCAmelCase = 2 * math.ceil(math.loga(len(lowerCAmelCase ) ) )
_lowerCAmelCase = 16
return intro_sort(lowerCAmelCase , 0 , len(lowerCAmelCase ) , lowerCAmelCase , lowerCAmelCase )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(lowerCAmelCase )
max_depth -= 1
_lowerCAmelCase = median_of_a(lowerCAmelCase , lowerCAmelCase , start + ((end - start) // 2) + 1 , end - 1 )
_lowerCAmelCase = partition(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
intro_sort(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
_lowerCAmelCase = p
return insertion_sort(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
A__ : Dict =input('''Enter numbers separated by a comma : ''').strip()
A__ : Dict =[float(item) for item in user_input.split(''',''')]
print(sort(unsorted))
| 70 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available
if is_torch_available() and is_datasets_available() and is_faiss_available():
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.tokenization_rag import RagTokenizer
@require_faiss
@require_torch
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = tempfile.mkdtemp()
lowerCAmelCase : Optional[int] = 8
# DPR tok
lowerCAmelCase : Dict = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
lowerCAmelCase : List[str] = os.path.join(self.tmpdirname , "dpr_tokenizer" )
os.makedirs(snake_case__ , exist_ok=snake_case__ )
lowerCAmelCase : Dict = os.path.join(snake_case__ , DPR_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] ) )
# BART tok
lowerCAmelCase : Optional[int] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
lowerCAmelCase : Optional[int] = dict(zip(snake_case__ , range(len(snake_case__ ) ) ) )
lowerCAmelCase : List[Any] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
lowerCAmelCase : str = {"unk_token": "<unk>"}
lowerCAmelCase : int = os.path.join(self.tmpdirname , "bart_tokenizer" )
os.makedirs(snake_case__ , exist_ok=snake_case__ )
lowerCAmelCase : int = os.path.join(snake_case__ , BART_VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase : Dict = os.path.join(snake_case__ , BART_VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(snake_case__ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(snake_case__ ) )
def lowercase__ ( self ):
"""simple docstring"""
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , "dpr_tokenizer" ) )
def lowercase__ ( self ):
"""simple docstring"""
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , "bart_tokenizer" ) )
def lowercase__ ( self ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
@require_tokenizers
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : str = os.path.join(self.tmpdirname , "rag_tokenizer" )
lowerCAmelCase : List[Any] = RagConfig(question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() )
lowerCAmelCase : Optional[Any] = RagTokenizer(question_encoder=self.get_dpr_tokenizer() , generator=self.get_bart_tokenizer() )
rag_config.save_pretrained(snake_case__ )
rag_tokenizer.save_pretrained(snake_case__ )
lowerCAmelCase : List[str] = RagTokenizer.from_pretrained(snake_case__ , config=snake_case__ )
self.assertIsInstance(new_rag_tokenizer.question_encoder , snake_case__ )
self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab() , rag_tokenizer.question_encoder.get_vocab() )
self.assertIsInstance(new_rag_tokenizer.generator , snake_case__ )
self.assertEqual(new_rag_tokenizer.generator.get_vocab() , rag_tokenizer.generator.get_vocab() )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = RagTokenizer.from_pretrained("facebook/rag-token-nq" )
lowerCAmelCase : Dict = [
"who got the first nobel prize in physics",
"when is the next deadpool movie being released",
"which mode is used for short wave broadcast service",
"who is the owner of reading football club",
"when is the next scandal episode coming out",
"when is the last time the philadelphia won the superbowl",
"what is the most current adobe flash player version",
"how many episodes are there in dragon ball z",
"what is the first step in the evolution of the eye",
"where is gall bladder situated in human body",
"what is the main mineral in lithium batteries",
"who is the president of usa right now",
"where do the greasers live in the outsiders",
"panda is a national animal of which country",
"what is the name of manchester united stadium",
]
lowerCAmelCase : Union[str, Any] = tokenizer(snake_case__ )
self.assertIsNotNone(snake_case__ )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : List[str] = RagTokenizer.from_pretrained("facebook/rag-sequence-nq" )
lowerCAmelCase : List[str] = [
"who got the first nobel prize in physics",
"when is the next deadpool movie being released",
"which mode is used for short wave broadcast service",
"who is the owner of reading football club",
"when is the next scandal episode coming out",
"when is the last time the philadelphia won the superbowl",
"what is the most current adobe flash player version",
"how many episodes are there in dragon ball z",
"what is the first step in the evolution of the eye",
"where is gall bladder situated in human body",
"what is the main mineral in lithium batteries",
"who is the president of usa right now",
"where do the greasers live in the outsiders",
"panda is a national animal of which country",
"what is the name of manchester united stadium",
]
lowerCAmelCase : str = tokenizer(snake_case__ )
self.assertIsNotNone(snake_case__ )
| 108 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if len(lowerCAmelCase__ ) == 0:
raise ValueError('''Scores cannot be empty''' )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1 , node_index * 2 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) , minimax(depth + 1 , node_index * 2 + 1 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) , )
return min(
minimax(depth + 1 , node_index * 2 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) , minimax(depth + 1 , node_index * 2 + 1 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) , )
def a__ ( ) -> None:
UpperCAmelCase__ : Optional[int] = [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
UpperCAmelCase__ : Any = math.log(len(lowerCAmelCase__ ) , 2 )
print('''Optimal value : ''' , end='''''' )
print(minimax(0 , 0 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 351 |
'''simple docstring'''
from __future__ import annotations
import math
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if not scores:
raise ValueError('''Scores cannot be empty''' )
if depth == height:
return scores[node_index]
return (
max(
minimax(depth + 1 , node_index * 2 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) , minimax(depth + 1 , node_index * 2 + 1 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) , )
if is_max
else min(
minimax(depth + 1 , node_index * 2 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) , minimax(depth + 1 , node_index * 2 + 1 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) , )
)
def a__ ( ) -> None:
UpperCAmelCase__ : Union[str, Any] = [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
UpperCAmelCase__ : Optional[Any] = math.log(len(lowerCAmelCase__ ) , 2 )
print(F"""Optimal value : {minimax(0 , 0 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )}""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 299 | 0 |
'''simple docstring'''
from statistics import mean
import numpy as np
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : str = 0
# Number of processes finished
lowerCAmelCase__ : Tuple = 0
# Displays the finished process.
# If it is 0, the performance is completed if it is 1, before the performance.
lowerCAmelCase__ : List[str] = [0] * no_of_process
# List to include calculation results
lowerCAmelCase__ : str = [0] * no_of_process
# Sort by arrival time.
lowerCAmelCase__ : Optional[int] = [burst_time[i] for i in np.argsort(UpperCamelCase )]
lowerCAmelCase__ : Tuple = [process_name[i] for i in np.argsort(UpperCamelCase )]
arrival_time.sort()
while no_of_process > finished_process_count:
lowerCAmelCase__ : Union[str, Any] = 0
while finished_process[i] == 1:
i += 1
if current_time < arrival_time[i]:
lowerCAmelCase__ : int = arrival_time[i]
lowerCAmelCase__ : Dict = 0
# Index showing the location of the process being performed
lowerCAmelCase__ : Tuple = 0
# Saves the current response ratio.
lowerCAmelCase__ : List[str] = 0
for i in range(0 , UpperCamelCase ):
if finished_process[i] == 0 and arrival_time[i] <= current_time:
lowerCAmelCase__ : Union[str, Any] = (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
i
]
if response_ratio < temp:
lowerCAmelCase__ : Tuple = temp
lowerCAmelCase__ : Any = i
# Calculate the turn around time
lowerCAmelCase__ : Dict = current_time + burst_time[loc] - arrival_time[loc]
current_time += burst_time[loc]
# Indicates that the process has been performed.
lowerCAmelCase__ : str = 1
# Increase finished_process_count by 1
finished_process_count += 1
return turn_around_time
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : Any = [0] * no_of_process
for i in range(0 , UpperCamelCase ):
lowerCAmelCase__ : str = turn_around_time[i] - burst_time[i]
return waiting_time
if __name__ == "__main__":
_lowerCAmelCase = 5
_lowerCAmelCase = ['''A''', '''B''', '''C''', '''D''', '''E''']
_lowerCAmelCase = [1, 2, 3, 4, 5]
_lowerCAmelCase = [1, 2, 3, 4, 5]
_lowerCAmelCase = calculate_turn_around_time(
process_name, arrival_time, burst_time, no_of_process
)
_lowerCAmelCase = calculate_waiting_time(
process_name, turn_around_time, burst_time, no_of_process
)
print('''Process name \tArrival time \tBurst time \tTurn around time \tWaiting time''')
for i in range(0, no_of_process):
print(
F"""{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t"""
F"""{turn_around_time[i]}\t\t\t{waiting_time[i]}"""
)
print(F"""average waiting time : {mean(waiting_time):.5f}""")
print(F"""average turn around time : {mean(turn_around_time):.5f}""")
| 37 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class lowerCAmelCase_:
'''simple docstring'''
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Optional[Any]:
return None
class lowerCAmelCase_:
'''simple docstring'''
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Tuple:
return None
class lowerCAmelCase_( unittest.TestCase ):
'''simple docstring'''
__lowercase : Dict = [
# (model_name, model_kwargs)
('''bert-base-cased''', {}),
('''gpt2''', {'''use_cache''': False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def UpperCAmelCase_ ( self ) -> int:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__UpperCAmelCase ,"""tf""" ,12 ,**__UpperCAmelCase )
@require_torch
@slow
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__UpperCAmelCase ,"""pt""" ,12 ,**__UpperCAmelCase )
@require_torch
@slow
def UpperCAmelCase_ ( self ) -> Any:
from transformers import BertModel
lowerCAmelCase__ : Optional[int] = ["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
with NamedTemporaryFile(mode="""w+t""" ) as vocab_file:
vocab_file.write("""\n""".join(__UpperCAmelCase ) )
vocab_file.flush()
lowerCAmelCase__ : Dict = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
lowerCAmelCase__ : Tuple = BertModel(BertConfig(vocab_size=len(__UpperCAmelCase ) ) )
model.save_pretrained(__UpperCAmelCase )
self._test_export(__UpperCAmelCase ,"""pt""" ,12 ,__UpperCAmelCase )
@require_tf
@slow
def UpperCAmelCase_ ( self ) -> List[str]:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowerCAmelCase__ : Dict = self._test_export(__UpperCAmelCase ,"""tf""" ,12 ,**__UpperCAmelCase )
lowerCAmelCase__ : List[str] = quantize(Path(__UpperCAmelCase ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__UpperCAmelCase ).stat().st_size:
self.fail("""Quantized model is bigger than initial ONNX model""" )
@require_torch
@slow
def UpperCAmelCase_ ( self ) -> List[Any]:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowerCAmelCase__ : Any = self._test_export(__UpperCAmelCase ,"""pt""" ,12 ,**__UpperCAmelCase )
lowerCAmelCase__ : Dict = quantize(__UpperCAmelCase )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__UpperCAmelCase ).stat().st_size:
self.fail("""Quantized model is bigger than initial ONNX model""" )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase=None ,**__UpperCAmelCase ) -> Optional[Any]:
try:
# Compute path
with TemporaryDirectory() as tempdir:
lowerCAmelCase__ : Optional[int] = Path(__UpperCAmelCase ).joinpath("""model.onnx""" )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,**__UpperCAmelCase )
return path
except Exception as e:
self.fail(__UpperCAmelCase )
@require_torch
@require_tokenizers
@slow
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
from transformers import BertModel
lowerCAmelCase__ : List[Any] = BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random""" ) )
lowerCAmelCase__ : Union[str, Any] = BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""" )
self._test_infer_dynamic_axis(__UpperCAmelCase ,__UpperCAmelCase ,"""pt""" )
@require_tf
@require_tokenizers
@slow
def UpperCAmelCase_ ( self ) -> Optional[int]:
from transformers import TFBertModel
lowerCAmelCase__ : int = TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random""" ) )
lowerCAmelCase__ : Optional[int] = BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""" )
self._test_infer_dynamic_axis(__UpperCAmelCase ,__UpperCAmelCase ,"""tf""" )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Tuple:
lowerCAmelCase__ : Any = FeatureExtractionPipeline(__UpperCAmelCase ,__UpperCAmelCase )
lowerCAmelCase__ : List[str] = ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = infer_shapes(__UpperCAmelCase ,__UpperCAmelCase )
# Assert all variables are present
self.assertEqual(len(__UpperCAmelCase ) ,len(__UpperCAmelCase ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] ,__UpperCAmelCase )
self.assertSequenceEqual(variable_names[3:] ,__UpperCAmelCase )
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name] ,{0: """batch""", 1: """sequence"""} )
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes["""output_0"""] ,{0: """batch""", 1: """sequence"""} )
self.assertDictEqual(shapes["""output_1"""] ,{0: """batch"""} )
def UpperCAmelCase_ ( self ) -> Optional[int]:
lowerCAmelCase__ : List[str] = ["""input_ids""", """attention_mask""", """token_type_ids"""]
lowerCAmelCase__ : Union[str, Any] = {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
lowerCAmelCase__ , lowerCAmelCase__ : List[Any] = ensure_valid_input(FuncContiguousArgs() ,__UpperCAmelCase ,__UpperCAmelCase )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(__UpperCAmelCase ) ,3 )
# Should have exactly the same input names
self.assertEqual(set(__UpperCAmelCase ) ,set(__UpperCAmelCase ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(__UpperCAmelCase ,(tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""]) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
lowerCAmelCase__ , lowerCAmelCase__ : int = ensure_valid_input(FuncNonContiguousArgs() ,__UpperCAmelCase ,__UpperCAmelCase )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(__UpperCAmelCase ) ,1 )
self.assertEqual(len(__UpperCAmelCase ) ,1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] ,tokens["""input_ids"""] )
self.assertEqual(ordered_input_names[0] ,"""input_ids""" )
def UpperCAmelCase_ ( self ) -> Tuple:
lowerCAmelCase__ : Dict = generate_identified_filename(Path("""/home/something/my_fake_model.onnx""" ) ,"""-test""" )
self.assertEqual("""/home/something/my_fake_model-test.onnx""" ,generated.as_posix() )
| 37 | 1 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
lowerCamelCase = logging.getLogger(__name__)
class _a :
def __init__( self : str )-> Any:
lowerCAmelCase__ : Any = False
def UpperCAmelCase__( self : Tuple , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : List[str] )-> List[str]:
if not self.initialized:
lowerCAmelCase__ : Optional[int] = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=__UpperCAmelCase , generator_tokenizer=__UpperCAmelCase , index=__UpperCAmelCase , init_retrieval=__UpperCAmelCase , )
lowerCAmelCase__ : str = True
def UpperCAmelCase__( self : Any )-> Tuple:
self.retriever.index.init_index()
def UpperCAmelCase__( self : Union[str, Any] , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Tuple )-> List[str]:
lowerCAmelCase__ , lowerCAmelCase__ : List[Any] = self.retriever._main_retrieve(__UpperCAmelCase , __UpperCAmelCase )
return doc_ids, retrieved_doc_embeds
class _a ( _lowercase):
def __init__( self : int , _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Dict=None )-> Optional[Any]:
if index is not None and index.is_initialized() and len(__UpperCAmelCase ) > 0:
raise ValueError(
'''When using Ray for distributed fine-tuning, '''
'''you\'ll need to provide the paths instead, '''
'''as the dataset and the index are loaded '''
'''separately. More info in examples/rag/use_own_knowledge_dataset.py ''' )
super().__init__(
__UpperCAmelCase , question_encoder_tokenizer=__UpperCAmelCase , generator_tokenizer=__UpperCAmelCase , index=__UpperCAmelCase , init_retrieval=__UpperCAmelCase , )
lowerCAmelCase__ : int = retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
for worker in self.retrieval_workers
] )
def UpperCAmelCase__( self : List[Any] )-> Tuple:
logger.info('''initializing retrieval''' )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def UpperCAmelCase__( self : Optional[Any] , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Tuple )-> Dict:
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
lowerCAmelCase__ : List[str] = self.retrieval_workers[random.randint(0 , len(self.retrieval_workers ) - 1 )]
lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = ray.get(random_worker.retrieve.remote(__UpperCAmelCase , __UpperCAmelCase ) )
else:
lowerCAmelCase__ , lowerCAmelCase__ : Dict = self._main_retrieve(__UpperCAmelCase , __UpperCAmelCase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__UpperCAmelCase )
@classmethod
def UpperCAmelCase__( cls : List[str] , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Tuple=None , **_SCREAMING_SNAKE_CASE : Optional[int] )-> List[Any]:
return super(__UpperCAmelCase , cls ).get_tokenizers(__UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase )
@classmethod
def UpperCAmelCase__( cls : Optional[int] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Optional[Any]=None , **_SCREAMING_SNAKE_CASE : int )-> str:
lowerCAmelCase__ : List[Any] = kwargs.pop('''config''' , __UpperCAmelCase ) or RagConfig.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
lowerCAmelCase__ : int = RagTokenizer.from_pretrained(__UpperCAmelCase , config=__UpperCAmelCase )
lowerCAmelCase__ : Any = rag_tokenizer.question_encoder
lowerCAmelCase__ : int = rag_tokenizer.generator
if indexed_dataset is not None:
lowerCAmelCase__ : List[Any] = '''custom'''
lowerCAmelCase__ : List[str] = CustomHFIndex(config.retrieval_vector_size , __UpperCAmelCase )
else:
lowerCAmelCase__ : str = cls._build_index(__UpperCAmelCase )
return cls(
__UpperCAmelCase , question_encoder_tokenizer=__UpperCAmelCase , generator_tokenizer=__UpperCAmelCase , retrieval_workers=__UpperCAmelCase , index=__UpperCAmelCase , )
| 354 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
'''YituTech/conv-bert-medium-small''': (
'''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
),
'''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class _a ( _lowercase):
_a : Dict = '''convbert'''
def __init__( self : Union[str, Any] , _SCREAMING_SNAKE_CASE : str=3_0522 , _SCREAMING_SNAKE_CASE : Union[str, Any]=768 , _SCREAMING_SNAKE_CASE : Tuple=12 , _SCREAMING_SNAKE_CASE : str=12 , _SCREAMING_SNAKE_CASE : Any=3072 , _SCREAMING_SNAKE_CASE : List[Any]="gelu" , _SCREAMING_SNAKE_CASE : Dict=0.1 , _SCREAMING_SNAKE_CASE : List[str]=0.1 , _SCREAMING_SNAKE_CASE : int=512 , _SCREAMING_SNAKE_CASE : Any=2 , _SCREAMING_SNAKE_CASE : Optional[int]=0.02 , _SCREAMING_SNAKE_CASE : Optional[int]=1E-12 , _SCREAMING_SNAKE_CASE : Union[str, Any]=1 , _SCREAMING_SNAKE_CASE : Optional[int]=0 , _SCREAMING_SNAKE_CASE : Dict=2 , _SCREAMING_SNAKE_CASE : Union[str, Any]=768 , _SCREAMING_SNAKE_CASE : Optional[Any]=2 , _SCREAMING_SNAKE_CASE : Tuple=9 , _SCREAMING_SNAKE_CASE : int=1 , _SCREAMING_SNAKE_CASE : Tuple=None , **_SCREAMING_SNAKE_CASE : List[str] , )-> List[str]:
super().__init__(
pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
lowerCAmelCase__ : Union[str, Any] = vocab_size
lowerCAmelCase__ : List[Any] = hidden_size
lowerCAmelCase__ : Tuple = num_hidden_layers
lowerCAmelCase__ : Union[str, Any] = num_attention_heads
lowerCAmelCase__ : Optional[Any] = intermediate_size
lowerCAmelCase__ : Tuple = hidden_act
lowerCAmelCase__ : Dict = hidden_dropout_prob
lowerCAmelCase__ : str = attention_probs_dropout_prob
lowerCAmelCase__ : str = max_position_embeddings
lowerCAmelCase__ : Tuple = type_vocab_size
lowerCAmelCase__ : Optional[Any] = initializer_range
lowerCAmelCase__ : List[Any] = layer_norm_eps
lowerCAmelCase__ : int = embedding_size
lowerCAmelCase__ : Union[str, Any] = head_ratio
lowerCAmelCase__ : Optional[int] = conv_kernel_size
lowerCAmelCase__ : List[str] = num_groups
lowerCAmelCase__ : Dict = classifier_dropout
class _a ( _lowercase):
@property
def UpperCAmelCase__( self : Tuple )-> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowerCAmelCase__ : Optional[Any] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowerCAmelCase__ : List[str] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 211 | 0 |
from string import ascii_uppercase
_lowercase : List[str] ={char: i for i, char in enumerate(ascii_uppercase)}
_lowercase : Dict =dict(enumerate(ascii_uppercase))
def lowerCAmelCase_ ( _lowercase : str , _lowercase : str) -> str:
"""simple docstring"""
a__ : Any = len(_lowercase)
a__ : Union[str, Any] = 0
while True:
if x == i:
a__ : Optional[int] = 0
if len(_lowercase) == len(_lowercase):
break
key += key[i]
i += 1
return key
def lowerCAmelCase_ ( _lowercase : str , _lowercase : str) -> str:
"""simple docstring"""
a__ : Optional[int] = """"""
a__ : Optional[int] = 0
for letter in message:
if letter == " ":
cipher_text += " "
else:
a__ : Optional[Any] = (dicta[letter] - dicta[key_new[i]]) % 26
i += 1
cipher_text += dicta[x]
return cipher_text
def lowerCAmelCase_ ( _lowercase : str , _lowercase : str) -> str:
"""simple docstring"""
a__ : Union[str, Any] = """"""
a__ : Union[str, Any] = 0
for letter in cipher_text:
if letter == " ":
or_txt += " "
else:
a__ : Dict = (dicta[letter] + dicta[key_new[i]] + 26) % 26
i += 1
or_txt += dicta[x]
return or_txt
def lowerCAmelCase_ ( ) -> None:
"""simple docstring"""
a__ : List[str] = """THE GERMAN ATTACK"""
a__ : List[str] = """SECRET"""
a__ : Optional[int] = generate_key(_lowercase , _lowercase)
a__ : int = cipher_text(_lowercase , _lowercase)
print(F'''Encrypted Text = {s}''')
print(F'''Original Text = {original_text(_lowercase , _lowercase)}''')
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 170 |
from __future__ import annotations
import unittest
from transformers import 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 numpy
import tensorflow as tf
from transformers import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
BertConfig,
DPRConfig,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
class snake_case__ :
"""simple docstring"""
def __init__( self , __lowercase , __lowercase=1_3 , __lowercase=7 , __lowercase=True , __lowercase=True , __lowercase=True , __lowercase=True , __lowercase=9_9 , __lowercase=3_2 , __lowercase=2 , __lowercase=4 , __lowercase=3_7 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=5_1_2 , __lowercase=1_6 , __lowercase=2 , __lowercase=0.0_2 , __lowercase=3 , __lowercase=4 , __lowercase=None , __lowercase=0 , ) -> Optional[Any]:
"""simple docstring"""
a__ : Optional[int] = parent
a__ : int = batch_size
a__ : Dict = seq_length
a__ : Optional[Any] = is_training
a__ : Optional[Any] = use_input_mask
a__ : str = use_token_type_ids
a__ : List[Any] = use_labels
a__ : int = vocab_size
a__ : List[Any] = hidden_size
a__ : int = num_hidden_layers
a__ : Optional[Any] = num_attention_heads
a__ : Tuple = intermediate_size
a__ : Dict = hidden_act
a__ : Any = hidden_dropout_prob
a__ : List[str] = attention_probs_dropout_prob
a__ : Optional[Any] = max_position_embeddings
a__ : List[Any] = type_vocab_size
a__ : Dict = type_sequence_label_size
a__ : List[Any] = initializer_range
a__ : Dict = num_labels
a__ : int = num_choices
a__ : Union[str, Any] = scope
a__ : str = projection_dim
def SCREAMING_SNAKE_CASE__( self ) -> List[str]:
"""simple docstring"""
a__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a__ : Optional[int] = None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
a__ : str = random_attention_mask([self.batch_size, self.seq_length] )
a__ : Tuple = None
if self.use_token_type_ids:
a__ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a__ : str = None
a__ : List[str] = None
a__ : int = None
if self.use_labels:
a__ : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a__ : Union[str, Any] = ids_tensor([self.batch_size] , self.num_choices )
a__ : int = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowercase , initializer_range=self.initializer_range , )
a__ : str = DPRConfig(projection_dim=self.projection_dim , **config.to_dict() )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> str:
"""simple docstring"""
a__ : Tuple = TFDPRContextEncoder(config=__lowercase )
a__ : Optional[int] = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase )
a__ : Dict = model(__lowercase , token_type_ids=__lowercase )
a__ : List[str] = model(__lowercase )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
"""simple docstring"""
a__ : List[str] = TFDPRQuestionEncoder(config=__lowercase )
a__ : Optional[Any] = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase )
a__ : str = model(__lowercase , token_type_ids=__lowercase )
a__ : Optional[int] = model(__lowercase )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Union[str, Any]:
"""simple docstring"""
a__ : int = TFDPRReader(config=__lowercase )
a__ : List[Any] = model(__lowercase , attention_mask=__lowercase )
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) )
self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,) )
def SCREAMING_SNAKE_CASE__( self ) -> str:
"""simple docstring"""
a__ : Union[str, Any] = self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) ,
) : Union[str, Any] = config_and_inputs
a__ : str = {"""input_ids""": input_ids}
return config, inputs_dict
@require_tf
class snake_case__ (A__ , A__ , unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase :Optional[int] = (
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
__lowerCAmelCase :Dict = {"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}
__lowerCAmelCase :List[Any] = False
__lowerCAmelCase :Optional[Any] = False
__lowerCAmelCase :Dict = False
__lowerCAmelCase :int = False
__lowerCAmelCase :Optional[Any] = False
def SCREAMING_SNAKE_CASE__( self ) -> List[str]:
"""simple docstring"""
a__ : Optional[Any] = TFDPRModelTester(self )
a__ : Dict = ConfigTester(self , config_class=__lowercase , hidden_size=3_7 )
def SCREAMING_SNAKE_CASE__( self ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__( self ) -> Dict:
"""simple docstring"""
a__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*__lowercase )
def SCREAMING_SNAKE_CASE__( self ) -> Dict:
"""simple docstring"""
a__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*__lowercase )
def SCREAMING_SNAKE_CASE__( self ) -> Any:
"""simple docstring"""
a__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*__lowercase )
@slow
def SCREAMING_SNAKE_CASE__( self ) -> int:
"""simple docstring"""
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : Optional[Any] = TFDPRContextEncoder.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : str = TFDPRContextEncoder.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : Optional[Any] = TFDPRQuestionEncoder.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : int = TFDPRReader.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
@require_tf
class snake_case__ (unittest.TestCase ):
"""simple docstring"""
@slow
def SCREAMING_SNAKE_CASE__( self ) -> Tuple:
"""simple docstring"""
a__ : Any = TFDPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base""" )
a__ : Union[str, Any] = tf.constant(
[[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]] ) # [CLS] hello, is my dog cute? [SEP]
a__ : Any = model(__lowercase )[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
a__ : Optional[int] = tf.constant(
[
[
0.0_3_2_3_6_2_5_3,
0.1_2_7_5_3_3_3_5,
0.1_6_8_1_8_5_0_9,
0.0_0_2_7_9_7_8_6,
0.3_8_9_6_9_3_3,
0.2_4_2_6_4_9_4_5,
0.2_1_7_8_9_7_1,
-0.0_2_3_3_5_2_2_7,
-0.0_8_4_8_1_9_5_9,
-0.1_4_3_2_4_1_1_7,
]
] )
self.assertTrue(numpy.allclose(output[:, :1_0].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 170 | 1 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ (__a : str = "AAPL" ):
"""simple docstring"""
_a : Tuple = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
_a : List[Any] = BeautifulSoup(requests.get(__a ).text , 'html.parser' )
_a : Any = 'My(6px) Pos(r) smartphone_Mt(6px)'
return soup.find('div' , class_=class_ ).find('span' ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(f'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')
| 5 |
'''simple docstring'''
__lowerCAmelCase = {
"""A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
"""H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": """.-..""", """M""": """--""", """N""": """-.""",
"""O""": """---""", """P""": """.--.""", """Q""": """--.-""", """R""": """.-.""", """S""": """...""", """T""": """-""", """U""": """..-""",
"""V""": """...-""", """W""": """.--""", """X""": """-..-""", """Y""": """-.--""", """Z""": """--..""", """1""": """.----""",
"""2""": """..---""", """3""": """...--""", """4""": """....-""", """5""": """.....""", """6""": """-....""", """7""": """--...""",
"""8""": """---..""", """9""": """----.""", """0""": """-----""", """&""": """.-...""", """@""": """.--.-.""",
""":""": """---...""", """,""": """--..--""", """.""": """.-.-.-""", """'""": """.----.""", """\"""": """.-..-.""",
"""?""": """..--..""", """/""": """-..-.""", """=""": """-...-""", """+""": """.-.-.""", """-""": """-....-""",
"""(""": """-.--.""", """)""": """-.--.-""", """!""": """-.-.--""", """ """: """/"""
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
__lowerCAmelCase = {value: key for key, value in MORSE_CODE_DICT.items()}
def UpperCAmelCase_ (__a : str ):
"""simple docstring"""
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def UpperCAmelCase_ (__a : str ):
"""simple docstring"""
return "".join(REVERSE_DICT[char] for char in message.split() )
def UpperCAmelCase_ ():
"""simple docstring"""
_a : List[str] = 'Morse code here!'
print(__a )
_a : Tuple = encrypt(__a )
print(__a )
_a : str = decrypt(__a )
print(__a )
if __name__ == "__main__":
main()
| 5 | 1 |
"""simple docstring"""
import numpy as np
def lowercase (snake_case__ : List[str] , snake_case__ : int , snake_case__ : List[Any] , snake_case__ : int , snake_case__ : Union[str, Any] ) -> Dict:
'''simple docstring'''
lowerCAmelCase = int(np.ceil((x_end - xa) / h ) )
lowerCAmelCase = np.zeros((n + 1,) )
lowerCAmelCase = ya
lowerCAmelCase = xa
for k in range(snake_case__ ):
lowerCAmelCase = f(snake_case__ , y[k] )
lowerCAmelCase = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
lowerCAmelCase = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
lowerCAmelCase = f(x + h , y[k] + h * ka )
lowerCAmelCase = y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 155 |
"""simple docstring"""
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
a = {
'169M': 1_2,
'430M': 2_4,
'1B5': 2_4,
'3B': 3_2,
'7B': 3_2,
'14B': 4_0,
}
a = {
'169M': 7_6_8,
'430M': 1_0_2_4,
'1B5': 2_0_4_8,
'3B': 2_5_6_0,
'7B': 4_0_9_6,
'14B': 5_1_2_0,
}
def lowercase (snake_case__ : str ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase = list(state_dict.keys() )
for name in state_dict_keys:
lowerCAmelCase = state_dict.pop(snake_case__ )
# emb -> embedding
if name.startswith("""emb.""" ):
lowerCAmelCase = name.replace("""emb.""" , """embeddings.""" )
# ln_0 -> pre_ln (only present at block 0)
if name.startswith("""blocks.0.ln0""" ):
lowerCAmelCase = name.replace("""blocks.0.ln0""" , """blocks.0.pre_ln""" )
# att -> attention
lowerCAmelCase = re.sub(R"""blocks\.(\d+)\.att""" , R"""blocks.\1.attention""" , snake_case__ )
# ffn -> feed_forward
lowerCAmelCase = re.sub(R"""blocks\.(\d+)\.ffn""" , R"""blocks.\1.feed_forward""" , snake_case__ )
# time_mix_k -> time_mix_key and reshape
if name.endswith(""".time_mix_k""" ):
lowerCAmelCase = name.replace(""".time_mix_k""" , """.time_mix_key""" )
# time_mix_v -> time_mix_value and reshape
if name.endswith(""".time_mix_v""" ):
lowerCAmelCase = name.replace(""".time_mix_v""" , """.time_mix_value""" )
# time_mix_r -> time_mix_key and reshape
if name.endswith(""".time_mix_r""" ):
lowerCAmelCase = name.replace(""".time_mix_r""" , """.time_mix_receptance""" )
if name != "head.weight":
lowerCAmelCase = """rwkv.""" + name
lowerCAmelCase = weight
return state_dict
def lowercase (snake_case__ : List[str] , snake_case__ : Optional[Any] , snake_case__ : Any , snake_case__ : int=None , snake_case__ : Any=None , snake_case__ : Optional[int]=False , snake_case__ : List[str]=None ) -> Optional[Any]:
'''simple docstring'''
if tokenizer_file is None:
print("""No `--tokenizer_file` provided, we will use the default tokenizer.""" )
lowerCAmelCase = 50_277
lowerCAmelCase = AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b""" )
else:
lowerCAmelCase = PreTrainedTokenizerFast(tokenizer_file=snake_case__ )
lowerCAmelCase = len(snake_case__ )
tokenizer.save_pretrained(snake_case__ )
# 2. Build the config
lowerCAmelCase = list(NUM_HIDDEN_LAYERS_MAPPING.keys() )
if size is None:
# Try to infer size from the checkpoint name
for candidate in possible_sizes:
if candidate in checkpoint_file:
lowerCAmelCase = candidate
break
if size is None:
raise ValueError("""Could not infer the size, please provide it with the `--size` argument.""" )
if size not in possible_sizes:
raise ValueError(f'''`size` should be one of {possible_sizes}, got {size}.''' )
lowerCAmelCase = RwkvConfig(
vocab_size=snake_case__ , num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] , hidden_size=HIDEN_SIZE_MAPPING[size] , )
config.save_pretrained(snake_case__ )
# 3. Download model file then convert state_dict
lowerCAmelCase = hf_hub_download(snake_case__ , snake_case__ )
lowerCAmelCase = torch.load(snake_case__ , map_location="""cpu""" )
lowerCAmelCase = convert_state_dict(snake_case__ )
# 4. Split in shards and save
lowerCAmelCase , lowerCAmelCase = shard_checkpoint(snake_case__ )
for shard_file, shard in shards.items():
torch.save(snake_case__ , os.path.join(snake_case__ , snake_case__ ) )
if index is not None:
lowerCAmelCase = os.path.join(snake_case__ , snake_case__ )
# Save the index as well
with open(snake_case__ , """w""" , encoding="""utf-8""" ) as f:
lowerCAmelCase = json.dumps(snake_case__ , indent=2 , sort_keys=snake_case__ ) + """\n"""
f.write(snake_case__ )
# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
print(
"""Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model.""" )
lowerCAmelCase = list(shards.keys() )
del state_dict
del shards
gc.collect()
for shard_file in shard_files:
lowerCAmelCase = torch.load(os.path.join(snake_case__ , snake_case__ ) )
torch.save({k: v.cpu().clone() for k, v in state_dict.items()} , os.path.join(snake_case__ , snake_case__ ) )
del state_dict
gc.collect()
if push_to_hub:
if model_name is None:
raise ValueError("""Please provide a `model_name` to push the model to the Hub.""" )
lowerCAmelCase = AutoModelForCausalLM.from_pretrained(snake_case__ )
model.push_to_hub(snake_case__ , max_shard_size="""2GB""" )
tokenizer.push_to_hub(snake_case__ )
if __name__ == "__main__":
a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--repo_id', default=None, type=str, required=True, help='Repo ID from which to pull the checkpoint.'
)
parser.add_argument(
'--checkpoint_file', default=None, type=str, required=True, help='Name of the checkpoint file in the repo.'
)
parser.add_argument(
'--output_dir', default=None, type=str, required=True, help='Where to save the converted model.'
)
parser.add_argument(
'--tokenizer_file',
default=None,
type=str,
help='Path to the tokenizer file to use (if not provided, only the model is converted).',
)
parser.add_argument(
'--size',
default=None,
type=str,
help='Size of the model. Will be inferred from the `checkpoint_file` if not passed.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Push to the Hub the converted model.',
)
parser.add_argument(
'--model_name',
default=None,
type=str,
help='Name of the pushed model on the Hub, including the username / organization.',
)
a = parser.parse_args()
convert_rmkv_checkpoint_to_hf_format(
args.repo_id,
args.checkpoint_file,
args.output_dir,
size=args.size,
tokenizer_file=args.tokenizer_file,
push_to_hub=args.push_to_hub,
model_name=args.model_name,
)
| 155 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {
"""configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["""AlbertTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["""AlbertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AlbertForMaskedLM""",
"""AlbertForMultipleChoice""",
"""AlbertForPreTraining""",
"""AlbertForQuestionAnswering""",
"""AlbertForSequenceClassification""",
"""AlbertForTokenClassification""",
"""AlbertModel""",
"""AlbertPreTrainedModel""",
"""load_tf_weights_in_albert""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFAlbertForMaskedLM""",
"""TFAlbertForMultipleChoice""",
"""TFAlbertForPreTraining""",
"""TFAlbertForQuestionAnswering""",
"""TFAlbertForSequenceClassification""",
"""TFAlbertForTokenClassification""",
"""TFAlbertMainLayer""",
"""TFAlbertModel""",
"""TFAlbertPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""FlaxAlbertForMaskedLM""",
"""FlaxAlbertForMultipleChoice""",
"""FlaxAlbertForPreTraining""",
"""FlaxAlbertForQuestionAnswering""",
"""FlaxAlbertForSequenceClassification""",
"""FlaxAlbertForTokenClassification""",
"""FlaxAlbertModel""",
"""FlaxAlbertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert import AlbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert_fast import AlbertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_albert import (
ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
AlbertPreTrainedModel,
load_tf_weights_in_albert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_albert import (
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAlbertForMaskedLM,
TFAlbertForMultipleChoice,
TFAlbertForPreTraining,
TFAlbertForQuestionAnswering,
TFAlbertForSequenceClassification,
TFAlbertForTokenClassification,
TFAlbertMainLayer,
TFAlbertModel,
TFAlbertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
FlaxAlbertPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 358 |
"""simple docstring"""
from __future__ import annotations
__UpperCAmelCase = 1.6021e-19 # units = C
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : float , ) -> tuple[str, float]:
'''simple docstring'''
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif conductivity < 0:
raise ValueError("""Conductivity cannot be negative""" )
elif electron_conc < 0:
raise ValueError("""Electron concentration cannot be negative""" )
elif mobility < 0:
raise ValueError("""mobility cannot be negative""" )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 | 0 |
'''simple docstring'''
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 _lowerCAmelCase ( __UpperCAmelCase ):
def _a (self ):
A_ : List[str] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowercase , """tf_padding""" ) )
self.parent.assertTrue(hasattr(lowercase , """depth_multiplier""" ) )
class _lowerCAmelCase :
def __init__(self , lowercase , lowercase=13 , lowercase=3 , lowercase=32 , lowercase=0.25 , lowercase=8 , lowercase=True , lowercase=1024 , lowercase=32 , lowercase="relu6" , lowercase=0.1 , lowercase=0.02 , lowercase=True , lowercase=True , lowercase=10 , lowercase=None , ):
A_ : Any = parent
A_ : int = batch_size
A_ : Any = num_channels
A_ : Tuple = image_size
A_ : List[str] = depth_multiplier
A_ : int = min_depth
A_ : Any = tf_padding
A_ : Dict = int(last_hidden_size * depth_multiplier )
A_ : Tuple = output_stride
A_ : List[str] = hidden_act
A_ : Tuple = classifier_dropout_prob
A_ : Tuple = use_labels
A_ : int = is_training
A_ : str = num_labels
A_ : int = initializer_range
A_ : int = scope
def _a (self ):
A_ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : List[str] = None
A_ : Tuple = None
if self.use_labels:
A_ : Dict = ids_tensor([self.batch_size] , self.num_labels )
A_ : Dict = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
A_ : int = self.get_config()
return config, pixel_values, labels, pixel_labels
def _a (self ):
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 , lowercase , lowercase , lowercase , lowercase ):
A_ : Optional[Any] = MobileNetVaModel(config=lowercase )
model.to(lowercase )
model.eval()
A_ : str = 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 , lowercase , lowercase , lowercase , lowercase ):
A_ : Any = self.num_labels
A_ : List[Any] = MobileNetVaForImageClassification(lowercase )
model.to(lowercase )
model.eval()
A_ : Union[str, Any] = model(lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a (self ):
A_ : List[Any] = self.prepare_config_and_inputs()
A_, A_, A_, A_ : str = config_and_inputs
A_ : List[str] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : int = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
__SCREAMING_SNAKE_CASE : Any = (
{'feature-extraction': MobileNetVaModel, 'image-classification': MobileNetVaForImageClassification}
if is_torch_available()
else {}
)
__SCREAMING_SNAKE_CASE : Dict = False
__SCREAMING_SNAKE_CASE : str = False
__SCREAMING_SNAKE_CASE : List[str] = False
__SCREAMING_SNAKE_CASE : Optional[int] = False
def _a (self ):
A_ : Union[str, Any] = MobileNetVaModelTester(self )
A_ : str = MobileNetVaConfigTester(self , config_class=lowercase , has_text_modality=lowercase )
def _a (self ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""MobileNetV1 does not use inputs_embeds""" )
def _a (self ):
pass
@unittest.skip(reason="""MobileNetV1 does not support input and output embeddings""" )
def _a (self ):
pass
@unittest.skip(reason="""MobileNetV1 does not output attentions""" )
def _a (self ):
pass
def _a (self ):
A_, A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : List[Any] = model_class(lowercase )
A_ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : str = [*signature.parameters.keys()]
A_ : Tuple = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowercase )
def _a (self ):
A_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def _a (self ):
def check_hidden_states_output(lowercase , lowercase , lowercase ):
A_ : Union[str, Any] = model_class(lowercase )
model.to(lowercase )
model.eval()
with torch.no_grad():
A_ : Tuple = model(**self._prepare_for_class(lowercase , lowercase ) )
A_ : List[Any] = outputs.hidden_states
A_ : Union[str, Any] = 26
self.assertEqual(len(lowercase ) , lowercase )
A_, A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Optional[int] = True
check_hidden_states_output(lowercase , lowercase , lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A_ : List[Any] = True
check_hidden_states_output(lowercase , lowercase , lowercase )
def _a (self ):
A_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase )
@slow
def _a (self ):
for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : str = MobileNetVaModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
def a ( ):
'''simple docstring'''
A_ : List[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class _lowerCAmelCase ( unittest.TestCase ):
@cached_property
def _a (self ):
return (
MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224""" ) if is_vision_available() else None
)
@slow
def _a (self ):
A_ : int = MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224""" ).to(lowercase )
A_ : Optional[int] = self.default_image_processor
A_ : int = prepare_img()
A_ : Union[str, Any] = image_processor(images=lowercase , return_tensors="""pt""" ).to(lowercase )
# forward pass
with torch.no_grad():
A_ : Any = model(**lowercase )
# verify the logits
A_ : Optional[Any] = torch.Size((1, 1001) )
self.assertEqual(outputs.logits.shape , lowercase )
A_ : List[str] = torch.tensor([-4.17_39, -1.12_33, 3.12_05] ).to(lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase , atol=1E-4 ) ) | 206 |
'''simple docstring'''
import re
def a ( lowerCamelCase__ ):
'''simple docstring'''
return [char.split() for char in re.split(r"""[^ a-z A-Z 0-9 \s]""" , str_ )]
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : Optional[int] = split_input(str_ )
return "".join(
["""""".join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
try:
A_ : List[Any] = split_input(lowerCamelCase__ )
if upper:
A_ : Tuple = """""".join(
[
separator.join([char.upper() for char in sub_str] )
for sub_str in string_split
] )
else:
A_ : Optional[int] = """""".join(
[
separator.join([char.lower() for char in sub_str] )
for sub_str in string_split
] )
return res_str
except IndexError:
return "not valid string"
def a ( lowerCamelCase__ ):
'''simple docstring'''
return to_simple_case(lowerCamelCase__ )
def a ( lowerCamelCase__ ):
'''simple docstring'''
try:
A_ : Tuple = to_simple_case(lowerCamelCase__ )
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
return to_complex_case(lowerCamelCase__ , lowerCamelCase__ , """_""" )
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
return to_complex_case(lowerCamelCase__ , lowerCamelCase__ , """-""" )
if __name__ == "__main__":
__import__('''doctest''').testmod() | 206 | 1 |
"""simple docstring"""
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Optional[Any], lowerCamelCase : List[Any], lowerCamelCase : str, lowerCamelCase : Tuple, lowerCamelCase : str, lowerCamelCase : str, lowerCamelCase : Union[str, Any]=0.2, lowerCamelCase : Union[str, Any]=0.2 )-> List[Any]:
lowerCamelCase__ : str =bp_numa
lowerCamelCase__ : Union[str, Any] =bp_numa
lowerCamelCase__ : Optional[int] =bp_numa
lowerCamelCase__ : List[str] =conva_get[:2]
lowerCamelCase__ : Dict =conva_get[2]
lowerCamelCase__ : Tuple =size_pa
lowerCamelCase__ : Dict =rate_w
lowerCamelCase__ : List[Any] =rate_t
lowerCamelCase__ : List[str] =[
np.mat(-1 * np.random.rand(self.conva[0], self.conva[0] ) + 0.5 )
for i in range(self.conva[1] )
]
lowerCamelCase__ : Optional[int] =np.mat(-1 * np.random.rand(self.num_bpa, self.num_bpa ) + 0.5 )
lowerCamelCase__ : Union[str, Any] =np.mat(-1 * np.random.rand(self.num_bpa, self.num_bpa ) + 0.5 )
lowerCamelCase__ : Union[str, Any] =-2 * np.random.rand(self.conva[1] ) + 1
lowerCamelCase__ : int =-2 * np.random.rand(self.num_bpa ) + 1
lowerCamelCase__ : Tuple =-2 * np.random.rand(self.num_bpa ) + 1
def snake_case ( self : Tuple, lowerCamelCase : Union[str, Any] )-> str:
# save model dict with pickle
lowerCamelCase__ : Any ={
'''num_bp1''': self.num_bpa,
'''num_bp2''': self.num_bpa,
'''num_bp3''': self.num_bpa,
'''conv1''': self.conva,
'''step_conv1''': self.step_conva,
'''size_pooling1''': self.size_poolinga,
'''rate_weight''': self.rate_weight,
'''rate_thre''': self.rate_thre,
'''w_conv1''': self.w_conva,
'''wkj''': self.wkj,
'''vji''': self.vji,
'''thre_conv1''': self.thre_conva,
'''thre_bp2''': self.thre_bpa,
'''thre_bp3''': self.thre_bpa,
}
with open(lowerCamelCase, '''wb''' ) as f:
pickle.dump(lowerCamelCase, lowerCamelCase )
print(F'''Model saved： {save_path}''' )
@classmethod
def snake_case ( cls : Union[str, Any], lowerCamelCase : Optional[Any] )-> Union[str, Any]:
# read saved model
with open(lowerCamelCase, '''rb''' ) as f:
lowerCamelCase__ : List[Any] =pickle.load(lowerCamelCase ) # noqa: S301
lowerCamelCase__ : Any =model_dic.get('''conv1''' )
conv_get.append(model_dic.get('''step_conv1''' ) )
lowerCamelCase__ : Optional[Any] =model_dic.get('''size_pooling1''' )
lowerCamelCase__ : Optional[Any] =model_dic.get('''num_bp1''' )
lowerCamelCase__ : List[str] =model_dic.get('''num_bp2''' )
lowerCamelCase__ : Any =model_dic.get('''num_bp3''' )
lowerCamelCase__ : Any =model_dic.get('''rate_weight''' )
lowerCamelCase__ : List[Any] =model_dic.get('''rate_thre''' )
# create model instance
lowerCamelCase__ : Optional[Any] =CNN(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase )
# modify model parameter
lowerCamelCase__ : Tuple =model_dic.get('''w_conv1''' )
lowerCamelCase__ : Dict =model_dic.get('''wkj''' )
lowerCamelCase__ : List[str] =model_dic.get('''vji''' )
lowerCamelCase__ : str =model_dic.get('''thre_conv1''' )
lowerCamelCase__ : List[Any] =model_dic.get('''thre_bp2''' )
lowerCamelCase__ : Dict =model_dic.get('''thre_bp3''' )
return conv_ins
def snake_case ( self : Union[str, Any], lowerCamelCase : Any )-> List[str]:
return 1 / (1 + np.exp(-1 * x ))
def snake_case ( self : Tuple, lowerCamelCase : List[str] )-> List[Any]:
return round(lowerCamelCase, 3 )
def snake_case ( self : Dict, lowerCamelCase : Tuple, lowerCamelCase : Optional[Any], lowerCamelCase : Any, lowerCamelCase : List[Any], lowerCamelCase : Union[str, Any] )-> List[str]:
# convolution process
lowerCamelCase__ : List[str] =convs[0]
lowerCamelCase__ : int =convs[1]
lowerCamelCase__ : List[str] =np.shape(lowerCamelCase )[0]
# get the data slice of original image data, data_focus
lowerCamelCase__ : List[Any] =[]
for i_focus in range(0, size_data - size_conv + 1, lowerCamelCase ):
for j_focus in range(0, size_data - size_conv + 1, lowerCamelCase ):
lowerCamelCase__ : Union[str, Any] =data[
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(lowerCamelCase )
# calculate the feature map of every single kernel, and saved as list of matrix
lowerCamelCase__ : str =[]
lowerCamelCase__ : Tuple =int((size_data - size_conv) / conv_step + 1 )
for i_map in range(lowerCamelCase ):
lowerCamelCase__ : Tuple =[]
for i_focus in range(len(lowerCamelCase ) ):
lowerCamelCase__ : Dict =(
np.sum(np.multiply(data_focus[i_focus], w_convs[i_map] ) )
- thre_convs[i_map]
)
featuremap.append(self.sig(lowerCamelCase ) )
lowerCamelCase__ : int =np.asmatrix(lowerCamelCase ).reshape(
lowerCamelCase, lowerCamelCase )
data_featuremap.append(lowerCamelCase )
# expanding the data slice to One dimenssion
lowerCamelCase__ : Optional[int] =[]
for each_focus in data_focus:
focusa_list.extend(self.Expand_Mat(lowerCamelCase ) )
lowerCamelCase__ : Union[str, Any] =np.asarray(lowerCamelCase )
return focus_list, data_featuremap
def snake_case ( self : Union[str, Any], lowerCamelCase : List[Any], lowerCamelCase : Optional[Any], lowerCamelCase : Optional[Any]="average_pool" )-> List[Any]:
# pooling process
lowerCamelCase__ : List[Any] =len(featuremaps[0] )
lowerCamelCase__ : Dict =int(size_map / size_pooling )
lowerCamelCase__ : int =[]
for i_map in range(len(lowerCamelCase ) ):
lowerCamelCase__ : List[str] =featuremaps[i_map]
lowerCamelCase__ : int =[]
for i_focus in range(0, lowerCamelCase, lowerCamelCase ):
for j_focus in range(0, lowerCamelCase, lowerCamelCase ):
lowerCamelCase__ : int =feature_map[
i_focus : i_focus + size_pooling,
j_focus : j_focus + size_pooling,
]
if pooling_type == "average_pool":
# average pooling
map_pooled.append(np.average(lowerCamelCase ) )
elif pooling_type == "max_pooling":
# max pooling
map_pooled.append(np.max(lowerCamelCase ) )
lowerCamelCase__ : Optional[int] =np.asmatrix(lowerCamelCase ).reshape(lowerCamelCase, lowerCamelCase )
featuremap_pooled.append(lowerCamelCase )
return featuremap_pooled
def snake_case ( self : str, lowerCamelCase : Optional[Any] )-> Optional[int]:
# expanding three dimension data to one dimension list
lowerCamelCase__ : List[str] =[]
for i in range(len(lowerCamelCase ) ):
lowerCamelCase__ : List[str] =np.shape(data[i] )
lowerCamelCase__ : int =data[i].reshape(1, shapes[0] * shapes[1] )
lowerCamelCase__ : Optional[Any] =data_listed.getA().tolist()[0]
data_expanded.extend(lowerCamelCase )
lowerCamelCase__ : Optional[int] =np.asarray(lowerCamelCase )
return data_expanded
def snake_case ( self : List[Any], lowerCamelCase : int )-> Union[str, Any]:
# expanding matrix to one dimension list
lowerCamelCase__ : Dict =np.asarray(lowerCamelCase )
lowerCamelCase__ : Tuple =np.shape(lowerCamelCase )
lowerCamelCase__ : Dict =data_mat.reshape(1, shapes[0] * shapes[1] )
return data_expanded
def snake_case ( self : List[Any], lowerCamelCase : List[Any], lowerCamelCase : List[str], lowerCamelCase : Tuple, lowerCamelCase : List[str], lowerCamelCase : Optional[Any] )-> Union[str, Any]:
lowerCamelCase__ : Optional[int] =[]
lowerCamelCase__ : Dict =0
for i_map in range(lowerCamelCase ):
lowerCamelCase__ : Union[str, Any] =np.ones((size_map, size_map) )
for i in range(0, lowerCamelCase, lowerCamelCase ):
for j in range(0, lowerCamelCase, lowerCamelCase ):
lowerCamelCase__ : List[Any] =pd_pool[
i_pool
]
lowerCamelCase__ : List[str] =i_pool + 1
lowerCamelCase__ : str =np.multiply(
lowerCamelCase, np.multiply(out_map[i_map], (1 - out_map[i_map]) ) )
pd_all.append(lowerCamelCase )
return pd_all
def snake_case ( self : List[str], lowerCamelCase : Optional[Any], lowerCamelCase : Any, lowerCamelCase : Dict, lowerCamelCase : List[str], lowerCamelCase : Dict, lowerCamelCase : List[Any]=bool )-> Any:
# model traning
print('''----------------------Start Training-------------------------''' )
print((''' - - Shape: Train_Data ''', np.shape(lowerCamelCase )) )
print((''' - - Shape: Teach_Data ''', np.shape(lowerCamelCase )) )
lowerCamelCase__ : List[Any] =0
lowerCamelCase__ : Optional[Any] =[]
lowerCamelCase__ : Dict =1_0000
while rp < n_repeat and mse >= error_accuracy:
lowerCamelCase__ : Tuple =0
print(F'''-------------Learning Time {rp}--------------''' )
for p in range(len(lowerCamelCase ) ):
# print('------------Learning Image: %d--------------'%p)
lowerCamelCase__ : int =np.asmatrix(datas_train[p] )
lowerCamelCase__ : Optional[Any] =np.asarray(datas_teach[p] )
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] =self.convolute(
lowerCamelCase, self.conva, self.w_conva, self.thre_conva, conv_step=self.step_conva, )
lowerCamelCase__ : Optional[int] =self.pooling(lowerCamelCase, self.size_poolinga )
lowerCamelCase__ : Optional[Any] =np.shape(lowerCamelCase )
lowerCamelCase__ : Optional[int] =self._expand(lowerCamelCase )
lowerCamelCase__ : Optional[int] =data_bp_input
lowerCamelCase__ : List[str] =np.dot(lowerCamelCase, self.vji.T ) - self.thre_bpa
lowerCamelCase__ : str =self.sig(lowerCamelCase )
lowerCamelCase__ : int =np.dot(lowerCamelCase, self.wkj.T ) - self.thre_bpa
lowerCamelCase__ : List[str] =self.sig(lowerCamelCase )
# --------------Model Leaning ------------------------
# calculate error and gradient---------------
lowerCamelCase__ : Optional[int] =np.multiply(
(data_teach - bp_outa), np.multiply(lowerCamelCase, (1 - bp_outa) ) )
lowerCamelCase__ : Optional[Any] =np.multiply(
np.dot(lowerCamelCase, self.wkj ), np.multiply(lowerCamelCase, (1 - bp_outa) ) )
lowerCamelCase__ : int =np.dot(lowerCamelCase, self.vji )
lowerCamelCase__ : int =pd_i_all / (self.size_poolinga * self.size_poolinga)
lowerCamelCase__ : Optional[int] =pd_conva_pooled.T.getA().tolist()
lowerCamelCase__ : int =self._calculate_gradient_from_pool(
lowerCamelCase, lowerCamelCase, shape_featuremapa[0], shape_featuremapa[1], self.size_poolinga, )
# weight and threshold learning process---------
# convolution layer
for k_conv in range(self.conva[1] ):
lowerCamelCase__ : Optional[Any] =self._expand_mat(pd_conva_all[k_conv] )
lowerCamelCase__ : List[Any] =self.rate_weight * np.dot(lowerCamelCase, lowerCamelCase )
lowerCamelCase__ : Dict =self.w_conva[k_conv] + delta_w.reshape(
(self.conva[0], self.conva[0]) )
lowerCamelCase__ : Optional[int] =(
self.thre_conva[k_conv]
- np.sum(pd_conva_all[k_conv] ) * self.rate_thre
)
# all connected layer
lowerCamelCase__ : int =self.wkj + pd_k_all.T * bp_outa * self.rate_weight
lowerCamelCase__ : Dict =self.vji + pd_j_all.T * bp_outa * self.rate_weight
lowerCamelCase__ : List[str] =self.thre_bpa - pd_k_all * self.rate_thre
lowerCamelCase__ : int =self.thre_bpa - pd_j_all * self.rate_thre
# calculate the sum error of all single image
lowerCamelCase__ : Tuple =np.sum(abs(data_teach - bp_outa ) )
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
lowerCamelCase__ : Optional[int] =rp + 1
lowerCamelCase__ : Tuple =error_count / patterns
all_mse.append(lowerCamelCase )
def draw_error():
lowerCamelCase__ : List[str] =[error_accuracy for i in range(int(n_repeat * 1.2 ) )]
plt.plot(lowerCamelCase, '''+-''' )
plt.plot(lowerCamelCase, '''r--''' )
plt.xlabel('''Learning Times''' )
plt.ylabel('''All_mse''' )
plt.grid(lowerCamelCase, alpha=0.5 )
plt.show()
print('''------------------Training Complished---------------------''' )
print((''' - - Training epoch: ''', rp, F''' - - Mse: {mse:.6f}''') )
if draw_e:
draw_error()
return mse
def snake_case ( self : Tuple, lowerCamelCase : Any )-> Any:
# model predict
lowerCamelCase__ : int =[]
print('''-------------------Start Testing-------------------------''' )
print((''' - - Shape: Test_Data ''', np.shape(lowerCamelCase )) )
for p in range(len(lowerCamelCase ) ):
lowerCamelCase__ : Any =np.asmatrix(datas_test[p] )
lowerCamelCase__ , lowerCamelCase__ : int =self.convolute(
lowerCamelCase, self.conva, self.w_conva, self.thre_conva, conv_step=self.step_conva, )
lowerCamelCase__ : List[Any] =self.pooling(lowerCamelCase, self.size_poolinga )
lowerCamelCase__ : Tuple =self._expand(lowerCamelCase )
lowerCamelCase__ : Any =data_bp_input
lowerCamelCase__ : Tuple =bp_outa * self.vji.T - self.thre_bpa
lowerCamelCase__ : str =self.sig(lowerCamelCase )
lowerCamelCase__ : Union[str, Any] =bp_outa * self.wkj.T - self.thre_bpa
lowerCamelCase__ : Any =self.sig(lowerCamelCase )
produce_out.extend(bp_outa.getA().tolist() )
lowerCamelCase__ : str =[list(map(self.do_round, lowerCamelCase ) ) for each in produce_out]
return np.asarray(lowerCamelCase )
def snake_case ( self : str, lowerCamelCase : List[str] )-> List[Any]:
# return the data of image after convoluting process so we can check it out
lowerCamelCase__ : Tuple =np.asmatrix(lowerCamelCase )
lowerCamelCase__ , lowerCamelCase__ : List[str] =self.convolute(
lowerCamelCase, self.conva, self.w_conva, self.thre_conva, conv_step=self.step_conva, )
lowerCamelCase__ : int =self.pooling(lowerCamelCase, self.size_poolinga )
return data_conveda, data_pooleda
if __name__ == "__main__":
pass
| 272 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
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 (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Dict, lowerCamelCase : str, lowerCamelCase : Dict=13, lowerCamelCase : Optional[Any]=7, lowerCamelCase : List[Any]=True, lowerCamelCase : Dict=True, lowerCamelCase : List[Any]=True, lowerCamelCase : Optional[int]=True, lowerCamelCase : int=99, lowerCamelCase : Optional[int]=[1, 1, 2], lowerCamelCase : str=1, lowerCamelCase : List[Any]=32, lowerCamelCase : str=4, lowerCamelCase : Dict=8, lowerCamelCase : List[Any]=37, lowerCamelCase : Optional[int]="gelu_new", lowerCamelCase : Union[str, Any]=0.1, lowerCamelCase : List[Any]=0.1, lowerCamelCase : List[Any]=0.0, lowerCamelCase : Dict=512, lowerCamelCase : Dict=3, lowerCamelCase : str=0.02, lowerCamelCase : str=3, lowerCamelCase : Optional[int]=4, lowerCamelCase : List[str]=None, lowerCamelCase : Tuple=False, )-> Union[str, Any]:
lowerCamelCase__ : int =parent
lowerCamelCase__ : Dict =batch_size
lowerCamelCase__ : Dict =seq_length
lowerCamelCase__ : Any =is_training
lowerCamelCase__ : int =use_input_mask
lowerCamelCase__ : Tuple =use_token_type_ids
lowerCamelCase__ : int =use_labels
lowerCamelCase__ : Tuple =vocab_size
lowerCamelCase__ : Union[str, Any] =block_sizes
lowerCamelCase__ : Any =num_decoder_layers
lowerCamelCase__ : Optional[Any] =d_model
lowerCamelCase__ : List[str] =n_head
lowerCamelCase__ : List[Any] =d_head
lowerCamelCase__ : Dict =d_inner
lowerCamelCase__ : Dict =hidden_act
lowerCamelCase__ : List[str] =hidden_dropout
lowerCamelCase__ : Union[str, Any] =attention_dropout
lowerCamelCase__ : Union[str, Any] =activation_dropout
lowerCamelCase__ : Dict =max_position_embeddings
lowerCamelCase__ : Dict =type_vocab_size
lowerCamelCase__ : Union[str, Any] =2
lowerCamelCase__ : Optional[int] =num_labels
lowerCamelCase__ : List[str] =num_choices
lowerCamelCase__ : Tuple =scope
lowerCamelCase__ : Optional[int] =initializer_std
# Used in the tests to check the size of the first attention layer
lowerCamelCase__ : List[str] =n_head
# Used in the tests to check the size of the first hidden state
lowerCamelCase__ : Tuple =self.d_model
# Used in the tests to check the number of output hidden states/attentions
lowerCamelCase__ : List[Any] =sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
lowerCamelCase__ : Union[str, Any] =self.num_hidden_layers + 2
def snake_case ( self : int )-> List[Any]:
lowerCamelCase__ : Dict =ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCamelCase__ : Union[str, Any] =None
if self.use_input_mask:
lowerCamelCase__ : Any =random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase__ : int =None
if self.use_token_type_ids:
lowerCamelCase__ : Union[str, Any] =ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
lowerCamelCase__ : List[str] =None
lowerCamelCase__ : Union[str, Any] =None
lowerCamelCase__ : List[str] =None
if self.use_labels:
lowerCamelCase__ : List[Any] =ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCamelCase__ : Optional[Any] =ids_tensor([self.batch_size, self.seq_length], self.num_labels )
lowerCamelCase__ : Union[str, Any] =ids_tensor([self.batch_size], self.num_choices )
lowerCamelCase__ : Optional[int] =FunnelConfig(
vocab_size=self.vocab_size, block_sizes=self.block_sizes, num_decoder_layers=self.num_decoder_layers, d_model=self.d_model, n_head=self.n_head, d_head=self.d_head, d_inner=self.d_inner, hidden_act=self.hidden_act, hidden_dropout=self.hidden_dropout, attention_dropout=self.attention_dropout, activation_dropout=self.activation_dropout, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_std=self.initializer_std, )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def snake_case ( self : List[Any], lowerCamelCase : Optional[Any], lowerCamelCase : Optional[int], lowerCamelCase : int, lowerCamelCase : int, lowerCamelCase : str, lowerCamelCase : List[str], lowerCamelCase : Dict, )-> Union[str, Any]:
lowerCamelCase__ : Tuple =TFFunnelModel(config=lowerCamelCase )
lowerCamelCase__ : Dict ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : Tuple =model(lowerCamelCase )
lowerCamelCase__ : Optional[Any] =[input_ids, input_mask]
lowerCamelCase__ : List[Any] =model(lowerCamelCase )
lowerCamelCase__ : Any =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model) )
lowerCamelCase__ : int =False
lowerCamelCase__ : Any =TFFunnelModel(config=lowerCamelCase )
lowerCamelCase__ : str =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model) )
lowerCamelCase__ : Dict =False
lowerCamelCase__ : Optional[int] =TFFunnelModel(config=lowerCamelCase )
lowerCamelCase__ : Tuple =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model) )
def snake_case ( self : Tuple, lowerCamelCase : List[Any], lowerCamelCase : Union[str, Any], lowerCamelCase : Union[str, Any], lowerCamelCase : str, lowerCamelCase : Optional[Any], lowerCamelCase : Tuple, lowerCamelCase : Dict, )-> Optional[Any]:
lowerCamelCase__ : List[str] =TFFunnelBaseModel(config=lowerCamelCase )
lowerCamelCase__ : str ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : Union[str, Any] =model(lowerCamelCase )
lowerCamelCase__ : Tuple =[input_ids, input_mask]
lowerCamelCase__ : Any =model(lowerCamelCase )
lowerCamelCase__ : Optional[Any] =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model) )
lowerCamelCase__ : List[Any] =False
lowerCamelCase__ : Dict =TFFunnelBaseModel(config=lowerCamelCase )
lowerCamelCase__ : int =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 3, self.d_model) )
lowerCamelCase__ : Union[str, Any] =False
lowerCamelCase__ : Optional[Any] =TFFunnelBaseModel(config=lowerCamelCase )
lowerCamelCase__ : str =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model) )
def snake_case ( self : str, lowerCamelCase : Dict, lowerCamelCase : Dict, lowerCamelCase : List[Any], lowerCamelCase : Dict, lowerCamelCase : List[Any], lowerCamelCase : Union[str, Any], lowerCamelCase : List[Any], )-> List[Any]:
lowerCamelCase__ : List[str] =TFFunnelForPreTraining(config=lowerCamelCase )
lowerCamelCase__ : List[Any] ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : Union[str, Any] =model(lowerCamelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length) )
def snake_case ( self : str, lowerCamelCase : Tuple, lowerCamelCase : str, lowerCamelCase : List[Any], lowerCamelCase : List[Any], lowerCamelCase : str, lowerCamelCase : Tuple, lowerCamelCase : int, )-> List[Any]:
lowerCamelCase__ : Union[str, Any] =TFFunnelForMaskedLM(config=lowerCamelCase )
lowerCamelCase__ : Tuple ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : List[Any] =model(lowerCamelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case ( self : Optional[int], lowerCamelCase : Tuple, lowerCamelCase : Any, lowerCamelCase : List[str], lowerCamelCase : Union[str, Any], lowerCamelCase : str, lowerCamelCase : Optional[int], lowerCamelCase : Dict, )-> Union[str, Any]:
lowerCamelCase__ : Optional[Any] =self.num_labels
lowerCamelCase__ : Tuple =TFFunnelForSequenceClassification(config=lowerCamelCase )
lowerCamelCase__ : Tuple ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : List[str] =model(lowerCamelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def snake_case ( self : Union[str, Any], lowerCamelCase : str, lowerCamelCase : Dict, lowerCamelCase : Dict, lowerCamelCase : Dict, lowerCamelCase : Optional[Any], lowerCamelCase : int, lowerCamelCase : Tuple, )-> int:
lowerCamelCase__ : int =self.num_choices
lowerCamelCase__ : List[Any] =TFFunnelForMultipleChoice(config=lowerCamelCase )
lowerCamelCase__ : int =tf.tile(tf.expand_dims(lowerCamelCase, 1 ), (1, self.num_choices, 1) )
lowerCamelCase__ : Union[str, Any] =tf.tile(tf.expand_dims(lowerCamelCase, 1 ), (1, self.num_choices, 1) )
lowerCamelCase__ : Optional[Any] =tf.tile(tf.expand_dims(lowerCamelCase, 1 ), (1, self.num_choices, 1) )
lowerCamelCase__ : Union[str, Any] ={
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
lowerCamelCase__ : str =model(lowerCamelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices) )
def snake_case ( self : str, lowerCamelCase : Dict, lowerCamelCase : Optional[Any], lowerCamelCase : Any, lowerCamelCase : str, lowerCamelCase : Union[str, Any], lowerCamelCase : Union[str, Any], lowerCamelCase : Dict, )-> Optional[int]:
lowerCamelCase__ : Optional[Any] =self.num_labels
lowerCamelCase__ : Optional[Any] =TFFunnelForTokenClassification(config=lowerCamelCase )
lowerCamelCase__ : Tuple ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : Union[str, Any] =model(lowerCamelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def snake_case ( self : Optional[int], lowerCamelCase : Dict, lowerCamelCase : str, lowerCamelCase : Union[str, Any], lowerCamelCase : Union[str, Any], lowerCamelCase : Union[str, Any], lowerCamelCase : str, lowerCamelCase : Optional[int], )-> Tuple:
lowerCamelCase__ : Tuple =TFFunnelForQuestionAnswering(config=lowerCamelCase )
lowerCamelCase__ : Union[str, Any] ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : Optional[int] =model(lowerCamelCase )
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 snake_case ( self : int )-> List[str]:
lowerCamelCase__ : List[Any] =self.prepare_config_and_inputs()
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) : Tuple =config_and_inputs
lowerCamelCase__ : str ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_a = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
_a = (
{
'feature-extraction': (TFFunnelBaseModel, TFFunnelModel),
'fill-mask': TFFunnelForMaskedLM,
'question-answering': TFFunnelForQuestionAnswering,
'text-classification': TFFunnelForSequenceClassification,
'token-classification': TFFunnelForTokenClassification,
'zero-shot': TFFunnelForSequenceClassification,
}
if is_tf_available()
else {}
)
_a = False
_a = False
def snake_case ( self : str )-> Tuple:
lowerCamelCase__ : Any =TFFunnelModelTester(self )
lowerCamelCase__ : Any =ConfigTester(self, config_class=lowerCamelCase )
def snake_case ( self : List[str] )-> Tuple:
self.config_tester.run_common_tests()
def snake_case ( self : str )-> List[Any]:
lowerCamelCase__ : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase )
def snake_case ( self : str )-> Dict:
lowerCamelCase__ : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowerCamelCase )
def snake_case ( self : int )-> List[Any]:
lowerCamelCase__ : Tuple =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase )
def snake_case ( self : Dict )-> Any:
lowerCamelCase__ : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase )
def snake_case ( self : Tuple )-> Optional[Any]:
lowerCamelCase__ : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase )
@require_tf
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_a = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
_a = False
_a = False
def snake_case ( self : int )-> Tuple:
lowerCamelCase__ : Union[str, Any] =TFFunnelModelTester(self, base=lowerCamelCase )
lowerCamelCase__ : Tuple =ConfigTester(self, config_class=lowerCamelCase )
def snake_case ( self : Any )-> Any:
self.config_tester.run_common_tests()
def snake_case ( self : Optional[Any] )-> Optional[Any]:
lowerCamelCase__ : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*lowerCamelCase )
def snake_case ( self : Union[str, Any] )-> int:
lowerCamelCase__ : Tuple =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase )
def snake_case ( self : List[str] )-> Optional[int]:
lowerCamelCase__ : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase )
| 272 | 1 |
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer
A__ : Optional[Any] = logging.get_logger(__name__)
A__ : Optional[Any] = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
A__ : Union[str, Any] = {
'vocab_file': {
'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
},
'merges_file': {
'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
},
'tokenizer_file': {
'Salesforce/codegen-350M-mono': (
'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'
),
},
}
A__ : Tuple = {
'Salesforce/codegen-350M-mono': 2_048,
}
class lowercase__ ( __a ):
_UpperCAmelCase :int = VOCAB_FILES_NAMES
_UpperCAmelCase :Tuple = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase :Dict = ["input_ids", "attention_mask"]
_UpperCAmelCase :Union[str, Any] = CodeGenTokenizer
def __init__( self : Union[str, Any] , snake_case__ : List[Any]=None , snake_case__ : str=None , snake_case__ : str=None , snake_case__ : Dict="<|endoftext|>" , snake_case__ : Tuple="<|endoftext|>" , snake_case__ : str="<|endoftext|>" , snake_case__ : List[Any]=False , **snake_case__ : List[str] , ):
super().__init__(
a_ , a_ , tokenizer_file=a_ , unk_token=a_ , bos_token=a_ , eos_token=a_ , add_prefix_space=a_ , **a_ , )
if kwargs.pop("add_bos_token" , a_ ):
lowerCamelCase_ : str =kwargs.pop("name_or_path" , "" )
raise ValueError(
"Currenty GPT2's fast tokenizer does NOT support adding a BOS token."
"Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"
F"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
F"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
"This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."
" so that the fast tokenizer works correctly." )
lowerCamelCase_ : Tuple =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , a_ ) != add_prefix_space:
lowerCamelCase_ : Dict =getattr(a_ , pre_tok_state.pop("type" ) )
lowerCamelCase_ : Dict =add_prefix_space
lowerCamelCase_ : str =pre_tok_class(**a_ )
lowerCamelCase_ : List[Any] =add_prefix_space
def UpperCAmelCase__ ( self : Any , *snake_case__ : Any , **snake_case__ : int ):
lowerCamelCase_ : Optional[int] =kwargs.get("is_split_into_words" , a_ )
assert self.add_prefix_space or not is_split_into_words, (
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*a_ , **a_ )
def UpperCAmelCase__ ( self : Optional[Any] , *snake_case__ : Any , **snake_case__ : List[str] ):
lowerCamelCase_ : Dict =kwargs.get("is_split_into_words" , a_ )
assert self.add_prefix_space or not is_split_into_words, (
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._encode_plus(*a_ , **a_ )
def UpperCAmelCase__ ( self : Optional[int] , snake_case__ : str , snake_case__ : Optional[str] = None ):
lowerCamelCase_ : List[Any] =self._tokenizer.model.save(a_ , name=a_ )
return tuple(a_ )
def UpperCAmelCase__ ( self : str , snake_case__ : Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"] , snake_case__ : bool = False , snake_case__ : bool = None , snake_case__ : Optional[List[str]] = None , **snake_case__ : List[str] , ):
lowerCamelCase_ : Any =super().decode(
token_ids=a_ , skip_special_tokens=a_ , clean_up_tokenization_spaces=a_ , **a_ , )
if truncate_before_pattern is not None and len(a_ ) > 0:
lowerCamelCase_ : List[str] =self.truncate(a_ , a_ )
return decoded_text
def UpperCAmelCase__ ( self : Dict , snake_case__ : Tuple , snake_case__ : Optional[Any] ):
def find_re(snake_case__ : Dict , snake_case__ : str , snake_case__ : Union[str, Any] ):
lowerCamelCase_ : Any =pattern.search(a_ , a_ )
return m.start() if m else -1
lowerCamelCase_ : Tuple =[re.compile(a_ , re.MULTILINE ) for pattern in truncate_before_pattern]
lowerCamelCase_ : List[Any] =list(re.finditer("^print" , a_ , re.MULTILINE ) )
if len(a_ ) > 1:
lowerCamelCase_ : int =completion[: prints[1].start()]
lowerCamelCase_ : List[str] =list(re.finditer("^def" , a_ , re.MULTILINE ) )
if len(a_ ) > 1:
lowerCamelCase_ : List[Any] =completion[: defs[1].start()]
lowerCamelCase_ : int =0
lowerCamelCase_ : List[Any] =[
pos for pos in [find_re(a_ , a_ , a_ ) for terminal in terminals] if pos != -1
]
if len(a_ ) > 0:
return completion[: min(a_ )]
else:
return completion
| 144 |
"""simple docstring"""
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
A_ = re.compile(r'''\s+''')
def UpperCAmelCase__ (snake_case__ : Optional[int] ):
"""simple docstring"""
return {"hash": hashlib.mda(re.sub(snake_case__ , """""" , example["""content"""] ).encode("""utf-8""" ) ).hexdigest()}
def UpperCAmelCase__ (snake_case__ : Dict ):
"""simple docstring"""
_snake_case : Any = [len(snake_case__ ) for line in example["""content"""].splitlines()]
return {"line_mean": np.mean(snake_case__ ), "line_max": max(snake_case__ )}
def UpperCAmelCase__ (snake_case__ : List[Any] ):
"""simple docstring"""
_snake_case : Tuple = np.mean([c.isalnum() for c in example["""content"""]] )
return {"alpha_frac": alpha_frac}
def UpperCAmelCase__ (snake_case__ : List[Any] , snake_case__ : List[Any] ):
"""simple docstring"""
if example["hash"] in uniques:
uniques.remove(example["""hash"""] )
return True
else:
return False
def UpperCAmelCase__ (snake_case__ : Optional[Any] , snake_case__ : List[str]=5 ):
"""simple docstring"""
_snake_case : Any = ["""auto-generated""", """autogenerated""", """automatically generated"""]
_snake_case : Tuple = example["""content"""].splitlines()
for _, line in zip(range(snake_case__ ) , snake_case__ ):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def UpperCAmelCase__ (snake_case__ : Any , snake_case__ : Union[str, Any]=5 , snake_case__ : Any=0.05 ):
"""simple docstring"""
_snake_case : Optional[Any] = ["""unit tests""", """test file""", """configuration file"""]
_snake_case : List[Any] = example["""content"""].splitlines()
_snake_case : Dict = 0
_snake_case : str = 0
# first test
for _, line in zip(range(snake_case__ ) , snake_case__ ):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
_snake_case : Optional[int] = example["""content"""].count("""\n""" )
_snake_case : Tuple = int(coeff * nlines )
for line in lines:
count_config += line.lower().count("""config""" )
count_test += line.lower().count("""test""" )
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def UpperCAmelCase__ (snake_case__ : str ):
"""simple docstring"""
_snake_case : Optional[int] = ["""def """, """class """, """for """, """while """]
_snake_case : str = example["""content"""].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def UpperCAmelCase__ (snake_case__ : List[str] , snake_case__ : List[str]=4 ):
"""simple docstring"""
_snake_case : List[Any] = example["""content"""].splitlines()
_snake_case : str = 0
for line in lines:
counter += line.lower().count("""=""" )
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def UpperCAmelCase__ (snake_case__ : List[str] ):
"""simple docstring"""
_snake_case : Optional[Any] = tokenizer(example["""content"""] , truncation=snake_case__ )["""input_ids"""]
_snake_case : Optional[Any] = len(example["""content"""] ) / len(snake_case__ )
return {"ratio": ratio}
def UpperCAmelCase__ (snake_case__ : Optional[int] ):
"""simple docstring"""
_snake_case : Optional[int] = {}
results.update(get_hash(snake_case__ ) )
results.update(line_stats(snake_case__ ) )
results.update(alpha_stats(snake_case__ ) )
results.update(char_token_ratio(snake_case__ ) )
results.update(is_autogenerated(snake_case__ ) )
results.update(is_config_or_test(snake_case__ ) )
results.update(has_no_keywords(snake_case__ ) )
results.update(has_few_assignments(snake_case__ ) )
return results
def UpperCAmelCase__ (snake_case__ : Tuple , snake_case__ : List[Any] , snake_case__ : List[str] ):
"""simple docstring"""
if not check_uniques(snake_case__ , snake_case__ ):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def UpperCAmelCase__ (snake_case__ : Optional[Any] ):
"""simple docstring"""
with open(snake_case__ , """rb""" ) as f_in:
with gzip.open(str(snake_case__ ) + """.gz""" , """wb""" , compresslevel=6 ) as f_out:
shutil.copyfileobj(snake_case__ , snake_case__ )
os.unlink(snake_case__ )
# Settings
A_ = HfArgumentParser(PreprocessingArguments)
A_ = parser.parse_args()
if args.num_workers is None:
A_ = multiprocessing.cpu_count()
A_ = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
A_ = time.time()
A_ = load_dataset(args.dataset_name, split='''train''')
print(F'''Time to load dataset: {time.time()-t_start:.2f}''')
# Run preprocessing
A_ = time.time()
A_ = ds.map(preprocess, num_proc=args.num_workers)
print(F'''Time to preprocess dataset: {time.time()-t_start:.2f}''')
# Deduplicate hashes
A_ = set(ds.unique('''hash'''))
A_ = len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')
# Deduplicate data and apply heuristics
A_ = time.time()
A_ = ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(F'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(F'''Size of filtered dataset: {len(ds_filter)}''')
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
A_ = time.time()
A_ , A_ = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(F'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
print(F'''Size of deduplicate dataset: {len(ds_filter)}''')
# Save data in batches of samples_per_file
A_ = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
json.dump(duplicate_clusters, f)
A_ = output_dir / '''data'''
data_dir.mkdir(exist_ok=True)
A_ = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
A_ = str(data_dir / F'''file-{file_number+1:012}.json''')
A_ = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(F'''Time to save dataset: {time.time()-t_start:.2f}''')
| 64 | 0 |
import math
import random
def a__ ( UpperCAmelCase : float , UpperCAmelCase : bool = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_lowerCamelCase : Optional[Any] = 0.0_2
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> float:
UpperCAmelCase : int = float(2 * (random.randint(1 , 100 )) - 1 )
for _ in range(UpperCAmelCase ):
# Forward propagation
UpperCAmelCase : List[str] = sigmoid_function(INITIAL_VALUE * weight )
# How much did we miss?
UpperCAmelCase : Optional[Any] = (expected / 100) - layer_a
# Error delta
UpperCAmelCase : List[str] = layer_1_error * sigmoid_function(UpperCAmelCase , UpperCAmelCase )
# Update weight
weight += INITIAL_VALUE * layer_1_delta
return layer_a * 100
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCamelCase : int = int(input("Expected value: "))
_lowerCamelCase : Optional[Any] = int(input("Number of propagations: "))
print(forward_propagation(expected, number_propagations))
| 99 |
from ..utils import DummyObject, requires_backends
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ["""onnx"""]
def __init__( self : int, *__A : Optional[Any], **__A : Dict ):
requires_backends(self, ['''onnx'''] )
@classmethod
def __magic_name__ ( cls : Any, *__A : Any, **__A : Dict ):
requires_backends(cls, ['''onnx'''] )
@classmethod
def __magic_name__ ( cls : Tuple, *__A : List[str], **__A : List[str] ):
requires_backends(cls, ['''onnx'''] )
| 99 | 1 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Tuple = {
'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',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
_snake_case : Optional[int] = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
]
def a_ ( lowerCAmelCase_ : List[str], lowerCAmelCase_ : Any, lowerCAmelCase_ : str, lowerCAmelCase_ : Union[str, Any], lowerCAmelCase_ : Any ):
for attribute in key.split('.' ):
__lowerCAmelCase = getattr(lowerCAmelCase_, lowerCAmelCase_ )
if weight_type is not None:
__lowerCAmelCase = getattr(lowerCAmelCase_, lowerCAmelCase_ ).shape
else:
__lowerCAmelCase = 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":
__lowerCAmelCase = value
elif weight_type == "weight_g":
__lowerCAmelCase = value
elif weight_type == "weight_v":
__lowerCAmelCase = value
elif weight_type == "bias":
__lowerCAmelCase = value
else:
__lowerCAmelCase = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def a_ ( lowerCAmelCase_ : int, lowerCAmelCase_ : List[str] ):
__lowerCAmelCase = []
__lowerCAmelCase = fairseq_model.state_dict()
__lowerCAmelCase = hf_model.feature_extractor
# if encoder has different dim to decoder -> use proj_weight
__lowerCAmelCase = None
for name, value in fairseq_dict.items():
__lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, hf_model.config.feat_extract_norm == 'group', )
__lowerCAmelCase = True
elif name.split('.' )[0] == "proj":
__lowerCAmelCase = fairseq_model.proj
__lowerCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
__lowerCAmelCase = True
if "*" in mapped_key:
__lowerCAmelCase = name.split(lowerCAmelCase_ )[0].split('.' )[-2]
__lowerCAmelCase = mapped_key.replace('*', lowerCAmelCase_ )
if "weight_g" in name:
__lowerCAmelCase = 'weight_g'
elif "weight_v" in name:
__lowerCAmelCase = 'weight_v'
elif "bias" in name:
__lowerCAmelCase = 'bias'
elif "weight" in name:
__lowerCAmelCase = 'weight'
else:
__lowerCAmelCase = None
set_recursively(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
continue
if not is_used:
unused_weights.append(lowerCAmelCase_ )
logger.warning(F"""Unused weights: {unused_weights}""" )
return proj_weight
def a_ ( lowerCAmelCase_ : List[Any], lowerCAmelCase_ : int, lowerCAmelCase_ : str, lowerCAmelCase_ : Union[str, Any], lowerCAmelCase_ : List[Any] ):
__lowerCAmelCase = full_name.split('conv_layers.' )[-1]
__lowerCAmelCase = name.split('.' )
__lowerCAmelCase = int(items[0] )
__lowerCAmelCase = 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."""
)
__lowerCAmelCase = 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."""
)
__lowerCAmelCase = 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."
)
__lowerCAmelCase = 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."""
)
__lowerCAmelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(lowerCAmelCase_ )
def a_ ( lowerCAmelCase_ : List[str] ):
__lowerCAmelCase , __lowerCAmelCase = emb.weight.shape
__lowerCAmelCase = nn.Linear(lowerCAmelCase_, lowerCAmelCase_, bias=lowerCAmelCase_ )
__lowerCAmelCase = emb.weight.data
return lin_layer
def a_ ( lowerCAmelCase_ : Optional[Any] ):
with open(lowerCAmelCase_, 'r', encoding='utf-8' ) as f:
__lowerCAmelCase = f.readlines()
__lowerCAmelCase = [line.split(' ' )[0] for line in lines]
__lowerCAmelCase = len(lowerCAmelCase_ )
__lowerCAmelCase = {
'<s>': 0,
'<pad>': 1,
'</s>': 2,
'<unk>': 3,
}
vocab_dict.update(dict(zip(lowerCAmelCase_, range(4, num_words + 4 ) ) ) )
return vocab_dict
@torch.no_grad()
def a_ ( lowerCAmelCase_ : Any, lowerCAmelCase_ : Tuple, lowerCAmelCase_ : Union[str, Any], lowerCAmelCase_ : str, lowerCAmelCase_ : Optional[int], lowerCAmelCase_ : Optional[Any], lowerCAmelCase_ : Optional[int], ):
__lowerCAmelCase = WavaVecaConfig.from_pretrained(lowerCAmelCase_ )
__lowerCAmelCase = SpeechaTextaConfig.from_pretrained(
lowerCAmelCase_, vocab_size=lowerCAmelCase_, decoder_layers=lowerCAmelCase_, do_stable_layer_norm=lowerCAmelCase_ )
__lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1, sampling_rate=1_6000, padding_value=0, do_normalize=lowerCAmelCase_, return_attention_mask=lowerCAmelCase_, )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path], arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
__lowerCAmelCase = model[0].eval()
# set weights for wav2vec2 encoder
__lowerCAmelCase = WavaVecaModel(lowerCAmelCase_ )
__lowerCAmelCase = recursively_load_weights_wavaveca(model.encoder, lowerCAmelCase_ )
__lowerCAmelCase = SpeechaTextaForCausalLM(lowerCAmelCase_ )
__lowerCAmelCase , __lowerCAmelCase = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(), strict=lowerCAmelCase_ )
# set output linear layer
unexpected_keys.remove('embed_out' )
__lowerCAmelCase = nn.Parameter(model.decoder.embed_out.detach() )
# layer norm is init to identity matrix so leaving it is fine
logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}""" )
logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""" )
__lowerCAmelCase = SpeechEncoderDecoderModel(encoder=lowerCAmelCase_, decoder=lowerCAmelCase_ )
__lowerCAmelCase = False
# add projection layer
__lowerCAmelCase = nn.Parameter(projection_layer.weight )
__lowerCAmelCase = nn.Parameter(projection_layer.bias )
__lowerCAmelCase = create_vocab_dict(lowerCAmelCase_ )
with open(os.path.join(lowerCAmelCase_, 'vocab.json' ), 'w' ) as fp:
json.dump(lowerCAmelCase_, lowerCAmelCase_ )
__lowerCAmelCase = SpeechaTextaTokenizer(os.path.join(lowerCAmelCase_, 'vocab.json' ) )
tokenizer.save_pretrained(lowerCAmelCase_ )
__lowerCAmelCase = hf_wavavec.config.to_dict()
__lowerCAmelCase = tokenizer.pad_token_id
__lowerCAmelCase = tokenizer.bos_token_id
__lowerCAmelCase = tokenizer.eos_token_id
__lowerCAmelCase = 'speech_to_text_2'
__lowerCAmelCase = 'wav2vec2'
__lowerCAmelCase = SpeechEncoderDecoderConfig.from_dict(lowerCAmelCase_ )
hf_wavavec.save_pretrained(lowerCAmelCase_ )
feature_extractor.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
_snake_case : 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(
'--encoder_config_path',
default='facebook/wav2vec2-large-lv60',
type=str,
help='Path to hf encoder wav2vec2 checkpoint config',
)
parser.add_argument(
'--decoder_config_path',
default='facebook/s2t-small-mustc-en-fr-st',
type=str,
help='Path to hf decoder s2t checkpoint config',
)
parser.add_argument('--vocab_size', default=10224, type=int, help='Vocab size of decoder')
parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')
_snake_case : Tuple = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
vocab_size=args.vocab_size,
num_decoder_layers=args.num_decoder_layers,
)
| 284 |
from timeit import timeit
def a_ ( lowerCAmelCase_ : int ):
if number < 0:
raise ValueError('the value of input must not be negative' )
__lowerCAmelCase = 0
while number:
number &= number - 1
result += 1
return result
def a_ ( lowerCAmelCase_ : int ):
if number < 0:
raise ValueError('the value of input must not be negative' )
__lowerCAmelCase = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def a_ ( ):
def do_benchmark(lowerCAmelCase_ : int ) -> None:
__lowerCAmelCase = 'import __main__ as z'
print(F"""Benchmark when {number = }:""" )
print(F"""{get_set_bits_count_using_modulo_operator(lowerCAmelCase_ ) = }""" )
__lowerCAmelCase = timeit('z.get_set_bits_count_using_modulo_operator(25)', setup=lowerCAmelCase_ )
print(F"""timeit() runs in {timing} seconds""" )
print(F"""{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase_ ) = }""" )
__lowerCAmelCase = timeit(
'z.get_set_bits_count_using_brian_kernighans_algorithm(25)', setup=lowerCAmelCase_, )
print(F"""timeit() runs in {timing} seconds""" )
for number in (25, 37, 58, 0):
do_benchmark(lowerCAmelCase_ )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 284 | 1 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class __a ( nn.Module ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = "geglu" , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = "layer_norm" , _SCREAMING_SNAKE_CASE = False , ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = only_cross_attention
_UpperCAmelCase = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
_UpperCAmelCase = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm'
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
f'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
f''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.''' )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
_UpperCAmelCase = AdaLayerNorm(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif self.use_ada_layer_norm_zero:
_UpperCAmelCase = AdaLayerNormZero(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = nn.LayerNorm(_SCREAMING_SNAKE_CASE , elementwise_affine=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = Attention(
query_dim=_SCREAMING_SNAKE_CASE , heads=_SCREAMING_SNAKE_CASE , dim_head=_SCREAMING_SNAKE_CASE , dropout=_SCREAMING_SNAKE_CASE , bias=_SCREAMING_SNAKE_CASE , cross_attention_dim=cross_attention_dim if only_cross_attention else None , upcast_attention=_SCREAMING_SNAKE_CASE , )
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
_UpperCAmelCase = (
AdaLayerNorm(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if self.use_ada_layer_norm
else nn.LayerNorm(_SCREAMING_SNAKE_CASE , elementwise_affine=_SCREAMING_SNAKE_CASE )
)
_UpperCAmelCase = Attention(
query_dim=_SCREAMING_SNAKE_CASE , cross_attention_dim=cross_attention_dim if not double_self_attention else None , heads=_SCREAMING_SNAKE_CASE , dim_head=_SCREAMING_SNAKE_CASE , dropout=_SCREAMING_SNAKE_CASE , bias=_SCREAMING_SNAKE_CASE , upcast_attention=_SCREAMING_SNAKE_CASE , ) # is self-attn if encoder_hidden_states is none
else:
_UpperCAmelCase = None
_UpperCAmelCase = None
# 3. Feed-forward
_UpperCAmelCase = nn.LayerNorm(_SCREAMING_SNAKE_CASE , elementwise_affine=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = FeedForward(_SCREAMING_SNAKE_CASE , dropout=_SCREAMING_SNAKE_CASE , activation_fn=_SCREAMING_SNAKE_CASE , final_dropout=_SCREAMING_SNAKE_CASE )
# let chunk size default to None
_UpperCAmelCase = None
_UpperCAmelCase = 0
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = chunk_size
_UpperCAmelCase = dim
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , ) -> Dict:
"""simple docstring"""
if self.use_ada_layer_norm:
_UpperCAmelCase = self.norma(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif self.use_ada_layer_norm_zero:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.norma(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , hidden_dtype=hidden_states.dtype )
else:
_UpperCAmelCase = self.norma(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = cross_attention_kwargs if cross_attention_kwargs is not None else {}
_UpperCAmelCase = self.attna(
_SCREAMING_SNAKE_CASE , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None , attention_mask=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
if self.use_ada_layer_norm_zero:
_UpperCAmelCase = gate_msa.unsqueeze(1 ) * attn_output
_UpperCAmelCase = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
_UpperCAmelCase = (
self.norma(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if self.use_ada_layer_norm else self.norma(_SCREAMING_SNAKE_CASE )
)
_UpperCAmelCase = self.attna(
_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
_UpperCAmelCase = attn_output + hidden_states
# 3. Feed-forward
_UpperCAmelCase = self.norma(_SCREAMING_SNAKE_CASE )
if self.use_ada_layer_norm_zero:
_UpperCAmelCase = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
f'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.''' )
_UpperCAmelCase = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
_UpperCAmelCase = torch.cat(
[self.ff(_SCREAMING_SNAKE_CASE ) for hid_slice in norm_hidden_states.chunk(_SCREAMING_SNAKE_CASE , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
_UpperCAmelCase = self.ff(_SCREAMING_SNAKE_CASE )
if self.use_ada_layer_norm_zero:
_UpperCAmelCase = gate_mlp.unsqueeze(1 ) * ff_output
_UpperCAmelCase = ff_output + hidden_states
return hidden_states
class __a ( nn.Module ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 4 , _SCREAMING_SNAKE_CASE = 0.0 , _SCREAMING_SNAKE_CASE = "geglu" , _SCREAMING_SNAKE_CASE = False , ) -> Any:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = int(dim * mult )
_UpperCAmelCase = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
_UpperCAmelCase = GELU(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if activation_fn == "gelu-approximate":
_UpperCAmelCase = GELU(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , approximate='tanh' )
elif activation_fn == "geglu":
_UpperCAmelCase = GEGLU(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif activation_fn == "geglu-approximate":
_UpperCAmelCase = ApproximateGELU(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = nn.ModuleList([] )
# project in
self.net.append(_SCREAMING_SNAKE_CASE )
# project dropout
self.net.append(nn.Dropout(_SCREAMING_SNAKE_CASE ) )
# project out
self.net.append(nn.Linear(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(_SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
for module in self.net:
_UpperCAmelCase = module(_SCREAMING_SNAKE_CASE )
return hidden_states
class __a ( nn.Module ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = "none" ) -> List[Any]:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = nn.Linear(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = approximate
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
if gate.device.type != "mps":
return F.gelu(_SCREAMING_SNAKE_CASE , approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) , approximate=self.approximate ).to(dtype=gate.dtype )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.proj(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = self.gelu(_SCREAMING_SNAKE_CASE )
return hidden_states
class __a ( nn.Module ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = nn.Linear(_SCREAMING_SNAKE_CASE , dim_out * 2 )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
if gate.device.type != "mps":
return F.gelu(_SCREAMING_SNAKE_CASE )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.proj(_SCREAMING_SNAKE_CASE ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(_SCREAMING_SNAKE_CASE )
class __a ( nn.Module ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = nn.Linear(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
_UpperCAmelCase = self.proj(_SCREAMING_SNAKE_CASE )
return x * torch.sigmoid(1.702 * x )
class __a ( nn.Module ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = nn.Embedding(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = nn.SiLU()
_UpperCAmelCase = nn.Linear(_SCREAMING_SNAKE_CASE , embedding_dim * 2 )
_UpperCAmelCase = nn.LayerNorm(_SCREAMING_SNAKE_CASE , elementwise_affine=_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
_UpperCAmelCase = self.linear(self.silu(self.emb(_SCREAMING_SNAKE_CASE ) ) )
_UpperCAmelCase , _UpperCAmelCase = torch.chunk(_SCREAMING_SNAKE_CASE , 2 )
_UpperCAmelCase = self.norm(_SCREAMING_SNAKE_CASE ) * (1 + scale) + shift
return x
class __a ( nn.Module ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = CombinedTimestepLabelEmbeddings(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = nn.SiLU()
_UpperCAmelCase = nn.Linear(_SCREAMING_SNAKE_CASE , 6 * embedding_dim , bias=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = nn.LayerNorm(_SCREAMING_SNAKE_CASE , elementwise_affine=_SCREAMING_SNAKE_CASE , eps=1e-6 )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> int:
"""simple docstring"""
_UpperCAmelCase = self.linear(self.silu(self.emb(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , hidden_dtype=_SCREAMING_SNAKE_CASE ) ) )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = emb.chunk(6 , dim=1 )
_UpperCAmelCase = self.norm(_SCREAMING_SNAKE_CASE ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class __a ( nn.Module ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1e-5 ) -> str:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = num_groups
_UpperCAmelCase = eps
if act_fn is None:
_UpperCAmelCase = None
else:
_UpperCAmelCase = get_activation(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = nn.Linear(_SCREAMING_SNAKE_CASE , out_dim * 2 )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
if self.act:
_UpperCAmelCase = self.act(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = self.linear(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = emb[:, :, None, None]
_UpperCAmelCase , _UpperCAmelCase = emb.chunk(2 , dim=1 )
_UpperCAmelCase = F.group_norm(_SCREAMING_SNAKE_CASE , self.num_groups , eps=self.eps )
_UpperCAmelCase = x * (1 + scale) + shift
return x
| 185 |
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 lowerCAmelCase__ ( a__: Dict , a__: Dict , a__: Any , a__: Optional[int]=None , a__: str=None , a__: List[Any]=None , a__: Optional[int]=None , a__: Union[str, Any]=None , ) -> Tuple:
'''simple docstring'''
if attention_mask is None:
_UpperCAmelCase = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
_UpperCAmelCase = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
_UpperCAmelCase = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=a__ )
if decoder_head_mask is None:
_UpperCAmelCase = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=a__ )
if cross_attn_head_mask is None:
_UpperCAmelCase = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=a__ )
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 __a :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE="relu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=20 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=0 , ) -> Any:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = encoder_layerdrop
_UpperCAmelCase = decoder_layerdrop
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = eos_token_id
_UpperCAmelCase = pad_token_id
_UpperCAmelCase = bos_token_id
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = self.eos_token_id # Eos Token
_UpperCAmelCase = 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
_UpperCAmelCase = input_ids.clamp(self.pad_token_id + 1 )
_UpperCAmelCase = decoder_input_ids.clamp(self.pad_token_id + 1 )
_UpperCAmelCase = self.get_config()
_UpperCAmelCase = prepare_mam_aaa_inputs_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return config, inputs_dict
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
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 UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaModel(config=_SCREAMING_SNAKE_CASE ).get_decoder().to(_SCREAMING_SNAKE_CASE ).eval()
_UpperCAmelCase = inputs_dict['input_ids']
_UpperCAmelCase = inputs_dict['attention_mask']
_UpperCAmelCase = inputs_dict['head_mask']
# first forward pass
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , head_mask=_SCREAMING_SNAKE_CASE , use_cache=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase , _UpperCAmelCase = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
_UpperCAmelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
_UpperCAmelCase = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
_UpperCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )['last_hidden_state']
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , past_key_values=_SCREAMING_SNAKE_CASE )[
'last_hidden_state'
]
# select random slice
_UpperCAmelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
_UpperCAmelCase = 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(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-2 ) )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaModel(config=_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE ).eval()
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = outputs.encoder_last_hidden_state
_UpperCAmelCase = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = model.get_encoder()
encoder.save_pretrained(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MaMaaaEncoder.from_pretrained(_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = 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:
_UpperCAmelCase = model.get_decoder()
decoder.save_pretrained(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MaMaaaDecoder.from_pretrained(_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = decoder(
input_ids=inputs_dict['decoder_input_ids'] , attention_mask=inputs_dict['decoder_attention_mask'] , encoder_hidden_states=_SCREAMING_SNAKE_CASE , 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 __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
_a : List[Any] = (
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
_a : List[str] = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
_a : int = (
{
'conversational': MaMaaaForConditionalGeneration,
'feature-extraction': MaMaaaModel,
'summarization': MaMaaaForConditionalGeneration,
'text2text-generation': MaMaaaForConditionalGeneration,
'translation': MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
_a : str = True
_a : Union[str, Any] = True
_a : Optional[int] = False
_a : Union[str, Any] = False
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
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 UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(_SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase , _UpperCAmelCase = model_class.from_pretrained(_SCREAMING_SNAKE_CASE , output_loading_info=_SCREAMING_SNAKE_CASE )
self.assertEqual(info['missing_keys'] , [] )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
_UpperCAmelCase = model_class(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
_UpperCAmelCase = copy.deepcopy(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
if not self.is_encoder_decoder:
_UpperCAmelCase = inputs['input_ids']
del inputs["input_ids"]
else:
_UpperCAmelCase = inputs['input_ids']
_UpperCAmelCase = inputs.get('decoder_input_ids' , _SCREAMING_SNAKE_CASE )
del inputs["input_ids"]
inputs.pop('decoder_input_ids' , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = model.get_input_embeddings()
if not self.is_encoder_decoder:
_UpperCAmelCase = wte(_SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = wte(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = wte(_SCREAMING_SNAKE_CASE )
with torch.no_grad():
model(**_SCREAMING_SNAKE_CASE )[0]
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = input_dict['input_ids']
_UpperCAmelCase = input_ids.ne(1 ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MaMaaaForConditionalGeneration(_SCREAMING_SNAKE_CASE ).eval().to(_SCREAMING_SNAKE_CASE )
if torch_device == "cuda":
model.half()
model.generate(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
model.generate(num_beams=4 , do_sample=_SCREAMING_SNAKE_CASE , early_stopping=_SCREAMING_SNAKE_CASE , num_return_sequences=3 )
def lowerCAmelCase__ ( a__: Tuple ) -> Optional[int]:
'''simple docstring'''
return torch.tensor(a__ , dtype=torch.long , device=a__ )
lowerCAmelCase__ :str = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class __a ( unittest.TestCase ):
@cached_property
def UpperCAmelCase__ ( self ) -> List[str]:
"""simple docstring"""
return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaModel.from_pretrained('facebook/m2m100_418M' ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
_UpperCAmelCase = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
_UpperCAmelCase = prepare_mam_aaa_inputs_dict(model.config , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
with torch.no_grad():
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )[0]
_UpperCAmelCase = torch.Size((1, 11, 1024) )
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
# change to expected output here
_UpperCAmelCase = torch.tensor(
[[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]] , device=_SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=_SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
_UpperCAmelCase = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(_SCREAMING_SNAKE_CASE )
# change to intended input
_UpperCAmelCase = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
_UpperCAmelCase = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
_UpperCAmelCase = prepare_mam_aaa_inputs_dict(model.config , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
with torch.no_grad():
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )[0]
_UpperCAmelCase = torch.Size((1, 11, model.config.vocab_size) )
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
# change to expected output here
_UpperCAmelCase = torch.tensor(
[[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]] , device=_SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=_SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' , src_lang='fr' , tgt_lang='en' )
_UpperCAmelCase = [
'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
_UpperCAmelCase = tokenizer(_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_tensors='pt' )
_UpperCAmelCase = model.generate(
input_ids=dct['input_ids'].to(_SCREAMING_SNAKE_CASE ) , attention_mask=dct['attention_mask'].to(_SCREAMING_SNAKE_CASE ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id('en' ) , )
_UpperCAmelCase = [
'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.',
]
_UpperCAmelCase = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE )
assert generated == expected_en
| 185 | 1 |
'''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 ( A__=None ):
"""simple docstring"""
if subparsers is not None:
__lowercase = subparsers.add_parser('''config''' , description=A__ )
else:
__lowercase = argparse.ArgumentParser('''Accelerate config command''' , description=A__ )
parser.add_argument(
'''--config_file''' , default=A__ , 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=A__ )
return parser
def _A ( A__ ):
"""simple docstring"""
__lowercase = get_user_input()
if args.config_file is not None:
__lowercase = args.config_file
else:
if not os.path.isdir(A__ ):
os.makedirs(A__ )
__lowercase = default_yaml_config_file
if config_file.endswith('''.json''' ):
config.to_json_file(A__ )
else:
config.to_yaml_file(A__ )
print(F"accelerate configuration saved at {config_file}" )
def _A ( ):
"""simple docstring"""
__lowercase = config_command_parser()
__lowercase = parser.parse_args()
config_command(A__ )
if __name__ == "__main__":
main()
| 104 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _A ( ):
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(A__ ):
requests.request('''GET''' , '''https://huggingface.co''' )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request('''GET''' , '''https://huggingface.co''' , timeout=1.0 )
@pytest.mark.integration
def _A ( ):
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request('''GET''' , '''https://huggingface.co''' )
def _A ( ):
"""simple docstring"""
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(A__ ):
http_head('''https://huggingface.co''' )
| 104 | 1 |
import doctest
from collections import deque
import numpy as np
class lowercase :
'''simple docstring'''
def __init__(self ) -> None:
"""simple docstring"""
UpperCAmelCase__ = [2, 1, 2, -1]
UpperCAmelCase__ = [1, 2, 3, 4]
def UpperCamelCase__ (self ) -> list[float]:
"""simple docstring"""
UpperCAmelCase__ = len(self.first_signal )
UpperCAmelCase__ = len(self.second_signal )
UpperCAmelCase__ = max(_lowercase , _lowercase )
# create a zero matrix of max_length x max_length
UpperCAmelCase__ = [[0] * max_length for i in range(_lowercase )]
# fills the smaller signal with zeros to make both signals of same length
if length_first_signal < length_second_signal:
self.first_signal += [0] * (max_length - length_first_signal)
elif length_first_signal > length_second_signal:
self.second_signal += [0] * (max_length - length_second_signal)
for i in range(_lowercase ):
UpperCAmelCase__ = deque(self.second_signal )
rotated_signal.rotate(_lowercase )
for j, item in enumerate(_lowercase ):
matrix[i][j] += item
# multiply the matrix with the first signal
UpperCAmelCase__ = np.matmul(np.transpose(_lowercase ) , np.transpose(self.first_signal ) )
# rounding-off to two decimal places
return [round(_lowercase , 2 ) for i in final_signal]
if __name__ == "__main__":
doctest.testmod()
| 366 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_UpperCamelCase = logging.get_logger(__name__)
class lowercase ( _UpperCamelCase ):
'''simple docstring'''
def __init__(self , *__a , **__a ) -> None:
"""simple docstring"""
warnings.warn(
'The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use MobileViTImageProcessor instead.' , __a , )
super().__init__(*__a , **__a )
| 335 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
_A = TypeVar('''T''')
_A = TypeVar('''U''')
class A ( Generic[T, U] ):
def __init__( self, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = key
lowerCAmelCase_ = val
lowerCAmelCase_ = None
lowerCAmelCase_ = None
def __repr__( self ):
"""simple docstring"""
return (
f"Node: key: {self.key}, val: {self.val}, "
f"has next: {bool(self.next )}, has prev: {bool(self.prev )}"
)
class A ( Generic[T, U] ):
def __init__( self ):
"""simple docstring"""
lowerCAmelCase_ = DoubleLinkedListNode(UpperCamelCase__, UpperCamelCase__ )
lowerCAmelCase_ = DoubleLinkedListNode(UpperCamelCase__, UpperCamelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ = self.rear, self.head
def __repr__( self ):
"""simple docstring"""
lowerCAmelCase_ = ['''DoubleLinkedList''']
lowerCAmelCase_ = self.head
while node.next is not None:
rep.append(str(UpperCamelCase__ ) )
lowerCAmelCase_ = node.next
rep.append(str(self.rear ) )
return ",\n ".join(UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
lowerCAmelCase_ = node
lowerCAmelCase_ = previous
lowerCAmelCase_ = node
lowerCAmelCase_ = self.rear
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
if node.prev is None or node.next is None:
return None
lowerCAmelCase_ = node.next
lowerCAmelCase_ = node.prev
lowerCAmelCase_ = None
lowerCAmelCase_ = None
return node
class A ( Generic[T, U] ):
__snake_case = {}
def __init__( self, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = DoubleLinkedList()
lowerCAmelCase_ = capacity
lowerCAmelCase_ = 0
lowerCAmelCase_ = 0
lowerCAmelCase_ = 0
lowerCAmelCase_ = {}
def __repr__( self ):
"""simple docstring"""
return (
f"CacheInfo(hits={self.hits}, misses={self.miss}, "
f"capacity={self.capacity}, current size={self.num_keys})"
)
def __contains__( self, UpperCamelCase__ ):
"""simple docstring"""
return key in self.cache
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
if key in self.cache:
self.hits += 1
lowerCAmelCase_ = self.cache[key]
lowerCAmelCase_ = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(UpperCamelCase__ )
return node.val
self.miss += 1
return None
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
lowerCAmelCase_ = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(UpperCamelCase__ ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
lowerCAmelCase_ = DoubleLinkedListNode(UpperCamelCase__, UpperCamelCase__ )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
lowerCAmelCase_ = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
lowerCAmelCase_ = value
self.list.add(UpperCamelCase__ )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls, UpperCamelCase__ = 128 ):
"""simple docstring"""
def cache_decorator_inner(UpperCamelCase__ ) -> Callable[..., U]:
def cache_decorator_wrapper(*UpperCamelCase__ ) -> U:
if func not in cls.decorator_function_to_instance_map:
lowerCAmelCase_ = LRUCache(UpperCamelCase__ )
lowerCAmelCase_ = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
lowerCAmelCase_ = func(*UpperCamelCase__ )
cls.decorator_function_to_instance_map[func].put(args[0], UpperCamelCase__ )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(UpperCamelCase__, '''cache_info''', UpperCamelCase__ ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 278 |
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 ):
@property
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCAmelCase_ = 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 SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.dummy_uncond_unet
lowerCAmelCase_ = KarrasVeScheduler()
lowerCAmelCase_ = KarrasVePipeline(unet=UpperCamelCase__, scheduler=UpperCamelCase__ )
pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
lowerCAmelCase_ = torch.manual_seed(0 )
lowerCAmelCase_ = pipe(num_inference_steps=2, generator=UpperCamelCase__, output_type='''numpy''' ).images
lowerCAmelCase_ = torch.manual_seed(0 )
lowerCAmelCase_ = pipe(num_inference_steps=2, generator=UpperCamelCase__, output_type='''numpy''', return_dict=UpperCamelCase__ )[0]
lowerCAmelCase_ = image[0, -3:, -3:, -1]
lowerCAmelCase_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCAmelCase_ = 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 ):
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = '''google/ncsnpp-celebahq-256'''
lowerCAmelCase_ = UNetaDModel.from_pretrained(UpperCamelCase__ )
lowerCAmelCase_ = KarrasVeScheduler()
lowerCAmelCase_ = KarrasVePipeline(unet=UpperCamelCase__, scheduler=UpperCamelCase__ )
pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
lowerCAmelCase_ = torch.manual_seed(0 )
lowerCAmelCase_ = pipe(num_inference_steps=20, generator=UpperCamelCase__, output_type='''numpy''' ).images
lowerCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
lowerCAmelCase_ = np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 278 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
a = logging.get_logger(__name__)
a = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
a = {
'''vocab_file''': {
'''squeezebert/squeezebert-uncased''': (
'''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'''
),
'''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt''',
'''squeezebert/squeezebert-mnli-headless''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''squeezebert/squeezebert-uncased''': (
'''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'''
),
'''squeezebert/squeezebert-mnli''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'''
),
'''squeezebert/squeezebert-mnli-headless''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'''
),
},
}
a = {
'''squeezebert/squeezebert-uncased''': 512,
'''squeezebert/squeezebert-mnli''': 512,
'''squeezebert/squeezebert-mnli-headless''': 512,
}
a = {
'''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
'''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
'''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Dict = VOCAB_FILES_NAMES
UpperCAmelCase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase : Union[str, Any] = PRETRAINED_INIT_CONFIGURATION
UpperCAmelCase : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase : Optional[int] = SqueezeBertTokenizer
def __init__( self : List[Any] , _UpperCAmelCase : Any=None , _UpperCAmelCase : Union[str, Any]=None , _UpperCAmelCase : str=True , _UpperCAmelCase : Tuple="[UNK]" , _UpperCAmelCase : Union[str, Any]="[SEP]" , _UpperCAmelCase : Tuple="[PAD]" , _UpperCAmelCase : List[str]="[CLS]" , _UpperCAmelCase : Tuple="[MASK]" , _UpperCAmelCase : Tuple=True , _UpperCAmelCase : int=None , **_UpperCAmelCase : Optional[int] , ):
super().__init__(
_UpperCAmelCase , tokenizer_file=_UpperCAmelCase , do_lower_case=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , tokenize_chinese_chars=_UpperCAmelCase , strip_accents=_UpperCAmelCase , **_UpperCAmelCase , )
_A = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , _UpperCAmelCase ) != do_lower_case
or normalizer_state.get('strip_accents' , _UpperCAmelCase ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , _UpperCAmelCase ) != tokenize_chinese_chars
):
_A = getattr(_UpperCAmelCase , normalizer_state.pop('type' ) )
_A = do_lower_case
_A = strip_accents
_A = tokenize_chinese_chars
_A = normalizer_class(**_UpperCAmelCase )
_A = do_lower_case
def lowerCAmelCase_ ( self : List[str] , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[str]=None ):
_A = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ):
_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]
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ):
_A = self._tokenizer.model.save(_UpperCAmelCase , name=_UpperCAmelCase )
return tuple(_UpperCAmelCase )
| 271 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
a = logging.get_logger(__name__)
def _snake_case ( _snake_case : bool , _snake_case : bool ) -> Tuple:
'''simple docstring'''
def run_func(_snake_case : Any ):
@wraps(_snake_case )
def run_in_eager_mode(*_snake_case : List[str] , **_snake_case : Tuple ):
return func(*_snake_case , **_snake_case )
@wraps(_snake_case )
@tf.function(experimental_compile=_snake_case )
def run_in_graph_mode(*_snake_case : Dict , **_snake_case : Tuple ):
return func(*_snake_case , **_snake_case )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
'Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.' )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def _snake_case ( _snake_case : int , _snake_case : int , _snake_case : int ) -> ["tf.Tensor"]:
'''simple docstring'''
_A = random.Random()
_A = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(_snake_case , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : TensorFlowBenchmarkArguments
UpperCAmelCase : PretrainedConfig
UpperCAmelCase : str = "TensorFlow"
@property
def lowerCAmelCase_ ( self : str ):
return tf.__version__
def lowerCAmelCase_ ( self : List[str] , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : int ):
# initialize GPU on separate process
_A = self.args.strategy
if strategy is None:
raise ValueError('A device strategy has to be initialized before using TensorFlow.' )
_A = self._prepare_inference_func(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
return self._measure_speed(_inference )
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : int ):
_A = self.args.strategy
if strategy is None:
raise ValueError('A device strategy has to be initialized before using TensorFlow.' )
_A = self._prepare_train_func(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
return self._measure_speed(_train )
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : int ):
# initialize GPU on separate process
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , _UpperCAmelCase )
_A = self.args.strategy
if strategy is None:
raise ValueError('A device strategy has to be initialized before using TensorFlow.' )
_A = self._prepare_inference_func(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
return self._measure_memory(_inference )
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : int ):
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , _UpperCAmelCase )
_A = self.args.strategy
if strategy is None:
raise ValueError('A device strategy has to be initialized before using TensorFlow.' )
_A = self._prepare_train_func(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
return self._measure_memory(_train )
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : int ):
_A = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError('Mixed precision is currently not supported.' )
_A = (
hasattr(_UpperCAmelCase , 'architectures' )
and isinstance(config.architectures , _UpperCAmelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
_A = 'TF' + config.architectures[0] # prepend 'TF' for tensorflow model
_A = __import__('transformers' , fromlist=[model_class] )
_A = getattr(_UpperCAmelCase , _UpperCAmelCase )
_A = model_cls(_UpperCAmelCase )
except ImportError:
raise ImportError(
F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
' set `--only_pretrain_model` or `args.only_pretrain_model=True`.' )
else:
_A = TF_MODEL_MAPPING[config.__class__](_UpperCAmelCase )
# encoder-decoder has vocab size saved differently
_A = config.vocab_size if hasattr(_UpperCAmelCase , 'vocab_size' ) else config.encoder.vocab_size
_A = random_input_ids(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(_UpperCAmelCase , decoder_input_ids=_UpperCAmelCase , training=_UpperCAmelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(_UpperCAmelCase , training=_UpperCAmelCase )
_A = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : int ):
_A = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError('Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.' )
if self.args.fpaa:
raise NotImplementedError('Mixed precision is currently not supported.' )
_A = (
hasattr(_UpperCAmelCase , 'architectures' )
and isinstance(config.architectures , _UpperCAmelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
_A = 'TF' + config.architectures[0] # prepend 'TF' for tensorflow model
_A = __import__('transformers' , fromlist=[model_class] )
_A = getattr(_UpperCAmelCase , _UpperCAmelCase )
_A = model_cls(_UpperCAmelCase )
except ImportError:
raise ImportError(
F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
' set `--only_pretrain_model` or `args.only_pretrain_model=True`.' )
else:
_A = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_UpperCAmelCase )
# encoder-decoder has vocab size saved differently
_A = config.vocab_size if hasattr(_UpperCAmelCase , 'vocab_size' ) else config.encoder.vocab_size
_A = random_input_ids(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
_A = model(_UpperCAmelCase , decoder_input_ids=_UpperCAmelCase , labels=_UpperCAmelCase , training=_UpperCAmelCase )[0]
_A = tf.gradients(_UpperCAmelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
_A = model(_UpperCAmelCase , labels=_UpperCAmelCase , training=_UpperCAmelCase )[0]
_A = tf.gradients(_UpperCAmelCase , model.trainable_variables )
return gradients
_A = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : int ):
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info('Do inference on TPU. Running model 5 times to stabilize compilation' )
timeit.repeat(_UpperCAmelCase , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
_A = timeit.repeat(
_UpperCAmelCase , repeat=self.args.repeat , number=10 , )
return min(_UpperCAmelCase ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(F'''Doesn\'t fit on GPU. {e}''' )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Callable[[], None] ):
logger.info(
'Note that TensorFlow allocates more memory than '
'it might need to speed up computation. '
'The memory reported here corresponds to the memory '
'reported by `nvidia-smi`, which can vary depending '
'on total available memory on the GPU that is used.' )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
'`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'
' consumption line by line.' )
_A = start_memory_tracing('transformers' )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
'Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'
' with `args.memory=False`' )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
'py3nvml not installed, we won\'t log GPU memory usage. '
'Install py3nvml (pip install py3nvml) to log information about GPU.' )
_A = 'N/A'
else:
logger.info(
'Measuring total GPU usage on GPU device. Make sure to not have additional processes'
' running on the same GPU.' )
# init nvml
nvml.nvmlInit()
func()
_A = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
_A = nvml.nvmlDeviceGetMemoryInfo(_UpperCAmelCase )
_A = meminfo.used
_A = Memory(_UpperCAmelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
'When enabling line by line tracing, the max peak memory for CPU is inaccurate in'
' TensorFlow.' )
_A = None
else:
_A = measure_peak_memory_cpu(_UpperCAmelCase )
_A = Memory(_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
_A = stop_memory_tracing(_UpperCAmelCase )
if memory is None:
_A = summary.total
else:
_A = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(F'''Doesn\'t fit on GPU. {e}''' )
return "N/A", None
| 271 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase :Union[str, Any] = R"""\w+[.]\d+"""
UpperCamelCase :int = re.findall(__magic_name__ , __magic_name__ )
for pat in pats:
UpperCamelCase :List[str] = key.replace(__magic_name__ , """_""".join(pat.split(""".""" ) ) )
return key
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Union[str, Any] , __magic_name__ : Union[str, Any] , __magic_name__ : Optional[int] ) -> Tuple:
"""simple docstring"""
UpperCamelCase :Optional[Any] = pt_tuple_key[:-1] + ("""scale""",)
if (
any("""norm""" in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
UpperCamelCase :List[str] = pt_tuple_key[:-1] + ("""scale""",)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
UpperCamelCase :Optional[Any] = pt_tuple_key[:-1] + ("""scale""",)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
UpperCamelCase :Optional[Any] = pt_tuple_key[:-1] + ("""embedding""",)
return renamed_pt_tuple_key, pt_tensor
# conv layer
UpperCamelCase :Dict = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
UpperCamelCase :Any = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
UpperCamelCase :Dict = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight":
UpperCamelCase :Optional[Any] = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
UpperCamelCase :str = pt_tuple_key[:-1] + ("""weight""",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
UpperCamelCase :Optional[int] = pt_tuple_key[:-1] + ("""bias""",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : List[Any] , __magic_name__ : List[Any] , __magic_name__ : Tuple=42 ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase :Any = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
UpperCamelCase :Tuple = flax_model.init_weights(PRNGKey(__magic_name__ ) )
UpperCamelCase :Any = flatten_dict(__magic_name__ )
UpperCamelCase :Optional[int] = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
UpperCamelCase :Union[str, Any] = rename_key(__magic_name__ )
UpperCamelCase :Optional[Any] = tuple(renamed_pt_key.split(""".""" ) )
# Correctly rename weight parameters
UpperCamelCase , UpperCamelCase :Tuple = rename_key_and_reshape_tensor(__magic_name__ , __magic_name__ , __magic_name__ )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# also add unexpected weight so that warning is thrown
UpperCamelCase :List[Any] = jnp.asarray(__magic_name__ )
return unflatten_dict(__magic_name__ )
| 38 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
snake_case_ : Union[str, Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Dict = ["GPTNeoXTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : str = [
"GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTNeoXForCausalLM",
"GPTNeoXForQuestionAnswering",
"GPTNeoXForSequenceClassification",
"GPTNeoXForTokenClassification",
"GPTNeoXLayer",
"GPTNeoXModel",
"GPTNeoXPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
snake_case_ : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 51 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> List[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
lowerCamelCase__ : Union[str, Any] = name.replace("""img_encoder.pos_embed""" , """vision_model.embeddings.position_embeddings""" )
if "img_encoder.patch_embed.proj" in name:
lowerCamelCase__ : Tuple = name.replace("""img_encoder.patch_embed.proj""" , """vision_model.embeddings.patch_embeddings.projection""" )
if "img_encoder.patch_embed.norm" in name:
lowerCamelCase__ : int = name.replace("""img_encoder.patch_embed.norm""" , """vision_model.embeddings.layernorm""" )
if "img_encoder.layers" in name:
lowerCamelCase__ : List[str] = name.replace("""img_encoder.layers""" , """vision_model.encoder.stages""" )
if "blocks" in name and "res" not in name:
lowerCamelCase__ : int = name.replace("""blocks""" , """layers""" )
if "attn" in name and "pre_assign" not in name:
lowerCamelCase__ : List[Any] = name.replace("""attn""" , """self_attn""" )
if "proj" in name and "self_attn" in name and "text" not in name:
lowerCamelCase__ : List[Any] = name.replace("""proj""" , """out_proj""" )
if "pre_assign_attn.attn.proj" in name:
lowerCamelCase__ : Optional[int] = name.replace("""pre_assign_attn.attn.proj""" , """pre_assign_attn.attn.out_proj""" )
if "norm1" in name:
lowerCamelCase__ : Optional[int] = name.replace("""norm1""" , """layer_norm1""" )
if "norm2" in name and "pre_assign" not in name:
lowerCamelCase__ : Tuple = name.replace("""norm2""" , """layer_norm2""" )
if "img_encoder.norm" in name:
lowerCamelCase__ : Any = name.replace("""img_encoder.norm""" , """vision_model.layernorm""" )
# text encoder
if "text_encoder.token_embedding" in name:
lowerCamelCase__ : List[str] = name.replace("""text_encoder.token_embedding""" , """text_model.embeddings.token_embedding""" )
if "text_encoder.positional_embedding" in name:
lowerCamelCase__ : List[str] = name.replace("""text_encoder.positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "text_encoder.transformer.resblocks." in name:
lowerCamelCase__ : Union[str, Any] = name.replace("""text_encoder.transformer.resblocks.""" , """text_model.encoder.layers.""" )
if "ln_1" in name:
lowerCamelCase__ : Optional[int] = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
lowerCamelCase__ : int = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
lowerCamelCase__ : List[str] = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
lowerCamelCase__ : List[str] = name.replace("""c_proj""" , """fc2""" )
if "text_encoder" in name:
lowerCamelCase__ : Dict = name.replace("""text_encoder""" , """text_model""" )
if "ln_final" in name:
lowerCamelCase__ : Any = name.replace("""ln_final""" , """final_layer_norm""" )
# projection layers
if "img_projector.linear_hidden." in name:
lowerCamelCase__ : int = name.replace("""img_projector.linear_hidden.""" , """visual_projection.""" )
if "img_projector.linear_out." in name:
lowerCamelCase__ : List[str] = name.replace("""img_projector.linear_out.""" , """visual_projection.3.""" )
if "text_projector.linear_hidden" in name:
lowerCamelCase__ : List[str] = name.replace("""text_projector.linear_hidden""" , """text_projection""" )
if "text_projector.linear_out" in name:
lowerCamelCase__ : Optional[Any] = name.replace("""text_projector.linear_out""" , """text_projection.3""" )
return name
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> List[str]:
for key in orig_state_dict.copy().keys():
lowerCamelCase__ : str = orig_state_dict.pop(UpperCamelCase )
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
lowerCamelCase__ : Optional[Any] = key.split(""".""" )
lowerCamelCase__ , lowerCamelCase__ : Dict = int(key_split[2] ), int(key_split[4] )
lowerCamelCase__ : List[str] = config.vision_config.hidden_size
if "weight" in key:
lowerCamelCase__ : Any = val[:dim, :]
lowerCamelCase__ : List[str] = val[dim : dim * 2, :]
lowerCamelCase__ : int = val[-dim:, :]
else:
lowerCamelCase__ : List[str] = val[:dim]
lowerCamelCase__ : str = val[dim : dim * 2]
lowerCamelCase__ : Dict = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
lowerCamelCase__ : Any = key.split(""".""" )
lowerCamelCase__ : Tuple = int(key_split[3] )
lowerCamelCase__ : Any = config.text_config.hidden_size
if "weight" in key:
lowerCamelCase__ : Optional[Any] = val[:dim, :]
lowerCamelCase__ : int = val[
dim : dim * 2, :
]
lowerCamelCase__ : int = val[-dim:, :]
else:
lowerCamelCase__ : Dict = val[:dim]
lowerCamelCase__ : str = val[dim : dim * 2]
lowerCamelCase__ : str = val[-dim:]
else:
lowerCamelCase__ : List[str] = rename_key(UpperCamelCase )
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
lowerCamelCase__ : Any = val.squeeze_()
else:
lowerCamelCase__ : Union[str, Any] = val
return orig_state_dict
def SCREAMING_SNAKE_CASE_ () -> str:
lowerCamelCase__ : Optional[int] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCamelCase__ : Any = Image.open(requests.get(UpperCamelCase , stream=UpperCamelCase ).raw )
return im
@torch.no_grad()
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase="groupvit-gcc-yfcc" , UpperCamelCase=False ) -> List[Any]:
lowerCamelCase__ : str = GroupViTConfig()
lowerCamelCase__ : List[Any] = GroupViTModel(UpperCamelCase ).eval()
lowerCamelCase__ : List[str] = torch.load(UpperCamelCase , map_location="""cpu""" )["""model"""]
lowerCamelCase__ : str = convert_state_dict(UpperCamelCase , UpperCamelCase )
lowerCamelCase__ , lowerCamelCase__ : Dict = model.load_state_dict(UpperCamelCase , strict=UpperCamelCase )
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(UpperCamelCase ) == 0)
# verify result
lowerCamelCase__ : int = CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
lowerCamelCase__ : List[Any] = prepare_img()
lowerCamelCase__ : Optional[int] = processor(text=["""a photo of a cat""", """a photo of a dog"""] , images=UpperCamelCase , padding=UpperCamelCase , return_tensors="""pt""" )
with torch.no_grad():
lowerCamelCase__ : Optional[int] = model(**UpperCamelCase )
if model_name == "groupvit-gcc-yfcc":
lowerCamelCase__ : Optional[int] = torch.tensor([[13.3523, 6.3629]] )
elif model_name == "groupvit-gcc-redcaps":
lowerCamelCase__ : List[str] = torch.tensor([[16.1873, 8.6230]] )
else:
raise ValueError(f'''Model name {model_name} not supported.''' )
assert torch.allclose(outputs.logits_per_image , UpperCamelCase , atol=1E-3 )
processor.save_pretrained(UpperCamelCase )
model.save_pretrained(UpperCamelCase )
print("""Successfully saved processor and model to""" , UpperCamelCase )
if push_to_hub:
print("""Pushing to the hub...""" )
processor.push_to_hub(UpperCamelCase , organization="""nielsr""" )
model.push_to_hub(UpperCamelCase , organization="""nielsr""" )
if __name__ == "__main__":
_A : int =argparse.ArgumentParser()
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to dump the processor and PyTorch model.'''
)
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to GroupViT checkpoint''')
parser.add_argument(
'''--model_name''',
default='''groupvit-gccy-fcc''',
type=str,
help='''Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'''',
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.''',
)
_A : List[str] =parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 129 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> List[Any]:
if "cls_token" in name:
lowerCamelCase__ : Any = name.replace("""cls_token""" , """vit.embeddings.cls_token""" )
if "mask_token" in name:
lowerCamelCase__ : Union[str, Any] = name.replace("""mask_token""" , """decoder.mask_token""" )
if "decoder_pos_embed" in name:
lowerCamelCase__ : Tuple = name.replace("""decoder_pos_embed""" , """decoder.decoder_pos_embed""" )
if "pos_embed" in name and "decoder" not in name:
lowerCamelCase__ : str = name.replace("""pos_embed""" , """vit.embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
lowerCamelCase__ : Optional[int] = name.replace("""patch_embed.proj""" , """vit.embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
lowerCamelCase__ : Any = name.replace("""patch_embed.norm""" , """vit.embeddings.norm""" )
if "decoder_blocks" in name:
lowerCamelCase__ : Dict = name.replace("""decoder_blocks""" , """decoder.decoder_layers""" )
if "blocks" in name:
lowerCamelCase__ : Union[str, Any] = name.replace("""blocks""" , """vit.encoder.layer""" )
if "attn.proj" in name:
lowerCamelCase__ : List[Any] = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
lowerCamelCase__ : List[str] = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
lowerCamelCase__ : Any = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
lowerCamelCase__ : Dict = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
lowerCamelCase__ : str = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
lowerCamelCase__ : Union[str, Any] = name.replace("""mlp.fc2""" , """output.dense""" )
if "decoder_embed" in name:
lowerCamelCase__ : Tuple = name.replace("""decoder_embed""" , """decoder.decoder_embed""" )
if "decoder_norm" in name:
lowerCamelCase__ : Optional[int] = name.replace("""decoder_norm""" , """decoder.decoder_norm""" )
if "decoder_pred" in name:
lowerCamelCase__ : int = name.replace("""decoder_pred""" , """decoder.decoder_pred""" )
if "norm.weight" in name and "decoder" not in name:
lowerCamelCase__ : Union[str, Any] = name.replace("""norm.weight""" , """vit.layernorm.weight""" )
if "norm.bias" in name and "decoder" not in name:
lowerCamelCase__ : Dict = name.replace("""norm.bias""" , """vit.layernorm.bias""" )
return name
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> Tuple:
for key in orig_state_dict.copy().keys():
lowerCamelCase__ : List[str] = orig_state_dict.pop(UpperCamelCase )
if "qkv" in key:
lowerCamelCase__ : List[Any] = key.split(""".""" )
lowerCamelCase__ : Optional[int] = int(key_split[1] )
if "decoder_blocks" in key:
lowerCamelCase__ : str = config.decoder_hidden_size
lowerCamelCase__ : List[Any] = """decoder.decoder_layers."""
if "weight" in key:
lowerCamelCase__ : int = val[:dim, :]
lowerCamelCase__ : int = val[dim : dim * 2, :]
lowerCamelCase__ : Tuple = val[-dim:, :]
elif "bias" in key:
lowerCamelCase__ : Tuple = val[:dim]
lowerCamelCase__ : Optional[int] = val[dim : dim * 2]
lowerCamelCase__ : List[Any] = val[-dim:]
else:
lowerCamelCase__ : List[Any] = config.hidden_size
lowerCamelCase__ : Optional[int] = """vit.encoder.layer."""
if "weight" in key:
lowerCamelCase__ : str = val[:dim, :]
lowerCamelCase__ : List[Any] = val[dim : dim * 2, :]
lowerCamelCase__ : Optional[int] = val[-dim:, :]
elif "bias" in key:
lowerCamelCase__ : int = val[:dim]
lowerCamelCase__ : List[Any] = val[dim : dim * 2]
lowerCamelCase__ : Optional[int] = val[-dim:]
else:
lowerCamelCase__ : int = val
return orig_state_dict
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> int:
lowerCamelCase__ : Any = ViTMAEConfig()
if "large" in checkpoint_url:
lowerCamelCase__ : Any = 1024
lowerCamelCase__ : Optional[Any] = 4096
lowerCamelCase__ : List[str] = 24
lowerCamelCase__ : Union[str, Any] = 16
elif "huge" in checkpoint_url:
lowerCamelCase__ : List[str] = 14
lowerCamelCase__ : Dict = 1280
lowerCamelCase__ : Tuple = 5120
lowerCamelCase__ : List[str] = 32
lowerCamelCase__ : Union[str, Any] = 16
lowerCamelCase__ : List[Any] = ViTMAEForPreTraining(UpperCamelCase )
lowerCamelCase__ : str = torch.hub.load_state_dict_from_url(UpperCamelCase , map_location="""cpu""" )["""model"""]
lowerCamelCase__ : Union[str, Any] = ViTMAEImageProcessor(size=config.image_size )
lowerCamelCase__ : List[str] = convert_state_dict(UpperCamelCase , UpperCamelCase )
model.load_state_dict(UpperCamelCase )
model.eval()
lowerCamelCase__ : Union[str, Any] = """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""
lowerCamelCase__ : List[Any] = Image.open(requests.get(UpperCamelCase , stream=UpperCamelCase ).raw )
lowerCamelCase__ : str = ViTMAEImageProcessor(size=config.image_size )
lowerCamelCase__ : Any = image_processor(images=UpperCamelCase , return_tensors="""pt""" )
# forward pass
torch.manual_seed(2 )
lowerCamelCase__ : Optional[Any] = model(**UpperCamelCase )
lowerCamelCase__ : Optional[Any] = outputs.logits
if "large" in checkpoint_url:
lowerCamelCase__ : List[Any] = torch.tensor(
[[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]] )
elif "huge" in checkpoint_url:
lowerCamelCase__ : Optional[Any] = torch.tensor(
[[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]] )
else:
lowerCamelCase__ : int = torch.tensor(
[[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]] )
# verify logits
assert torch.allclose(logits[0, :3, :3] , UpperCamelCase , atol=1E-4 )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCamelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCamelCase )
if __name__ == "__main__":
_A : str =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
type=str,
help='''URL of the checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
_A : Tuple =parser.parse_args()
convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 129 | 1 |
"""simple docstring"""
def a__ ( snake_case__ , snake_case__ ) -> str:
lowerCamelCase = len(snake_case__ )
lowerCamelCase = len(snake_case__ )
lowerCamelCase = (
first_str_length if first_str_length > second_str_length else second_str_length
)
lowerCamelCase = []
for char_count in range(snake_case__ ):
if char_count < first_str_length:
output_list.append(first_str[char_count] )
if char_count < second_str_length:
output_list.append(second_str[char_count] )
return "".join(snake_case__ )
if __name__ == "__main__":
print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)
| 291 |
"""simple docstring"""
def a__ ( snake_case__ ) -> list:
if len(snake_case__ ) < 2:
return collection
def circle_sort_util(snake_case__ , snake_case__ , snake_case__ ) -> bool:
lowerCamelCase = False
if low == high:
return swapped
lowerCamelCase = low
lowerCamelCase = high
while left < right:
if collection[left] > collection[right]:
lowerCamelCase , lowerCamelCase = (
collection[right],
collection[left],
)
lowerCamelCase = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
lowerCamelCase , lowerCamelCase = (
collection[right + 1],
collection[left],
)
lowerCamelCase = True
lowerCamelCase = low + int((high - low) / 2 )
lowerCamelCase = circle_sort_util(snake_case__ , snake_case__ , snake_case__ )
lowerCamelCase = circle_sort_util(snake_case__ , mid + 1 , snake_case__ )
return swapped or left_swap or right_swap
lowerCamelCase = True
while is_not_sorted is True:
lowerCamelCase = circle_sort_util(snake_case__ , 0 , len(snake_case__ ) - 1 )
return collection
if __name__ == "__main__":
lowerCAmelCase : Tuple = input("""Enter numbers separated by a comma:\n""").strip()
lowerCAmelCase : List[Any] = [int(item) for item in user_input.split(""",""")]
print(circle_sort(unsorted))
| 291 | 1 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : List[Any] ):
lowerCAmelCase__ : List[Any] = {}
def __lowerCAmelCase ( self : int ,lowercase_ : str ,lowercase_ : Any ,lowercase_ : Union[str, Any]=1 ):
if self.graph.get(lowercase_ ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
lowerCAmelCase__ : Optional[Any] = [[w, v]]
if not self.graph.get(lowercase_ ):
lowerCAmelCase__ : Tuple = []
def __lowerCAmelCase ( self : Any ):
return list(self.graph )
def __lowerCAmelCase ( self : List[Any] ,lowercase_ : Tuple ,lowercase_ : List[str] ):
if self.graph.get(lowercase_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowercase_ )
def __lowerCAmelCase ( self : List[str] ,lowercase_ : str=-2 ,lowercase_ : Dict=-1 ):
if s == d:
return []
lowerCAmelCase__ : List[str] = []
lowerCAmelCase__ : Tuple = []
if s == -2:
lowerCAmelCase__ : str = list(self.graph )[0]
stack.append(lowercase_ )
visited.append(lowercase_ )
lowerCAmelCase__ : Optional[int] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCAmelCase__ : Union[str, Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowercase_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
lowerCAmelCase__ : Optional[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowercase_ ) != 0:
lowerCAmelCase__ : Optional[int] = stack[len(lowercase_ ) - 1]
else:
lowerCAmelCase__ : Any = ss
# check if se have reached the starting point
if len(lowercase_ ) == 0:
return visited
def __lowerCAmelCase ( self : List[Any] ,lowercase_ : List[Any]=-1 ):
if c == -1:
lowerCAmelCase__ : Dict = floor(random() * 1_0_0_0_0 ) + 1_0
for i in range(lowercase_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 1_0_2 ) + 1 ):
lowerCAmelCase__ : Any = floor(random() * c ) + 1
if n != i:
self.add_pair(lowercase_ ,lowercase_ ,1 )
def __lowerCAmelCase ( self : str ,lowercase_ : int=-2 ):
lowerCAmelCase__ : Any = deque()
lowerCAmelCase__ : Optional[Any] = []
if s == -2:
lowerCAmelCase__ : Any = list(self.graph )[0]
d.append(lowercase_ )
visited.append(lowercase_ )
while d:
lowerCAmelCase__ : List[str] = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def __lowerCAmelCase ( self : str ,lowercase_ : Dict ):
lowerCAmelCase__ : List[Any] = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def __lowerCAmelCase ( self : Optional[Any] ,lowercase_ : List[str] ):
return len(self.graph[u] )
def __lowerCAmelCase ( self : Optional[int] ,lowercase_ : List[str]=-2 ):
lowerCAmelCase__ : str = []
lowerCAmelCase__ : str = []
if s == -2:
lowerCAmelCase__ : List[str] = list(self.graph )[0]
stack.append(lowercase_ )
visited.append(lowercase_ )
lowerCAmelCase__ : str = s
lowerCAmelCase__ : Optional[int] = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCAmelCase__ : Tuple = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCAmelCase__ : str = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(lowercase_ ) != 0:
lowerCAmelCase__ : List[str] = stack[len(lowercase_ ) - 1]
else:
lowerCAmelCase__ : Optional[Any] = ss
# check if se have reached the starting point
if len(lowercase_ ) == 0:
return sorted_nodes
def __lowerCAmelCase ( self : Union[str, Any] ):
lowerCAmelCase__ : str = []
lowerCAmelCase__ : str = []
lowerCAmelCase__ : str = list(self.graph )[0]
stack.append(lowercase_ )
visited.append(lowercase_ )
lowerCAmelCase__ : Union[str, Any] = -2
lowerCAmelCase__ : Union[str, Any] = []
lowerCAmelCase__ : Tuple = s
lowerCAmelCase__ : Optional[Any] = False
lowerCAmelCase__ : Any = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCAmelCase__ : List[str] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowerCAmelCase__ : Tuple = len(lowercase_ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCAmelCase__ : List[str] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowerCAmelCase__ : int = True
if len(lowercase_ ) != 0:
lowerCAmelCase__ : Optional[int] = stack[len(lowercase_ ) - 1]
else:
lowerCAmelCase__ : Optional[Any] = False
indirect_parents.append(lowercase_ )
lowerCAmelCase__ : str = s
lowerCAmelCase__ : Optional[int] = ss
# check if se have reached the starting point
if len(lowercase_ ) == 0:
return list(lowercase_ )
def __lowerCAmelCase ( self : Any ):
lowerCAmelCase__ : Union[str, Any] = []
lowerCAmelCase__ : Union[str, Any] = []
lowerCAmelCase__ : Optional[Any] = list(self.graph )[0]
stack.append(lowercase_ )
visited.append(lowercase_ )
lowerCAmelCase__ : Optional[Any] = -2
lowerCAmelCase__ : List[str] = []
lowerCAmelCase__ : Tuple = s
lowerCAmelCase__ : Any = False
lowerCAmelCase__ : Dict = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCAmelCase__ : Optional[Any] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowerCAmelCase__ : Union[str, Any] = len(lowercase_ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCAmelCase__ : int = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowerCAmelCase__ : List[Any] = True
if len(lowercase_ ) != 0:
lowerCAmelCase__ : Optional[Any] = stack[len(lowercase_ ) - 1]
else:
lowerCAmelCase__ : Union[str, Any] = False
indirect_parents.append(lowercase_ )
lowerCAmelCase__ : Optional[Any] = s
lowerCAmelCase__ : Tuple = ss
# check if se have reached the starting point
if len(lowercase_ ) == 0:
return False
def __lowerCAmelCase ( self : Optional[int] ,lowercase_ : Dict=-2 ,lowercase_ : List[str]=-1 ):
lowerCAmelCase__ : str = time()
self.dfs(lowercase_ ,lowercase_ )
lowerCAmelCase__ : int = time()
return end - begin
def __lowerCAmelCase ( self : Tuple ,lowercase_ : Optional[int]=-2 ):
lowerCAmelCase__ : List[str] = time()
self.bfs(lowercase_ )
lowerCAmelCase__ : Union[str, Any] = time()
return end - begin
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Optional[Any] ):
lowerCAmelCase__ : Tuple = {}
def __lowerCAmelCase ( self : Dict ,lowercase_ : Optional[int] ,lowercase_ : Union[str, Any] ,lowercase_ : str=1 ):
# check if the u exists
if self.graph.get(lowercase_ ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
lowerCAmelCase__ : List[Any] = [[w, v]]
# add the other way
if self.graph.get(lowercase_ ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
lowerCAmelCase__ : Dict = [[w, u]]
def __lowerCAmelCase ( self : Optional[int] ,lowercase_ : Optional[Any] ,lowercase_ : Tuple ):
if self.graph.get(lowercase_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowercase_ )
# the other way round
if self.graph.get(lowercase_ ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(lowercase_ )
def __lowerCAmelCase ( self : int ,lowercase_ : str=-2 ,lowercase_ : str=-1 ):
if s == d:
return []
lowerCAmelCase__ : Tuple = []
lowerCAmelCase__ : int = []
if s == -2:
lowerCAmelCase__ : str = list(self.graph )[0]
stack.append(lowercase_ )
visited.append(lowercase_ )
lowerCAmelCase__ : Optional[Any] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCAmelCase__ : str = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowercase_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
lowerCAmelCase__ : Tuple = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowercase_ ) != 0:
lowerCAmelCase__ : Optional[Any] = stack[len(lowercase_ ) - 1]
else:
lowerCAmelCase__ : Optional[int] = ss
# check if se have reached the starting point
if len(lowercase_ ) == 0:
return visited
def __lowerCAmelCase ( self : Union[str, Any] ,lowercase_ : Optional[Any]=-1 ):
if c == -1:
lowerCAmelCase__ : int = floor(random() * 1_0_0_0_0 ) + 1_0
for i in range(lowercase_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 1_0_2 ) + 1 ):
lowerCAmelCase__ : List[Any] = floor(random() * c ) + 1
if n != i:
self.add_pair(lowercase_ ,lowercase_ ,1 )
def __lowerCAmelCase ( self : Optional[Any] ,lowercase_ : Optional[Any]=-2 ):
lowerCAmelCase__ : Tuple = deque()
lowerCAmelCase__ : List[str] = []
if s == -2:
lowerCAmelCase__ : Union[str, Any] = list(self.graph )[0]
d.append(lowercase_ )
visited.append(lowercase_ )
while d:
lowerCAmelCase__ : Optional[int] = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def __lowerCAmelCase ( self : Any ,lowercase_ : int ):
return len(self.graph[u] )
def __lowerCAmelCase ( self : List[Any] ):
lowerCAmelCase__ : Union[str, Any] = []
lowerCAmelCase__ : List[str] = []
lowerCAmelCase__ : Optional[int] = list(self.graph )[0]
stack.append(lowercase_ )
visited.append(lowercase_ )
lowerCAmelCase__ : int = -2
lowerCAmelCase__ : str = []
lowerCAmelCase__ : Optional[Any] = s
lowerCAmelCase__ : Union[str, Any] = False
lowerCAmelCase__ : str = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCAmelCase__ : List[str] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowerCAmelCase__ : Union[str, Any] = len(lowercase_ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCAmelCase__ : int = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowerCAmelCase__ : int = True
if len(lowercase_ ) != 0:
lowerCAmelCase__ : Union[str, Any] = stack[len(lowercase_ ) - 1]
else:
lowerCAmelCase__ : Union[str, Any] = False
indirect_parents.append(lowercase_ )
lowerCAmelCase__ : Tuple = s
lowerCAmelCase__ : List[Any] = ss
# check if se have reached the starting point
if len(lowercase_ ) == 0:
return list(lowercase_ )
def __lowerCAmelCase ( self : List[Any] ):
lowerCAmelCase__ : Dict = []
lowerCAmelCase__ : str = []
lowerCAmelCase__ : str = list(self.graph )[0]
stack.append(lowercase_ )
visited.append(lowercase_ )
lowerCAmelCase__ : str = -2
lowerCAmelCase__ : Optional[Any] = []
lowerCAmelCase__ : Optional[int] = s
lowerCAmelCase__ : Union[str, Any] = False
lowerCAmelCase__ : Any = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCAmelCase__ : List[Any] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowerCAmelCase__ : List[str] = len(lowercase_ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCAmelCase__ : Any = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowerCAmelCase__ : str = True
if len(lowercase_ ) != 0:
lowerCAmelCase__ : int = stack[len(lowercase_ ) - 1]
else:
lowerCAmelCase__ : int = False
indirect_parents.append(lowercase_ )
lowerCAmelCase__ : List[Any] = s
lowerCAmelCase__ : str = ss
# check if se have reached the starting point
if len(lowercase_ ) == 0:
return False
def __lowerCAmelCase ( self : List[Any] ):
return list(self.graph )
def __lowerCAmelCase ( self : Any ,lowercase_ : List[str]=-2 ,lowercase_ : Tuple=-1 ):
lowerCAmelCase__ : List[str] = time()
self.dfs(lowercase_ ,lowercase_ )
lowerCAmelCase__ : Optional[Any] = time()
return end - begin
def __lowerCAmelCase ( self : Tuple ,lowercase_ : List[str]=-2 ):
lowerCAmelCase__ : int = time()
self.bfs(lowercase_ )
lowerCAmelCase__ : Optional[int] = time()
return end - begin
| 74 |
"""simple docstring"""
from manim import *
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def __lowerCAmelCase ( self : List[Any] ):
lowerCAmelCase__ : List[str] = Rectangle(height=0.5 ,width=0.5 )
lowerCAmelCase__ : List[Any] = Rectangle(height=0.46 ,width=0.46 ).set_stroke(width=0 )
lowerCAmelCase__ : Tuple = [mem.copy() for i in range(6 )]
lowerCAmelCase__ : str = [mem.copy() for i in range(6 )]
lowerCAmelCase__ : Optional[Any] = VGroup(*lowercase_ ).arrange(lowercase_ ,buff=0 )
lowerCAmelCase__ : Optional[Any] = VGroup(*lowercase_ ).arrange(lowercase_ ,buff=0 )
lowerCAmelCase__ : Optional[int] = VGroup(lowercase_ ,lowercase_ ).arrange(lowercase_ ,buff=0 )
lowerCAmelCase__ : Dict = Text('''CPU''' ,font_size=2_4 )
lowerCAmelCase__ : Optional[int] = Group(lowercase_ ,lowercase_ ).arrange(lowercase_ ,buff=0.5 ,aligned_edge=lowercase_ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(lowercase_ )
lowerCAmelCase__ : Tuple = [mem.copy() for i in range(4 )]
lowerCAmelCase__ : List[Any] = VGroup(*lowercase_ ).arrange(lowercase_ ,buff=0 )
lowerCAmelCase__ : Any = Text('''GPU''' ,font_size=2_4 )
lowerCAmelCase__ : str = Group(lowercase_ ,lowercase_ ).arrange(lowercase_ ,buff=0.5 ,aligned_edge=lowercase_ )
gpu.move_to([-1, -1, 0] )
self.add(lowercase_ )
lowerCAmelCase__ : str = [mem.copy() for i in range(6 )]
lowerCAmelCase__ : List[str] = VGroup(*lowercase_ ).arrange(lowercase_ ,buff=0 )
lowerCAmelCase__ : Tuple = Text('''Model''' ,font_size=2_4 )
lowerCAmelCase__ : List[str] = Group(lowercase_ ,lowercase_ ).arrange(lowercase_ ,buff=0.5 ,aligned_edge=lowercase_ )
model.move_to([3, -1.0, 0] )
self.add(lowercase_ )
lowerCAmelCase__ : Tuple = []
for i, rect in enumerate(lowercase_ ):
rect.set_stroke(lowercase_ )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
lowerCAmelCase__ : List[Any] = Rectangle(height=0.46 / 4 ,width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(lowercase_ ,opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) ,buff=0.02 ,direction=lowercase_ )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] ,direction=lowercase_ ,buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] ,direction=lowercase_ ,buff=0.0 )
self.add(lowercase_ )
cpu_targs.append(lowercase_ )
lowerCAmelCase__ : List[str] = [mem.copy() for i in range(6 )]
lowerCAmelCase__ : Optional[int] = VGroup(*lowercase_ ).arrange(lowercase_ ,buff=0 )
lowerCAmelCase__ : Dict = Text('''Loaded Checkpoint''' ,font_size=2_4 )
lowerCAmelCase__ : List[str] = Group(lowercase_ ,lowercase_ ).arrange(lowercase_ ,aligned_edge=lowercase_ ,buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
lowerCAmelCase__ : List[Any] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
lowerCAmelCase__ : Optional[Any] = MarkupText(
F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' ,font_size=1_8 ,)
key_text.move_to([-5, 2.4, 0] )
self.add(lowercase_ ,lowercase_ )
lowerCAmelCase__ : Tuple = MarkupText(
F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint' ,font_size=1_8 ,)
blue_text.next_to(lowercase_ ,DOWN * 2.4 ,aligned_edge=key_text.get_left() )
lowerCAmelCase__ : Tuple = MarkupText(
F'Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.' ,font_size=2_4 ,)
step_a.move_to([2, 2, 0] )
self.play(Write(lowercase_ ) ,Write(lowercase_ ) )
self.play(Write(lowercase_ ,run_time=1 ) ,Create(lowercase_ ,run_time=1 ) )
lowerCAmelCase__ : int = []
lowerCAmelCase__ : Optional[Any] = []
for i, rect in enumerate(lowercase_ ):
lowerCAmelCase__ : List[str] = fill.copy().set_fill(lowercase_ ,opacity=0.7 )
target.move_to(lowercase_ )
first_animations.append(GrowFromCenter(lowercase_ ,run_time=1 ) )
lowerCAmelCase__ : List[Any] = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(lowercase_ ,run_time=1.5 ) )
self.play(*lowercase_ )
self.play(*lowercase_ )
self.wait()
| 74 | 1 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowerCAmelCase__ = get_logger(__name__)
class a__ ( enum.Enum ):
"""simple docstring"""
__lowerCamelCase = 'all_checks'
__lowerCamelCase = 'basic_checks'
__lowerCamelCase = 'no_checks'
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[dict] , SCREAMING_SNAKE_CASE_: dict , SCREAMING_SNAKE_CASE_: Tuple=None ) -> Union[str, Any]:
'''simple docstring'''
if expected_checksums is None:
logger.info("Unable to verify checksums." )
return
if len(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) > 0:
raise ExpectedMoreDownloadedFiles(str(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) )
if len(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) > 0:
raise UnexpectedDownloadedFile(str(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) )
A__ = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
A__ = " for " + verification_name if verification_name is not None else ""
if len(SCREAMING_SNAKE_CASE_ ) > 0:
raise NonMatchingChecksumError(
F'Checksums didn\'t match{for_verification_name}:\n'
F'{bad_urls}\n'
"Set `verification_mode='no_checks'` to skip checksums verification and ignore this error" )
logger.info("All the checksums matched successfully" + for_verification_name )
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[dict] , SCREAMING_SNAKE_CASE_: dict ) -> List[str]:
'''simple docstring'''
if expected_splits is None:
logger.info("Unable to verify splits sizes." )
return
if len(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) > 0:
raise ExpectedMoreSplits(str(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) )
if len(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) > 0:
raise UnexpectedSplits(str(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) )
A__ = [
{"expected": expected_splits[name], "recorded": recorded_splits[name]}
for name in expected_splits
if expected_splits[name].num_examples != recorded_splits[name].num_examples
]
if len(SCREAMING_SNAKE_CASE_ ) > 0:
raise NonMatchingSplitsSizesError(str(SCREAMING_SNAKE_CASE_ ) )
logger.info("All the splits matched successfully." )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: bool = True ) -> dict:
'''simple docstring'''
if record_checksum:
A__ = shaaaa()
with open(SCREAMING_SNAKE_CASE_ , "rb" ) as f:
for chunk in iter(lambda: f.read(1 << 2_0 ) , b"" ):
m.update(SCREAMING_SNAKE_CASE_ )
A__ = m.hexdigest()
else:
A__ = None
return {"num_bytes": os.path.getsize(SCREAMING_SNAKE_CASE_ ), "checksum": checksum}
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] ) -> str:
'''simple docstring'''
if dataset_size and config.IN_MEMORY_MAX_SIZE:
return dataset_size < config.IN_MEMORY_MAX_SIZE
else:
return False
| 68 |
# Copyright 2021 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.
from packaging import version
from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
copy_func,
replace_return_docstrings,
)
from .generic import (
ContextManagers,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
TensorType,
add_model_info_to_auto_map,
cached_property,
can_return_loss,
expand_dims,
find_labels,
flatten_dict,
infer_framework,
is_jax_tensor,
is_numpy_array,
is_tensor,
is_tf_symbolic_tensor,
is_tf_tensor,
is_torch_device,
is_torch_dtype,
is_torch_tensor,
reshape,
squeeze,
strtobool,
tensor_size,
to_numpy,
to_py_obj,
transpose,
working_or_temp_dir,
)
from .hub import (
CLOUDFRONT_DISTRIB_PREFIX,
DISABLE_TELEMETRY,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
EntryNotFoundError,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
cached_file,
default_cache_path,
define_sagemaker_information,
download_url,
extract_commit_hash,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
has_file,
http_user_agent,
is_offline_mode,
is_remote_url,
move_cache,
send_example_telemetry,
try_to_load_from_cache,
)
from .import_utils import (
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
TORCH_FX_REQUIRED_VERSION,
USE_JAX,
USE_TF,
USE_TORCH,
DummyObject,
OptionalDependencyNotAvailable,
_LazyModule,
ccl_version,
direct_transformers_import,
get_torch_version,
is_accelerate_available,
is_apex_available,
is_bitsandbytes_available,
is_bsa_available,
is_coloredlogs_available,
is_cython_available,
is_datasets_available,
is_decord_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_jieba_available,
is_jumanpp_available,
is_kenlm_available,
is_keras_nlp_available,
is_librosa_available,
is_natten_available,
is_ninja_available,
is_onnx_available,
is_openai_available,
is_optimum_available,
is_pandas_available,
is_peft_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytest_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sacremoses_available,
is_safetensors_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_sudachi_available,
is_tensorflow_probability_available,
is_tensorflow_text_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_bfaa_cpu_available,
is_torch_bfaa_gpu_available,
is_torch_compile_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_neuroncore_available,
is_torch_tensorrt_fx_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_torchdistx_available,
is_torchdynamo_available,
is_torchvision_available,
is_training_run_on_sagemaker,
is_vision_available,
requires_backends,
torch_only_method,
)
SCREAMING_SNAKE_CASE :List[str] = 'pytorch_model.bin'
SCREAMING_SNAKE_CASE :str = 'pytorch_model.bin.index.json'
SCREAMING_SNAKE_CASE :Optional[int] = 'adapter_config.json'
SCREAMING_SNAKE_CASE :Dict = 'adapter_model.bin'
SCREAMING_SNAKE_CASE :Dict = 'adapter_model.safetensors'
SCREAMING_SNAKE_CASE :str = 'tf_model.h5'
SCREAMING_SNAKE_CASE :List[Any] = 'tf_model.h5.index.json'
SCREAMING_SNAKE_CASE :str = 'model.ckpt'
SCREAMING_SNAKE_CASE :List[Any] = 'flax_model.msgpack'
SCREAMING_SNAKE_CASE :Optional[int] = 'flax_model.msgpack.index.json'
SCREAMING_SNAKE_CASE :Tuple = 'model.safetensors'
SCREAMING_SNAKE_CASE :List[Any] = 'model.safetensors.index.json'
SCREAMING_SNAKE_CASE :str = 'config.json'
SCREAMING_SNAKE_CASE :int = 'preprocessor_config.json'
SCREAMING_SNAKE_CASE :Optional[Any] = FEATURE_EXTRACTOR_NAME
SCREAMING_SNAKE_CASE :Optional[int] = 'generation_config.json'
SCREAMING_SNAKE_CASE :List[str] = 'modelcard.json'
SCREAMING_SNAKE_CASE :Optional[int] = '▁'
SCREAMING_SNAKE_CASE :Optional[Any] = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility
SCREAMING_SNAKE_CASE :str = [
[[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2 # Needs to have 0s and 1s only since XLM uses it for langs too.
SCREAMING_SNAKE_CASE :Optional[Any] = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
SCREAMING_SNAKE_CASE :List[Any] = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
def UpperCAmelCase ( a_ ) -> Dict:
"""simple docstring"""
if version.parse(a_ ) < version.parse(a_ ):
if "dev" in min_version:
__A = (
"This example requires a source install from HuggingFace Transformers (see "
"`https://huggingface.co/docs/transformers/installation#install-from-source`),"
)
else:
__A = F'''This example requires a minimum version of {min_version},'''
error_message += F''' but the version found is {__version__}.\n'''
raise ImportError(
error_message
+ "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
"versions of HuggingFace Transformers." )
| 15 | 0 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCamelCase = '''src/diffusers'''
UpperCamelCase = '''.'''
# This is to make sure the diffusers module imported is the one in the repo.
UpperCamelCase = importlib.util.spec_from_file_location(
'''diffusers''',
os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
submodule_search_locations=[DIFFUSERS_PATH],
)
UpperCamelCase = spec.loader.load_module()
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> str:
return line.startswith(__lowercase ) or len(__lowercase ) <= 1 or re.search(r'''^\s*\)(\s*->.*:|:)\s*$''' , __lowercase ) is not None
def SCREAMING_SNAKE_CASE( __lowercase ) -> Tuple:
A: int = object_name.split('''.''' )
A: Optional[Any] = 0
# First let's find the module where our object lives.
A: Union[str, Any] = parts[i]
while i < len(__lowercase ) and not os.path.isfile(os.path.join(__lowercase , F"""{module}.py""" ) ):
i += 1
if i < len(__lowercase ):
A: Tuple = os.path.join(__lowercase , parts[i] )
if i >= len(__lowercase ):
raise ValueError(F"""`object_name` should begin with the name of a module of diffusers but got {object_name}.""" )
with open(os.path.join(__lowercase , F"""{module}.py""" ) , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
A: List[str] = f.readlines()
# Now let's find the class / func in the code!
A: Optional[Any] = ''''''
A: List[Any] = 0
for name in parts[i + 1 :]:
while (
line_index < len(__lowercase ) and re.search(rF"""^{indent}(class|def)\s+{name}(\(|\:)""" , lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(__lowercase ):
raise ValueError(F""" {object_name} does not match any function or class in {module}.""" )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
A: Optional[Any] = line_index
while line_index < len(__lowercase ) and _should_continue(lines[line_index] , __lowercase ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
A: Optional[Any] = lines[start_index:line_index]
return "".join(__lowercase )
UpperCamelCase = re.compile(R'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
UpperCamelCase = re.compile(R'''^\s*(\S+)->(\S+)(\s+.*|$)''')
UpperCamelCase = re.compile(R'''<FILL\s+[^>]*>''')
def SCREAMING_SNAKE_CASE( __lowercase ) -> int:
A: Union[str, Any] = code.split('''\n''' )
A: int = 0
while idx < len(__lowercase ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(__lowercase ):
return re.search(r'''^(\s*)\S''' , lines[idx] ).groups()[0]
return ""
def SCREAMING_SNAKE_CASE( __lowercase ) -> Union[str, Any]:
A: int = len(get_indent(__lowercase ) ) > 0
if has_indent:
A: List[str] = F"""class Bla:\n{code}"""
A: List[str] = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_1_9 , preview=__lowercase )
A: int = black.format_str(__lowercase , mode=__lowercase )
A , A: List[Any] = style_docstrings_in_code(__lowercase )
return result[len('''class Bla:\n''' ) :] if has_indent else result
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase=False ) -> str:
with open(__lowercase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
A: List[Any] = f.readlines()
A: Tuple = []
A: Dict = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(__lowercase ):
A: Tuple = _re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
A , A , A: Any = search.groups()
A: List[Any] = find_code_in_diffusers(__lowercase )
A: Tuple = get_indent(__lowercase )
A: List[Any] = line_index + 1 if indent == theoretical_indent else line_index + 2
A: Dict = theoretical_indent
A: Optional[Any] = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
A: Tuple = True
while line_index < len(__lowercase ) and should_continue:
line_index += 1
if line_index >= len(__lowercase ):
break
A: Optional[Any] = lines[line_index]
A: int = _should_continue(__lowercase , __lowercase ) and re.search(F"""^{indent}# End copy""" , __lowercase ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
A: Any = lines[start_index:line_index]
A: Dict = ''''''.join(__lowercase )
# Remove any nested `Copied from` comments to avoid circular copies
A: Any = [line for line in theoretical_code.split('''\n''' ) if _re_copy_warning.search(__lowercase ) is None]
A: Union[str, Any] = '''\n'''.join(__lowercase )
# Before comparing, use the `replace_pattern` on the original code.
if len(__lowercase ) > 0:
A: Optional[Any] = replace_pattern.replace('''with''' , '''''' ).split(''',''' )
A: List[str] = [_re_replace_pattern.search(__lowercase ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
A , A , A: List[str] = pattern.groups()
A: Tuple = re.sub(__lowercase , __lowercase , __lowercase )
if option.strip() == "all-casing":
A: str = re.sub(obja.lower() , obja.lower() , __lowercase )
A: Dict = re.sub(obja.upper() , obja.upper() , __lowercase )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
A: Any = blackify(lines[start_index - 1] + theoretical_code )
A: Any = theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
A: Tuple = lines[:start_index] + [theoretical_code] + lines[line_index:]
A: str = start_index + 1
if overwrite and len(__lowercase ) > 0:
# Warn the user a file has been modified.
print(F"""Detected changes, rewriting {filename}.""" )
with open(__lowercase , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(__lowercase )
return diffs
def SCREAMING_SNAKE_CASE( __lowercase = False ) -> Any:
A: Dict = glob.glob(os.path.join(__lowercase , '''**/*.py''' ) , recursive=__lowercase )
A: Tuple = []
for filename in all_files:
A: List[Any] = is_copy_consistent(__lowercase , __lowercase )
diffs += [F"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
if not overwrite and len(__lowercase ) > 0:
A: Tuple = '''\n'''.join(__lowercase )
raise Exception(
'''Found the following copy inconsistencies:\n'''
+ diff
+ '''\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.''' )
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
UpperCamelCase = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 334 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE( __lowercase = 4 ) -> list[list[int]]:
A: Tuple = abs(__lowercase ) or 4
return [[1 + x + y * row_size for x in range(__lowercase )] for y in range(__lowercase )]
def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]:
return reverse_row(transpose(__lowercase ) )
# OR.. transpose(reverse_column(matrix))
def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]:
return reverse_row(reverse_column(__lowercase ) )
# OR.. reverse_column(reverse_row(matrix))
def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]:
return reverse_column(transpose(__lowercase ) )
# OR.. transpose(reverse_row(matrix))
def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]:
A: Union[str, Any] = [list(__lowercase ) for x in zip(*__lowercase )]
return matrix
def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]:
A: Optional[int] = matrix[::-1]
return matrix
def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]:
A: Optional[Any] = [x[::-1] for x in matrix]
return matrix
def SCREAMING_SNAKE_CASE( __lowercase ) -> None:
for i in matrix:
print(*__lowercase )
if __name__ == "__main__":
UpperCamelCase = make_matrix()
print('''\norigin:\n''')
print_matrix(matrix)
print('''\nrotate 90 counterclockwise:\n''')
print_matrix(rotate_aa(matrix))
UpperCamelCase = make_matrix()
print('''\norigin:\n''')
print_matrix(matrix)
print('''\nrotate 180:\n''')
print_matrix(rotate_aaa(matrix))
UpperCamelCase = make_matrix()
print('''\norigin:\n''')
print_matrix(matrix)
print('''\nrotate 270 counterclockwise:\n''')
print_matrix(rotate_aaa(matrix))
| 334 | 1 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class __A( nn.Module ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "geglu" , SCREAMING_SNAKE_CASE_ = None , ):
super().__init__()
UpperCamelCase__ = 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
UpperCamelCase__ = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
UpperCamelCase__ = [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])`
UpperCamelCase__ = [1, 0]
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_ = True , ):
UpperCamelCase__ = hidden_states
UpperCamelCase__ = []
UpperCamelCase__ = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
UpperCamelCase__ = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
UpperCamelCase__ = self.transformer_index_for_condition[i]
UpperCamelCase__ = 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]
UpperCamelCase__ = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
UpperCamelCase__ = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE_ )
| 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 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Union[str, 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 __UpperCamelCase ( _A ):
SCREAMING_SNAKE_CASE = "speech_to_text_2"
SCREAMING_SNAKE_CASE = ["past_key_values"]
SCREAMING_SNAKE_CASE = {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}
def __init__(self : Any , __SCREAMING_SNAKE_CASE : List[str]=1_0_0_0_0 , __SCREAMING_SNAKE_CASE : Dict=6 , __SCREAMING_SNAKE_CASE : Dict=2_0_4_8 , __SCREAMING_SNAKE_CASE : str=4 , __SCREAMING_SNAKE_CASE : int=0.0 , __SCREAMING_SNAKE_CASE : Tuple=True , __SCREAMING_SNAKE_CASE : List[Any]="relu" , __SCREAMING_SNAKE_CASE : Union[str, Any]=2_5_6 , __SCREAMING_SNAKE_CASE : int=0.1 , __SCREAMING_SNAKE_CASE : List[str]=0.0 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , __SCREAMING_SNAKE_CASE : Tuple=0.0_2 , __SCREAMING_SNAKE_CASE : int=2 , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : Any=1 , __SCREAMING_SNAKE_CASE : List[str]=0 , __SCREAMING_SNAKE_CASE : Optional[Any]=2 , __SCREAMING_SNAKE_CASE : int=1_0_2_4 , **__SCREAMING_SNAKE_CASE : str , ):
A = vocab_size
A = d_model
A = decoder_ffn_dim
A = decoder_layers
A = decoder_attention_heads
A = dropout
A = attention_dropout
A = activation_dropout
A = activation_function
A = init_std
A = decoder_layerdrop
A = use_cache
A = decoder_layers
A = scale_embedding # scale factor will be sqrt(d_model) if True
A = max_target_positions
super().__init__(
pad_token_id=__SCREAMING_SNAKE_CASE , bos_token_id=__SCREAMING_SNAKE_CASE , eos_token_id=__SCREAMING_SNAKE_CASE , decoder_start_token_id=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
| 57 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : str = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : int = [
'PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST',
'PegasusXForConditionalGeneration',
'PegasusXModel',
'PegasusXPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
__A : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 57 | 1 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCAmelCase : List[Any] = logging.get_logger(__name__)
def __snake_case ( _lowerCAmelCase : Union[str, Any] ) -> Tuple:
A_ : Optional[int] = torch.load(_lowerCAmelCase , map_location="cpu" )
if "model" in sd.keys():
A_ : List[str] = torch.load(_lowerCAmelCase , map_location="cpu" )["model"]
# pop unnecessary weights
A_ : str = [
"decoder.version",
"decoder.output_projection.weight",
]
for key in keys_to_delete:
if key in sd:
sd.pop(_lowerCAmelCase )
A_ : Optional[int] = {
"decoder.project_in_dim.weight": "decoder.project_in.weight",
"decoder.project_out_dim.weight": "decoder.project_out.weight",
"decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
"decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
A_ : Any = sd.pop(_lowerCAmelCase )
A_ : Any = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
A_ : Union[str, Any] = sd[key]
# We split QKV in separate Q,K,V
A_ : Optional[Any] = key.replace(".qkv_proj." , ".q_proj." )
A_ : int = key.replace(".qkv_proj." , ".k_proj." )
A_ : Optional[Any] = key.replace(".qkv_proj." , ".v_proj." )
A_ : str = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
A_ , A_ , A_ : Dict = torch.split(_lowerCAmelCase , depth // 3 , dim=0 )
A_ : List[Any] = q
A_ : Dict = k
A_ : Tuple = v
del sd[key]
return sd
@torch.no_grad()
def __snake_case ( _lowerCAmelCase : int , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str]=None ) -> List[Any]:
A_ : Any = load_checkpoint(_lowerCAmelCase )
if config is not None:
A_ : Tuple = OPTConfig.from_pretrained(_lowerCAmelCase )
else:
A_ : Any = OPTConfig()
A_ : int = OPTModel(_lowerCAmelCase ).half().eval()
model.load_state_dict(_lowerCAmelCase )
# Check results
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
_lowerCAmelCase : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--fairseq_path''',
type=str,
help=(
'''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
''' https://huggingface.co/models?other=opt_metasq'''
),
)
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
_lowerCAmelCase : List[str] = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 300 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __snake_case ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] ) -> str:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def __snake_case ( _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any] ) -> Optional[int]:
A_ : Tuple = tmp_path / "cache"
A_ : Optional[int] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
A_ : Optional[Any] = ParquetDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase , keep_in_memory=_lowerCAmelCase ).read()
_check_parquet_dataset(_lowerCAmelCase , _lowerCAmelCase )
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def __snake_case ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] ) -> str:
A_ : List[Any] = tmp_path / "cache"
A_ : List[str] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
A_ : int = features.copy() if features else default_expected_features
A_ : str = (
Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
A_ : Union[str, Any] = ParquetDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read()
_check_parquet_dataset(_lowerCAmelCase , _lowerCAmelCase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def __snake_case ( _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : Any ) -> Optional[Any]:
A_ : Dict = tmp_path / "cache"
A_ : int = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
A_ : Optional[int] = ParquetDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase , split=_lowerCAmelCase ).read()
_check_parquet_dataset(_lowerCAmelCase , _lowerCAmelCase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type" , [str, list] )
def __snake_case ( _lowerCAmelCase : List[str] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] ) -> List[str]:
if issubclass(_lowerCAmelCase , _lowerCAmelCase ):
A_ : int = parquet_path
elif issubclass(_lowerCAmelCase , _lowerCAmelCase ):
A_ : Optional[int] = [parquet_path]
A_ : Optional[int] = tmp_path / "cache"
A_ : Union[str, Any] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
A_ : Optional[int] = ParquetDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read()
_check_parquet_dataset(_lowerCAmelCase , _lowerCAmelCase )
def __snake_case ( _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Union[str, Any]=("train",) ) -> Tuple:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
for split in splits:
A_ : List[str] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def __snake_case ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict ) -> Optional[int]:
A_ : Optional[Any] = tmp_path / "cache"
A_ : Union[str, Any] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
A_ : Union[str, Any] = ParquetDatasetReader(
{"train": parquet_path} , cache_dir=_lowerCAmelCase , keep_in_memory=_lowerCAmelCase ).read()
_check_parquet_datasetdict(_lowerCAmelCase , _lowerCAmelCase )
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def __snake_case ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : str ) -> Tuple:
A_ : Optional[Any] = tmp_path / "cache"
A_ : Any = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
A_ : List[str] = features.copy() if features else default_expected_features
A_ : Tuple = (
Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
A_ : Optional[int] = ParquetDatasetReader({"train": parquet_path} , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read()
_check_parquet_datasetdict(_lowerCAmelCase , _lowerCAmelCase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def __snake_case ( _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : Any ) -> Union[str, Any]:
if split:
A_ : Any = {split: parquet_path}
else:
A_ : Optional[Any] = "train"
A_ : str = {"train": parquet_path, "test": parquet_path}
A_ : Any = tmp_path / "cache"
A_ : str = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
A_ : Dict = ParquetDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read()
_check_parquet_datasetdict(_lowerCAmelCase , _lowerCAmelCase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def __snake_case ( _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[Any] ) -> Dict:
A_ : List[str] = ParquetDatasetWriter(_lowerCAmelCase , tmp_path / "foo.parquet" )
assert writer.write() > 0
A_ : Tuple = pq.ParquetFile(tmp_path / "foo.parquet" )
A_ : Dict = pf.read()
assert dataset.data.table == output_table
def __snake_case ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : int ) -> List[Any]:
A_ : Tuple = str(shared_datadir / "test_image_rgb.jpg" )
A_ : int = {"image": [image_path]}
A_ : Optional[Any] = Features({"image": Image()} )
A_ : Union[str, Any] = Dataset.from_dict(_lowerCAmelCase , features=_lowerCAmelCase )
A_ : Tuple = ParquetDatasetWriter(_lowerCAmelCase , tmp_path / "foo.parquet" )
assert writer.write() > 0
A_ : str = Dataset.from_parquet(str(tmp_path / "foo.parquet" ) )
assert dataset.features == reloaded_dataset.features
A_ : int = ParquetDatasetReader(str(tmp_path / "foo.parquet" ) , streaming=_lowerCAmelCase ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"feature, expected" , [
(Features({"foo": Value("int32" )} ), None),
(Features({"image": Image(), "foo": Value("int32" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"nested": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def __snake_case ( _lowerCAmelCase : str , _lowerCAmelCase : List[Any] ) -> Any:
assert get_writer_batch_size(_lowerCAmelCase ) == expected
| 300 | 1 |
"""simple docstring"""
def A_ ( snake_case_ : List[Any] ,snake_case_ : Any ,snake_case_ : Optional[Any] ,snake_case_ : Dict=None ):
'''simple docstring'''
UpperCamelCase : str = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
UpperCamelCase : Any = True, True
UpperCamelCase : Optional[Any] = dfs(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
return path
def A_ ( snake_case_ : List[Any] ,snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : int = 0
UpperCamelCase : Any = -1
for i in range(snake_case_ ):
if i not in graph.keys():
continue
if len(graph[i] ) % 2 == 1:
odd_degree_nodes += 1
UpperCamelCase : int = i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def A_ ( snake_case_ : int ,snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : int = [[False for _ in range(max_node + 1 )] for _ in range(max_node + 1 )]
UpperCamelCase : int = check_circuit_or_path(snake_case_ ,snake_case_ )
if check == 3:
print("""graph is not Eulerian""" )
print("""no path""" )
return
UpperCamelCase : str = 1
if check == 2:
UpperCamelCase : Optional[Any] = odd_node
print("""graph has a Euler path""" )
if check == 1:
print("""graph has a Euler cycle""" )
UpperCamelCase : Tuple = dfs(snake_case_ ,snake_case_ ,snake_case_ )
print(snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Optional[int] = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
UpperCamelCase : int = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
UpperCamelCase : Union[str, Any] = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
UpperCamelCase : List[Any] = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
UpperCamelCase : int = {
1: [],
2: []
# all degree is zero
}
UpperCamelCase : Any = 1_0
check_euler(snake_case_ ,snake_case_ )
check_euler(snake_case_ ,snake_case_ )
check_euler(snake_case_ ,snake_case_ )
check_euler(snake_case_ ,snake_case_ )
check_euler(snake_case_ ,snake_case_ )
if __name__ == "__main__":
main()
| 352 |
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):
return f'Node({self.data})'
class lowerCamelCase :
def __init__( self ):
UpperCamelCase : Dict = None
def __iter__( self ):
UpperCamelCase : int = self.head
while node:
yield node.data
UpperCamelCase : Union[str, Any] = node.next
def __len__( self ):
return sum(1 for _ in self )
def __repr__( self ):
return "->".join([str(SCREAMING_SNAKE_CASE_ ) for item in self] )
def __getitem__( self , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
UpperCamelCase : List[Any] = self.head
for _ in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = current.next
UpperCamelCase : Optional[Any] = data
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(len(self ) , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(0 , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index <= len(self ):
raise IndexError("""list index out of range""" )
UpperCamelCase : Optional[Any] = Node(SCREAMING_SNAKE_CASE_ )
if self.head is None:
UpperCamelCase : Dict = new_node
elif index == 0:
UpperCamelCase : Any = self.head # link new_node to head
UpperCamelCase : Any = new_node
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : str = temp.next
UpperCamelCase : Any = temp.next
UpperCamelCase : Optional[Any] = new_node
def a_ ( self ): # print every node data
print(self )
def a_ ( self ):
return self.delete_nth(0 )
def a_ ( self ): # delete from tail
return self.delete_nth(len(self ) - 1 )
def a_ ( self , SCREAMING_SNAKE_CASE_ = 0 ):
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("""List index out of range.""" )
UpperCamelCase : Union[str, Any] = self.head # default first node
if index == 0:
UpperCamelCase : Optional[Any] = self.head.next
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : int = temp.next
UpperCamelCase : Optional[Any] = temp.next
UpperCamelCase : Dict = temp.next.next
return delete_node.data
def a_ ( self ):
return self.head is None
def a_ ( self ):
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Union[str, Any] = self.head
while current:
# Store the current node's next node.
UpperCamelCase : Optional[int] = current.next
# Make the current node's next point backwards
UpperCamelCase : Optional[Any] = prev
# Make the previous node be the current node
UpperCamelCase : int = current
# Make the current node the next node (to progress iteration)
UpperCamelCase : Optional[int] = next_node
# Return prev in order to put the head at the end
UpperCamelCase : Optional[int] = prev
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = LinkedList()
assert linked_list.is_empty() is True
assert str(snake_case_ ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(1_0 ):
assert len(snake_case_ ) == i
linked_list.insert_nth(snake_case_ ,i + 1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_1 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(1_1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(0 ,1_2 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 1_0
assert linked_list.delete_tail() == 1_1
assert len(snake_case_ ) == 9
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_0 ) )
assert all(linked_list[i] == i + 1 for i in range(0 ,9 ) ) is True
for i in range(0 ,9 ):
UpperCamelCase : Optional[Any] = -i
assert all(linked_list[i] == -i for i in range(0 ,9 ) ) is True
linked_list.reverse()
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(-8 ,1 ) )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = [
-9,
1_0_0,
Node(7_7_3_4_5_1_1_2 ),
"""dlrow olleH""",
7,
5_5_5_5,
0,
-192.55555,
"""Hello, world!""",
77.9,
Node(1_0 ),
None,
None,
12.20,
]
UpperCamelCase : List[Any] = LinkedList()
for i in test_input:
linked_list.insert_tail(snake_case_ )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(snake_case_ ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
UpperCamelCase : Dict = linked_list.delete_head()
assert result == -9
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
UpperCamelCase : int = linked_list.delete_tail()
assert result == 12.2
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
UpperCamelCase : Optional[Any] = linked_list.delete_nth(1_0 )
assert result is None
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("""Hello again, world!""" ) )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(snake_case_ )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(snake_case_ )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def A_ ( ):
'''simple docstring'''
from doctest import testmod
testmod()
UpperCamelCase : List[Any] = LinkedList()
linked_list.insert_head(input("""Inserting 1st at head """ ).strip() )
linked_list.insert_head(input("""Inserting 2nd at head """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
linked_list.insert_tail(input("""\nInserting 1st at tail """ ).strip() )
linked_list.insert_tail(input("""Inserting 2nd at tail """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nDelete head""" )
linked_list.delete_head()
print("""Delete tail""" )
linked_list.delete_tail()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nReverse linked list""" )
linked_list.reverse()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nString representation of linked list:""" )
print(snake_case_ )
print("""\nReading/changing Node data using indexing:""" )
print(f'Element at Position 1: {linked_list[1]}' )
UpperCamelCase : List[Any] = input("""Enter New Value: """ ).strip()
print("""New list:""" )
print(snake_case_ )
print(f'length of linked_list is : {len(snake_case_ )}' )
if __name__ == "__main__":
main()
| 27 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_canine''': ['''CanineTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase = [
'''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CanineForMultipleChoice''',
'''CanineForQuestionAnswering''',
'''CanineForSequenceClassification''',
'''CanineForTokenClassification''',
'''CanineLayer''',
'''CanineModel''',
'''CaninePreTrainedModel''',
'''load_tf_weights_in_canine''',
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 188 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
lowerCamelCase = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase__ )
class __magic_name__ ( lowerCamelCase__ ):
'''simple docstring'''
def __init__( self, **lowercase_ ) -> Dict:
"""simple docstring"""
super().__init__(**lowercase_ )
if self.framework != "pt":
raise ValueError(F"""The {self.__class__} is only available in PyTorch.""" )
# No specific FOR_XXX available yet
def __call__( self, lowercase_, **lowercase_ ) -> Tuple:
"""simple docstring"""
return super().__call__(lowercase_, **lowercase_ )
def _UpperCAmelCase ( self, **lowercase_ ) -> int:
"""simple docstring"""
a__ ={}
if "candidate_labels" in kwargs:
a__ =kwargs['''candidate_labels''']
if "hypothesis_template" in kwargs:
a__ =kwargs['''hypothesis_template''']
return preprocess_params, {}, {}
def _UpperCAmelCase ( self, lowercase_, lowercase_=None, lowercase_="This is a sound of {}." ) -> Union[str, Any]:
"""simple docstring"""
if isinstance(lowercase_, lowercase_ ):
if audio.startswith('''http://''' ) or audio.startswith('''https://''' ):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
a__ =requests.get(lowercase_ ).content
else:
with open(lowercase_, '''rb''' ) as f:
a__ =f.read()
if isinstance(lowercase_, lowercase_ ):
a__ =ffmpeg_read(lowercase_, self.feature_extractor.sampling_rate )
if not isinstance(lowercase_, np.ndarray ):
raise ValueError('''We expect a numpy ndarray as input''' )
if len(audio.shape ) != 1:
raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline''' )
a__ =self.feature_extractor(
[audio], sampling_rate=self.feature_extractor.sampling_rate, return_tensors='''pt''' )
a__ =candidate_labels
a__ =[hypothesis_template.format(lowercase_ ) for x in candidate_labels]
a__ =self.tokenizer(lowercase_, return_tensors=self.framework, padding=lowercase_ )
a__ =[text_inputs]
return inputs
def _UpperCAmelCase ( self, lowercase_ ) -> str:
"""simple docstring"""
a__ =model_inputs.pop('''candidate_labels''' )
a__ =model_inputs.pop('''text_inputs''' )
if isinstance(text_inputs[0], lowercase_ ):
a__ =text_inputs[0]
else:
# Batching case.
a__ =text_inputs[0][0]
a__ =self.model(**lowercase_, **lowercase_ )
a__ ={
'''candidate_labels''': candidate_labels,
'''logits''': outputs.logits_per_audio,
}
return model_outputs
def _UpperCAmelCase ( self, lowercase_ ) -> Any:
"""simple docstring"""
a__ =model_outputs.pop('''candidate_labels''' )
a__ =model_outputs['''logits'''][0]
if self.framework == "pt":
a__ =logits.softmax(dim=0 )
a__ =probs.tolist()
else:
raise ValueError('''`tf` framework not supported.''' )
a__ =[
{'''score''': score, '''label''': candidate_label}
for score, candidate_label in sorted(zip(lowercase_, lowercase_ ), key=lambda lowercase_ : -x[0] )
]
return result
| 188 | 1 |
import os
import pytest
from attr import dataclass
UpperCamelCase__ : int = """us-east-1""" # defaults region
@dataclass
class lowerCamelCase_ :
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
SCREAMING_SNAKE_CASE_ = {
'task_name': 'mnli',
'per_device_train_batch_size': 16,
'per_device_eval_batch_size': 16,
'do_train': True,
'do_eval': True,
'do_predict': True,
'output_dir': '/opt/ml/model',
'overwrite_output_dir': True,
'max_steps': 5_00,
'save_steps': 55_00,
}
SCREAMING_SNAKE_CASE_ = {**hyperparameters, 'max_steps': 10_00}
@property
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
'''simple docstring'''
if self.framework == "pytorch":
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
{"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
]
else:
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
{"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
]
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
'''simple docstring'''
return F"""{self.framework}-transfromers-test"""
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
return F"""./tests/sagemaker/scripts/{self.framework}"""
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
if self.framework == "pytorch":
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
else:
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"
@pytest.fixture(scope='''class''' )
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> Optional[int]:
"""simple docstring"""
a = SageMakerTestEnvironment(framework=request.cls.framework )
| 350 |
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> int:
"""simple docstring"""
a = ''''''
for i in table:
res += inp[i - 1]
return res
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> int:
"""simple docstring"""
return data[1:] + data[0]
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> List[str]:
"""simple docstring"""
a = ''''''
for i in range(len(snake_case_ ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> Dict:
"""simple docstring"""
a = int('''0b''' + data[0] + data[-1], 2 )
a = int('''0b''' + data[1:3], 2 )
return bin(s[row][col] )[2:]
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ ) -> Optional[int]:
"""simple docstring"""
a = message[:4]
a = message[4:]
a = apply_table(snake_case_, snake_case_ )
a = xor(snake_case_, snake_case_ )
a = apply_sbox(snake_case_, temp[:4] ) # noqa: E741
a = apply_sbox(snake_case_, temp[4:] )
a = '''0''' * (2 - len(snake_case_ )) + l # noqa: E741
a = '''0''' * (2 - len(snake_case_ )) + r
a = apply_table(l + r, snake_case_ )
a = xor(snake_case_, snake_case_ )
return temp + right
if __name__ == "__main__":
UpperCamelCase__ : int = input("""Enter 10 bit key: """)
UpperCamelCase__ : Union[str, Any] = input("""Enter 8 bit message: """)
UpperCamelCase__ : Dict = [6, 3, 7, 4, 8, 5, 10, 9]
UpperCamelCase__ : Union[str, Any] = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
UpperCamelCase__ : Optional[int] = [2, 4, 3, 1]
UpperCamelCase__ : List[Any] = [2, 6, 3, 1, 4, 8, 5, 7]
UpperCamelCase__ : str = [4, 1, 3, 5, 7, 2, 8, 6]
UpperCamelCase__ : List[Any] = [4, 1, 2, 3, 2, 3, 4, 1]
UpperCamelCase__ : int = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
UpperCamelCase__ : Dict = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
UpperCamelCase__ : Optional[Any] = apply_table(key, paa_table)
UpperCamelCase__ : str = temp[:5]
UpperCamelCase__ : List[Any] = temp[5:]
UpperCamelCase__ : Dict = left_shift(left)
UpperCamelCase__ : Any = left_shift(right)
UpperCamelCase__ : Optional[Any] = apply_table(left + right, pa_table)
UpperCamelCase__ : List[str] = left_shift(left)
UpperCamelCase__ : int = left_shift(right)
UpperCamelCase__ : List[str] = left_shift(left)
UpperCamelCase__ : Dict = left_shift(right)
UpperCamelCase__ : List[str] = apply_table(left + right, pa_table)
# encryption
UpperCamelCase__ : Tuple = apply_table(message, IP)
UpperCamelCase__ : Optional[Any] = function(expansion, sa, sa, keya, temp)
UpperCamelCase__ : Optional[int] = temp[4:] + temp[:4]
UpperCamelCase__ : Any = function(expansion, sa, sa, keya, temp)
UpperCamelCase__ : Tuple = apply_table(temp, IP_inv)
print("""Cipher text is:""", CT)
# decryption
UpperCamelCase__ : Union[str, Any] = apply_table(CT, IP)
UpperCamelCase__ : List[str] = function(expansion, sa, sa, keya, temp)
UpperCamelCase__ : Optional[Any] = temp[4:] + temp[:4]
UpperCamelCase__ : Optional[int] = function(expansion, sa, sa, keya, temp)
UpperCamelCase__ : Any = apply_table(temp, IP_inv)
print("""Plain text after decypting is:""", PT)
| 330 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( _a , _a , _a):
if days_between_payments <= 0:
raise ValueError("days_between_payments must be > 0")
if daily_interest_rate < 0:
raise ValueError("daily_interest_rate must be >= 0")
if principal <= 0:
raise ValueError("principal must be > 0")
return principal * daily_interest_rate * days_between_payments
def lowerCamelCase__ ( _a , _a , _a , ):
if number_of_compounding_periods <= 0:
raise ValueError("number_of_compounding_periods must be > 0")
if nominal_annual_interest_rate_percentage < 0:
raise ValueError("nominal_annual_interest_rate_percentage must be >= 0")
if principal <= 0:
raise ValueError("principal must be > 0")
return principal * (
(1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
- 1
)
def lowerCamelCase__ ( _a , _a , _a , ):
if number_of_years <= 0:
raise ValueError("number_of_years must be > 0")
if nominal_annual_percentage_rate < 0:
raise ValueError("nominal_annual_percentage_rate must be >= 0")
if principal <= 0:
raise ValueError("principal must be > 0")
return compound_interest(
_a , nominal_annual_percentage_rate / 365 , number_of_years * 365)
if __name__ == "__main__":
import doctest
doctest.testmod() | 76 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
a_ = logging.getLogger(__name__)
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Any ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = False
def __UpperCamelCase ( self : str , a : str , a : Optional[int] , a : Any , a : str ) -> List[Any]:
"""simple docstring"""
if not self.initialized:
SCREAMING_SNAKE_CASE : List[str] = RagRetriever(
a , question_encoder_tokenizer=a , generator_tokenizer=a , index=a , init_retrieval=a , )
SCREAMING_SNAKE_CASE : Optional[int] = True
def __UpperCamelCase ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
self.retriever.index.init_index()
def __UpperCamelCase ( self : Optional[Any] , a : List[Any] , a : Any ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Dict = self.retriever._main_retrieve(a , a )
return doc_ids, retrieved_doc_embeds
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Tuple , a : Any , a : Tuple , a : Tuple , a : Tuple , a : List[Any]=None ) -> Optional[int]:
"""simple docstring"""
if index is not None and index.is_initialized() and len(a ) > 0:
raise ValueError(
"When using Ray for distributed fine-tuning, "
"you'll need to provide the paths instead, "
"as the dataset and the index are loaded "
"separately. More info in examples/rag/use_own_knowledge_dataset.py " )
super().__init__(
a , question_encoder_tokenizer=a , generator_tokenizer=a , index=a , init_retrieval=a , )
SCREAMING_SNAKE_CASE : Optional[Any] = retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(a , a , a , a )
for worker in self.retrieval_workers
] )
def __UpperCamelCase ( self : Any ) -> Dict:
"""simple docstring"""
logger.info("initializing retrieval" )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def __UpperCamelCase ( self : Tuple , a : Optional[int] , a : Any ) -> int:
"""simple docstring"""
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
SCREAMING_SNAKE_CASE : Optional[Any] = self.retrieval_workers[random.randint(0 , len(self.retrieval_workers ) - 1 )]
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : str = ray.get(random_worker.retrieve.remote(a , a ) )
else:
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Any = self._main_retrieve(a , a )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(a )
@classmethod
def __UpperCamelCase ( cls : str , a : Optional[Any] , a : Any=None , **a : List[Any] ) -> str:
"""simple docstring"""
return super(a , cls ).get_tokenizers(a , a , **a )
@classmethod
def __UpperCamelCase ( cls : Union[str, Any] , a : int , a : Any , a : List[Any]=None , **a : Optional[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = kwargs.pop("config" , a ) or RagConfig.from_pretrained(a , **a )
SCREAMING_SNAKE_CASE : List[Any] = RagTokenizer.from_pretrained(a , config=a )
SCREAMING_SNAKE_CASE : List[Any] = rag_tokenizer.question_encoder
SCREAMING_SNAKE_CASE : List[Any] = rag_tokenizer.generator
if indexed_dataset is not None:
SCREAMING_SNAKE_CASE : str = "custom"
SCREAMING_SNAKE_CASE : List[Any] = CustomHFIndex(config.retrieval_vector_size , a )
else:
SCREAMING_SNAKE_CASE : List[str] = cls._build_index(a )
return cls(
a , question_encoder_tokenizer=a , generator_tokenizer=a , retrieval_workers=a , index=a , ) | 76 | 1 |
def __lowerCamelCase ( lowerCamelCase__ = 1_000 ):
"""simple docstring"""
lowercase__ : int = 1, 1
lowercase__ : List[Any] = []
for i in range(1 , n + 1 ):
lowercase__ : Dict = prev_numerator + 2 * prev_denominator
lowercase__ : Tuple = prev_numerator + prev_denominator
if len(str(lowerCamelCase__ ) ) > len(str(lowerCamelCase__ ) ):
result.append(lowerCamelCase__ )
lowercase__ : int = numerator
lowercase__ : int = denominator
return len(lowerCamelCase__ )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 366 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = '''▁'''
lowerCAmelCase__ = {'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}
lowerCAmelCase__ = {
'''vocab_file''': {
'''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
},
'''monolingual_vocab_file''': {
'''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
},
}
lowerCAmelCase__ = {'''vinai/bartpho-syllable''': 1_0_2_4}
class snake_case__(_UpperCamelCase ):
"""simple docstring"""
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = ["""input_ids""", """attention_mask"""]
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Optional[int]="<s>" , SCREAMING_SNAKE_CASE : Optional[int]="</s>" , SCREAMING_SNAKE_CASE : str="</s>" , SCREAMING_SNAKE_CASE : List[str]="<s>" , SCREAMING_SNAKE_CASE : Union[str, Any]="<unk>" , SCREAMING_SNAKE_CASE : Tuple="<pad>" , SCREAMING_SNAKE_CASE : List[str]="<mask>" , SCREAMING_SNAKE_CASE : Optional[Dict[str, Any]] = None , **SCREAMING_SNAKE_CASE : int , ):
# Mask token behave like a normal word, i.e. include the space before it
lowercase__ : Dict = AddedToken(SCREAMING_SNAKE_CASE , lstrip=SCREAMING_SNAKE_CASE , rstrip=SCREAMING_SNAKE_CASE ) if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else mask_token
lowercase__ : int = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=SCREAMING_SNAKE_CASE , eos_token=SCREAMING_SNAKE_CASE , unk_token=SCREAMING_SNAKE_CASE , sep_token=SCREAMING_SNAKE_CASE , cls_token=SCREAMING_SNAKE_CASE , pad_token=SCREAMING_SNAKE_CASE , mask_token=SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **SCREAMING_SNAKE_CASE , )
lowercase__ : Dict = vocab_file
lowercase__ : Union[str, Any] = monolingual_vocab_file
lowercase__ : Union[str, Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(SCREAMING_SNAKE_CASE ) )
# Load the reduced vocab
# Keep order of special tokens for backward compatibility
lowercase__ : Any = {}
lowercase__ : int = 0
for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
if str(SCREAMING_SNAKE_CASE ) not in self.fairseq_tokens_to_ids:
lowercase__ : Dict = cnt
cnt += 1
with open(SCREAMING_SNAKE_CASE , "r" , encoding="utf-8" ) as f:
for line in f.readlines():
lowercase__ : int = line.strip().split()[0]
lowercase__ : List[str] = len(self.fairseq_tokens_to_ids )
if str(SCREAMING_SNAKE_CASE ) not in self.fairseq_tokens_to_ids:
lowercase__ : Optional[Any] = len(self.fairseq_tokens_to_ids )
lowercase__ : List[str] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : int ):
lowercase__ : Dict = self.__dict__.copy()
lowercase__ : Union[str, Any] = None
lowercase__ : Dict = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Union[str, Any] , SCREAMING_SNAKE_CASE : Optional[Any] ):
lowercase__ : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
lowercase__ : Dict = {}
lowercase__ : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def snake_case ( self : Optional[int] , SCREAMING_SNAKE_CASE : List[int] , SCREAMING_SNAKE_CASE : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowercase__ : List[Any] = [self.cls_token_id]
lowercase__ : str = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def snake_case ( self : Dict , SCREAMING_SNAKE_CASE : List[int] , SCREAMING_SNAKE_CASE : Optional[List[int]] = None , SCREAMING_SNAKE_CASE : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE , token_ids_a=SCREAMING_SNAKE_CASE , already_has_special_tokens=SCREAMING_SNAKE_CASE )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE )) + [1]
def snake_case ( self : str , SCREAMING_SNAKE_CASE : List[int] , SCREAMING_SNAKE_CASE : Optional[List[int]] = None ):
lowercase__ : Tuple = [self.sep_token_id]
lowercase__ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def snake_case ( self : Optional[int] ):
return len(self.fairseq_ids_to_tokens )
def snake_case ( self : List[Any] ):
lowercase__ : Any = {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : str ):
return self.sp_model.encode(SCREAMING_SNAKE_CASE , out_type=SCREAMING_SNAKE_CASE )
def snake_case ( self : List[str] , SCREAMING_SNAKE_CASE : int ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
else:
return self.unk_token_id
def snake_case ( self : List[str] , SCREAMING_SNAKE_CASE : Any ):
return self.fairseq_ids_to_tokens[index]
def snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE : Union[str, Any] ):
lowercase__ : str = "".join(SCREAMING_SNAKE_CASE ).replace(SCREAMING_SNAKE_CASE , " " ).strip()
return out_string
def snake_case ( self : Optional[int] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[str] = None ):
if not os.path.isdir(SCREAMING_SNAKE_CASE ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase__ : str = os.path.join(
SCREAMING_SNAKE_CASE , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowercase__ : List[str] = os.path.join(
SCREAMING_SNAKE_CASE , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_vocab_file"] , )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(SCREAMING_SNAKE_CASE , "wb" ) as fi:
lowercase__ : str = self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE )
if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
SCREAMING_SNAKE_CASE ) and os.path.isfile(self.monolingual_vocab_file ):
copyfile(self.monolingual_vocab_file , SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.monolingual_vocab_file ):
with open(SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as fp:
for token in self.fairseq_tokens_to_ids:
if token not in self.all_special_tokens:
fp.write(f"""{str(SCREAMING_SNAKE_CASE )} \n""" )
return out_vocab_file, out_monolingual_vocab_file
| 121 | 0 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_snake_case = False
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self , __A=32 ):
"""simple docstring"""
set_seed(0 )
lowerCamelCase : int = UNetaDModel(sample_size=__A , in_channels=3 , out_channels=3 )
lowerCamelCase : Optional[int] = torch.optim.SGD(model.parameters() , lr=0.0001 )
return model, optimizer
@slow
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Dict = '''cpu''' # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
lowerCamelCase : Optional[Any] = DDPMScheduler(
num_train_timesteps=1000 , beta_start=0.0001 , beta_end=0.02 , beta_schedule="linear" , clip_sample=__A , )
lowerCamelCase : Optional[Any] = DDIMScheduler(
num_train_timesteps=1000 , beta_start=0.0001 , beta_end=0.02 , beta_schedule="linear" , clip_sample=__A , )
assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps
# shared batches for DDPM and DDIM
set_seed(0 )
lowerCamelCase : Any = [torch.randn((4, 3, 32, 32) ).clip(-1 , 1 ).to(__A ) for _ in range(4 )]
lowerCamelCase : Union[str, Any] = [torch.randn((4, 3, 32, 32) ).to(__A ) for _ in range(4 )]
lowerCamelCase : Union[str, Any] = [torch.randint(0 , 1000 , (4,) ).long().to(__A ) for _ in range(4 )]
# train with a DDPM scheduler
lowerCamelCase : Any = self.get_model_optimizer(resolution=32 )
model.train().to(__A )
for i in range(4 ):
optimizer.zero_grad()
lowerCamelCase : Union[str, Any] = ddpm_scheduler.add_noise(clean_images[i] , noise[i] , timesteps[i] )
lowerCamelCase : Dict = model(__A , timesteps[i] ).sample
lowerCamelCase : Any = torch.nn.functional.mse_loss(__A , noise[i] )
loss.backward()
optimizer.step()
del model, optimizer
# recreate the model and optimizer, and retry with DDIM
lowerCamelCase : List[str] = self.get_model_optimizer(resolution=32 )
model.train().to(__A )
for i in range(4 ):
optimizer.zero_grad()
lowerCamelCase : Optional[Any] = ddim_scheduler.add_noise(clean_images[i] , noise[i] , timesteps[i] )
lowerCamelCase : Any = model(__A , timesteps[i] ).sample
lowerCamelCase : Tuple = torch.nn.functional.mse_loss(__A , noise[i] )
loss.backward()
optimizer.step()
del model, optimizer
self.assertTrue(torch.allclose(__A , __A , atol=1e-5 ) )
self.assertTrue(torch.allclose(__A , __A , atol=1e-5 ) )
| 283 |
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 __lowerCamelCase ( *A : Dict , **A : Optional[int] ) ->Dict:
pass
@is_pipeline_test
@require_vision
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
_UpperCAmelCase : Optional[int] = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def __lowerCamelCase ( self : Any , A : List[str] , A : Tuple , A : List[str] ) ->List[Any]:
lowerCamelCase__ : List[str] = pipeline(
'''zero-shot-object-detection''' , model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCamelCase__ : Union[str, Any] = [
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
]
return object_detector, examples
def __lowerCamelCase ( self : List[Any] , A : Optional[int] , A : Tuple ) ->Optional[Any]:
lowerCamelCase__ : str = object_detector(examples[0] , threshold=0.0 )
lowerCamelCase__ : Union[str, Any] = len(A )
self.assertGreater(A , 0 )
self.assertEqual(
A , [
{
'''score''': ANY(A ),
'''label''': ANY(A ),
'''box''': {'''xmin''': ANY(A ), '''ymin''': ANY(A ), '''xmax''': ANY(A ), '''ymax''': ANY(A )},
}
for i in range(A )
] , )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def __lowerCamelCase ( self : Dict ) ->List[Any]:
pass
@require_torch
def __lowerCamelCase ( self : Optional[Any] ) ->List[Any]:
lowerCamelCase__ : Optional[int] = pipeline(
'''zero-shot-object-detection''' , model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCamelCase__ : List[Any] = object_detector(
'''./tests/fixtures/tests_samples/COCO/000000039769.png''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , threshold=0.64 , )
self.assertEqual(
nested_simplify(A , decimals=4 ) , [
{'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
{'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}},
{'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
] , )
lowerCamelCase__ : str = object_detector(
[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
] , threshold=0.64 , )
self.assertEqual(
nested_simplify(A , decimals=4 ) , [
[
{'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
{'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}},
{'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
]
] , )
@require_torch
@slow
def __lowerCamelCase ( self : Union[str, Any] ) ->Optional[Any]:
lowerCamelCase__ : Tuple = pipeline('''zero-shot-object-detection''' )
lowerCamelCase__ : str = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , )
self.assertEqual(
nested_simplify(A , decimals=4 ) , [
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
{'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
{'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
] , )
lowerCamelCase__ : List[Any] = 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(A , decimals=4 ) , [
[
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
{'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
{'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
],
[
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
{'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
{'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
],
] , )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def __lowerCamelCase ( self : int ) ->Union[str, Any]:
pass
@require_torch
@slow
def __lowerCamelCase ( self : Optional[int] ) ->Optional[int]:
lowerCamelCase__ : Optional[Any] = 0.2
lowerCamelCase__ : List[Any] = pipeline('''zero-shot-object-detection''' )
lowerCamelCase__ : Any = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , threshold=A , )
self.assertEqual(
nested_simplify(A , decimals=4 ) , [
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
] , )
@require_torch
@slow
def __lowerCamelCase ( self : Any ) ->str:
lowerCamelCase__ : List[Any] = 2
lowerCamelCase__ : Union[str, Any] = pipeline('''zero-shot-object-detection''' )
lowerCamelCase__ : List[str] = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , top_k=A , )
self.assertEqual(
nested_simplify(A , decimals=4 ) , [
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
] , )
| 142 | 0 |
def lowerCAmelCase_ ( __a ) -> Optional[int]:
"""simple docstring"""
if collection == []:
return []
# get some information about the collection
lowerCamelCase__: int =len(__a )
lowerCamelCase__: str =max(__a )
lowerCamelCase__: Optional[Any] =min(__a )
# create the counting array
lowerCamelCase__: List[str] =coll_max + 1 - coll_min
lowerCamelCase__: Optional[Any] =[0] * counting_arr_length
# count how much a number appears in the collection
for number in collection:
counting_arr[number - coll_min] += 1
# sum each position with it's predecessors. now, counting_arr[i] tells
# us how many elements <= i has in the collection
for i in range(1 , __a ):
lowerCamelCase__: List[Any] =counting_arr[i] + counting_arr[i - 1]
# create the output collection
lowerCamelCase__: List[str] =[0] * coll_len
# place the elements in the output, respecting the original order (stable
# sort) from end to begin, updating counting_arr
for i in reversed(range(0 , __a ) ):
lowerCamelCase__: Tuple =collection[i]
counting_arr[collection[i] - coll_min] -= 1
return ordered
def lowerCAmelCase_ ( __a ) -> str:
"""simple docstring"""
return "".join([chr(__a ) for i in counting_sort([ord(__a ) for c in string] )] )
if __name__ == "__main__":
# Test string sort
assert counting_sort_string("thisisthestring") == "eghhiiinrsssttt"
__A = input("Enter numbers separated by a comma:\n").strip()
__A = [int(item) for item in user_input.split(",")]
print(counting_sort(unsorted))
| 273 |
def lowerCAmelCase_ ( __a , __a ) -> Tuple:
"""simple docstring"""
assert x is not None
assert y is not None
lowerCamelCase__: Any =len(__a )
lowerCamelCase__: int =len(__a )
# declaring the array for storing the dp values
lowerCamelCase__: List[Any] =[[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__: str =1 if x[i - 1] == y[j - 1] else 0
lowerCamelCase__: str =max(l[i - 1][j] , l[i][j - 1] , l[i - 1][j - 1] + match )
lowerCamelCase__: Any =""
lowerCamelCase__ , lowerCamelCase__: str =m, n
while i > 0 and j > 0:
lowerCamelCase__: Union[str, Any] =1 if x[i - 1] == y[j - 1] else 0
if l[i][j] == l[i - 1][j - 1] + match:
if match == 1:
lowerCamelCase__: Any =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__":
__A = "AGGTAB"
__A = "GXTXAYB"
__A = 4
__A = "GTAB"
__A , __A = longest_common_subsequence(a, b)
print("len =", ln, ", sub-sequence =", subseq)
import doctest
doctest.testmod()
| 273 | 1 |
"""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
lowerCAmelCase_ = logging.get_logger(__name__)
@add_end_docstrings(A_ )
class __A ( A_ ):
'''simple docstring'''
def __init__( self : List[str] ,**_snake_case : Dict ) -> List[Any]:
"""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 : Optional[int] ,_snake_case : Union[str, List[str], "Image", List["Image"]] ,**_snake_case : int ) -> Optional[Any]:
"""simple docstring"""
return super().__call__(_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Dict ,**_snake_case : Optional[int] ) -> List[Any]:
"""simple docstring"""
lowercase__ : List[str] = {}
if "candidate_labels" in kwargs:
lowercase__ : Any = kwargs['''candidate_labels''']
if "hypothesis_template" in kwargs:
lowercase__ : Optional[Any] = kwargs['''hypothesis_template''']
return preprocess_params, {}, {}
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : Optional[int] ,_snake_case : Dict=None ,_snake_case : Union[str, Any]="This is a photo of {}." ) -> List[str]:
"""simple docstring"""
lowercase__ : List[Any] = load_image(_snake_case )
lowercase__ : int = self.image_processor(images=[image] ,return_tensors=self.framework )
lowercase__ : str = candidate_labels
lowercase__ : Dict = [hypothesis_template.format(_snake_case ) for x in candidate_labels]
lowercase__ : Any = self.tokenizer(_snake_case ,return_tensors=self.framework ,padding=_snake_case )
lowercase__ : Optional[int] = [text_inputs]
return inputs
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : Optional[int] ) -> List[Any]:
"""simple docstring"""
lowercase__ : Optional[int] = model_inputs.pop('''candidate_labels''' )
lowercase__ : Union[str, Any] = model_inputs.pop('''text_inputs''' )
if isinstance(text_inputs[0] ,_snake_case ):
lowercase__ : List[str] = text_inputs[0]
else:
# Batching case.
lowercase__ : int = text_inputs[0][0]
lowercase__ : Tuple = self.model(**_snake_case ,**_snake_case )
lowercase__ : Union[str, Any] = {
'''candidate_labels''': candidate_labels,
'''logits''': outputs.logits_per_image,
}
return model_outputs
def UpperCAmelCase ( self : Any ,_snake_case : Tuple ) -> Any:
"""simple docstring"""
lowercase__ : Dict = model_outputs.pop('''candidate_labels''' )
lowercase__ : Optional[Any] = model_outputs['''logits'''][0]
if self.framework == "pt":
lowercase__ : Optional[int] = logits.softmax(dim=-1 ).squeeze(-1 )
lowercase__ : Tuple = probs.tolist()
if not isinstance(_snake_case ,_snake_case ):
lowercase__ : Any = [scores]
elif self.framework == "tf":
lowercase__ : List[str] = stable_softmax(_snake_case ,axis=-1 )
lowercase__ : Optional[Any] = probs.numpy().tolist()
else:
raise ValueError(f"""Unsupported framework: {self.framework}""" )
lowercase__ : Union[str, Any] = [
{'''score''': score, '''label''': candidate_label}
for score, candidate_label in sorted(zip(_snake_case ,_snake_case ) ,key=lambda _snake_case : -x[0] )
]
return result
| 16 |
# flake8: noqa
# Lint as: python3
lowerCamelCase : Optional[Any] = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental | 233 | 0 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCamelCase ( __snake_case , unittest.TestCase ):
lowerCamelCase_ : Any = CTRLTokenizer
lowerCamelCase_ : Optional[int] = False
lowerCamelCase_ : List[str] = False
def lowerCAmelCase_ ( self ) -> str:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case_ = ["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
snake_case_ = dict(zip(lowerCamelCase , range(len(lowerCamelCase ) ) ) )
snake_case_ = ["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
snake_case_ = {"""unk_token""": """<unk>"""}
snake_case_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
snake_case_ = 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(lowerCamelCase ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(lowerCamelCase ) )
def lowerCAmelCase_ ( self , **lowerCamelCase ) -> Union[str, Any]:
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase )
def lowerCAmelCase_ ( self , lowerCamelCase ) -> Any:
snake_case_ = """adapt react readapt apt"""
snake_case_ = """adapt react readapt apt"""
return input_text, output_text
def lowerCAmelCase_ ( self ) -> Any:
snake_case_ = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case_ = """adapt react readapt apt"""
snake_case_ = """adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
snake_case_ = tokenizer.tokenize(lowerCamelCase )
self.assertListEqual(lowerCamelCase , lowerCamelCase )
snake_case_ = tokens + [tokenizer.unk_token]
snake_case_ = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase ) , lowerCamelCase ) | 34 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCamelCase_ = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
lowerCamelCase_ = None
def UpperCamelCase( ) -> List[Any]:
'''simple docstring'''
snake_case_ = argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0.""" )
parser.add_argument("""data_file""" , metavar="""data.json""" , help="""Input data JSON file.""" )
parser.add_argument("""pred_file""" , metavar="""pred.json""" , help="""Model predictions.""" )
parser.add_argument(
"""--out-file""" , """-o""" , metavar="""eval.json""" , help="""Write accuracy metrics to file (default is stdout).""" )
parser.add_argument(
"""--na-prob-file""" , """-n""" , metavar="""na_prob.json""" , help="""Model estimates of probability of no answer.""" )
parser.add_argument(
"""--na-prob-thresh""" , """-t""" , type=lowercase_ , default=1.0 , help="""Predict \"\" if no-answer probability exceeds this (default = 1.0).""" , )
parser.add_argument(
"""--out-image-dir""" , """-p""" , metavar="""out_images""" , default=lowercase_ , help="""Save precision-recall curves to directory.""" )
parser.add_argument("""--verbose""" , """-v""" , action="""store_true""" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def UpperCamelCase( lowercase_ ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case_ = bool(qa["""answers"""]["""text"""] )
return qid_to_has_ans
def UpperCamelCase( lowercase_ ) -> Tuple:
'''simple docstring'''
def remove_articles(lowercase_ ):
return ARTICLES_REGEX.sub(""" """ , lowercase_ )
def white_space_fix(lowercase_ ):
return " ".join(text.split() )
def remove_punc(lowercase_ ):
snake_case_ = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase_ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def UpperCamelCase( lowercase_ ) -> Dict:
'''simple docstring'''
if not s:
return []
return normalize_answer(lowercase_ ).split()
def UpperCamelCase( lowercase_ , lowercase_ ) -> Dict:
'''simple docstring'''
return int(normalize_answer(lowercase_ ) == normalize_answer(lowercase_ ) )
def UpperCamelCase( lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ = get_tokens(lowercase_ )
snake_case_ = get_tokens(lowercase_ )
snake_case_ = collections.Counter(lowercase_ ) & collections.Counter(lowercase_ )
snake_case_ = sum(common.values() )
if len(lowercase_ ) == 0 or len(lowercase_ ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
snake_case_ = 1.0 * num_same / len(lowercase_ )
snake_case_ = 1.0 * num_same / len(lowercase_ )
snake_case_ = (2 * precision * recall) / (precision + recall)
return fa
def UpperCamelCase( lowercase_ , lowercase_ ) -> List[str]:
'''simple docstring'''
snake_case_ = {}
snake_case_ = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case_ = qa["""id"""]
snake_case_ = [t for t in qa["""answers"""]["""text"""] if normalize_answer(lowercase_ )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
snake_case_ = [""""""]
if qid not in preds:
print(f'''Missing prediction for {qid}''' )
continue
snake_case_ = preds[qid]
# Take max over all gold answers
snake_case_ = max(compute_exact(lowercase_ , lowercase_ ) for a in gold_answers )
snake_case_ = max(compute_fa(lowercase_ , lowercase_ ) for a in gold_answers )
return exact_scores, fa_scores
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = {}
for qid, s in scores.items():
snake_case_ = na_probs[qid] > na_prob_thresh
if pred_na:
snake_case_ = float(not qid_to_has_ans[qid] )
else:
snake_case_ = s
return new_scores
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_=None ) -> Dict:
'''simple docstring'''
if not qid_list:
snake_case_ = len(lowercase_ )
return collections.OrderedDict(
[
("""exact""", 1_00.0 * sum(exact_scores.values() ) / total),
("""f1""", 1_00.0 * sum(fa_scores.values() ) / total),
("""total""", total),
] )
else:
snake_case_ = len(lowercase_ )
return collections.OrderedDict(
[
("""exact""", 1_00.0 * sum(exact_scores[k] for k in qid_list ) / total),
("""f1""", 1_00.0 * sum(fa_scores[k] for k in qid_list ) / total),
("""total""", total),
] )
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
for k in new_eval:
snake_case_ = new_eval[k]
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Dict:
'''simple docstring'''
plt.step(lowercase_ , lowercase_ , color="""b""" , alpha=0.2 , where="""post""" )
plt.fill_between(lowercase_ , lowercase_ , step="""post""" , alpha=0.2 , color="""b""" )
plt.xlabel("""Recall""" )
plt.ylabel("""Precision""" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(lowercase_ )
plt.savefig(lowercase_ )
plt.clf()
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_=None , lowercase_=None ) -> Dict:
'''simple docstring'''
snake_case_ = sorted(lowercase_ , key=lambda lowercase_ : na_probs[k] )
snake_case_ = 0.0
snake_case_ = 1.0
snake_case_ = 0.0
snake_case_ = [1.0]
snake_case_ = [0.0]
snake_case_ = 0.0
for i, qid in enumerate(lowercase_ ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
snake_case_ = true_pos / float(i + 1 )
snake_case_ = true_pos / float(lowercase_ )
if i == len(lowercase_ ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(lowercase_ )
recalls.append(lowercase_ )
if out_image:
plot_pr_curve(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
return {"ap": 1_00.0 * avg_prec}
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
if out_image_dir and not os.path.exists(lowercase_ ):
os.makedirs(lowercase_ )
snake_case_ = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
snake_case_ = make_precision_recall_eval(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , out_image=os.path.join(lowercase_ , """pr_exact.png""" ) , title="""Precision-Recall curve for Exact Match score""" , )
snake_case_ = make_precision_recall_eval(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , out_image=os.path.join(lowercase_ , """pr_f1.png""" ) , title="""Precision-Recall curve for F1 score""" , )
snake_case_ = {k: float(lowercase_ ) for k, v in qid_to_has_ans.items()}
snake_case_ = make_precision_recall_eval(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , out_image=os.path.join(lowercase_ , """pr_oracle.png""" ) , title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)""" , )
merge_eval(lowercase_ , lowercase_ , """pr_exact""" )
merge_eval(lowercase_ , lowercase_ , """pr_f1""" )
merge_eval(lowercase_ , lowercase_ , """pr_oracle""" )
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> int:
'''simple docstring'''
if not qid_list:
return
snake_case_ = [na_probs[k] for k in qid_list]
snake_case_ = np.ones_like(lowercase_ ) / float(len(lowercase_ ) )
plt.hist(lowercase_ , weights=lowercase_ , bins=20 , range=(0.0, 1.0) )
plt.xlabel("""Model probability of no-answer""" )
plt.ylabel("""Proportion of dataset""" )
plt.title(f'''Histogram of no-answer probability: {name}''' )
plt.savefig(os.path.join(lowercase_ , f'''na_prob_hist_{name}.png''' ) )
plt.clf()
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Optional[int]:
'''simple docstring'''
snake_case_ = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
snake_case_ = num_no_ans
snake_case_ = cur_score
snake_case_ = 0.0
snake_case_ = sorted(lowercase_ , key=lambda lowercase_ : na_probs[k] )
for i, qid in enumerate(lowercase_ ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
snake_case_ = scores[qid]
else:
if preds[qid]:
snake_case_ = -1
else:
snake_case_ = 0
cur_score += diff
if cur_score > best_score:
snake_case_ = cur_score
snake_case_ = na_probs[qid]
return 1_00.0 * best_score / len(lowercase_ ), best_thresh
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ , snake_case_ = find_best_thresh(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
snake_case_ , snake_case_ = find_best_thresh(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
snake_case_ = best_exact
snake_case_ = exact_thresh
snake_case_ = best_fa
snake_case_ = fa_thresh
def UpperCamelCase( ) -> Union[str, Any]:
'''simple docstring'''
with open(OPTS.data_file ) as f:
snake_case_ = json.load(lowercase_ )
snake_case_ = dataset_json["""data"""]
with open(OPTS.pred_file ) as f:
snake_case_ = json.load(lowercase_ )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
snake_case_ = json.load(lowercase_ )
else:
snake_case_ = {k: 0.0 for k in preds}
snake_case_ = make_qid_to_has_ans(lowercase_ ) # maps qid to True/False
snake_case_ = [k for k, v in qid_to_has_ans.items() if v]
snake_case_ = [k for k, v in qid_to_has_ans.items() if not v]
snake_case_ , snake_case_ = get_raw_scores(lowercase_ , lowercase_ )
snake_case_ = apply_no_ans_threshold(lowercase_ , lowercase_ , lowercase_ , OPTS.na_prob_thresh )
snake_case_ = apply_no_ans_threshold(lowercase_ , lowercase_ , lowercase_ , OPTS.na_prob_thresh )
snake_case_ = make_eval_dict(lowercase_ , lowercase_ )
if has_ans_qids:
snake_case_ = make_eval_dict(lowercase_ , lowercase_ , qid_list=lowercase_ )
merge_eval(lowercase_ , lowercase_ , """HasAns""" )
if no_ans_qids:
snake_case_ = make_eval_dict(lowercase_ , lowercase_ , qid_list=lowercase_ )
merge_eval(lowercase_ , lowercase_ , """NoAns""" )
if OPTS.na_prob_file:
find_all_best_thresh(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , OPTS.out_image_dir )
histogram_na_prob(lowercase_ , lowercase_ , OPTS.out_image_dir , """hasAns""" )
histogram_na_prob(lowercase_ , lowercase_ , OPTS.out_image_dir , """noAns""" )
if OPTS.out_file:
with open(OPTS.out_file , """w""" ) as f:
json.dump(lowercase_ , lowercase_ )
else:
print(json.dumps(lowercase_ , indent=2 ) )
if __name__ == "__main__":
lowerCamelCase_ = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('''Agg''')
import matplotlib.pyplot as plt
main() | 34 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ["ReformerTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ["ReformerTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"ReformerAttention",
"ReformerForMaskedLM",
"ReformerForQuestionAnswering",
"ReformerForSequenceClassification",
"ReformerLayer",
"ReformerModel",
"ReformerModelWithLMHead",
"ReformerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer import ReformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer_fast import ReformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_reformer import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerAttention,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerLayer,
ReformerModel,
ReformerModelWithLMHead,
ReformerPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 26 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
a_ :List[Any] = logging.get_logger(__name__)
a_ :List[Any] = {
"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",
"adapter_layer": "encoder.layers.*.adapter_layer",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
"pooling_layer.linear": "projector",
"pooling_layer.projection": "classifier",
}
a_ :List[Any] = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
"projector",
"classifier",
]
def lowercase_ (A : Dict ):
snake_case__ : Optional[Any] = {}
with open(A , 'r' ) as file:
for line_number, line in enumerate(A ):
snake_case__ : Dict = line.strip()
if line:
snake_case__ : int = line.split()
snake_case__ : List[str] = line_number
snake_case__ : Dict = words[0]
snake_case__ : Optional[Any] = value
return result
def lowercase_ (A : int , A : int , A : Optional[int] , A : Optional[Any] , A : Tuple ):
for attribute in key.split('.' ):
snake_case__ : Optional[int] = getattr(A , A )
snake_case__ : Union[str, Any] = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(A ):
snake_case__ : List[str] = PARAM_MAPPING[full_name.split('.' )[-1]]
snake_case__ : Dict = 'param'
if weight_type is not None and weight_type != "param":
snake_case__ : Union[str, Any] = getattr(A , A ).shape
elif weight_type is not None and weight_type == "param":
snake_case__ : Optional[int] = hf_pointer
for attribute in hf_param_name.split('.' ):
snake_case__ : Optional[Any] = getattr(A , A )
snake_case__ : Dict = shape_pointer.shape
# let's reduce dimension
snake_case__ : List[Any] = value[0]
else:
snake_case__ : Union[str, Any] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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":
snake_case__ : Any = value
elif weight_type == "weight_g":
snake_case__ : List[Any] = value
elif weight_type == "weight_v":
snake_case__ : Any = value
elif weight_type == "bias":
snake_case__ : List[Any] = value
elif weight_type == "param":
for attribute in hf_param_name.split('.' ):
snake_case__ : int = getattr(A , A )
snake_case__ : Optional[int] = value
else:
snake_case__ : Optional[Any] = value
logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def lowercase_ (A : Tuple , A : List[Any] , A : int , A : str , A : Tuple ):
snake_case__ : Optional[int] = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(A ):
snake_case__ : List[str] = PARAM_MAPPING[full_name.split('.' )[-1]]
snake_case__ : str = 'param'
if weight_type is not None and weight_type != "param":
snake_case__ : int = '.'.join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
snake_case__ : Any = '.'.join([key, hf_param_name] )
else:
snake_case__ : Dict = key
snake_case__ : List[str] = value if 'lm_head' in full_key else value[0]
a_ :List[str] = {
"W_a": "linear_1.weight",
"W_b": "linear_2.weight",
"b_a": "linear_1.bias",
"b_b": "linear_2.bias",
"ln_W": "norm.weight",
"ln_b": "norm.bias",
}
def lowercase_ (A : str , A : Optional[Any] , A : Optional[Any]=None , A : List[str]=None ):
snake_case__ : Optional[int] = False
for key, mapped_key in MAPPING.items():
snake_case__ : Tuple = 'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
snake_case__ : Optional[int] = True
if "*" in mapped_key:
snake_case__ : List[Any] = name.split(A )[0].split('.' )[-2]
snake_case__ : Union[str, Any] = mapped_key.replace('*' , A )
if "weight_g" in name:
snake_case__ : Tuple = 'weight_g'
elif "weight_v" in name:
snake_case__ : List[str] = 'weight_v'
elif "bias" in name:
snake_case__ : Dict = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
snake_case__ : Optional[int] = 'weight'
else:
snake_case__ : str = None
if hf_dict is not None:
rename_dict(A , A , A , A , A )
else:
set_recursively(A , A , A , A , A )
return is_used
return is_used
def lowercase_ (A : Optional[Any] , A : Dict , A : Optional[int] ):
snake_case__ : Dict = []
snake_case__ : Tuple = fairseq_model.state_dict()
snake_case__ : str = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
snake_case__ : str = False
if "conv_layers" in name:
load_conv_layer(
A , A , A , A , hf_model.config.feat_extract_norm == 'group' , )
snake_case__ : Any = True
else:
snake_case__ : Dict = load_wavaveca_layer(A , A , A )
if not is_used:
unused_weights.append(A )
logger.warning(F'''Unused weights: {unused_weights}''' )
def lowercase_ (A : Dict , A : Optional[Any] , A : Tuple , A : str , A : List[str] ):
snake_case__ : List[Any] = full_name.split('conv_layers.' )[-1]
snake_case__ : List[str] = name.split('.' )
snake_case__ : List[Any] = int(items[0] )
snake_case__ : str = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
snake_case__ : Any = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
snake_case__ : 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
snake_case__ : str = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' )
snake_case__ : int = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(A )
@torch.no_grad()
def lowercase_ (A : Union[str, Any] , A : str , A : Tuple=None , A : List[str]=None , A : Any=True , A : Optional[int]=False ):
if config_path is not None:
snake_case__ : List[Any] = WavaVecaConfig.from_pretrained(A )
else:
snake_case__ : List[Any] = WavaVecaConfig()
if is_seq_class:
snake_case__ : Dict = read_txt_into_dict(A )
snake_case__ : Any = idalabel
snake_case__ : Union[str, Any] = WavaVecaForSequenceClassification(A )
snake_case__ : Any = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6_0_0_0 , padding_value=0 , do_normalize=A , return_attention_mask=A , )
feature_extractor.save_pretrained(A )
elif is_finetuned:
if dict_path:
snake_case__ : str = Dictionary.load(A )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
snake_case__ : List[str] = target_dict.pad_index
snake_case__ : Optional[int] = target_dict.bos_index
snake_case__ : Optional[int] = target_dict.eos_index
snake_case__ : List[Any] = len(target_dict.symbols )
snake_case__ : str = os.path.join(A , 'vocab.json' )
if not os.path.isdir(A ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(A ) )
return
os.makedirs(A , exist_ok=A )
snake_case__ : Optional[Any] = target_dict.indices
# fairseq has the <pad> and <s> switched
snake_case__ : Optional[Any] = 0
snake_case__ : Union[str, Any] = 1
with open(A , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(A , A )
snake_case__ : List[Any] = WavaVecaCTCTokenizer(
A , 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=A , )
snake_case__ : str = True if config.feat_extract_norm == 'layer' else False
snake_case__ : Optional[Any] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6_0_0_0 , padding_value=0 , do_normalize=A , return_attention_mask=A , )
snake_case__ : Union[str, Any] = WavaVecaProcessor(feature_extractor=A , tokenizer=A )
processor.save_pretrained(A )
snake_case__ : str = WavaVecaForCTC(A )
else:
snake_case__ : int = WavaVecaForPreTraining(A )
if is_finetuned or is_seq_class:
snake_case__ , snake_case__ , snake_case__ : str = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
snake_case__ : Tuple = argparse.Namespace(task='audio_pretraining' )
snake_case__ : str = fairseq.tasks.setup_task(A )
snake_case__ , snake_case__ , snake_case__ : Any = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=A )
snake_case__ : List[Any] = model[0].eval()
recursively_load_weights(A , A , not is_finetuned )
hf_wavavec.save_pretrained(A )
if __name__ == "__main__":
a_ :List[Any] = 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"
)
parser.add_argument(
"--is_seq_class",
action="store_true",
help="Whether the model to convert is a fine-tuned sequence classification model or not",
)
a_ :str = parser.parse_args()
a_ :Tuple = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 277 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : Any =logging.get_logger(__name__)
def UpperCamelCase ( _lowerCamelCase : List[str] , _lowerCamelCase : Tuple=False ):
A__ = []
# fmt: off
# stem:
rename_keys.append(("cls_token", "vit.embeddings.cls_token") )
rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings") )
rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias") )
# backbone
rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight") )
rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias") )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight") )
rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias") )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((F"blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append((F"blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append((F"blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((F"blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((F"blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias") )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
("pre_logits.fc.weight", "pooler.dense.weight"),
("pre_logits.fc.bias", "pooler.dense.bias"),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
A__ = [(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
# fmt: on
return rename_keys
def UpperCamelCase ( _lowerCamelCase : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : Union[str, Any]=False ):
for i in range(config.num_hidden_layers ):
if base_model:
A__ = ""
else:
A__ = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
A__ = state_dict.pop(F"blocks.{i}.attn.qkv.weight" )
A__ = state_dict.pop(F"blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
A__ = in_proj_weight[
: config.hidden_size, :
]
A__ = in_proj_bias[: config.hidden_size]
A__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
A__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
A__ = in_proj_weight[
-config.hidden_size :, :
]
A__ = in_proj_bias[-config.hidden_size :]
def UpperCamelCase ( _lowerCamelCase : Any ):
A__ = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(_lowerCamelCase , _lowerCamelCase )
def UpperCamelCase ( _lowerCamelCase : str , _lowerCamelCase : Optional[int] , _lowerCamelCase : Union[str, Any] ):
A__ = dct.pop(_lowerCamelCase )
A__ = val
def UpperCamelCase ( ):
A__ = "http://images.cocodataset.org/val2017/000000039769.jpg"
A__ = Image.open(requests.get(_lowerCamelCase , stream=_lowerCamelCase ).raw )
return im
@torch.no_grad()
def UpperCamelCase ( _lowerCamelCase : Dict , _lowerCamelCase : int , _lowerCamelCase : int=False ):
A__ = BitConfig(
global_padding="same" , layer_type="bottleneck" , depths=(3, 4, 9) , out_features=["stage3"] , embedding_dynamic_padding=_lowerCamelCase , )
A__ = ViTHybridConfig(backbone_config=_lowerCamelCase , image_size=3_84 , num_labels=10_00 )
A__ = False
# load original model from timm
A__ = timm.create_model(_lowerCamelCase , pretrained=_lowerCamelCase )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
A__ = timm_model.state_dict()
if base_model:
remove_classification_head_(_lowerCamelCase )
A__ = create_rename_keys(_lowerCamelCase , _lowerCamelCase )
for src, dest in rename_keys:
rename_key(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
read_in_q_k_v(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
A__ = "huggingface/label-files"
A__ = "imagenet-1k-id2label.json"
A__ = json.load(open(hf_hub_download(_lowerCamelCase , _lowerCamelCase , repo_type="dataset" ) , "r" ) )
A__ = {int(_lowerCamelCase ): v for k, v in idalabel.items()}
A__ = idalabel
A__ = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
A__ = ViTHybridModel(_lowerCamelCase ).eval()
else:
A__ = ViTHybridForImageClassification(_lowerCamelCase ).eval()
model.load_state_dict(_lowerCamelCase )
# create image processor
A__ = create_transform(**resolve_data_config({} , model=_lowerCamelCase ) )
A__ = transform.transforms
A__ = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
A__ = ViTHybridImageProcessor(
do_resize=_lowerCamelCase , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_lowerCamelCase , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=_lowerCamelCase , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
A__ = prepare_img()
A__ = transform(_lowerCamelCase ).unsqueeze(0 )
A__ = processor(_lowerCamelCase , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(_lowerCamelCase , _lowerCamelCase )
# verify logits
with torch.no_grad():
A__ = model(_lowerCamelCase )
A__ = outputs.logits
print("Predicted class:" , logits.argmax(-1 ).item() )
if base_model:
A__ = timm_model.forward_features(_lowerCamelCase )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_lowerCamelCase , outputs.pooler_output , atol=1e-3 )
else:
A__ = timm_model(_lowerCamelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_lowerCamelCase , outputs.logits , atol=1e-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
print(F"Saving model {vit_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_lowerCamelCase )
print(F"Saving processor to {pytorch_dump_folder_path}" )
processor.save_pretrained(_lowerCamelCase )
if push_to_hub:
print(F"Pushing model and processor to the hub {vit_name}" )
model.push_to_hub(F"ybelkada/{vit_name}" )
processor.push_to_hub(F"ybelkada/{vit_name}" )
if __name__ == "__main__":
__lowerCAmelCase : Dict =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--vit_name",
default="vit_base_r50_s16_384",
type=str,
help="Name of the hybrid ViT timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
)
__lowerCAmelCase : Optional[Any] =parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 123 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
__lowerCAmelCase : List[Any] ="examples/"
__lowerCAmelCase : Dict ={
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
"setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
"doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
__lowerCAmelCase : List[str] ={
"init": "src/transformers/__init__.py",
"setup": "setup.py",
}
__lowerCAmelCase : str ="README.md"
def UpperCamelCase ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Optional[Any] ):
with open(_lowerCamelCase , "r" , encoding="utf-8" , newline="\n" ) as f:
A__ = f.read()
A__, A__ = REPLACE_PATTERNS[pattern]
A__ = replace.replace("VERSION" , _lowerCamelCase )
A__ = re_pattern.sub(_lowerCamelCase , _lowerCamelCase )
with open(_lowerCamelCase , "w" , encoding="utf-8" , newline="\n" ) as f:
f.write(_lowerCamelCase )
def UpperCamelCase ( _lowerCamelCase : int ):
for folder, directories, fnames in os.walk(_lowerCamelCase ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("research_projects" )
if "legacy" in directories:
directories.remove("legacy" )
for fname in fnames:
if fname.endswith(".py" ):
update_version_in_file(os.path.join(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase , pattern="examples" )
def UpperCamelCase ( _lowerCamelCase : int , _lowerCamelCase : Union[str, Any]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
if not patch:
update_version_in_examples(_lowerCamelCase )
def UpperCamelCase ( ):
A__ = "🤗 Transformers currently provides the following architectures"
A__ = "1. Want to contribute a new model?"
with open(_lowerCamelCase , "r" , encoding="utf-8" , newline="\n" ) as f:
A__ = f.readlines()
# Find the start of the list.
A__ = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
A__ = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("1." ):
A__ = lines[index].replace(
"https://huggingface.co/docs/transformers/main/model_doc" , "https://huggingface.co/docs/transformers/model_doc" , )
index += 1
with open(_lowerCamelCase , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(_lowerCamelCase )
def UpperCamelCase ( ):
with open(REPLACE_FILES["init"] , "r" ) as f:
A__ = f.read()
A__ = REPLACE_PATTERNS["init"][0].search(_lowerCamelCase ).groups()[0]
return packaging.version.parse(_lowerCamelCase )
def UpperCamelCase ( _lowerCamelCase : Dict=False ):
A__ = get_version()
if patch and default_version.is_devrelease:
raise ValueError("Can't create a patch version from the dev branch, checkout a released version!" )
if default_version.is_devrelease:
A__ = default_version.base_version
elif patch:
A__ = F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
else:
A__ = F"{default_version.major}.{default_version.minor + 1}.0"
# Now let's ask nicely if that's the right one.
A__ = input(F"Which version are you releasing? [{default_version}]" )
if len(_lowerCamelCase ) == 0:
A__ = default_version
print(F"Updating version to {version}." )
global_version_update(_lowerCamelCase , patch=_lowerCamelCase )
if not patch:
print("Cleaning main README, don't forget to run `make fix-copies`." )
clean_main_ref_in_model_list()
def UpperCamelCase ( ):
A__ = get_version()
A__ = F"{current_version.major}.{current_version.minor + 1}.0.dev0"
A__ = current_version.base_version
# Check with the user we got that right.
A__ = input(F"Which version are we developing now? [{dev_version}]" )
if len(_lowerCamelCase ) == 0:
A__ = dev_version
print(F"Updating version to {version}." )
global_version_update(_lowerCamelCase )
print("Cleaning main README, don't forget to run `make fix-copies`." )
clean_main_ref_in_model_list()
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] =argparse.ArgumentParser()
parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
__lowerCAmelCase : int =parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print("Nothing to do after a patch :-)")
else:
post_release_work()
| 123 | 1 |
from __future__ import annotations
import numpy as np
def __lowerCAmelCase ( a__ ) -> tuple[np.ndarray, np.ndarray]:
__a , __a = np.shape(a__ )
if rows != columns:
__a = (
'''\'table\' has to be of square shaped array but got a '''
F"""{rows}x{columns} array:\n{table}"""
)
raise ValueError(a__ )
__a = np.zeros((rows, columns) )
__a = np.zeros((rows, columns) )
for i in range(a__ ):
for j in range(a__ ):
__a = sum(lower[i][k] * upper[k][j] for k in range(a__ ) )
if upper[j][j] == 0:
raise ArithmeticError('''No LU decomposition exists''' )
__a = (table[i][j] - total) / upper[j][j]
__a = 1
for j in range(a__ , a__ ):
__a = sum(lower[i][k] * upper[k][j] for k in range(a__ ) )
__a = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod() | 6 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_mbart import MBartTokenizer
else:
_UpperCamelCase = None
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
_UpperCamelCase = {
'vocab_file': {
'facebook/mbart-large-en-ro': (
'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
),
'facebook/mbart-large-cc25': (
'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
),
},
'tokenizer_file': {
'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
},
}
_UpperCamelCase = {
'facebook/mbart-large-en-ro': 1024,
'facebook/mbart-large-cc25': 1024,
}
# fmt: off
_UpperCamelCase = ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']
class lowerCamelCase_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
a_ =VOCAB_FILES_NAMES
a_ =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ =PRETRAINED_VOCAB_FILES_MAP
a_ =["""input_ids""", """attention_mask"""]
a_ =MBartTokenizer
a_ =[]
a_ =[]
def __init__( self : Optional[Any] , _a : Optional[int]=None , _a : Any=None , _a : Any="<s>" , _a : Optional[Any]="</s>" , _a : List[str]="</s>" , _a : List[Any]="<s>" , _a : Union[str, Any]="<unk>" , _a : str="<pad>" , _a : Any="<mask>" , _a : Optional[Any]=None , _a : str=None , _a : Tuple=None , **_a : Dict , ) -> List[str]:
# Mask token behave like a normal word, i.e. include the space before it
__lowerCamelCase : List[Any] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
super().__init__(
vocab_file=_a , tokenizer_file=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , src_lang=_a , tgt_lang=_a , additional_special_tokens=_a , **_a , )
__lowerCamelCase : Optional[Any] = vocab_file
__lowerCamelCase : List[str] = False if not self.vocab_file else True
__lowerCamelCase : str = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'additional_special_tokens': _additional_special_tokens} )
__lowerCamelCase : Optional[Any] = {
lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
__lowerCamelCase : Optional[Any] = src_lang if src_lang is not None else 'en_XX'
__lowerCamelCase : int = self.convert_tokens_to_ids(self._src_lang )
__lowerCamelCase : List[str] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _lowercase ( self : List[Any] ) -> str:
return self._src_lang
@src_lang.setter
def _lowercase ( self : Union[str, Any] , _a : str ) -> None:
__lowerCamelCase : Union[str, Any] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _lowercase ( self : List[Any] , _a : List[int] , _a : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _lowercase ( self : int , _a : List[int] , _a : Optional[List[int]] = None ) -> List[int]:
__lowerCamelCase : Optional[int] = [self.sep_token_id]
__lowerCamelCase : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _lowercase ( self : Optional[Any] , _a : Optional[Any] , _a : str , _a : Optional[str] , _a : Optional[str] , **_a : Optional[int] ) -> Any:
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
__lowerCamelCase : Optional[Any] = src_lang
__lowerCamelCase : Dict = self(_a , add_special_tokens=_a , return_tensors=_a , **_a )
__lowerCamelCase : Tuple = self.convert_tokens_to_ids(_a )
__lowerCamelCase : Optional[Any] = tgt_lang_id
return inputs
def _lowercase ( self : Any , _a : List[str] , _a : str = "en_XX" , _a : Optional[List[str]] = None , _a : str = "ro_RO" , **_a : Tuple , ) -> BatchEncoding:
__lowerCamelCase : List[Any] = src_lang
__lowerCamelCase : str = tgt_lang
return super().prepare_seqaseq_batch(_a , _a , **_a )
def _lowercase ( self : List[Any] ) -> Any:
return self.set_src_lang_special_tokens(self.src_lang )
def _lowercase ( self : Dict ) -> Optional[Any]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _lowercase ( self : Tuple , _a : List[str] ) -> None:
__lowerCamelCase : Tuple = self.convert_tokens_to_ids(_a )
__lowerCamelCase : Optional[Any] = []
__lowerCamelCase : List[str] = [self.eos_token_id, self.cur_lang_code]
__lowerCamelCase : Dict = self.convert_ids_to_tokens(self.prefix_tokens )
__lowerCamelCase : Optional[Any] = self.convert_ids_to_tokens(self.suffix_tokens )
__lowerCamelCase : Union[str, Any] = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str , pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _lowercase ( self : Optional[Any] , _a : str ) -> None:
__lowerCamelCase : Union[str, Any] = self.convert_tokens_to_ids(_a )
__lowerCamelCase : int = []
__lowerCamelCase : List[str] = [self.eos_token_id, self.cur_lang_code]
__lowerCamelCase : int = self.convert_ids_to_tokens(self.prefix_tokens )
__lowerCamelCase : Any = self.convert_ids_to_tokens(self.suffix_tokens )
__lowerCamelCase : Optional[int] = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str , pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _lowercase ( self : Any , _a : str , _a : Optional[str] = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(_a ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory.' )
return
__lowerCamelCase : List[str] = os.path.join(
_a , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 208 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : float , _UpperCamelCase : float ) -> float:
"""simple docstring"""
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(_UpperCamelCase ) * abs(_UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 114 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _lowerCAmelCase ( ) -> int:
"""simple docstring"""
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
_SCREAMING_SNAKE_CASE ='__test_patch_submodule_mock__'
with patch_submodule(_test_patching , 'os.path.join' , _UpperCamelCase ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os , _PatchedModuleObj )
assert isinstance(_test_patching.os.path , _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path , _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os , _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path , _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def _lowerCAmelCase ( ) -> Optional[Any]:
"""simple docstring"""
assert _test_patching.open is open
_SCREAMING_SNAKE_CASE ='__test_patch_submodule_builtin_mock__'
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching , 'open' , _UpperCamelCase ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def _lowerCAmelCase ( ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE ='__test_patch_submodule_missing_mock__'
with patch_submodule(_test_patching , 'pandas.read_csv' , _UpperCamelCase ):
pass
def _lowerCAmelCase ( ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE ='__test_patch_submodule_missing_builtin_mock__'
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching , 'len' , _UpperCamelCase ) is None
with patch_submodule(_test_patching , 'len' , _UpperCamelCase ):
assert _test_patching.len is mock
assert _test_patching.len is len
def _lowerCAmelCase ( ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE ='__test_patch_submodule_start_and_stop_mock__'
_SCREAMING_SNAKE_CASE =patch_submodule(_test_patching , 'open' , _UpperCamelCase )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def _lowerCAmelCase ( ) -> Optional[Any]:
"""simple docstring"""
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
_SCREAMING_SNAKE_CASE ='__test_patch_submodule_successive_join__'
_SCREAMING_SNAKE_CASE ='__test_patch_submodule_successive_dirname__'
_SCREAMING_SNAKE_CASE ='__test_patch_submodule_successive_rename__'
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching , 'os.path.join' , _UpperCamelCase ):
with patch_submodule(_test_patching , 'os.rename' , _UpperCamelCase ):
with patch_submodule(_test_patching , 'os.path.dirname' , _UpperCamelCase ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching , 'os.rename' , _UpperCamelCase ):
with patch_submodule(_test_patching , 'os.path.join' , _UpperCamelCase ):
with patch_submodule(_test_patching , 'os.path.dirname' , _UpperCamelCase ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def _lowerCAmelCase ( ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE ='__test_patch_submodule_doesnt_exist_mock__'
with patch_submodule(_test_patching , '__module_that_doesn_exist__.__attribute_that_doesn_exist__' , _UpperCamelCase ):
pass
with patch_submodule(_test_patching , 'os.__attribute_that_doesn_exist__' , _UpperCamelCase ):
pass
| 114 | 1 |
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def a__ ( lowerCAmelCase__ ) -> tuple:
return (data["data"], data["target"])
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> XGBClassifier:
UpperCAmelCase__ : int = XGBClassifier()
classifier.fit(lowerCAmelCase__ , lowerCAmelCase__ )
return classifier
def a__ ( ) -> None:
UpperCAmelCase__ : Dict = load_iris()
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = data_handling(lowerCAmelCase__ )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : int = train_test_split(
lowerCAmelCase__ , lowerCAmelCase__ , test_size=0.2_5 )
UpperCAmelCase__ : Dict = iris['''target_names''']
# Create an XGBoost Classifier from the training data
UpperCAmelCase__ : List[Any] = xgboost(lowerCAmelCase__ , lowerCAmelCase__ )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , display_labels=lowerCAmelCase__ , cmap='''Blues''' , normalize='''true''' , )
plt.title('''Normalized Confusion Matrix - IRIS Dataset''' )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 181 |
'''simple docstring'''
# Copyright 2023 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.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase__ = {
'''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VivitConfig'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = ['''VivitImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
'''VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''VivitModel''',
'''VivitPreTrainedModel''',
'''VivitForVideoClassification''',
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 181 | 1 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def A ( *_UpperCAmelCase : Any , _UpperCAmelCase : Optional[Union[Dict, Any]] = None , _UpperCAmelCase : Optional[int]=True , _UpperCAmelCase : str=2 ) -> Any:
'''simple docstring'''
from .. import __version__
_UpperCAmelCase = take_from
_UpperCAmelCase = ()
if not isinstance(args[0] , _UpperCAmelCase ):
_UpperCAmelCase = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(_UpperCAmelCase ).base_version ) >= version.parse(_UpperCAmelCase ):
raise ValueError(
F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
F" version {__version__} is >= {version_name}" )
_UpperCAmelCase = None
if isinstance(_UpperCAmelCase , _UpperCAmelCase ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(_UpperCAmelCase ),)
_UpperCAmelCase = F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
elif hasattr(_UpperCAmelCase , _UpperCAmelCase ):
values += (getattr(_UpperCAmelCase , _UpperCAmelCase ),)
_UpperCAmelCase = F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
elif deprecated_kwargs is None:
_UpperCAmelCase = F"`{attribute}` is deprecated and will be removed in version {version_name}."
if warning is not None:
_UpperCAmelCase = warning + ' ' if standard_warn else ''
warnings.warn(warning + message , _UpperCAmelCase , stacklevel=_UpperCAmelCase )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ) and len(_UpperCAmelCase ) > 0:
_UpperCAmelCase = inspect.getouterframes(inspect.currentframe() )[1]
_UpperCAmelCase = call_frame.filename
_UpperCAmelCase = call_frame.lineno
_UpperCAmelCase = call_frame.function
_UpperCAmelCase , _UpperCAmelCase = next(iter(deprecated_kwargs.items() ) )
raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`" )
if len(_UpperCAmelCase ) == 0:
return
elif len(_UpperCAmelCase ) == 1:
return values[0]
return values
| 290 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class __lowerCAmelCase :
def __init__( self : Any , A : str = "cpu" , A : str = "openai/clip-vit-large-patch14") -> None:
"""simple docstring"""
_UpperCAmelCase = device
_UpperCAmelCase = CLIPTokenizerFast.from_pretrained(A)
_UpperCAmelCase = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
_UpperCAmelCase = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
_UpperCAmelCase = torchvision.transforms.Normalize(self.image_mean , self.image_std)
_UpperCAmelCase = torchvision.transforms.Resize(2_24)
_UpperCAmelCase = torchvision.transforms.CenterCrop(2_24)
def _lowerCamelCase ( self : str , A : Any) -> str:
"""simple docstring"""
_UpperCAmelCase = self.resize(A)
_UpperCAmelCase = self.center_crop(A)
_UpperCAmelCase = self.normalize(A)
return images
def __call__( self : Any , A : Dict=None , A : Dict=None , **A : List[Any]) -> Dict:
"""simple docstring"""
_UpperCAmelCase = self.tokenizer(text=A , **A)
_UpperCAmelCase = self.preprocess_img(A)
_UpperCAmelCase = {key: value.to(self.device) for (key, value) in encoding.items()}
return encoding
class __lowerCAmelCase ( nn.Module ):
def __init__( self : List[Any] , A : Any=10 , A : List[Any]=0.0_1 , A : Optional[int]=None , A : int=None , A : Dict=None , A : Tuple=None , A : str=None , A : Dict=None , A : Union[str, Any]=False , A : Any=True , A : Any="image" , A : Tuple=True , A : List[Any]=False , A : int=False , A : int=False , ) -> None:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = None
_UpperCAmelCase = device if device else get_device()
if vqgan:
_UpperCAmelCase = vqgan
else:
_UpperCAmelCase = load_vqgan(self.device , conf_path=A , ckpt_path=A)
self.vqgan.eval()
if clip:
_UpperCAmelCase = clip
else:
_UpperCAmelCase = CLIPModel.from_pretrained('openai/clip-vit-base-patch32')
self.clip.to(self.device)
_UpperCAmelCase = ProcessorGradientFlow(device=self.device)
_UpperCAmelCase = iterations
_UpperCAmelCase = lr
_UpperCAmelCase = log
_UpperCAmelCase = make_grid
_UpperCAmelCase = return_val
_UpperCAmelCase = quantize
_UpperCAmelCase = self.vqgan.decoder.z_shape
def _lowerCamelCase ( self : Optional[int] , A : int=None , A : Union[str, Any]=None , A : Dict=5 , A : Optional[Any]=True) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = []
if output_path is None:
_UpperCAmelCase = './animation.gif'
if input_path is None:
_UpperCAmelCase = self.save_path
_UpperCAmelCase = sorted(glob(input_path + '/*'))
if not len(A):
raise ValueError(
'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
' function?)')
if len(A) == 1:
print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)')
_UpperCAmelCase = total_duration / len(A)
_UpperCAmelCase = [frame_duration] * len(A)
if extend_frames:
_UpperCAmelCase = 1.5
_UpperCAmelCase = 3
for file_name in paths:
if file_name.endswith('.png'):
images.append(imageio.imread(A))
imageio.mimsave(A , A , duration=A)
print(F"gif saved to {output_path}")
def _lowerCamelCase ( self : List[str] , A : Optional[Any]=None , A : Optional[int]=None) -> int:
"""simple docstring"""
if not (path or img):
raise ValueError('Input either path or tensor')
if img is not None:
raise NotImplementedError
_UpperCAmelCase = preprocess(Image.open(A) , target_image_size=2_56).to(self.device)
_UpperCAmelCase = preprocess_vqgan(A)
_UpperCAmelCase , *_UpperCAmelCase = self.vqgan.encode(A)
return z
def _lowerCamelCase ( self : List[str] , A : int) -> Dict:
"""simple docstring"""
_UpperCAmelCase = self.latent.detach().requires_grad_()
_UpperCAmelCase = base_latent + transform_vector
if self.quantize:
_UpperCAmelCase , *_UpperCAmelCase = self.vqgan.quantize(A)
else:
_UpperCAmelCase = trans_latent
return self.vqgan.decode(A)
def _lowerCamelCase ( self : Any , A : Dict , A : Dict , A : Optional[Any]=None) -> Any:
"""simple docstring"""
_UpperCAmelCase = self.clip_preprocessor(text=A , images=A , return_tensors='pt' , padding=A)
_UpperCAmelCase = self.clip(**A)
_UpperCAmelCase = clip_outputs.logits_per_image
if weights is not None:
_UpperCAmelCase = similarity_logits * weights
return similarity_logits.sum()
def _lowerCamelCase ( self : Optional[int] , A : Dict , A : int , A : Tuple) -> str:
"""simple docstring"""
_UpperCAmelCase = self._get_clip_similarity(pos_prompts['prompts'] , A , weights=(1 / pos_prompts['weights']))
if neg_prompts:
_UpperCAmelCase = self._get_clip_similarity(neg_prompts['prompts'] , A , weights=neg_prompts['weights'])
else:
_UpperCAmelCase = torch.tensor([1] , device=self.device)
_UpperCAmelCase = -torch.log(A) + torch.log(A)
return loss
def _lowerCamelCase ( self : Tuple , A : Optional[int] , A : List[Any] , A : Optional[int]) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = torch.randn_like(self.latent , requires_grad=A , device=self.device)
_UpperCAmelCase = torch.optim.Adam([vector] , lr=self.lr)
for i in range(self.iterations):
optim.zero_grad()
_UpperCAmelCase = self._add_vector(A)
_UpperCAmelCase = loop_post_process(A)
_UpperCAmelCase = self._get_CLIP_loss(A , A , A)
print('CLIP loss' , A)
if self.log:
wandb.log({'CLIP Loss': clip_loss})
clip_loss.backward(retain_graph=A)
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0])
else:
yield vector
def _lowerCamelCase ( self : Dict , A : Any , A : Optional[int] , A : str) -> Any:
"""simple docstring"""
wandb.init(reinit=A , project='face-editor')
wandb.config.update({'Positive Prompts': positive_prompts})
wandb.config.update({'Negative Prompts': negative_prompts})
wandb.config.update({'lr': self.lr, 'iterations': self.iterations})
if image_path:
_UpperCAmelCase = Image.open(A)
_UpperCAmelCase = image.resize((2_56, 2_56))
wandb.log('Original Image' , wandb.Image(A))
def _lowerCamelCase ( self : Dict , A : int) -> Dict:
"""simple docstring"""
if not prompts:
return []
_UpperCAmelCase = []
_UpperCAmelCase = []
if isinstance(A , A):
_UpperCAmelCase = [prompt.strip() for prompt in prompts.split('|')]
for prompt in prompts:
if isinstance(A , (tuple, list)):
_UpperCAmelCase = prompt[0]
_UpperCAmelCase = float(prompt[1])
elif ":" in prompt:
_UpperCAmelCase , _UpperCAmelCase = prompt.split(':')
_UpperCAmelCase = float(A)
else:
_UpperCAmelCase = prompt
_UpperCAmelCase = 1.0
processed_prompts.append(A)
weights.append(A)
return {
"prompts": processed_prompts,
"weights": torch.tensor(A , device=self.device),
}
def _lowerCamelCase ( self : Optional[int] , A : Union[str, Any] , A : Union[str, Any]=None , A : int=None , A : Optional[Any]=True , A : Dict=False , A : Union[str, Any]=True , A : Any=True , A : Any=None , ) -> Dict:
"""simple docstring"""
if image_path:
_UpperCAmelCase = self._get_latent(A)
else:
_UpperCAmelCase = torch.randn(self.latent_dim , device=self.device)
if self.log:
self._init_logging(A , A , A)
assert pos_prompts, "You must provide at least one positive prompt."
_UpperCAmelCase = self.process_prompts(A)
_UpperCAmelCase = self.process_prompts(A)
if save_final and save_path is None:
_UpperCAmelCase = os.path.join('./outputs/' , '_'.join(pos_prompts['prompts']))
if not os.path.exists(A):
os.makedirs(A)
else:
_UpperCAmelCase = save_path + '_' + get_timestamp()
os.makedirs(A)
_UpperCAmelCase = save_path
_UpperCAmelCase = self.vqgan.decode(self.latent)[0]
if show_intermediate:
print('Original Image')
show_pil(custom_to_pil(A))
_UpperCAmelCase = loop_post_process(A)
for iter, transformed_img in enumerate(self._optimize_CLIP(A , A , A)):
if show_intermediate:
show_pil(A)
if save_intermediate:
transformed_img.save(os.path.join(self.save_path , F"iter_{iter:03d}.png"))
if self.log:
wandb.log({'Image': wandb.Image(A)})
if show_final:
show_pil(A)
if save_final:
transformed_img.save(os.path.join(self.save_path , F"iter_{iter:03d}_final.png"))
| 290 | 1 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : Union[str, Any] = logging.get_logger(__name__)
lowercase : str = "▁"
lowercase : Any = {
"vocab_file": "vocab.json",
"spm_file": "sentencepiece.bpe.model",
}
lowercase : Tuple = {
"vocab_file": {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"
),
},
"spm_file": {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"
)
},
}
lowercase : Dict = {
"facebook/s2t-small-librispeech-asr": 1024,
}
lowercase : Tuple = ["pt", "fr", "ru", "nl", "ro", "it", "es", "de"]
lowercase : Any = {"mustc": MUSTC_LANGS}
class __UpperCAmelCase ( _lowerCamelCase ):
__lowercase = VOCAB_FILES_NAMES
__lowercase = PRETRAINED_VOCAB_FILES_MAP
__lowercase = MAX_MODEL_INPUT_SIZES
__lowercase = ["""input_ids""", """attention_mask"""]
__lowercase = []
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_="<s>" , lowerCAmelCase_="</s>" , lowerCAmelCase_="<pad>" , lowerCAmelCase_="<unk>" , lowerCAmelCase_=False , lowerCAmelCase_=False , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_ = None , **lowerCAmelCase_ , ):
"""simple docstring"""
_snake_case = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowerCAmelCase_ , eos_token=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , pad_token=lowerCAmelCase_ , do_upper_case=lowerCAmelCase_ , do_lower_case=lowerCAmelCase_ , tgt_lang=lowerCAmelCase_ , lang_codes=lowerCAmelCase_ , sp_model_kwargs=self.sp_model_kwargs , **lowerCAmelCase_ , )
_snake_case = do_upper_case
_snake_case = do_lower_case
_snake_case = load_json(lowerCAmelCase_ )
_snake_case = {v: k for k, v in self.encoder.items()}
_snake_case = spm_file
_snake_case = load_spm(lowerCAmelCase_ , self.sp_model_kwargs )
if lang_codes is not None:
_snake_case = lang_codes
_snake_case = LANGUAGES[lang_codes]
_snake_case = [F'<lang:{lang}>' for lang in self.langs]
_snake_case = {lang: self.sp_model.PieceToId(F'<lang:{lang}>' ) for lang in self.langs}
_snake_case = self.lang_tokens
_snake_case = tgt_lang if tgt_lang is not None else self.langs[0]
self.set_tgt_lang_special_tokens(self._tgt_lang )
else:
_snake_case = {}
@property
def lowerCamelCase ( self ):
"""simple docstring"""
return len(self.encoder )
@property
def lowerCamelCase ( self ):
"""simple docstring"""
return self._tgt_lang
@tgt_lang.setter
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = new_tgt_lang
self.set_tgt_lang_special_tokens(lowerCAmelCase_ )
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = self.lang_code_to_id[tgt_lang]
_snake_case = [lang_code_id]
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
return self.sp_model.encode(lowerCAmelCase_ , out_type=lowerCAmelCase_ )
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
return self.encoder.get(lowerCAmelCase_ , self.encoder[self.unk_token] )
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
return self.decoder.get(lowerCAmelCase_ , self.unk_token )
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = []
_snake_case = ''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
_snake_case = self.sp_model.decode(lowerCAmelCase_ )
out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
_snake_case = []
else:
current_sub_tokens.append(lowerCAmelCase_ )
_snake_case = self.sp_model.decode(lowerCAmelCase_ )
out_string += decoded.upper() if self.do_upper_case else decoded
return out_string.strip()
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_=None ):
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = None , lowerCAmelCase_ = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase_ , token_ids_a=lowerCAmelCase_ , already_has_special_tokens=lowerCAmelCase_ )
_snake_case = [1] * len(self.prefix_tokens )
_snake_case = [1]
if token_ids_a is None:
return prefix_ones + ([0] * len(lowerCAmelCase_ )) + suffix_ones
return prefix_ones + ([0] * len(lowerCAmelCase_ )) + ([0] * len(lowerCAmelCase_ )) + suffix_ones
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.encoder.copy()
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
"""simple docstring"""
_snake_case = self.__dict__.copy()
_snake_case = None
return state
def __setstate__( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
_snake_case = {}
_snake_case = load_spm(self.spm_file , self.sp_model_kwargs )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = None ):
"""simple docstring"""
_snake_case = Path(lowerCAmelCase_ )
assert save_dir.is_dir(), F'{save_directory} should be a directory'
_snake_case = save_dir / (
(filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
)
_snake_case = save_dir / (
(filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
)
save_json(self.encoder , lowerCAmelCase_ )
if os.path.abspath(self.spm_file ) != os.path.abspath(lowerCAmelCase_ ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , lowerCAmelCase_ )
elif not os.path.isfile(self.spm_file ):
with open(lowerCAmelCase_ , 'wb' ) as fi:
_snake_case = self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase_ )
return (str(lowerCAmelCase_ ), str(lowerCAmelCase_ ))
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> sentencepiece.SentencePieceProcessor:
_snake_case = sentencepiece.SentencePieceProcessor(**__A )
spm.Load(str(__A ) )
return spm
def SCREAMING_SNAKE_CASE__ ( __A ) -> Union[Dict, List]:
with open(__A , 'r' ) as f:
return json.load(__A )
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> None:
with open(__A , 'w' ) as f:
json.dump(__A , __A , indent=2 )
| 42 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __UpperCAmelCase ( _lowerCamelCase ):
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
return 0.0
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> tuple[int | float, int | float]:
_snake_case = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
_snake_case = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> None:
_snake_case = 512
_snake_case = [1] + [0] * (size - 1)
_snake_case = [filter_type.process(__A ) for item in inputs]
_snake_case = [0] * (samplerate - size) # zero-padding
outputs += filler
_snake_case = np.abs(np.fft.fft(__A ) )
_snake_case = 20 * np.logaa(__A )
# 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
_snake_case = get_bounds(__A , __A )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel('Gain (dB)' )
plt.plot(__A )
plt.show()
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> None:
_snake_case = 512
_snake_case = [1] + [0] * (size - 1)
_snake_case = [filter_type.process(__A ) for item in inputs]
_snake_case = [0] * (samplerate - size) # zero-padding
outputs += filler
_snake_case = np.angle(np.fft.fft(__A ) )
# 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(__A , -2 * pi ) )
plt.show()
| 42 | 1 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class __lowerCamelCase ( __snake_case , unittest.TestCase ):
lowerCamelCase_ : Optional[Any] = VideoToVideoSDPipeline
lowerCamelCase_ : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({'video'} ) - {'image', 'width', 'height'}
lowerCamelCase_ : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'video'} ) - {'image'}
lowerCamelCase_ : int = PipelineTesterMixin.required_optional_params - {'latents'}
lowerCamelCase_ : List[Any] = False
# No `output_type`.
lowerCamelCase_ : Tuple = frozenset(
[
'num_inference_steps',
'generator',
'latents',
'return_dict',
'callback',
'callback_steps',
] )
def lowerCAmelCase_ ( self ) -> Dict:
torch.manual_seed(0 )
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 , )
snake_case_ = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=lowerCamelCase , set_alpha_to_one=lowerCamelCase , )
torch.manual_seed(0 )
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 )
snake_case_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""gelu""" , projection_dim=512 , )
snake_case_ = CLIPTextModel(lowerCamelCase )
snake_case_ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
snake_case_ = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def lowerCAmelCase_ ( self , lowerCamelCase , lowerCamelCase=0 ) -> List[Any]:
# 3 frames
snake_case_ = floats_tensor((1, 3, 3, 32, 32) , rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
if str(lowerCamelCase ).startswith("""mps""" ):
snake_case_ = torch.manual_seed(lowerCamelCase )
else:
snake_case_ = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
snake_case_ = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""video""": video,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """pt""",
}
return inputs
def lowerCAmelCase_ ( self ) -> int:
snake_case_ = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case_ = self.get_dummy_components()
snake_case_ = VideoToVideoSDPipeline(**lowerCamelCase )
snake_case_ = sd_pipe.to(lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase )
snake_case_ = self.get_dummy_inputs(lowerCamelCase )
snake_case_ = """np"""
snake_case_ = sd_pipe(**lowerCamelCase ).frames
snake_case_ = frames[0][-3:, -3:, -1]
assert frames[0].shape == (32, 32, 3)
snake_case_ = np.array([106, 117, 113, 174, 137, 112, 148, 151, 131] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase , expected_max_diff=5e-3 )
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def lowerCAmelCase_ ( self ) -> List[str]:
pass
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def lowerCAmelCase_ ( self ) -> str:
pass
@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline.""" )
def lowerCAmelCase_ ( self ) -> int:
pass
def lowerCAmelCase_ ( self ) -> List[Any]:
return super().test_progress_bar()
@slow
@skip_mps
class __lowerCamelCase ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> str:
snake_case_ = VideoToVideoSDPipeline.from_pretrained("""cerspense/zeroscope_v2_XL""" , torch_dtype=torch.floataa )
pipe.enable_model_cpu_offload()
# 10 frames
snake_case_ = torch.Generator(device="""cpu""" ).manual_seed(0 )
snake_case_ = torch.randn((1, 10, 3, 1024, 576) , generator=lowerCamelCase )
snake_case_ = video.to("""cuda""" )
snake_case_ = """Spiderman is surfing"""
snake_case_ = pipe(lowerCamelCase , video=lowerCamelCase , generator=lowerCamelCase , num_inference_steps=3 , output_type="""pt""" ).frames
snake_case_ = np.array([-1.045_8984, -1.127_9297, -0.966_3086, -0.9150_3906, -0.7509_7656] )
assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array ).sum() < 1e-2
| 357 |
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_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : List[str] = 'mobilenet_v1'
def __init__( self , lowerCamelCase=3 , lowerCamelCase=224 , lowerCamelCase=1.0 , lowerCamelCase=8 , lowerCamelCase="relu6" , lowerCamelCase=True , lowerCamelCase=0.999 , lowerCamelCase=0.02 , lowerCamelCase=0.001 , **lowerCamelCase , ) -> List[str]:
super().__init__(**lowerCamelCase )
if depth_multiplier <= 0:
raise ValueError("""depth_multiplier must be greater than zero.""" )
snake_case_ = num_channels
snake_case_ = image_size
snake_case_ = depth_multiplier
snake_case_ = min_depth
snake_case_ = hidden_act
snake_case_ = tf_padding
snake_case_ = classifier_dropout_prob
snake_case_ = initializer_range
snake_case_ = layer_norm_eps
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : str = version.parse('1.11' )
@property
def lowerCAmelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict([("""pixel_values""", {0: """batch"""})] )
@property
def lowerCAmelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
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 ) -> float:
return 1e-4 | 34 | 0 |
def UpperCamelCase__ ( A__ ) -> Optional[int]:
if not head:
return True
# split the list to two parts
snake_case__ : List[str] = head.next, head
while fast and fast.next:
snake_case__ : Any = fast.next.next
snake_case__ : List[str] = slow.next
snake_case__ : Tuple = slow.next
snake_case__ : Dict = None # Don't forget here! But forget still works!
# reverse the second part
snake_case__ : Dict = None
while second:
snake_case__ : Tuple = second.next
snake_case__ : Any = node
snake_case__ : Tuple = second
snake_case__ : List[Any] = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
snake_case__ : Optional[Any] = node.next
snake_case__ : Union[str, Any] = head.next
return True
def UpperCamelCase__ ( A__ ) -> Optional[Any]:
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
snake_case__ : List[str] = head
while fast and fast.next:
snake_case__ : Optional[int] = fast.next.next, slow.next
# 2. Push the second half into the stack
snake_case__ : List[Any] = [slow.val]
while slow.next:
snake_case__ : Any = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
snake_case__ : str = cur.next
return True
def UpperCamelCase__ ( A__ ) -> str:
if not head or not head.next:
return True
snake_case__ : str = {}
snake_case__ : int = 0
while head:
if head.val in d:
d[head.val].append(__UpperCAmelCase )
else:
snake_case__ : str = [pos]
snake_case__ : Any = head.next
pos += 1
snake_case__ : Optional[int] = pos - 1
snake_case__ : Any = 0
for v in d.values():
if len(__UpperCAmelCase ) % 2 != 0:
middle += 1
else:
snake_case__ : Dict = 0
for i in range(0 , len(__UpperCAmelCase ) ):
if v[i] + v[len(__UpperCAmelCase ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 143 |
"""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
_A = logging.get_logger(__name__)
_A = {
"""facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class _lowerCamelCase ( a_ ):
_lowerCamelCase :Optional[Any] = "levit"
def __init__( self : List[Any] , UpperCamelCase : List[str]=2_24 , UpperCamelCase : Any=3 , UpperCamelCase : Optional[Any]=3 , UpperCamelCase : Union[str, Any]=2 , UpperCamelCase : Any=1 , UpperCamelCase : int=16 , UpperCamelCase : List[str]=[1_28, 2_56, 3_84] , UpperCamelCase : Optional[Any]=[4, 8, 12] , UpperCamelCase : Optional[int]=[4, 4, 4] , UpperCamelCase : str=[16, 16, 16] , UpperCamelCase : Tuple=0 , UpperCamelCase : List[str]=[2, 2, 2] , UpperCamelCase : Optional[int]=[2, 2, 2] , UpperCamelCase : Optional[int]=0.02 , **UpperCamelCase : Dict , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**UpperCamelCase )
lowerCAmelCase__ : int = image_size
lowerCAmelCase__ : Any = num_channels
lowerCAmelCase__ : int = kernel_size
lowerCAmelCase__ : Any = stride
lowerCAmelCase__ : List[str] = padding
lowerCAmelCase__ : Tuple = hidden_sizes
lowerCAmelCase__ : str = num_attention_heads
lowerCAmelCase__ : List[Any] = depths
lowerCAmelCase__ : List[str] = key_dim
lowerCAmelCase__ : List[str] = drop_path_rate
lowerCAmelCase__ : List[Any] = patch_size
lowerCAmelCase__ : Dict = attention_ratio
lowerCAmelCase__ : Tuple = mlp_ratio
lowerCAmelCase__ : Any = initializer_range
lowerCAmelCase__ : Dict = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class _lowerCamelCase ( a_ ):
_lowerCamelCase :Tuple = version.parse("1.11" )
@property
def _lowerCAmelCase ( self : int ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _lowerCAmelCase ( self : List[str] ) -> float:
"""simple docstring"""
return 1E-4
| 242 | 0 |
'''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 lowerCamelCase ( __lowerCAmelCase ):
snake_case_ = 42
snake_case_ = jnp.floataa
snake_case_ = True
def _lowerCamelCase ( self ) -> List[Any]:
super().setup()
snake_case = nn.Dense(5, dtype=self.dtype )
def __call__( self, *lowercase_, **lowercase_ ) -> int:
snake_case = super().__call__(*lowerCamelCase_, **lowerCamelCase_ )
snake_case = self.cls(outputs[2] )
return outputs[:2] + (cls_out,)
class lowerCamelCase ( __lowerCAmelCase ):
snake_case_ = FlaxBigBirdForNaturalQuestionsModule
def __magic_name__ ( A , A , A , A , A , A ) -> Optional[Any]:
def cross_entropy(A , A , A=None ):
snake_case = logits.shape[-1]
snake_case = (labels[..., None] == jnp.arange(_a )[None]).astype('f4' )
snake_case = jax.nn.log_softmax(_a , axis=-1 )
snake_case = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
snake_case = reduction(_a )
return loss
snake_case = partial(_a , reduction=jnp.mean )
snake_case = cross_entropy(_a , _a )
snake_case = cross_entropy(_a , _a )
snake_case = cross_entropy(_a , _a )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class lowerCamelCase :
snake_case_ = '''google/bigbird-roberta-base'''
snake_case_ = 3000
snake_case_ = 10500
snake_case_ = 128
snake_case_ = 3
snake_case_ = 1
snake_case_ = 5
# tx_args
snake_case_ = 3e-5
snake_case_ = 0.0
snake_case_ = 20000
snake_case_ = 0.0_095
snake_case_ = '''bigbird-roberta-natural-questions'''
snake_case_ = '''training-expt'''
snake_case_ = '''data/nq-training.jsonl'''
snake_case_ = '''data/nq-validation.jsonl'''
def _lowerCamelCase ( self ) -> int:
os.makedirs(self.base_dir, exist_ok=lowerCamelCase_ )
snake_case = os.path.join(self.base_dir, self.save_dir )
snake_case = self.batch_size_per_device * jax.device_count()
@dataclass
class lowerCamelCase :
snake_case_ = 42
snake_case_ = 4096 # no dynamic padding on TPUs
def __call__( self, lowercase_ ) -> Tuple:
snake_case = self.collate_fn(lowerCamelCase_ )
snake_case = jax.tree_util.tree_map(lowerCamelCase_, lowerCamelCase_ )
return batch
def _lowerCamelCase ( self, lowercase_ ) -> Dict:
snake_case , snake_case = self.fetch_inputs(features['input_ids'] )
snake_case = {
'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 _lowerCamelCase ( self, lowercase_ ) -> Union[str, Any]:
snake_case = [self._fetch_inputs(lowerCamelCase_ ) for ids in input_ids]
return zip(*lowerCamelCase_ )
def _lowerCamelCase ( self, lowercase_ ) -> Tuple:
snake_case = [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 __magic_name__ ( A , A , A=None ) -> Any:
if seed is not None:
snake_case = dataset.shuffle(seed=_a )
for i in range(len(_a ) // batch_size ):
snake_case = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(_a )
@partial(jax.pmap , axis_name='batch' )
def __magic_name__ ( A , A , **A ) -> List[str]:
def loss_fn(A ):
snake_case = model_inputs.pop('start_labels' )
snake_case = model_inputs.pop('end_labels' )
snake_case = model_inputs.pop('pooled_labels' )
snake_case = state.apply_fn(**_a , params=_a , dropout_rng=_a , train=_a )
snake_case , snake_case , snake_case = outputs
return state.loss_fn(
_a , _a , _a , _a , _a , _a , )
snake_case , snake_case = jax.random.split(_a )
snake_case = jax.value_and_grad(_a )
snake_case , snake_case = grad_fn(state.params )
snake_case = jax.lax.pmean({'loss': loss} , axis_name='batch' )
snake_case = jax.lax.pmean(_a , 'batch' )
snake_case = state.apply_gradients(grads=_a )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name='batch' )
def __magic_name__ ( A , **A ) -> Any:
snake_case = model_inputs.pop('start_labels' )
snake_case = model_inputs.pop('end_labels' )
snake_case = model_inputs.pop('pooled_labels' )
snake_case = state.apply_fn(**_a , params=state.params , train=_a )
snake_case , snake_case , snake_case = outputs
snake_case = state.loss_fn(_a , _a , _a , _a , _a , _a )
snake_case = jax.lax.pmean({'loss': loss} , axis_name='batch' )
return metrics
class lowerCamelCase ( train_state.TrainState ):
snake_case_ = struct.field(pytree_node=__lowerCAmelCase )
@dataclass
class lowerCamelCase :
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
snake_case_ = None
def _lowerCamelCase ( self, lowercase_, lowercase_, lowercase_, lowercase_=None ) -> List[str]:
snake_case = model.params
snake_case = TrainState.create(
apply_fn=model.__call__, params=lowerCamelCase_, tx=lowerCamelCase_, loss_fn=lowerCamelCase_, )
if ckpt_dir is not None:
snake_case , snake_case , snake_case , snake_case , snake_case = restore_checkpoint(lowerCamelCase_, lowerCamelCase_ )
snake_case = {
'lr': args.lr,
'init_lr': args.init_lr,
'warmup_steps': args.warmup_steps,
'num_train_steps': num_train_steps,
'weight_decay': args.weight_decay,
}
snake_case , snake_case = build_tx(**lowerCamelCase_ )
snake_case = train_state.TrainState(
step=lowerCamelCase_, apply_fn=model.__call__, params=lowerCamelCase_, tx=lowerCamelCase_, opt_state=lowerCamelCase_, )
snake_case = args
snake_case = data_collator
snake_case = lr
snake_case = params
snake_case = jax_utils.replicate(lowerCamelCase_ )
return state
def _lowerCamelCase ( self, lowercase_, lowercase_, lowercase_ ) -> Any:
snake_case = self.args
snake_case = len(lowerCamelCase_ ) // args.batch_size
snake_case = jax.random.PRNGKey(0 )
snake_case = jax.random.split(lowerCamelCase_, jax.device_count() )
for epoch in range(args.max_epochs ):
snake_case = jnp.array(0, dtype=jnp.floataa )
snake_case = get_batched_dataset(lowerCamelCase_, args.batch_size, seed=lowerCamelCase_ )
snake_case = 0
for batch in tqdm(lowerCamelCase_, total=lowerCamelCase_, desc=F'''Running EPOCH-{epoch}''' ):
snake_case = self.data_collator(lowerCamelCase_ )
snake_case , snake_case , snake_case = self.train_step_fn(lowerCamelCase_, lowerCamelCase_, **lowerCamelCase_ )
running_loss += jax_utils.unreplicate(metrics['loss'] )
i += 1
if i % args.logging_steps == 0:
snake_case = jax_utils.unreplicate(state.step )
snake_case = running_loss.item() / i
snake_case = self.scheduler_fn(state_step - 1 )
snake_case = self.evaluate(lowerCamelCase_, lowerCamelCase_ )
snake_case = {
'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 _lowerCamelCase ( self, lowercase_, lowercase_ ) -> int:
snake_case = get_batched_dataset(lowerCamelCase_, self.args.batch_size )
snake_case = len(lowerCamelCase_ ) // self.args.batch_size
snake_case = jnp.array(0, dtype=jnp.floataa )
snake_case = 0
for batch in tqdm(lowerCamelCase_, total=lowerCamelCase_, desc='Evaluating ... ' ):
snake_case = self.data_collator(lowerCamelCase_ )
snake_case = self.val_step_fn(lowerCamelCase_, **lowerCamelCase_ )
running_loss += jax_utils.unreplicate(metrics['loss'] )
i += 1
return running_loss / i
def _lowerCamelCase ( self, lowercase_, lowercase_ ) -> List[Any]:
snake_case = 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 __magic_name__ ( A , A ) -> Dict:
print(F'''RESTORING CHECKPOINT FROM {save_dir}''' , end=' ... ' )
with open(os.path.join(_a , 'flax_model.msgpack' ) , 'rb' ) as f:
snake_case = from_bytes(state.params , f.read() )
with open(os.path.join(_a , 'opt_state.msgpack' ) , 'rb' ) as f:
snake_case = from_bytes(state.opt_state , f.read() )
snake_case = joblib.load(os.path.join(_a , 'args.joblib' ) )
snake_case = joblib.load(os.path.join(_a , 'data_collator.joblib' ) )
with open(os.path.join(_a , 'training_state.json' ) , 'r' ) as f:
snake_case = json.load(_a )
snake_case = training_state['step']
print('DONE' )
return params, opt_state, step, args, data_collator
def __magic_name__ ( A , A , A , A ) -> Union[str, Any]:
snake_case = num_train_steps - warmup_steps
snake_case = optax.linear_schedule(init_value=_a , end_value=_a , transition_steps=_a )
snake_case = optax.linear_schedule(init_value=_a , end_value=1E-7 , transition_steps=_a )
snake_case = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def __magic_name__ ( A , A , A , A , A ) -> Any:
def weight_decay_mask(A ):
snake_case = traverse_util.flatten_dict(_a )
snake_case = {k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
return traverse_util.unflatten_dict(_a )
snake_case = scheduler_fn(_a , _a , _a , _a )
snake_case = optax.adamw(learning_rate=_a , weight_decay=_a , mask=_a )
return tx, lr
| 361 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
"roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}
class lowerCamelCase ( __lowerCAmelCase ):
snake_case_ = '''roberta'''
def __init__( self, lowercase_=50265, lowercase_=768, lowercase_=12, lowercase_=12, lowercase_=3072, lowercase_="gelu", lowercase_=0.1, lowercase_=0.1, lowercase_=512, lowercase_=2, lowercase_=0.02, lowercase_=1E-12, lowercase_=1, lowercase_=0, lowercase_=2, lowercase_="absolute", lowercase_=True, lowercase_=None, **lowercase_, ) -> Tuple:
super().__init__(pad_token_id=lowercase_, bos_token_id=lowercase_, eos_token_id=lowercase_, **lowercase_ )
snake_case = vocab_size
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = hidden_act
snake_case = intermediate_size
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = max_position_embeddings
snake_case = type_vocab_size
snake_case = initializer_range
snake_case = layer_norm_eps
snake_case = position_embedding_type
snake_case = use_cache
snake_case = classifier_dropout
class lowerCamelCase ( __lowerCAmelCase ):
@property
def _lowerCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
snake_case = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
snake_case = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 332 | 0 |
import unittest
from typing import Dict, List, Optional, Union
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 BridgeTowerImageProcessor
class a__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : List[Any] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict = True , UpperCAmelCase__ : List[str] = None , UpperCAmelCase__ : Union[str, Any] = 3_2 , UpperCAmelCase__ : str = True , UpperCAmelCase__ : Union[str, Any] = 1 / 2_5_5 , UpperCAmelCase__ : Tuple = True , UpperCAmelCase__ : List[Any] = True , UpperCAmelCase__ : Dict = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] , UpperCAmelCase__ : Dict = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] , UpperCAmelCase__ : Dict = True , UpperCAmelCase__ : int=7 , UpperCAmelCase__ : Optional[int]=3_0 , UpperCAmelCase__ : int=4_0_0 , UpperCAmelCase__ : Optional[int]=3 , ) ->Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = parent
SCREAMING_SNAKE_CASE : Dict = do_resize
SCREAMING_SNAKE_CASE : Union[str, Any] = size if size is not None else {"""shortest_edge""": 2_8_8}
SCREAMING_SNAKE_CASE : Union[str, Any] = size_divisor
SCREAMING_SNAKE_CASE : Union[str, Any] = do_rescale
SCREAMING_SNAKE_CASE : Dict = rescale_factor
SCREAMING_SNAKE_CASE : Union[str, Any] = do_normalize
SCREAMING_SNAKE_CASE : List[str] = do_center_crop
SCREAMING_SNAKE_CASE : Tuple = image_mean
SCREAMING_SNAKE_CASE : Tuple = image_std
SCREAMING_SNAKE_CASE : Tuple = do_pad
SCREAMING_SNAKE_CASE : int = batch_size
SCREAMING_SNAKE_CASE : List[Any] = num_channels
SCREAMING_SNAKE_CASE : int = min_resolution
SCREAMING_SNAKE_CASE : str = max_resolution
def _lowercase ( self : Optional[Any] ) ->str:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"size_divisor": self.size_divisor,
}
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=False ) ->int:
"""simple docstring"""
if not batched:
SCREAMING_SNAKE_CASE : Dict = self.size["""shortest_edge"""]
SCREAMING_SNAKE_CASE : List[str] = image_inputs[0]
if isinstance(lowerCAmelCase__ , Image.Image ):
SCREAMING_SNAKE_CASE : List[Any] = image.size
else:
SCREAMING_SNAKE_CASE : Dict = image.shape[1], image.shape[2]
SCREAMING_SNAKE_CASE : List[Any] = size / min(lowerCAmelCase__ , lowerCAmelCase__ )
if h < w:
SCREAMING_SNAKE_CASE : str = size, scale * w
else:
SCREAMING_SNAKE_CASE : Optional[Any] = scale * h, size
SCREAMING_SNAKE_CASE : Tuple = int((1_3_3_3 / 8_0_0) * size )
if max(lowerCAmelCase__ , lowerCAmelCase__ ) > max_size:
SCREAMING_SNAKE_CASE : Union[str, Any] = max_size / max(lowerCAmelCase__ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = newh * scale
SCREAMING_SNAKE_CASE : Any = neww * scale
SCREAMING_SNAKE_CASE : str = int(newh + 0.5 ), int(neww + 0.5 )
SCREAMING_SNAKE_CASE : int = (
newh // self.size_divisor * self.size_divisor,
neww // self.size_divisor * self.size_divisor,
)
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = []
for image in image_inputs:
SCREAMING_SNAKE_CASE : int = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
SCREAMING_SNAKE_CASE : Optional[Any] = max(lowerCAmelCase__ , key=lambda UpperCAmelCase__ : item[0] )[0]
SCREAMING_SNAKE_CASE : Tuple = max(lowerCAmelCase__ , key=lambda UpperCAmelCase__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class a__ ( _lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : int =BridgeTowerImageProcessor if is_vision_available() else None
def _lowercase ( self : Dict ) ->Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = BridgeTowerImageProcessingTester(self )
@property
def _lowercase ( self : Dict ) ->List[Any]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _lowercase ( self : Optional[int] ) ->Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , """image_mean""" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , """image_std""" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , """do_normalize""" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , """do_resize""" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , """size""" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , """size_divisor""" ) )
def _lowercase ( self : Optional[int] ) ->Optional[int]:
"""simple docstring"""
pass
def _lowercase ( self : Optional[int] ) ->Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE : List[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
SCREAMING_SNAKE_CASE : List[str] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE : str = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE : Any = image_processing(lowerCAmelCase__ , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE : List[str] = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _lowercase ( self : List[Any] ) ->Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE : str = 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
SCREAMING_SNAKE_CASE : str = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE : List[Any] = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE : Any = image_processing(lowerCAmelCase__ , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE : Optional[int] = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _lowercase ( self : List[Any] ) ->str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE : str = 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
SCREAMING_SNAKE_CASE : Dict = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE : Dict = image_processing(lowerCAmelCase__ , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE : List[str] = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
| 245 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snake_case__ ( _lowerCAmelCase , unittest.TestCase ):
lowercase__ : Optional[Any] = MgpstrTokenizer
lowercase__ : int = False
lowercase__ : Any = {}
lowercase__ : Optional[int] = False
def __magic_name__ ( self ) -> Optional[Any]:
super().setUp()
# fmt: off
__magic_name__ : List[str] = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
# fmt: on
__magic_name__ : List[Any] = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
__magic_name__ : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + """\n""" )
def __magic_name__ ( self , **lowerCAmelCase__ ) -> Optional[int]:
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def __magic_name__ ( self , lowerCAmelCase__ ) -> Optional[int]:
__magic_name__ : List[str] = """tester"""
__magic_name__ : int = """tester"""
return input_text, output_text
@unittest.skip("""MGP-STR always lower cases letters.""" )
def __magic_name__ ( self ) -> str:
pass
def __magic_name__ ( self ) -> List[str]:
__magic_name__ : List[Any] = self.get_tokenizers(do_lower_case=lowerCAmelCase__ )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
__magic_name__ : Dict = """[SPECIAL_TOKEN]"""
tokenizer.add_special_tokens({"""cls_token""": special_token} )
__magic_name__ : List[str] = tokenizer.encode([special_token] , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(len(lowerCAmelCase__ ) , 1 )
__magic_name__ : Tuple = tokenizer.decode(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__ )
self.assertTrue(special_token not in decoded )
def __magic_name__ ( self ) -> Union[str, Any]:
__magic_name__ : int = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
__magic_name__ ,__magic_name__ : Optional[Any] = self.get_input_output_texts(lowerCAmelCase__ )
__magic_name__ : List[Any] = tokenizer.tokenize(lowerCAmelCase__ )
__magic_name__ : Any = tokenizer.convert_tokens_to_ids(lowerCAmelCase__ )
__magic_name__ : Union[str, Any] = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
__magic_name__ : List[Any] = tokenizer.convert_ids_to_tokens(lowerCAmelCase__ )
self.assertNotEqual(len(lowerCAmelCase__ ) , 0 )
__magic_name__ : Optional[int] = tokenizer.decode(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertEqual(text_a.replace(""" """ , """""" ) , lowerCAmelCase__ )
@unittest.skip("""MGP-STR tokenizer only handles one sequence.""" )
def __magic_name__ ( self ) -> Tuple:
pass
@unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer""" )
def __magic_name__ ( self ) -> Optional[Any]:
pass
| 342 | 0 |
'''simple docstring'''
from __future__ import annotations
import queue
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowerCamelCase ) -> Union[str, Any]:
A_ : Tuple = data
A_ : List[str] = None
A_ : str = None
def UpperCAmelCase ( ) -> TreeNode:
"""simple docstring"""
print("""\n********Press N to stop entering at any point of time********\n""" )
A_ : List[Any] = input("""Enter the value of the root node: """ ).strip().lower()
A_ : Union[str, Any] = queue.Queue()
A_ : Optional[int] = TreeNode(int(a_ ) )
q.put(a_ )
while not q.empty():
A_ : Optional[int] = q.get()
A_ : Any = F"Enter the left node of {node_found.data}: "
A_ : int = input(a_ ).strip().lower() or """n"""
if check == "n":
return tree_node
A_ : List[str] = TreeNode(int(a_ ) )
A_ : Optional[int] = left_node
q.put(a_ )
A_ : Union[str, Any] = F"Enter the right node of {node_found.data}: "
A_ : Tuple = input(a_ ).strip().lower() or """n"""
if check == "n":
return tree_node
A_ : List[Any] = TreeNode(int(a_ ) )
A_ : Tuple = right_node
q.put(a_ )
raise
def UpperCAmelCase ( a_ ) -> None:
"""simple docstring"""
if not isinstance(a_ , a_ ) or not node:
return
print(node.data , end=""",""" )
pre_order(node.left )
pre_order(node.right )
def UpperCAmelCase ( a_ ) -> None:
"""simple docstring"""
if not isinstance(a_ , a_ ) or not node:
return
in_order(node.left )
print(node.data , end=""",""" )
in_order(node.right )
def UpperCAmelCase ( a_ ) -> None:
"""simple docstring"""
if not isinstance(a_ , a_ ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data , end=""",""" )
def UpperCAmelCase ( a_ ) -> None:
"""simple docstring"""
if not isinstance(a_ , a_ ) or not node:
return
A_ : List[str] = queue.Queue()
q.put(a_ )
while not q.empty():
A_ : Any = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def UpperCAmelCase ( a_ ) -> None:
"""simple docstring"""
if not isinstance(a_ , a_ ) or not node:
return
A_ : List[Any] = queue.Queue()
q.put(a_ )
while not q.empty():
A_ : Optional[int] = []
while not q.empty():
A_ : Any = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(a_ )
def UpperCAmelCase ( a_ ) -> None:
"""simple docstring"""
if not isinstance(a_ , a_ ) or not node:
return
A_ : Tuple = []
A_ : Tuple = node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end=""",""" )
stack.append(a_ )
A_ : Any = n.left
# end of while means current node doesn't have left child
A_ : Dict = stack.pop()
# start to traverse its right child
A_ : Optional[Any] = n.right
def UpperCAmelCase ( a_ ) -> None:
"""simple docstring"""
if not isinstance(a_ , a_ ) or not node:
return
A_ : str = []
A_ : str = node
while n or stack:
while n:
stack.append(a_ )
A_ : str = n.left
A_ : List[Any] = stack.pop()
print(n.data , end=""",""" )
A_ : int = n.right
def UpperCAmelCase ( a_ ) -> None:
"""simple docstring"""
if not isinstance(a_ , a_ ) or not node:
return
A_ , A_ : List[Any] = [], []
A_ : Optional[int] = node
stacka.append(a_ )
while stacka: # to find the reversed order of post order, store it in stack2
A_ : str = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(a_ )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end=""",""" )
def UpperCAmelCase ( a_ = "" , a_=5_0 , a_="*" ) -> str:
"""simple docstring"""
if not s:
return "\n" + width * char
A_ , A_ : List[Any] = divmod(width - len(a_ ) - 2 , 2 )
return F"{left * char} {s} {(left + extra) * char}"
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt('Binary Tree Traversals'))
UpperCamelCase__ : Dict = build_tree()
print(prompt('Pre Order Traversal'))
pre_order(node)
print(prompt() + '\n')
print(prompt('In Order Traversal'))
in_order(node)
print(prompt() + '\n')
print(prompt('Post Order Traversal'))
post_order(node)
print(prompt() + '\n')
print(prompt('Level Order Traversal'))
level_order(node)
print(prompt() + '\n')
print(prompt('Actual Level Order Traversal'))
level_order_actual(node)
print('*' * 50 + '\n')
print(prompt('Pre Order Traversal - Iteration Version'))
pre_order_iter(node)
print(prompt() + '\n')
print(prompt('In Order Traversal - Iteration Version'))
in_order_iter(node)
print(prompt() + '\n')
print(prompt('Post Order Traversal - Iteration Version'))
post_order_iter(node)
print(prompt())
| 360 |
'''simple docstring'''
import math
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
A_ : List[Any] = [True] * n
A_ : List[Any] = False
A_ : Union[str, Any] = False
A_ : List[Any] = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
A_ : Optional[Any] = i * 2
while index < n:
A_ : Any = False
A_ : str = index + i
A_ : List[str] = [2]
for i in range(3 , a_ , 2 ):
if is_prime[i]:
primes.append(a_ )
return primes
def UpperCAmelCase ( a_ = 9_9_9_9_6_6_6_6_3_3_3_3 ) -> int:
"""simple docstring"""
A_ : Any = math.floor(math.sqrt(a_ ) ) + 1_0_0
A_ : int = prime_sieve(a_ )
A_ : int = 0
A_ : Union[str, Any] = 0
A_ : List[str] = primes[prime_index]
while (last_prime**2) <= limit:
A_ : Tuple = primes[prime_index + 1]
A_ : List[Any] = last_prime**2
A_ : Union[str, Any] = next_prime**2
# Get numbers divisible by lps(current)
A_ : Tuple = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
A_ : Optional[Any] = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
A_ : str = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
A_ : Any = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 164 | 0 |
'''simple docstring'''
import torch
def snake_case_ ( ):
"""simple docstring"""
if torch.cuda.is_available():
lowercase_ : int = torch.cuda.device_count()
else:
lowercase_ : Optional[int] = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main()
| 93 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase ):
assert isinstance(__lowerCAmelCase , __lowerCAmelCase )
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_UpperCAmelCase : Union[str, Any] = tmp_path / "cache"
_UpperCAmelCase : int = {"text": "string"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_UpperCAmelCase : Union[str, Any] = TextDatasetReader(__lowerCAmelCase , cache_dir=__lowerCAmelCase , keep_in_memory=__lowerCAmelCase ).read()
_check_text_dataset(__lowerCAmelCase , __lowerCAmelCase )
@pytest.mark.parametrize(
"features" , [
None,
{"text": "string"},
{"text": "int32"},
{"text": "float32"},
] , )
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_UpperCAmelCase : List[Any] = tmp_path / "cache"
_UpperCAmelCase : Any = {"text": "string"}
_UpperCAmelCase : Optional[Any] = features.copy() if features else default_expected_features
_UpperCAmelCase : Union[str, Any] = (
Features({feature: Value(__lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
_UpperCAmelCase : Union[str, Any] = TextDatasetReader(__lowerCAmelCase , features=__lowerCAmelCase , cache_dir=__lowerCAmelCase ).read()
_check_text_dataset(__lowerCAmelCase , __lowerCAmelCase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_UpperCAmelCase : Any = tmp_path / "cache"
_UpperCAmelCase : Dict = {"text": "string"}
_UpperCAmelCase : Union[str, Any] = TextDatasetReader(__lowerCAmelCase , cache_dir=__lowerCAmelCase , split=__lowerCAmelCase ).read()
_check_text_dataset(__lowerCAmelCase , __lowerCAmelCase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type" , [str, list] )
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if issubclass(__lowerCAmelCase , __lowerCAmelCase ):
_UpperCAmelCase : Union[str, Any] = text_path
elif issubclass(__lowerCAmelCase , __lowerCAmelCase ):
_UpperCAmelCase : int = [text_path]
_UpperCAmelCase : List[Any] = tmp_path / "cache"
_UpperCAmelCase : Union[str, Any] = {"text": "string"}
_UpperCAmelCase : Optional[int] = TextDatasetReader(__lowerCAmelCase , cache_dir=__lowerCAmelCase ).read()
_check_text_dataset(__lowerCAmelCase , __lowerCAmelCase )
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=("train",) ):
assert isinstance(__lowerCAmelCase , __lowerCAmelCase )
for split in splits:
_UpperCAmelCase : List[str] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_UpperCAmelCase : List[Any] = tmp_path / "cache"
_UpperCAmelCase : Tuple = {"text": "string"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_UpperCAmelCase : Any = TextDatasetReader({"train": text_path} , cache_dir=__lowerCAmelCase , keep_in_memory=__lowerCAmelCase ).read()
_check_text_datasetdict(__lowerCAmelCase , __lowerCAmelCase )
@pytest.mark.parametrize(
"features" , [
None,
{"text": "string"},
{"text": "int32"},
{"text": "float32"},
] , )
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_UpperCAmelCase : List[Any] = tmp_path / "cache"
# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
_UpperCAmelCase : List[Any] = {"text": "string"}
_UpperCAmelCase : List[str] = features.copy() if features else default_expected_features
_UpperCAmelCase : Optional[int] = (
Features({feature: Value(__lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
_UpperCAmelCase : Tuple = TextDatasetReader({"train": text_path} , features=__lowerCAmelCase , cache_dir=__lowerCAmelCase ).read()
_check_text_datasetdict(__lowerCAmelCase , __lowerCAmelCase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if split:
_UpperCAmelCase : int = {split: text_path}
else:
_UpperCAmelCase : Tuple = "train"
_UpperCAmelCase : List[str] = {"train": text_path, "test": text_path}
_UpperCAmelCase : Optional[Any] = tmp_path / "cache"
_UpperCAmelCase : Optional[int] = {"text": "string"}
_UpperCAmelCase : int = TextDatasetReader(__lowerCAmelCase , cache_dir=__lowerCAmelCase ).read()
_check_text_datasetdict(__lowerCAmelCase , __lowerCAmelCase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
| 234 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase : List[str] = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase : Tuple = []
for i in range(6):
# 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 encoder + 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.encoder.norm.weight""", """encoder.layernorm.weight"""),
("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
("""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"""),
]
)
def SCREAMING_SNAKE_CASE__ ( snake_case : int , snake_case : Any , snake_case : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a : Tuple = state_dict.pop(_lowerCamelCase )
a : str = val
def SCREAMING_SNAKE_CASE__ ( snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
a : str = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
a : List[str] = key.replace('backbone.0.body' , 'backbone.conv_encoder.model' )
a : Optional[Any] = value
else:
a : Optional[int] = value
return new_state_dict
def SCREAMING_SNAKE_CASE__ ( snake_case : List[Any] ) -> Dict:
"""simple docstring"""
a : Optional[int] = ""
# 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)
a : Any = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
a : List[str] = 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
a : Dict = in_proj_weight[:256, :]
a : str = in_proj_bias[:256]
a : str = in_proj_weight[256:512, :]
a : List[Any] = in_proj_bias[256:512]
a : List[str] = in_proj_weight[-256:, :]
a : Dict = in_proj_bias[-256:]
# 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
a : Union[str, Any] = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
a : Optional[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
a : Any = in_proj_weight[:256, :]
a : int = in_proj_bias[:256]
a : Union[str, Any] = in_proj_weight[256:512, :]
a : Union[str, Any] = in_proj_bias[256:512]
a : Optional[int] = in_proj_weight[-256:, :]
a : str = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
a : Optional[Any] = state_dict.pop(
F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""" )
a : Tuple = 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
a : Tuple = in_proj_weight_cross_attn[:256, :]
a : Union[str, Any] = in_proj_bias_cross_attn[:256]
a : List[str] = in_proj_weight_cross_attn[256:512, :]
a : Union[str, Any] = in_proj_bias_cross_attn[256:512]
a : int = in_proj_weight_cross_attn[-256:, :]
a : Dict = in_proj_bias_cross_attn[-256:]
def SCREAMING_SNAKE_CASE__ ( snake_case : str , snake_case : List[Any] ) -> Optional[int]:
"""simple docstring"""
a : Union[str, Any] = image.size
a : Tuple = max(_lowerCamelCase , _lowerCamelCase )
a : List[str] = 800 if "detection" in checkpoint_url else 1_000
a : Tuple = target_max_size / current_max_size
a : List[str] = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def SCREAMING_SNAKE_CASE__ ( snake_case : Tuple ) -> Optional[int]:
"""simple docstring"""
a : Optional[int] = F.to_tensor(_lowerCamelCase )
a : Optional[int] = F.normalize(_lowerCamelCase , mean=[0.4_85, 0.4_56, 0.4_06] , std=[0.2_29, 0.2_24, 0.2_25] )
return image
@torch.no_grad()
def SCREAMING_SNAKE_CASE__ ( snake_case : Tuple , snake_case : Tuple , snake_case : Dict ) -> str:
"""simple docstring"""
logger.info('Converting model...' )
# load original state dict
a : Any = torch.hub.load_state_dict_from_url(_lowerCamelCase , map_location='cpu' )
# rename keys
for src, dest in rename_keys:
rename_key(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
a : Optional[Any] = rename_backbone_keys(_lowerCamelCase )
# query, key and value matrices need special treatment
read_in_q_k_v(_lowerCamelCase )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
a : Optional[Any] = "model."
for key in state_dict.copy().keys():
if not key.startswith('class_labels_classifier' ) and not key.startswith('bbox_predictor' ):
a : List[Any] = state_dict.pop(_lowerCamelCase )
a : int = val
# create HuggingFace model and load state dict
a : int = TableTransformerConfig(
backbone='resnet18' , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
a : Dict = 15
a : Optional[int] = 2
a : Union[str, Any] = {0: "table", 1: "table rotated"}
a : Union[str, Any] = idalabel
a : str = {v: k for k, v in idalabel.items()}
else:
a : Optional[int] = 125
a : Dict = 6
a : str = {
0: "table",
1: "table column",
2: "table row",
3: "table column header",
4: "table projected row header",
5: "table spanning cell",
}
a : Dict = idalabel
a : Optional[Any] = {v: k for k, v in idalabel.items()}
a : List[str] = DetrImageProcessor(
format='coco_detection' , max_size=800 if 'detection' in checkpoint_url else 1_000 )
a : Optional[Any] = TableTransformerForObjectDetection(_lowerCamelCase )
model.load_state_dict(_lowerCamelCase )
model.eval()
# verify our conversion
a : List[str] = "example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
a : Union[str, Any] = hf_hub_download(repo_id='nielsr/example-pdf' , repo_type='dataset' , filename=_lowerCamelCase )
a : Tuple = Image.open(_lowerCamelCase ).convert('RGB' )
a : List[Any] = normalize(resize(_lowerCamelCase , _lowerCamelCase ) ).unsqueeze(0 )
a : List[Any] = model(_lowerCamelCase )
if "detection" in checkpoint_url:
a : str = (1, 15, 3)
a : List[Any] = torch.tensor(
[[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]] )
a : Optional[Any] = torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]] )
else:
a : Optional[Any] = (1, 125, 7)
a : Optional[Any] = torch.tensor(
[[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]] )
a : Optional[Any] = torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , _lowerCamelCase , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , _lowerCamelCase , 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(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
model.save_pretrained(_lowerCamelCase )
image_processor.save_pretrained(_lowerCamelCase )
if push_to_hub:
# Push model to HF hub
logger.info('Pushing model to the hub...' )
a : Union[str, Any] = (
"microsoft/table-transformer-detection"
if "detection" in checkpoint_url
else "microsoft/table-transformer-structure-recognition"
)
model.push_to_hub(_lowerCamelCase )
image_processor.push_to_hub(_lowerCamelCase )
if __name__ == "__main__":
UpperCamelCase : Any = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
type=str,
choices=[
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
],
help="""URL of the Table Transformer checkpoint 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 or not to push the converted model to the 🤗 hub."""
)
UpperCamelCase : List[str] = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 370 | '''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( snake_case : int | float | str , snake_case : int | float | str ) -> list[str]:
"""simple docstring"""
if nth_term == "":
return [""]
a : Dict = int(snake_case )
a : Optional[int] = int(snake_case )
a : list[str] = []
for temp in range(int(snake_case ) ):
series.append(F"""1 / {pow(temp + 1 , int(snake_case ) )}""" if series else '1' )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase : Optional[int] = int(input("""Enter the last number (nth term) of the P-Series"""))
UpperCamelCase : List[Any] = int(input("""Enter the power for P-Series"""))
print("""Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p""")
print(p_series(nth_term, power))
| 345 | 0 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
UpperCAmelCase_ = {
'vocab_file': {
'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
},
'merges_file': {
'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
},
'tokenizer_config_file': {
'facebook/blenderbot_small-90M': (
'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
)
},
}
UpperCAmelCase_ = {
'facebook/blenderbot_small-90M': 512,
}
class lowercase__ ( __lowerCamelCase ):
'''simple docstring'''
a : Dict = VOCAB_FILES_NAMES
a : str = PRETRAINED_VOCAB_FILES_MAP
a : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a : Optional[int] = BlenderbotSmallTokenizer
def __init__( self, __magic_name__=None, __magic_name__=None, __magic_name__="<|endoftext|>", __magic_name__="<|endoftext|>", __magic_name__="<|endoftext|>", __magic_name__=False, __magic_name__=True, **__magic_name__, ) -> Optional[Any]:
"""simple docstring"""
super().__init__(
ByteLevelBPETokenizer(
vocab=__magic_name__, merges=__magic_name__, add_prefix_space=__magic_name__, trim_offsets=__magic_name__, ), bos_token=__magic_name__, eos_token=__magic_name__, unk_token=__magic_name__, **__magic_name__, )
UpperCamelCase__ : Optional[Any] = add_prefix_space
def UpperCamelCase__ ( self, __magic_name__, __magic_name__=None ) -> Dict:
"""simple docstring"""
UpperCamelCase__ : str = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def UpperCamelCase__ ( self, __magic_name__, __magic_name__ = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase__ : Optional[int] = [self.sep_token_id]
UpperCamelCase__ : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 201 |
from math import factorial
def lowerCAmelCase_ ( __UpperCAmelCase: int , __UpperCAmelCase: int ) -> int:
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
return factorial(__UpperCAmelCase ) // (factorial(__UpperCAmelCase ) * factorial(n - k ))
if __name__ == "__main__":
print(
'The number of five-card hands possible from a standard',
F'''fifty-two card deck is: {combinations(52, 5)}\n''',
)
print(
'If a class of 40 students must be arranged into groups of',
F'''4 for group projects, there are {combinations(40, 4)} ways''',
'to arrange them.\n',
)
print(
'If 10 teams are competing in a Formula One race, there',
F'''are {combinations(10, 3)} ways that first, second and''',
'third place can be awarded.',
)
| 201 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase_ : Optional[int] = {
"""facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}
class __A ( a__ ):
"""simple docstring"""
__lowerCAmelCase = "data2vec-text"
def __init__( self , __A=3_0522 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=2 , __A=0.02 , __A=1E-1_2 , __A=1 , __A=0 , __A=2 , __A="absolute" , __A=True , __A=None , **__A , ) -> Optional[int]:
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
a =vocab_size
a =hidden_size
a =num_hidden_layers
a =num_attention_heads
a =hidden_act
a =intermediate_size
a =hidden_dropout_prob
a =attention_probs_dropout_prob
a =max_position_embeddings
a =type_vocab_size
a =initializer_range
a =layer_norm_eps
a =position_embedding_type
a =use_cache
a =classifier_dropout
class __A ( a__ ):
"""simple docstring"""
@property
def SCREAMING_SNAKE_CASE ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
a ={0: 'batch', 1: 'choice', 2: 'sequence'}
else:
a ={0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] ) | 365 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
lowerCamelCase_ : str = ["""bert-base-uncased""", """bert-base-cased"""]
lowerCamelCase_ : List[str] = """hf-internal-testing/tiny-bert-tf-only"""
if is_tf_available():
class __A ( tf.keras.Model ):
"""simple docstring"""
def __init__( self , __A ) -> Dict:
super().__init__()
a =tokenizer
a =AutoConfig.from_pretrained(__A )
a =TFAutoModel.from_config(__A )
def SCREAMING_SNAKE_CASE ( self , __A ) -> int:
a =self.tokenizer(__A )
a =self.bert(**__A )
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class __A ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self ) -> str:
super().setUp()
a =[
BertTokenizer.from_pretrained(__A ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
] # repeat for when fast_bert_tokenizer=false
a =[TFBertTokenizer.from_pretrained(__A ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
TFBertTokenizer.from_pretrained(__A , use_fast_bert_tokenizer=__A )
for checkpoint in TOKENIZER_CHECKPOINTS
]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
a =[
'''This is a straightforward English test sentence.''',
'''This one has some weird characters\rto\nsee\r\nif those\u00E9break things.''',
'''Now we\'re going to add some Chinese: 一 二 三 一二三''',
'''And some much more rare Chinese: 齉 堃 齉堃''',
'''Je vais aussi écrire en français pour tester les accents''',
'''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
]
a =list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in (self.test_sentences, self.paired_sentences):
a =tokenizer(__A , return_tensors='''tf''' , padding='''longest''' )
a =tf_tokenizer(__A )
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) )
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] , tf.intaa ) == tf_outputs[key] ) )
@slow
def SCREAMING_SNAKE_CASE ( self ) -> str:
for tf_tokenizer in self.tf_tokenizers:
a =tf_tokenizer(self.paired_sentences )
a =tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences] , text_pair=[sentence[1] for sentence in self.paired_sentences] , )
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] , tf.intaa ) == separated_outputs[key] ) )
@slow
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
for tf_tokenizer in self.tf_tokenizers:
a =tf.function(__A )
for test_inputs in (self.test_sentences, self.paired_sentences):
a =tf.constant(__A )
a =compiled_tokenizer(__A )
a =tf_tokenizer(__A )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
for tf_tokenizer in self.tf_tokenizers:
a =ModelToSave(tokenizer=__A )
a =tf.convert_to_tensor(self.test_sentences )
a =model(__A ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
a =Path(__A ) / '''saved.model'''
model.save(__A )
a =tf.keras.models.load_model(__A )
a =loaded_model(__A )
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) ) , 1E-5 ) | 215 | 0 |
import os
import string
import sys
_UpperCAmelCase : Tuple = 1 << 8
_UpperCAmelCase : str = {
"""tab""": ord("\t"),
"""newline""": ord("\r"),
"""esc""": 27,
"""up""": 65 + ARROW_KEY_FLAG,
"""down""": 66 + ARROW_KEY_FLAG,
"""right""": 67 + ARROW_KEY_FLAG,
"""left""": 68 + ARROW_KEY_FLAG,
"""mod_int""": 91,
"""undefined""": sys.maxsize,
"""interrupt""": 3,
"""insert""": 50,
"""delete""": 51,
"""pg_up""": 53,
"""pg_down""": 54,
}
_UpperCAmelCase : int = KEYMAP["""up"""]
_UpperCAmelCase : Dict = KEYMAP["""left"""]
if sys.platform == "win32":
_UpperCAmelCase : Union[str, Any] = []
_UpperCAmelCase : List[str] = {
b"""\xe0H""": KEYMAP["""up"""] - ARROW_KEY_FLAG,
b"""\x00H""": KEYMAP["""up"""] - ARROW_KEY_FLAG,
b"""\xe0P""": KEYMAP["""down"""] - ARROW_KEY_FLAG,
b"""\x00P""": KEYMAP["""down"""] - ARROW_KEY_FLAG,
b"""\xe0M""": KEYMAP["""right"""] - ARROW_KEY_FLAG,
b"""\x00M""": KEYMAP["""right"""] - ARROW_KEY_FLAG,
b"""\xe0K""": KEYMAP["""left"""] - ARROW_KEY_FLAG,
b"""\x00K""": KEYMAP["""left"""] - ARROW_KEY_FLAG,
}
for i in range(10):
_UpperCAmelCase : Dict = ord(str(i))
def A ( ) -> Tuple:
'''simple docstring'''
if os.name == "nt":
import msvcrt
UpperCamelCase = 'mbcs'
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(snake_case__ ) == 0:
# Read the keystroke
UpperCamelCase = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
UpperCamelCase = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
UpperCamelCase = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP['mod_int'] ) )
WIN_CH_BUFFER.append(snake_case__ )
if ord(snake_case__ ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(126 ) )
UpperCamelCase = chr(KEYMAP['esc'] )
except KeyError:
UpperCamelCase = cha[1]
else:
UpperCamelCase = ch.decode(snake_case__ )
else:
UpperCamelCase = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
UpperCamelCase = sys.stdin.fileno()
UpperCamelCase = termios.tcgetattr(snake_case__ )
try:
tty.setraw(snake_case__ )
UpperCamelCase = sys.stdin.read(1 )
finally:
termios.tcsetattr(snake_case__ , termios.TCSADRAIN , snake_case__ )
return ch
def A ( ) -> List[str]:
'''simple docstring'''
UpperCamelCase = get_raw_chars()
if ord(snake_case__ ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(snake_case__ ) == KEYMAP["esc"]:
UpperCamelCase = get_raw_chars()
if ord(snake_case__ ) == KEYMAP["mod_int"]:
UpperCamelCase = get_raw_chars()
if ord(snake_case__ ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(snake_case__ ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(snake_case__ ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 222 |
"""simple docstring"""
import operator as op
lowerCAmelCase : Dict = """scaler.pt"""
lowerCAmelCase : Tuple = """pytorch_model"""
lowerCAmelCase : Union[str, Any] = """random_states"""
lowerCAmelCase : Union[str, Any] = """optimizer"""
lowerCAmelCase : Dict = """scheduler"""
lowerCAmelCase : int = """pytorch_model.bin"""
lowerCAmelCase : str = """pytorch_model.bin.index.json"""
lowerCAmelCase : Union[str, Any] = """model.safetensors"""
lowerCAmelCase : List[Any] = """model.safetensors.index.json"""
lowerCAmelCase : List[Any] = """1.10.2"""
lowerCAmelCase : Any = """py38"""
lowerCAmelCase : Optional[int] = """4.17.0"""
lowerCAmelCase : str = ["""ml.p3.16xlarge""", """ml.p3dn.24xlarge""", """ml.p4dn.24xlarge"""]
lowerCAmelCase : Tuple = ["""FULL_SHARD""", """SHARD_GRAD_OP""", """NO_SHARD""", """HYBRID_SHARD""", """HYBRID_SHARD_ZERO2"""]
lowerCAmelCase : List[Any] = ["""TRANSFORMER_BASED_WRAP""", """SIZE_BASED_WRAP""", """NO_WRAP"""]
lowerCAmelCase : List[str] = ["""BACKWARD_PRE""", """BACKWARD_POST""", """NO_PREFETCH"""]
lowerCAmelCase : List[str] = ["""FULL_STATE_DICT""", """LOCAL_STATE_DICT""", """SHARDED_STATE_DICT"""]
lowerCAmelCase : Any = """2.0.1"""
lowerCAmelCase : List[Any] = ["""pdsh""", """standard""", """openmpi""", """mvapich"""]
lowerCAmelCase : Union[str, Any] = ["""default""", """reduce-overhead""", """max-autotune"""]
lowerCAmelCase : Optional[int] = {""">""": op.gt, """>=""": op.ge, """==""": op.eq, """!=""": op.ne, """<=""": op.le, """<""": op.lt}
# These are the args for `torch.distributed.launch` for pytorch < 1.9
lowerCAmelCase : Union[str, Any] = [
"""nnodes""",
"""nproc_per_node""",
"""rdzv_backend""",
"""rdzv_endpoint""",
"""rdzv_id""",
"""rdzv_conf""",
"""standalone""",
"""max_restarts""",
"""monitor_interval""",
"""start_method""",
"""role""",
"""module""",
"""m""",
"""no_python""",
"""run_path""",
"""log_dir""",
"""r""",
"""redirects""",
"""t""",
"""tee""",
"""node_rank""",
"""master_addr""",
"""master_port""",
]
lowerCAmelCase : List[str] = ["""DEEPSPEED""", """MULTI_GPU""", """FSDP""", """MEGATRON_LM"""]
lowerCAmelCase : Optional[Any] = ["""DEEPSPEED""", """MULTI_XPU""", """FSDP"""]
| 291 | 0 |
"""simple docstring"""
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : Dict ="autoformer"
a : Dict ={
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
"num_hidden_layers": "encoder_layers",
}
def __init__( self , snake_case__ = None , snake_case__ = None , snake_case__ = "student_t" , snake_case__ = "nll" , snake_case__ = 1 , snake_case__ = [1, 2, 3, 4, 5, 6, 7] , snake_case__ = True , snake_case__ = 0 , snake_case__ = 0 , snake_case__ = 0 , snake_case__ = 0 , snake_case__ = None , snake_case__ = None , snake_case__ = 64 , snake_case__ = 2 , snake_case__ = 2 , snake_case__ = 2 , snake_case__ = 2 , snake_case__ = 32 , snake_case__ = 32 , snake_case__ = "gelu" , snake_case__ = 0.1 , snake_case__ = 0.1 , snake_case__ = 0.1 , snake_case__ = 0.1 , snake_case__ = 0.1 , snake_case__ = 100 , snake_case__ = 0.02 , snake_case__ = True , snake_case__=True , snake_case__ = 10 , snake_case__ = 25 , snake_case__ = 3 , **snake_case__ , ):
"""simple docstring"""
lowerCAmelCase : Any = prediction_length
lowerCAmelCase : Dict = context_length if context_length is not None else prediction_length
lowerCAmelCase : Tuple = distribution_output
lowerCAmelCase : List[Any] = loss
lowerCAmelCase : int = input_size
lowerCAmelCase : str = num_time_features
lowerCAmelCase : str = lags_sequence
lowerCAmelCase : List[str] = scaling
lowerCAmelCase : List[Any] = num_dynamic_real_features
lowerCAmelCase : Tuple = num_static_real_features
lowerCAmelCase : Dict = num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(snake_case__ ) != num_static_categorical_features:
raise ValueError(
"The cardinality should be a list of the same length as `num_static_categorical_features`" )
lowerCAmelCase : Any = cardinality
else:
lowerCAmelCase : Union[str, Any] = [0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(snake_case__ ) != num_static_categorical_features:
raise ValueError(
"The embedding dimension should be a list of the same length as `num_static_categorical_features`" )
lowerCAmelCase : Tuple = embedding_dimension
else:
lowerCAmelCase : Any = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
lowerCAmelCase : Any = num_parallel_samples
# Transformer architecture configuration
lowerCAmelCase : str = input_size * len(self.lags_sequence ) + self._number_of_features
lowerCAmelCase : Any = d_model
lowerCAmelCase : List[str] = encoder_attention_heads
lowerCAmelCase : Union[str, Any] = decoder_attention_heads
lowerCAmelCase : Optional[int] = encoder_ffn_dim
lowerCAmelCase : Optional[Any] = decoder_ffn_dim
lowerCAmelCase : int = encoder_layers
lowerCAmelCase : int = decoder_layers
lowerCAmelCase : List[Any] = dropout
lowerCAmelCase : Optional[int] = attention_dropout
lowerCAmelCase : Union[str, Any] = activation_dropout
lowerCAmelCase : Optional[int] = encoder_layerdrop
lowerCAmelCase : Dict = decoder_layerdrop
lowerCAmelCase : Tuple = activation_function
lowerCAmelCase : Optional[Any] = init_std
lowerCAmelCase : List[Any] = use_cache
# Autoformer
lowerCAmelCase : Any = label_length
lowerCAmelCase : Any = moving_average
lowerCAmelCase : Optional[Any] = autocorrelation_factor
super().__init__(is_encoder_decoder=snake_case__ , **snake_case__ )
@property
def lowercase__ ( self ):
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 133 |
"""simple docstring"""
from typing import Any
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Optional[Any] = data
lowerCAmelCase : Tuple = None
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = None
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Optional[Any] = self.head
while temp is not None:
print(temp.data , end=" " )
lowerCAmelCase : List[str] = temp.next
print()
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : int = Node(snake_case__ )
lowerCAmelCase : Union[str, Any] = self.head
lowerCAmelCase : Optional[Any] = new_node
def lowercase__ ( self , snake_case__ , snake_case__ ):
"""simple docstring"""
if node_data_a == node_data_a:
return
else:
lowerCAmelCase : str = self.head
while node_a is not None and node_a.data != node_data_a:
lowerCAmelCase : Union[str, Any] = node_a.next
lowerCAmelCase : Any = self.head
while node_a is not None and node_a.data != node_data_a:
lowerCAmelCase : List[Any] = node_a.next
if node_a is None or node_a is None:
return
lowerCAmelCase , lowerCAmelCase : str = node_a.data, node_a.data
if __name__ == "__main__":
lowerCAmelCase__ = LinkedList()
for i in range(5, 0, -1):
ll.push(i)
ll.print_list()
ll.swap_nodes(1, 4)
print('''After swapping''')
ll.print_list()
| 133 | 1 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
raise Exception('''requires gluonnlp == 0.8.3''')
if version.parse(mx.__version__) != version.parse('''1.5.0'''):
raise Exception('''requires mxnet == 1.5.0''')
logging.set_verbosity_info()
__snake_case = logging.get_logger(__name__)
__snake_case = '''The Nymphenburg Palace is a beautiful palace in Munich!'''
def _A ( _lowercase , _lowercase ) -> Any:
"""simple docstring"""
__UpperCamelCase = {
'attention_cell': 'multi_head',
'num_layers': 4,
'units': 10_24,
'hidden_size': 7_68,
'max_length': 5_12,
'num_heads': 8,
'scaled': True,
'dropout': 0.1,
'use_residual': True,
'embed_size': 10_24,
'embed_dropout': 0.1,
'word_embed': None,
'layer_norm_eps': 1e-5,
'token_type_vocab_size': 2,
}
__UpperCamelCase = bort_4_8_768_1024_hparams
# Let's construct the original Bort model here
# Taken from official BERT implementation, see:
# https://github.com/alexa/bort/blob/master/bort/bort.py
__UpperCamelCase = BERTEncoder(
attention_cell=predefined_args['attention_cell'] , num_layers=predefined_args['num_layers'] , units=predefined_args['units'] , hidden_size=predefined_args['hidden_size'] , max_length=predefined_args['max_length'] , num_heads=predefined_args['num_heads'] , scaled=predefined_args['scaled'] , dropout=predefined_args['dropout'] , output_attention=_lowercase , output_all_encodings=_lowercase , use_residual=predefined_args['use_residual'] , activation=predefined_args.get('activation' , 'gelu' ) , layer_norm_eps=predefined_args.get('layer_norm_eps' , _lowercase ) , )
# Vocab information needs to be fetched first
# It's the same as RoBERTa, so RobertaTokenizer can be used later
__UpperCamelCase = 'openwebtext_ccnews_stories_books_cased'
# Specify download folder to Gluonnlp's vocab
__UpperCamelCase = os.path.join(get_home_dir() , 'models' )
__UpperCamelCase = _load_vocab(_lowercase , _lowercase , _lowercase , cls=_lowercase )
__UpperCamelCase = nlp.model.BERTModel(
_lowercase , len(_lowercase ) , units=predefined_args['units'] , embed_size=predefined_args['embed_size'] , embed_dropout=predefined_args['embed_dropout'] , word_embed=predefined_args['word_embed'] , use_pooler=_lowercase , use_token_type_embed=_lowercase , token_type_vocab_size=predefined_args['token_type_vocab_size'] , use_classifier=_lowercase , use_decoder=_lowercase , )
original_bort.load_parameters(_lowercase , cast_dtype=_lowercase , ignore_extra=_lowercase )
__UpperCamelCase = original_bort._collect_params_with_prefix()
# Build our config 🤗
__UpperCamelCase = {
'architectures': ['BertForMaskedLM'],
'attention_probs_dropout_prob': predefined_args['dropout'],
'hidden_act': 'gelu',
'hidden_dropout_prob': predefined_args['dropout'],
'hidden_size': predefined_args['embed_size'],
'initializer_range': 0.02,
'intermediate_size': predefined_args['hidden_size'],
'layer_norm_eps': predefined_args['layer_norm_eps'],
'max_position_embeddings': predefined_args['max_length'],
'model_type': 'bort',
'num_attention_heads': predefined_args['num_heads'],
'num_hidden_layers': predefined_args['num_layers'],
'pad_token_id': 1, # 2 = BERT, 1 = RoBERTa
'type_vocab_size': 1, # 2 = BERT, 1 = RoBERTa
'vocab_size': len(_lowercase ),
}
__UpperCamelCase = BertConfig.from_dict(_lowercase )
__UpperCamelCase = BertForMaskedLM(_lowercase )
hf_bort_model.eval()
# Parameter mapping table (Gluonnlp to Transformers)
# * denotes layer index
#
# | Gluon Parameter | Transformers Parameter
# | -------------------------------------------------------------- | ----------------------
# | `encoder.layer_norm.beta` | `bert.embeddings.LayerNorm.bias`
# | `encoder.layer_norm.gamma` | `bert.embeddings.LayerNorm.weight`
# | `encoder.position_weight` | `bert.embeddings.position_embeddings.weight`
# | `word_embed.0.weight` | `bert.embeddings.word_embeddings.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_key.bias` | `bert.encoder.layer.*.attention.self.key.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_key.weight` | `bert.encoder.layer.*.attention.self.key.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_query.bias` | `bert.encoder.layer.*.attention.self.query.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_value.bias` | `bert.encoder.layer.*.attention.self.value.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
# | `encoder.transformer_cells.*.ffn.ffn_2.bias` | `bert.encoder.layer.*.attention.output.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_2.weight` | `bert.encoder.layer.*.attention.output.dense.weight`
# | `encoder.transformer_cells.*.layer_norm.beta` | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.layer_norm.gamma` | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.ffn.ffn_1.bias` | `bert.encoder.layer.*.intermediate.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_1.weight` | `bert.encoder.layer.*.intermediate.dense.weight`
# | `encoder.transformer_cells.*.ffn.layer_norm.beta` | `bert.encoder.layer.*.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.ffn.layer_norm.gamma` | `bert.encoder.layer.*.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.proj.bias` | `bert.encoder.layer.*.output.dense.bias`
# | `encoder.transformer_cells.*.proj.weight` | `bert.encoder.layer.*.output.dense.weight`
# Helper function to convert MXNET Arrays to PyTorch
def to_torch(_lowercase ) -> nn.Parameter:
return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy() ) )
# Check param shapes and map new HF param back
def check_and_map_params(_lowercase , _lowercase ):
__UpperCamelCase = hf_param.shape
__UpperCamelCase = to_torch(params[gluon_param] )
__UpperCamelCase = gluon_param.shape
assert (
shape_hf == shape_gluon
), f'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'''
return gluon_param
__UpperCamelCase = check_and_map_params(
hf_bort_model.bert.embeddings.word_embeddings.weight , 'word_embed.0.weight' )
__UpperCamelCase = check_and_map_params(
hf_bort_model.bert.embeddings.position_embeddings.weight , 'encoder.position_weight' )
__UpperCamelCase = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.bias , 'encoder.layer_norm.beta' )
__UpperCamelCase = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.weight , 'encoder.layer_norm.gamma' )
# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
__UpperCamelCase = torch.zeros_like(
hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )
for i in range(hf_bort_config.num_hidden_layers ):
__UpperCamelCase = hf_bort_model.bert.encoder.layer[i]
# self attention
__UpperCamelCase = layer.attention.self
__UpperCamelCase = check_and_map_params(
self_attn.key.bias.data , f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias''' )
__UpperCamelCase = check_and_map_params(
self_attn.key.weight.data , f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight''' )
__UpperCamelCase = check_and_map_params(
self_attn.query.bias.data , f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias''' )
__UpperCamelCase = check_and_map_params(
self_attn.query.weight.data , f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight''' )
__UpperCamelCase = check_and_map_params(
self_attn.value.bias.data , f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias''' )
__UpperCamelCase = check_and_map_params(
self_attn.value.weight.data , f'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight''' )
# self attention output
__UpperCamelCase = layer.attention.output
__UpperCamelCase = check_and_map_params(
self_output.dense.bias , f'''encoder.transformer_cells.{i}.proj.bias''' )
__UpperCamelCase = check_and_map_params(
self_output.dense.weight , f'''encoder.transformer_cells.{i}.proj.weight''' )
__UpperCamelCase = check_and_map_params(
self_output.LayerNorm.bias , f'''encoder.transformer_cells.{i}.layer_norm.beta''' )
__UpperCamelCase = check_and_map_params(
self_output.LayerNorm.weight , f'''encoder.transformer_cells.{i}.layer_norm.gamma''' )
# intermediate
__UpperCamelCase = layer.intermediate
__UpperCamelCase = check_and_map_params(
intermediate.dense.bias , f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias''' )
__UpperCamelCase = check_and_map_params(
intermediate.dense.weight , f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight''' )
# output
__UpperCamelCase = layer.output
__UpperCamelCase = check_and_map_params(
bert_output.dense.bias , f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias''' )
__UpperCamelCase = check_and_map_params(
bert_output.dense.weight , f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight''' )
__UpperCamelCase = check_and_map_params(
bert_output.LayerNorm.bias , f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta''' )
__UpperCamelCase = check_and_map_params(
bert_output.LayerNorm.weight , f'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma''' )
# Save space and energy 🎄
hf_bort_model.half()
# Compare output of both models
__UpperCamelCase = RobertaTokenizer.from_pretrained('roberta-base' )
__UpperCamelCase = tokenizer.encode_plus(_lowercase )['input_ids']
# Get gluon output
__UpperCamelCase = mx.nd.array([input_ids] )
__UpperCamelCase = original_bort(inputs=_lowercase , token_types=[] )
# Get Transformer output (save and reload model again)
hf_bort_model.save_pretrained(_lowercase )
__UpperCamelCase = BertModel.from_pretrained(_lowercase )
hf_bort_model.eval()
__UpperCamelCase = tokenizer.encode_plus(_lowercase , return_tensors='pt' )
__UpperCamelCase = hf_bort_model(**_lowercase )[0]
__UpperCamelCase = output_gluon[0].asnumpy()
__UpperCamelCase = output_hf[0].detach().numpy()
__UpperCamelCase = np.max(np.abs(hf_layer - gluon_layer ) ).item()
__UpperCamelCase = np.allclose(_lowercase , _lowercase , atol=1e-3 )
if success:
print('✔️ Both model do output the same tensors' )
else:
print('❌ Both model do **NOT** output the same tensors' )
print('Absolute difference is:' , _lowercase )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__snake_case = parser.parse_args()
convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)
| 310 |
def _A ( _lowercase ) -> list[int]:
"""simple docstring"""
if length <= 0 or not isinstance(_lowercase , _lowercase ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(_lowercase )]
if __name__ == "__main__":
print(hexagonal_numbers(length=5))
print(hexagonal_numbers(length=1_0))
| 310 | 1 |
_lowercase: Tuple = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
| 361 |
# using dfs for finding eulerian path traversal
def a( A : int , A : Optional[Any] , A : Any , A : Optional[int]=None ) -> List[str]:
"""simple docstring"""
a = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
a , a = True, True
a = dfs(A , A , A , A )
return path
def a( A : List[str] , A : Optional[int] ) -> List[str]:
"""simple docstring"""
a = 0
a = -1
for i in range(A ):
if i not in graph.keys():
continue
if len(graph[i] ) % 2 == 1:
odd_degree_nodes += 1
a = i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def a( A : str , A : str ) -> List[Any]:
"""simple docstring"""
a = [[False for _ in range(max_node + 1 )] for _ in range(max_node + 1 )]
a , a = check_circuit_or_path(A , A )
if check == 3:
print("graph is not Eulerian" )
print("no path" )
return
a = 1
if check == 2:
a = odd_node
print("graph has a Euler path" )
if check == 1:
print("graph has a Euler cycle" )
a = dfs(A , A , A )
print(A )
def a( ) -> int:
"""simple docstring"""
a = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
a = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
a = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
a = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
a = {
1: [],
2: []
# all degree is zero
}
a = 10
check_euler(A , A )
check_euler(A , A )
check_euler(A , A )
check_euler(A , A )
check_euler(A , A )
if __name__ == "__main__":
main()
| 71 | 0 |
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 A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Optional[Any] = DownBlockaD # noqa F405
_UpperCAmelCase :Tuple = 'down'
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = [-0.02_32, -0.98_69, 0.80_54, -0.06_37, -0.16_88, -1.42_64, 0.44_70, -1.33_94, 0.09_04]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Optional[Any] = ResnetDownsampleBlockaD # noqa F405
_UpperCAmelCase :Any = 'down'
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = [0.07_10, 0.24_10, -0.73_20, -1.07_57, -1.13_43, 0.35_40, -0.01_33, -0.25_76, 0.09_48]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :int = AttnDownBlockaD # noqa F405
_UpperCAmelCase :str = 'down'
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Tuple = [0.06_36, 0.89_64, -0.62_34, -1.01_31, 0.08_44, 0.49_35, 0.34_37, 0.09_11, -0.29_57]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Union[str, Any] = CrossAttnDownBlockaD # noqa F405
_UpperCAmelCase :Optional[int] = 'down'
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : Any = super().prepare_init_args_and_inputs_for_common()
UpperCamelCase : Optional[Any] = 32
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = [0.22_38, -0.73_96, -0.22_55, -0.38_29, 0.19_25, 1.16_65, 0.06_03, -0.72_95, 0.19_83]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :int = SimpleCrossAttnDownBlockaD # noqa F405
_UpperCAmelCase :Dict = 'down'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_encoder_hidden_states=A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : Optional[int] = super().prepare_init_args_and_inputs_for_common()
UpperCamelCase : int = 32
return init_dict, inputs_dict
@unittest.skipIf(torch_device == "mps" , "MPS result is not consistent" )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int = [0.79_21, -0.09_92, -0.19_62, -0.76_95, -0.42_42, 0.78_04, 0.47_37, 0.27_65, 0.33_38]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Optional[Any] = SkipDownBlockaD # noqa F405
_UpperCAmelCase :Optional[Any] = 'down'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_skip_sample=A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = [-0.08_45, -0.20_87, -0.24_65, 0.09_71, 0.19_00, -0.04_84, 0.26_64, 0.41_79, 0.50_69]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :List[Any] = AttnSkipDownBlockaD # noqa F405
_UpperCAmelCase :str = 'down'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_skip_sample=A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = [0.55_39, 0.16_09, 0.49_24, 0.05_37, -0.19_95, 0.40_50, 0.09_79, -0.27_21, -0.06_42]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Tuple = DownEncoderBlockaD # noqa F405
_UpperCAmelCase :Tuple = 'down'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_temb=A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[int] = {
"in_channels": 32,
"out_channels": 32,
}
UpperCamelCase : Any = self.dummy_input
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Any = [1.11_02, 0.53_02, 0.48_72, -0.00_23, -0.80_42, 0.04_83, -0.34_89, -0.56_32, 0.76_26]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Tuple = AttnDownEncoderBlockaD # noqa F405
_UpperCAmelCase :int = 'down'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_temb=A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : str = {
"in_channels": 32,
"out_channels": 32,
}
UpperCamelCase : Tuple = self.dummy_input
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Tuple = [0.89_66, -0.14_86, 0.85_68, 0.81_41, -0.90_46, -0.13_42, -0.09_72, -0.74_17, 0.15_38]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Dict = UNetMidBlockaD # noqa F405
_UpperCAmelCase :int = 'mid'
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = {
"in_channels": 32,
"temb_channels": 128,
}
UpperCamelCase : int = self.dummy_input
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Tuple = [-0.10_62, 1.72_48, 0.34_94, 1.45_69, -0.09_10, -1.24_21, -0.99_84, 0.67_36, 1.00_28]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Union[str, Any] = UNetMidBlockaDCrossAttn # noqa F405
_UpperCAmelCase :Tuple = 'mid'
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : List[Any] = super().prepare_init_args_and_inputs_for_common()
UpperCamelCase : Optional[int] = 32
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = [0.01_87, 2.42_20, 0.44_84, 1.12_03, -0.61_21, -1.51_22, -0.82_70, 0.78_51, 1.83_35]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Dict = UNetMidBlockaDSimpleCrossAttn # noqa F405
_UpperCAmelCase :Tuple = 'mid'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_encoder_hidden_states=A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : str = super().prepare_init_args_and_inputs_for_common()
UpperCamelCase : Dict = 32
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = [0.71_43, 1.99_74, 0.54_48, 1.39_77, 0.12_82, -1.12_37, -1.42_38, 0.55_30, 0.88_80]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :List[Any] = UpBlockaD # noqa F405
_UpperCAmelCase :int = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = [-0.20_41, -0.41_65, -0.30_22, 0.00_41, -0.66_28, -0.70_53, 0.19_28, -0.03_25, 0.05_23]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Optional[Any] = ResnetUpsampleBlockaD # noqa F405
_UpperCAmelCase :Optional[int] = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = [0.22_87, 0.35_49, -0.13_46, 0.47_97, -0.17_15, -0.96_49, 0.73_05, -0.58_64, -0.62_44]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :int = CrossAttnUpBlockaD # noqa F405
_UpperCAmelCase :Optional[Any] = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : Union[str, Any] = super().prepare_init_args_and_inputs_for_common()
UpperCamelCase : Dict = 32
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int = [-0.14_03, -0.35_15, -0.04_20, -0.14_25, 0.31_67, 0.50_94, -0.21_81, 0.59_31, 0.55_82]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Optional[Any] = SimpleCrossAttnUpBlockaD # noqa F405
_UpperCAmelCase :Dict = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=A_ , include_encoder_hidden_states=A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : List[Any] = super().prepare_init_args_and_inputs_for_common()
UpperCamelCase : str = 32
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = [0.26_45, 0.14_80, 0.09_09, 0.80_44, -0.97_58, -0.90_83, 0.09_94, -1.14_53, -0.74_02]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :int = AttnUpBlockaD # noqa F405
_UpperCAmelCase :int = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=A_ )
@unittest.skipIf(torch_device == "mps" , "MPS result is not consistent" )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Any = [0.09_79, 0.13_26, 0.00_21, 0.06_59, 0.22_49, 0.00_59, 0.11_32, 0.59_52, 0.10_33]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :List[str] = SkipUpBlockaD # noqa F405
_UpperCAmelCase :int = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Tuple = [-0.08_93, -0.12_34, -0.15_06, -0.03_32, 0.01_23, -0.02_11, 0.05_66, 0.01_43, 0.03_62]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Optional[Any] = AttnSkipUpBlockaD # noqa F405
_UpperCAmelCase :Any = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_res_hidden_states_tuple=A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = [0.03_61, 0.06_17, 0.27_87, -0.03_50, 0.03_42, 0.34_21, -0.08_43, 0.09_13, 0.30_15]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :int = UpDecoderBlockaD # noqa F405
_UpperCAmelCase :Union[str, Any] = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_temb=A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = {"in_channels": 32, "out_channels": 32}
UpperCamelCase : Dict = self.dummy_input
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = [0.44_04, 0.19_98, -0.98_86, -0.33_20, -0.31_28, -0.70_34, -0.69_55, -0.23_38, -0.31_37]
super().test_output(A_ )
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :List[str] = AttnUpDecoderBlockaD # noqa F405
_UpperCAmelCase :Any = 'up'
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return super().get_dummy_input(include_temb=A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = {"in_channels": 32, "out_channels": 32}
UpperCamelCase : Optional[int] = self.dummy_input
return init_dict, inputs_dict
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Tuple = [0.67_38, 0.44_91, 0.10_55, 1.07_10, 0.73_16, 0.33_39, 0.33_52, 0.10_23, 0.35_68]
super().test_output(A_ )
| 52 |
def A_ ( _lowerCAmelCase ) -> str:
UpperCamelCase : List[Any] = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def A_ ( _lowerCAmelCase ) -> dict[str, str]:
UpperCamelCase : Optional[Any] = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
UpperCamelCase : Tuple = remove_duplicates(key.upper() )
UpperCamelCase : int = len(_lowerCAmelCase )
# First fill cipher with key characters
UpperCamelCase : int = {alphabet[i]: char for i, char in enumerate(_lowerCAmelCase )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(_lowerCAmelCase ) , 26 ):
UpperCamelCase : Optional[Any] = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
UpperCamelCase : List[str] = alphabet[i - offset]
UpperCamelCase : List[Any] = char
return cipher_alphabet
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> str:
return "".join(cipher_map.get(_lowerCAmelCase , _lowerCAmelCase ) for ch in message.upper() )
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> str:
UpperCamelCase : Union[str, Any] = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(_lowerCAmelCase , _lowerCAmelCase ) for ch in message.upper() )
def A_ ( ) -> None:
UpperCamelCase : int = input("Enter message to encode or decode: " ).strip()
UpperCamelCase : str = input("Enter keyword: " ).strip()
UpperCamelCase : Union[str, Any] = input("Encipher or decipher? E/D:" ).strip()[0].lower()
try:
UpperCamelCase : List[str] = {"e": encipher, "d": decipher}[option]
except KeyError:
raise KeyError("invalid input option" )
UpperCamelCase : str = create_cipher_map(_lowerCAmelCase )
print(func(_lowerCAmelCase , _lowerCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 52 | 1 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__lowerCAmelCase : int =importlib.util.find_spec("s3fs") is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
__lowerCAmelCase : List[compression.BaseCompressedFileFileSystem] =[
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(f"""A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.""")
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def UpperCamelCase ( _lowerCamelCase : str ):
if "://" in dataset_path:
A__ = dataset_path.split("://" )[1]
return dataset_path
def UpperCamelCase ( _lowerCamelCase : fsspec.AbstractFileSystem ):
if fs is not None and fs.protocol != "file":
return True
else:
return False
def UpperCamelCase ( _lowerCamelCase : fsspec.AbstractFileSystem , _lowerCamelCase : str , _lowerCamelCase : str ):
A__ = not is_remote_filesystem(_lowerCamelCase )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(_lowerCamelCase ) , fs._strip_protocol(_lowerCamelCase ) )
else:
fs.mv(_lowerCamelCase , _lowerCamelCase , recursive=_lowerCamelCase )
def UpperCamelCase ( ):
if hasattr(fsspec.asyn , "reset_lock" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
A__ = None
A__ = None
A__ = threading.Lock()
| 123 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : Tuple =logging.get_logger(__name__)
def UpperCamelCase ( _lowerCamelCase : Tuple ):
A__ = DPTConfig(embedding_type="hybrid" )
if "large" in checkpoint_url:
A__ = 10_24
A__ = 40_96
A__ = 24
A__ = 16
A__ = [5, 11, 17, 23]
A__ = [2_56, 5_12, 10_24, 10_24]
A__ = (1, 3_84, 3_84)
if "nyu" or "midas" in checkpoint_url:
A__ = 7_68
A__ = [1, 1, 1, 0.5]
A__ = [2_56, 5_12, 7_68, 7_68]
A__ = 1_50
A__ = 16
A__ = (1, 3_84, 3_84)
A__ = False
A__ = "project"
if "ade" in checkpoint_url:
A__ = True
A__ = 7_68
A__ = [1, 1, 1, 0.5]
A__ = 1_50
A__ = 16
A__ = "huggingface/label-files"
A__ = "ade20k-id2label.json"
A__ = json.load(open(cached_download(hf_hub_url(_lowerCamelCase , _lowerCamelCase , repo_type="dataset" ) ) , "r" ) )
A__ = {int(_lowerCamelCase ): v for k, v in idalabel.items()}
A__ = idalabel
A__ = {v: k for k, v in idalabel.items()}
A__ = [1, 1_50, 4_80, 4_80]
return config, expected_shape
def UpperCamelCase ( _lowerCamelCase : Optional[Any] ):
A__ = ["pretrained.model.head.weight", "pretrained.model.head.bias"]
for k in ignore_keys:
state_dict.pop(_lowerCamelCase , _lowerCamelCase )
def UpperCamelCase ( _lowerCamelCase : int ):
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
A__ = name.replace("pretrained.model" , "dpt.encoder" )
if "pretrained.model" in name:
A__ = name.replace("pretrained.model" , "dpt.embeddings" )
if "patch_embed" in name:
A__ = name.replace("patch_embed" , "" )
if "pos_embed" in name:
A__ = name.replace("pos_embed" , "position_embeddings" )
if "attn.proj" in name:
A__ = name.replace("attn.proj" , "attention.output.dense" )
if "proj" in name and "project" not in name:
A__ = name.replace("proj" , "projection" )
if "blocks" in name:
A__ = name.replace("blocks" , "layer" )
if "mlp.fc1" in name:
A__ = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
A__ = name.replace("mlp.fc2" , "output.dense" )
if "norm1" in name and "backbone" not in name:
A__ = name.replace("norm1" , "layernorm_before" )
if "norm2" in name and "backbone" not in name:
A__ = name.replace("norm2" , "layernorm_after" )
if "scratch.output_conv" in name:
A__ = name.replace("scratch.output_conv" , "head" )
if "scratch" in name:
A__ = name.replace("scratch" , "neck" )
if "layer1_rn" in name:
A__ = name.replace("layer1_rn" , "convs.0" )
if "layer2_rn" in name:
A__ = name.replace("layer2_rn" , "convs.1" )
if "layer3_rn" in name:
A__ = name.replace("layer3_rn" , "convs.2" )
if "layer4_rn" in name:
A__ = name.replace("layer4_rn" , "convs.3" )
if "refinenet" in name:
A__ = int(name[len("neck.refinenet" ) : len("neck.refinenet" ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
A__ = name.replace(F"refinenet{layer_idx}" , F"fusion_stage.layers.{abs(layer_idx-4 )}" )
if "out_conv" in name:
A__ = name.replace("out_conv" , "projection" )
if "resConfUnit1" in name:
A__ = name.replace("resConfUnit1" , "residual_layer1" )
if "resConfUnit2" in name:
A__ = name.replace("resConfUnit2" , "residual_layer2" )
if "conv1" in name:
A__ = name.replace("conv1" , "convolution1" )
if "conv2" in name:
A__ = name.replace("conv2" , "convolution2" )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
A__ = name.replace("pretrained.act_postprocess1.0.project.0" , "neck.reassemble_stage.readout_projects.0.0" )
if "pretrained.act_postprocess2.0.project.0" in name:
A__ = name.replace("pretrained.act_postprocess2.0.project.0" , "neck.reassemble_stage.readout_projects.1.0" )
if "pretrained.act_postprocess3.0.project.0" in name:
A__ = name.replace("pretrained.act_postprocess3.0.project.0" , "neck.reassemble_stage.readout_projects.2.0" )
if "pretrained.act_postprocess4.0.project.0" in name:
A__ = name.replace("pretrained.act_postprocess4.0.project.0" , "neck.reassemble_stage.readout_projects.3.0" )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
A__ = name.replace("pretrained.act_postprocess1.3" , "neck.reassemble_stage.layers.0.projection" )
if "pretrained.act_postprocess1.4" in name:
A__ = name.replace("pretrained.act_postprocess1.4" , "neck.reassemble_stage.layers.0.resize" )
if "pretrained.act_postprocess2.3" in name:
A__ = name.replace("pretrained.act_postprocess2.3" , "neck.reassemble_stage.layers.1.projection" )
if "pretrained.act_postprocess2.4" in name:
A__ = name.replace("pretrained.act_postprocess2.4" , "neck.reassemble_stage.layers.1.resize" )
if "pretrained.act_postprocess3.3" in name:
A__ = name.replace("pretrained.act_postprocess3.3" , "neck.reassemble_stage.layers.2.projection" )
if "pretrained.act_postprocess4.3" in name:
A__ = name.replace("pretrained.act_postprocess4.3" , "neck.reassemble_stage.layers.3.projection" )
if "pretrained.act_postprocess4.4" in name:
A__ = name.replace("pretrained.act_postprocess4.4" , "neck.reassemble_stage.layers.3.resize" )
if "pretrained" in name:
A__ = name.replace("pretrained" , "dpt" )
if "bn" in name:
A__ = name.replace("bn" , "batch_norm" )
if "head" in name:
A__ = name.replace("head" , "head.head" )
if "encoder.norm" in name:
A__ = name.replace("encoder.norm" , "layernorm" )
if "auxlayer" in name:
A__ = name.replace("auxlayer" , "auxiliary_head.head" )
if "backbone" in name:
A__ = name.replace("backbone" , "backbone.bit.encoder" )
if ".." in name:
A__ = name.replace(".." , "." )
if "stem.conv" in name:
A__ = name.replace("stem.conv" , "bit.embedder.convolution" )
if "blocks" in name:
A__ = name.replace("blocks" , "layers" )
if "convolution" in name and "backbone" in name:
A__ = name.replace("convolution" , "conv" )
if "layer" in name and "backbone" in name:
A__ = name.replace("layer" , "layers" )
if "backbone.bit.encoder.bit" in name:
A__ = name.replace("backbone.bit.encoder.bit" , "backbone.bit" )
if "embedder.conv" in name:
A__ = name.replace("embedder.conv" , "embedder.convolution" )
if "backbone.bit.encoder.stem.norm" in name:
A__ = name.replace("backbone.bit.encoder.stem.norm" , "backbone.bit.embedder.norm" )
return name
def UpperCamelCase ( _lowerCamelCase : Dict , _lowerCamelCase : int ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
A__ = state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight" )
A__ = state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
A__ = in_proj_weight[: config.hidden_size, :]
A__ = in_proj_bias[: config.hidden_size]
A__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
A__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
A__ = in_proj_weight[
-config.hidden_size :, :
]
A__ = in_proj_bias[-config.hidden_size :]
def UpperCamelCase ( ):
A__ = "http://images.cocodataset.org/val2017/000000039769.jpg"
A__ = Image.open(requests.get(_lowerCamelCase , stream=_lowerCamelCase ).raw )
return im
@torch.no_grad()
def UpperCamelCase ( _lowerCamelCase : List[str] , _lowerCamelCase : Tuple , _lowerCamelCase : Optional[int] , _lowerCamelCase : Any , _lowerCamelCase : str ):
A__, A__ = get_dpt_config(_lowerCamelCase )
# load original state_dict from URL
# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
A__ = torch.load(_lowerCamelCase , map_location="cpu" )
# remove certain keys
remove_ignore_keys_(_lowerCamelCase )
# rename keys
for key in state_dict.copy().keys():
A__ = state_dict.pop(_lowerCamelCase )
A__ = val
# read in qkv matrices
read_in_q_k_v(_lowerCamelCase , _lowerCamelCase )
# load HuggingFace model
A__ = DPTForSemanticSegmentation(_lowerCamelCase ) if "ade" in checkpoint_url else DPTForDepthEstimation(_lowerCamelCase )
model.load_state_dict(_lowerCamelCase )
model.eval()
# Check outputs on an image
A__ = 4_80 if "ade" in checkpoint_url else 3_84
A__ = DPTImageProcessor(size=_lowerCamelCase )
A__ = prepare_img()
A__ = image_processor(_lowerCamelCase , return_tensors="pt" )
# forward pass
A__ = model(**_lowerCamelCase ).logits if "ade" in checkpoint_url else model(**_lowerCamelCase ).predicted_depth
if show_prediction:
A__ = (
torch.nn.functional.interpolate(
outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode="bicubic" , align_corners=_lowerCamelCase , )
.squeeze()
.cpu()
.numpy()
)
Image.fromarray((prediction / prediction.max()) * 2_55 ).show()
if pytorch_dump_folder_path is not None:
Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
print(F"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(_lowerCamelCase )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(_lowerCamelCase )
if push_to_hub:
model.push_to_hub("ybelkada/dpt-hybrid-midas" )
image_processor.push_to_hub("ybelkada/dpt-hybrid-midas" )
if __name__ == "__main__":
__lowerCAmelCase : Tuple =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
type=str,
help="URL of the original DPT checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=False,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
parser.add_argument(
"--model_name",
default="dpt-large",
type=str,
help="Name of the model, in case you're pushing to the hub.",
)
parser.add_argument(
"--show_prediction",
action="store_true",
)
__lowerCAmelCase : List[Any] =parser.parse_args()
convert_dpt_checkpoint(
args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
)
| 123 | 1 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
find_tied_parameters,
get_balanced_memory,
infer_auto_device_map,
load_checkpoint_in_model,
offload_weight,
set_module_tensor_to_device,
)
if is_bnb_available():
import bitsandbytes as bnb
from copy import deepcopy
snake_case__ : Dict = logging.getLogger(__name__)
def _snake_case ( _snake_case : torch.nn.Module , _snake_case : BnbQuantizationConfig , _snake_case : Union[str, os.PathLike] = None , _snake_case : Optional[Dict[str, Union[int, str, torch.device]]] = None , _snake_case : Optional[List[str]] = None , _snake_case : Optional[Dict[Union[int, str], Union[int, str]]] = None , _snake_case : Optional[Union[str, os.PathLike]] = None , _snake_case : bool = False , ):
lowerCAmelCase : Any = bnb_quantization_config.load_in_abit
lowerCAmelCase : List[str] = bnb_quantization_config.load_in_abit
if load_in_abit and not is_abit_bnb_available():
raise ImportError(
'''You have a version of `bitsandbytes` that is not compatible with 8bit quantization,'''
''' make sure you have the latest version of `bitsandbytes` installed.''' )
if load_in_abit and not is_abit_bnb_available():
raise ValueError(
'''You have a version of `bitsandbytes` that is not compatible with 4bit quantization,'''
'''make sure you have the latest version of `bitsandbytes` installed.''' )
lowerCAmelCase : Dict = []
# custom device map
if isinstance(_snake_case , _snake_case ) and len(device_map.keys() ) > 1:
lowerCAmelCase : Any = [key for key, value in device_map.items() if value in ['''disk''', '''cpu''']]
# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
if bnb_quantization_config.skip_modules is None:
lowerCAmelCase : List[Any] = get_keys_to_not_convert(_snake_case )
# add cpu modules to skip modules only for 4-bit modules
if load_in_abit:
bnb_quantization_config.skip_modules.extend(_snake_case )
lowerCAmelCase : str = bnb_quantization_config.skip_modules
# We add the modules we want to keep in full precision
if bnb_quantization_config.keep_in_fpaa_modules is None:
lowerCAmelCase : Optional[Any] = []
lowerCAmelCase : int = bnb_quantization_config.keep_in_fpaa_modules
modules_to_not_convert.extend(_snake_case )
# compatibility with peft
lowerCAmelCase : str = load_in_abit
lowerCAmelCase : str = load_in_abit
lowerCAmelCase : str = get_parameter_device(_snake_case )
if model_device.type != "meta":
# quantization of an already loaded model
logger.warning(
'''It is not recommended to quantize a loaded model. '''
'''The model should be instantiated under the `init_empty_weights` context manager.''' )
lowerCAmelCase : Optional[int] = replace_with_bnb_layers(_snake_case , _snake_case , modules_to_not_convert=_snake_case )
# convert param to the right dtype
lowerCAmelCase : Any = bnb_quantization_config.torch_dtype
for name, param in model.state_dict().items():
if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules ):
param.to(torch.floataa )
if param.dtype != torch.floataa:
lowerCAmelCase : Any = name.replace('''.weight''' , '''''' ).replace('''.bias''' , '''''' )
lowerCAmelCase : Optional[Any] = getattr(_snake_case , _snake_case , _snake_case )
if param is not None:
param.to(torch.floataa )
elif torch.is_floating_point(_snake_case ):
param.to(_snake_case )
if model_device.type == "cuda":
# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
model.cuda(torch.cuda.current_device() )
torch.cuda.empty_cache()
elif torch.cuda.is_available():
model.to(torch.cuda.current_device() )
else:
raise RuntimeError('''No GPU found. A GPU is needed for quantization.''' )
logger.info(
f'''The model device type is {model_device.type}. However, cuda is needed for quantization.'''
'''We move the model to cuda.''' )
return model
elif weights_location is None:
raise RuntimeError(
f'''`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} ''' )
else:
with init_empty_weights():
lowerCAmelCase : List[str] = replace_with_bnb_layers(
_snake_case , _snake_case , modules_to_not_convert=_snake_case )
lowerCAmelCase : List[str] = get_quantized_model_device_map(
_snake_case , _snake_case , _snake_case , max_memory=_snake_case , no_split_module_classes=_snake_case , )
if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
lowerCAmelCase : List[Any] = True
lowerCAmelCase : List[str] = any(x in list(device_map.values() ) for x in ['''cpu''', '''disk'''] )
load_checkpoint_in_model(
_snake_case , _snake_case , _snake_case , dtype=bnb_quantization_config.torch_dtype , offload_folder=_snake_case , offload_state_dict=_snake_case , keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules , offload_abit_bnb=load_in_abit and offload , )
return dispatch_model(_snake_case , device_map=_snake_case , offload_dir=_snake_case )
def _snake_case ( _snake_case : str , _snake_case : List[str] , _snake_case : Tuple=None , _snake_case : Any=None , _snake_case : Dict=None ):
if device_map is None:
if torch.cuda.is_available():
lowerCAmelCase : int = {'''''': torch.cuda.current_device()}
else:
raise RuntimeError('''No GPU found. A GPU is needed for quantization.''' )
logger.info('''The device_map was not initialized.''' '''Setting device_map to `{\'\':torch.cuda.current_device()}`.''' )
if isinstance(_snake_case , _snake_case ):
if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
raise ValueError(
'''If passing a string for `device_map`, please choose \'auto\', \'balanced\', \'balanced_low_0\' or '''
'''\'sequential\'.''' )
lowerCAmelCase : int = {}
special_dtypes.update(
{
name: bnb_quantization_config.torch_dtype
for name, _ in model.named_parameters()
if any(m in name for m in bnb_quantization_config.skip_modules )
} )
special_dtypes.update(
{
name: torch.floataa
for name, _ in model.named_parameters()
if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules )
} )
lowerCAmelCase : Tuple = {}
lowerCAmelCase : List[Any] = special_dtypes
lowerCAmelCase : List[str] = no_split_module_classes
lowerCAmelCase : Optional[int] = bnb_quantization_config.target_dtype
# get max_memory for each device.
if device_map != "sequential":
lowerCAmelCase : str = get_balanced_memory(
_snake_case , low_zero=(device_map == '''balanced_low_0''') , max_memory=_snake_case , **_snake_case , )
lowerCAmelCase : Tuple = max_memory
lowerCAmelCase : int = infer_auto_device_map(_snake_case , **_snake_case )
if isinstance(_snake_case , _snake_case ):
# check if don't have any quantized module on the cpu
lowerCAmelCase : Dict = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules
lowerCAmelCase : Tuple = {
key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
}
for device in ["cpu", "disk"]:
if device in device_map_without_some_modules.values():
if bnb_quantization_config.load_in_abit:
raise ValueError(
'''
Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
these modules in `torch_dtype`, you need to pass a custom `device_map` to
`load_and_quantize_model`. Check
https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
for more details.
''' )
else:
logger.info(
'''Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit''' )
del device_map_without_some_modules
return device_map
def _snake_case ( _snake_case : Tuple , _snake_case : List[Any] , _snake_case : int=None , _snake_case : Any=None ):
if modules_to_not_convert is None:
lowerCAmelCase : str = []
lowerCAmelCase, lowerCAmelCase : List[str] = _replace_with_bnb_layers(
_snake_case , _snake_case , _snake_case , _snake_case )
if not has_been_replaced:
logger.warning(
'''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
''' this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers.'''
''' Please double check your model architecture, or submit an issue on github if you think this is'''
''' a bug.''' )
return model
def _snake_case ( _snake_case : Dict , _snake_case : Optional[Any] , _snake_case : List[Any]=None , _snake_case : Dict=None , ):
lowerCAmelCase : List[str] = False
for name, module in model.named_children():
if current_key_name is None:
lowerCAmelCase : List[Any] = []
current_key_name.append(_snake_case )
if isinstance(_snake_case , nn.Linear ) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
lowerCAmelCase : Optional[int] = '''.'''.join(_snake_case )
lowerCAmelCase : Optional[int] = True
for key in modules_to_not_convert:
if (
(key in current_key_name_str) and (key + "." in current_key_name_str)
) or key == current_key_name_str:
lowerCAmelCase : List[str] = False
break
if proceed:
# Load bnb module with empty weight and replace ``nn.Linear` module
if bnb_quantization_config.load_in_abit:
lowerCAmelCase : Any = bnb.nn.LinearabitLt(
module.in_features , module.out_features , module.bias is not None , has_fpaa_weights=_snake_case , threshold=bnb_quantization_config.llm_inta_threshold , )
elif bnb_quantization_config.load_in_abit:
lowerCAmelCase : Dict = bnb.nn.Linearabit(
module.in_features , module.out_features , module.bias is not None , bnb_quantization_config.bnb_abit_compute_dtype , compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant , quant_type=bnb_quantization_config.bnb_abit_quant_type , )
else:
raise ValueError('''load_in_8bit and load_in_4bit can\'t be both False''' )
lowerCAmelCase : Dict = module.weight.data
if module.bias is not None:
lowerCAmelCase : Union[str, Any] = module.bias.data
bnb_module.requires_grad_(_snake_case )
setattr(_snake_case , _snake_case , _snake_case )
lowerCAmelCase : Any = True
if len(list(module.children() ) ) > 0:
lowerCAmelCase, lowerCAmelCase : Any = _replace_with_bnb_layers(
_snake_case , _snake_case , _snake_case , _snake_case )
lowerCAmelCase : Optional[Any] = has_been_replaced | _has_been_replaced
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def _snake_case ( _snake_case : List[str] ):
# Create a copy of the model
with init_empty_weights():
lowerCAmelCase : str = deepcopy(_snake_case ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
lowerCAmelCase : List[Any] = find_tied_parameters(_snake_case )
# For compatibility with Accelerate < 0.18
if isinstance(_snake_case , _snake_case ):
lowerCAmelCase : List[Any] = sum(list(tied_params.values() ) , [] ) + list(tied_params.keys() )
else:
lowerCAmelCase : Tuple = sum(_snake_case , [] )
lowerCAmelCase : Optional[Any] = len(_snake_case ) > 0
# Check if it is a base model
lowerCAmelCase : Union[str, Any] = False
if hasattr(_snake_case , '''base_model_prefix''' ):
lowerCAmelCase : int = not hasattr(_snake_case , model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
lowerCAmelCase : Union[str, Any] = list(model.named_children() )
lowerCAmelCase : Dict = [list_modules[-1][0]]
# add last module together with tied weights
lowerCAmelCase : Optional[Any] = set(_snake_case ) - set(_snake_case )
lowerCAmelCase : Dict = list(set(_snake_case ) ) + list(_snake_case )
# remove ".weight" from the keys
lowerCAmelCase : Dict = ['''.weight''', '''.bias''']
lowerCAmelCase : List[str] = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
lowerCAmelCase : Optional[int] = name.replace(_snake_case , '''''' )
filtered_module_names.append(_snake_case )
return filtered_module_names
def _snake_case ( _snake_case : Any ):
for m in model.modules():
if isinstance(_snake_case , bnb.nn.Linearabit ):
return True
return False
def _snake_case ( _snake_case : nn.Module ):
return next(parameter.parameters() ).device
def _snake_case ( _snake_case : Dict , _snake_case : str , _snake_case : List[str] , _snake_case : List[str] , _snake_case : List[str] , _snake_case : str , _snake_case : Tuple ):
# if it is not quantized, we quantize and offload the quantized weights and the SCB stats
if fpaa_statistics is None:
set_module_tensor_to_device(_snake_case , _snake_case , 0 , dtype=_snake_case , value=_snake_case )
lowerCAmelCase : List[str] = param_name
lowerCAmelCase : Union[str, Any] = model
if "." in tensor_name:
lowerCAmelCase : int = tensor_name.split('''.''' )
for split in splits[:-1]:
lowerCAmelCase : str = getattr(_snake_case , _snake_case )
if new_module is None:
raise ValueError(f'''{module} has no attribute {split}.''' )
lowerCAmelCase : List[str] = new_module
lowerCAmelCase : int = splits[-1]
# offload weights
lowerCAmelCase : str = False
offload_weight(module._parameters[tensor_name] , _snake_case , _snake_case , index=_snake_case )
if hasattr(module._parameters[tensor_name] , '''SCB''' ):
offload_weight(
module._parameters[tensor_name].SCB , param_name.replace('''weight''' , '''SCB''' ) , _snake_case , index=_snake_case , )
else:
offload_weight(_snake_case , _snake_case , _snake_case , index=_snake_case )
offload_weight(_snake_case , param_name.replace('''weight''' , '''SCB''' ) , _snake_case , index=_snake_case )
set_module_tensor_to_device(_snake_case , _snake_case , '''meta''' , dtype=_snake_case , value=torch.empty(*param.size() ) )
| 60 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __magic_name__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase = [r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']
@register_to_config
def __init__( self :List[Any] , snake_case :int , snake_case :int , snake_case :Optional[int] = None , snake_case :int = 50_257 , snake_case :int = 1_024 , snake_case :int = 768 , snake_case :int = 12 , snake_case :int = 12 , snake_case :Optional[int] = None , snake_case :str = "gelu_new" , snake_case :float = 0.1 , snake_case :float = 0.1 , snake_case :float = 0.1 , snake_case :float = 1e-5 , snake_case :float = 0.02 , snake_case :bool = True , snake_case :bool = True , snake_case :bool = False , snake_case :bool = False , ):
'''simple docstring'''
super().__init__()
A_ : Tuple = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
f"`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"
f" `n_embd`: {n_embd} are not equal." )
A_ : List[Any] = prefix_inner_dim
A_ : Union[str, Any] = prefix_hidden_dim
A_ : List[str] = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
A_ : List[Any] = (
nn.Linear(self.prefix_hidden_dim , snake_case ) if self.prefix_hidden_dim is not None else nn.Identity()
)
A_ : List[Any] = GPTaConfig(
vocab_size=snake_case , n_positions=snake_case , n_embd=snake_case , n_layer=snake_case , n_head=snake_case , n_inner=snake_case , activation_function=snake_case , resid_pdrop=snake_case , embd_pdrop=snake_case , attn_pdrop=snake_case , layer_norm_epsilon=snake_case , initializer_range=snake_case , scale_attn_weights=snake_case , use_cache=snake_case , scale_attn_by_inverse_layer_idx=snake_case , reorder_and_upcast_attn=snake_case , )
A_ : Optional[Any] = GPTaLMHeadModel(snake_case )
def SCREAMING_SNAKE_CASE ( self :Tuple , snake_case :torch.Tensor , snake_case :torch.Tensor , snake_case :Optional[torch.Tensor] = None , snake_case :Optional[torch.Tensor] = None , ):
'''simple docstring'''
A_ : Any = self.transformer.transformer.wte(snake_case )
A_ : str = self.encode_prefix(snake_case )
A_ : Union[str, Any] = self.decode_prefix(snake_case )
A_ : int = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
A_ : Dict = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
A_ : int = torch.cat((dummy_token, input_ids) , dim=1 )
A_ : Union[str, Any] = self.transformer(inputs_embeds=snake_case , labels=snake_case , attention_mask=snake_case )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def SCREAMING_SNAKE_CASE ( self :str , snake_case :int , snake_case :torch.device ):
'''simple docstring'''
return torch.zeros(snake_case , self.prefix_length , dtype=torch.intaa , device=snake_case )
def SCREAMING_SNAKE_CASE ( self :Optional[int] , snake_case :int ):
'''simple docstring'''
return self.encode_prefix(snake_case )
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( self :List[Any] , snake_case :Dict , snake_case :Optional[int] , snake_case :Any ):
'''simple docstring'''
A_ : Any = torch.split(snake_case , 1 , dim=0 )
A_ : Optional[int] = []
A_ : Union[str, Any] = []
for feature in features:
A_ : Tuple = self.decode_prefix(feature.to(snake_case ) ) # back to the clip feature
# Only support beam search for now
A_ , A_ : Dict = self.generate_beam(
input_embeds=snake_case , device=snake_case , eos_token_id=snake_case )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
A_ : int = torch.stack(snake_case )
A_ : int = torch.stack(snake_case )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( self :Union[str, Any] , snake_case :int=None , snake_case :str=None , snake_case :int=None , snake_case :int = 5 , snake_case :int = 67 , snake_case :float = 1.0 , snake_case :Optional[int] = None , ):
'''simple docstring'''
A_ : Optional[Any] = eos_token_id
A_ : List[Any] = None
A_ : List[Any] = None
A_ : str = torch.ones(snake_case , device=snake_case , dtype=torch.int )
A_ : Any = torch.zeros(snake_case , device=snake_case , dtype=torch.bool )
if input_embeds is not None:
A_ : Any = input_embeds
else:
A_ : Optional[Any] = self.transformer.transformer.wte(snake_case )
for i in range(snake_case ):
A_ : Optional[Any] = self.transformer(inputs_embeds=snake_case )
A_ : str = outputs.logits
A_ : int = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
A_ : List[str] = logits.softmax(-1 ).log()
if scores is None:
A_ , A_ : Union[str, Any] = logits.topk(snake_case , -1 )
A_ : Tuple = generated.expand(snake_case , *generated.shape[1:] )
A_ , A_ : str = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
A_ : Union[str, Any] = next_tokens
else:
A_ : List[str] = tokens.expand(snake_case , *tokens.shape[1:] )
A_ : Union[str, Any] = torch.cat((tokens, next_tokens) , dim=1 )
else:
A_ : List[str] = -float(np.inf )
A_ : List[Any] = 0
A_ : Union[str, Any] = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
A_ : Optional[Any] = scores_sum / seq_lengths[:, None]
A_ , A_ : List[str] = scores_sum_average.view(-1 ).topk(snake_case , -1 )
A_ : str = next_tokens // scores_sum.shape[1]
A_ : Union[str, Any] = seq_lengths[next_tokens_source]
A_ : Optional[int] = next_tokens % scores_sum.shape[1]
A_ : Tuple = next_tokens.unsqueeze(1 )
A_ : Tuple = tokens[next_tokens_source]
A_ : Dict = torch.cat((tokens, next_tokens) , dim=1 )
A_ : Dict = generated[next_tokens_source]
A_ : Union[str, Any] = scores_sum_average * seq_lengths
A_ : Optional[int] = is_stopped[next_tokens_source]
A_ : Tuple = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
A_ : Union[str, Any] = torch.cat((generated, next_token_embed) , dim=1 )
A_ : Any = is_stopped + next_tokens.eq(snake_case ).squeeze()
if is_stopped.all():
break
A_ : int = scores / seq_lengths
A_ : str = scores.argsort(descending=snake_case )
# tokens tensors are already padded to max_seq_length
A_ : Dict = [tokens[i] for i in order]
A_ : int = torch.stack(snake_case , dim=0 )
A_ : List[Any] = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 300 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__a :Union[str, Any] = logging.get_logger(__name__)
__a :Optional[int] = {"vocab_file": "vocab.txt"}
__a :Any = {
"vocab_file": {
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt",
"YituTech/conv-bert-medium-small": (
"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"
),
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt",
}
}
__a :List[str] = {
"YituTech/conv-bert-base": 512,
"YituTech/conv-bert-medium-small": 512,
"YituTech/conv-bert-small": 512,
}
__a :Optional[Any] = {
"YituTech/conv-bert-base": {"do_lower_case": True},
"YituTech/conv-bert-medium-small": {"do_lower_case": True},
"YituTech/conv-bert-small": {"do_lower_case": True},
}
class _a ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_lowerCamelCase : Tuple = VOCAB_FILES_NAMES
_lowerCamelCase : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : int = PRETRAINED_INIT_CONFIGURATION
_lowerCamelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase : Optional[Any] = ConvBertTokenizer
def __init__( self : int , UpperCAmelCase : Any=None , UpperCAmelCase : List[Any]=None , UpperCAmelCase : List[Any]=True , UpperCAmelCase : str="[UNK]" , UpperCAmelCase : Tuple="[SEP]" , UpperCAmelCase : Optional[int]="[PAD]" , UpperCAmelCase : Tuple="[CLS]" , UpperCAmelCase : Any="[MASK]" , UpperCAmelCase : Any=True , UpperCAmelCase : List[str]=None , **UpperCAmelCase : List[Any] , ):
super().__init__(
UpperCAmelCase , tokenizer_file=UpperCAmelCase , do_lower_case=UpperCAmelCase , unk_token=UpperCAmelCase , sep_token=UpperCAmelCase , pad_token=UpperCAmelCase , cls_token=UpperCAmelCase , mask_token=UpperCAmelCase , tokenize_chinese_chars=UpperCAmelCase , strip_accents=UpperCAmelCase , **UpperCAmelCase , )
A_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , UpperCAmelCase ) != do_lower_case
or normalizer_state.get("strip_accents" , UpperCAmelCase ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , UpperCAmelCase ) != tokenize_chinese_chars
):
A_ = getattr(UpperCAmelCase , normalizer_state.pop("type" ) )
A_ = do_lower_case
A_ = strip_accents
A_ = tokenize_chinese_chars
A_ = normalizer_class(**UpperCAmelCase )
A_ = do_lower_case
def __A ( self : Tuple , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple=None ):
A_ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __A ( self : int , UpperCAmelCase : List[int] , UpperCAmelCase : Optional[List[int]] = None ):
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]
def __A ( self : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : Optional[str] = None ):
A_ = self._tokenizer.model.save(UpperCAmelCase , name=UpperCAmelCase )
return tuple(UpperCAmelCase ) | 351 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__a :int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__a :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')
__a :Tuple = [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')
__a :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')
__a :List[str] = [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')
__a :Any = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files) | 329 | 0 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : List[Any] = logging.get_logger(__name__)
# TODO Update this
snake_case : Union[str, Any] = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = 'esm'
def __init__( self , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=768 , _lowerCamelCase=12 , _lowerCamelCase=12 , _lowerCamelCase=3072 , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase=1026 , _lowerCamelCase=0.02 , _lowerCamelCase=1e-12 , _lowerCamelCase="absolute" , _lowerCamelCase=True , _lowerCamelCase=None , _lowerCamelCase=False , _lowerCamelCase=False , _lowerCamelCase=None , _lowerCamelCase=None , **_lowerCamelCase , ):
super().__init__(pad_token_id=_lowerCamelCase , mask_token_id=_lowerCamelCase , **_lowerCamelCase )
a :Tuple = vocab_size
a :List[str] = hidden_size
a :int = num_hidden_layers
a :int = num_attention_heads
a :Union[str, Any] = intermediate_size
a :Union[str, Any] = hidden_dropout_prob
a :Any = attention_probs_dropout_prob
a :List[Any] = max_position_embeddings
a :str = initializer_range
a :Tuple = layer_norm_eps
a :Union[str, Any] = position_embedding_type
a :List[str] = use_cache
a :str = emb_layer_norm_before
a :List[str] = token_dropout
a :str = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('''No esmfold_config supplied for folding model, using default values.''' )
a :Optional[Any] = EsmFoldConfig()
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
a :Dict = EsmFoldConfig(**_lowerCamelCase )
a :Optional[Any] = esmfold_config
if vocab_list is None:
logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!''' )
a :str = get_default_vocab_list()
else:
a :Dict = vocab_list
else:
a :Tuple = None
a :List[str] = None
if self.esmfold_config is not None and getattr(self.esmfold_config , '''use_esm_attn_map''' , _lowerCamelCase ):
raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!''' )
def SCREAMING_SNAKE_CASE__ ( self ):
a :Any = super().to_dict()
if isinstance(self.esmfold_config , _lowerCamelCase ):
a :Optional[Any] = self.esmfold_config.to_dict()
return output
@dataclass
class _snake_case :
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = 128
SCREAMING_SNAKE_CASE__ = None
def SCREAMING_SNAKE_CASE__ ( self ):
if self.trunk is None:
a :List[str] = TrunkConfig()
elif isinstance(self.trunk , _lowerCamelCase ):
a :List[Any] = TrunkConfig(**self.trunk )
def SCREAMING_SNAKE_CASE__ ( self ):
a :Any = asdict(self )
a :Any = self.trunk.to_dict()
return output
@dataclass
class _snake_case :
SCREAMING_SNAKE_CASE__ = 48
SCREAMING_SNAKE_CASE__ = 1024
SCREAMING_SNAKE_CASE__ = 128
SCREAMING_SNAKE_CASE__ = 32
SCREAMING_SNAKE_CASE__ = 32
SCREAMING_SNAKE_CASE__ = 32
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = 4
SCREAMING_SNAKE_CASE__ = 128
SCREAMING_SNAKE_CASE__ = None
def SCREAMING_SNAKE_CASE__ ( self ):
if self.structure_module is None:
a :List[Any] = StructureModuleConfig()
elif isinstance(self.structure_module , _lowerCamelCase ):
a :Optional[int] = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
F''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
a :Tuple = self.sequence_state_dim // self.sequence_head_width
a :Union[str, Any] = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def SCREAMING_SNAKE_CASE__ ( self ):
a :Union[str, Any] = asdict(self )
a :Dict = self.structure_module.to_dict()
return output
@dataclass
class _snake_case :
SCREAMING_SNAKE_CASE__ = 384
SCREAMING_SNAKE_CASE__ = 128
SCREAMING_SNAKE_CASE__ = 16
SCREAMING_SNAKE_CASE__ = 128
SCREAMING_SNAKE_CASE__ = 12
SCREAMING_SNAKE_CASE__ = 4
SCREAMING_SNAKE_CASE__ = 8
SCREAMING_SNAKE_CASE__ = 0.1
SCREAMING_SNAKE_CASE__ = 8
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = 2
SCREAMING_SNAKE_CASE__ = 7
SCREAMING_SNAKE_CASE__ = 10
SCREAMING_SNAKE_CASE__ = 1e-8
SCREAMING_SNAKE_CASE__ = 1e5
def SCREAMING_SNAKE_CASE__ ( self ):
return asdict(self )
def __lowerCamelCase ( ):
"""simple docstring"""
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 94 |
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 SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
__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
| 302 | 0 |
"""simple docstring"""
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
__UpperCAmelCase = data_utils.TransfoXLTokenizer
__UpperCAmelCase = data_utils.TransfoXLCorpus
__UpperCAmelCase = data_utils
__UpperCAmelCase = data_utils
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : int , lowercase__ : str , lowercase__ : Any ) -> Optional[int]:
'''simple docstring'''
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(lowercase__ , """rb""" ) as fp:
lowerCAmelCase_ :Tuple = pickle.load(lowercase__ , encoding="""latin1""" )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
lowerCAmelCase_ :Optional[int] = pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""pretrained_vocab_file"""]
print(f"""Save vocabulary to {pytorch_vocab_dump_path}""" )
lowerCAmelCase_ :Union[str, Any] = corpus.vocab.__dict__
torch.save(lowercase__ , lowercase__ )
lowerCAmelCase_ :str = corpus.__dict__
corpus_dict_no_vocab.pop("""vocab""" , lowercase__ )
lowerCAmelCase_ :str = pytorch_dump_folder_path + """/""" + CORPUS_NAME
print(f"""Save dataset to {pytorch_dataset_dump_path}""" )
torch.save(lowercase__ , lowercase__ )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
lowerCAmelCase_ :int = os.path.abspath(lowercase__ )
lowerCAmelCase_ :Any = os.path.abspath(lowercase__ )
print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.""" )
# Initialise PyTorch model
if transfo_xl_config_file == "":
lowerCAmelCase_ :Optional[int] = TransfoXLConfig()
else:
lowerCAmelCase_ :Optional[Any] = TransfoXLConfig.from_json_file(lowercase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase_ :List[str] = TransfoXLLMHeadModel(lowercase__ )
lowerCAmelCase_ :Any = load_tf_weights_in_transfo_xl(lowercase__ , lowercase__ , lowercase__ )
# Save pytorch-model
lowerCAmelCase_ :Any = os.path.join(lowercase__ , lowercase__ )
lowerCAmelCase_ :Optional[int] = os.path.join(lowercase__ , lowercase__ )
print(f"""Save PyTorch model to {os.path.abspath(lowercase__ )}""" )
torch.save(model.state_dict() , lowercase__ )
print(f"""Save configuration file to {os.path.abspath(lowercase__ )}""" )
with open(lowercase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__UpperCAmelCase = 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.',
)
__UpperCAmelCase = 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,
)
| 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *__A , **__A ) -> None:
warnings.warn(
"""The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use CLIPImageProcessor instead.""" , __A , )
super().__init__(*__A , **__A )
| 1 | 1 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class A( nn.Module ):
'''simple docstring'''
UpperCamelCase = 42
UpperCamelCase = jnp.floataa
def a__ ( self : int ) -> str:
"""simple docstring"""
lowerCamelCase_ = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : List[Any] , A_ : List[str] ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = hidden_states.shape
lowerCamelCase_ = jax.image.resize(
A_ , shape=(batch, height * 2, width * 2, channels) , method='nearest' , )
lowerCamelCase_ = self.conv(A_ )
return hidden_states
class A( nn.Module ):
'''simple docstring'''
UpperCamelCase = 42
UpperCamelCase = jnp.floataa
def a__ ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : Optional[int] , A_ : Tuple ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ = self.conv(A_ )
return hidden_states
class A( nn.Module ):
'''simple docstring'''
UpperCamelCase = 42
UpperCamelCase = None
UpperCamelCase = 0.0
UpperCamelCase = None
UpperCamelCase = jnp.floataa
def a__ ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = self.in_channels if self.out_channels is None else self.out_channels
lowerCamelCase_ = nn.GroupNorm(num_groups=32 , epsilon=1E-5 )
lowerCamelCase_ = nn.Conv(
A_ , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
lowerCamelCase_ = nn.Dense(A_ , dtype=self.dtype )
lowerCamelCase_ = nn.GroupNorm(num_groups=32 , epsilon=1E-5 )
lowerCamelCase_ = nn.Dropout(self.dropout_prob )
lowerCamelCase_ = nn.Conv(
A_ , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
lowerCamelCase_ = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
lowerCamelCase_ = None
if use_nin_shortcut:
lowerCamelCase_ = nn.Conv(
A_ , kernel_size=(1, 1) , strides=(1, 1) , padding='VALID' , dtype=self.dtype , )
def __call__( self : List[str] , A_ : Any , A_ : str , A_ : Tuple=True ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ = hidden_states
lowerCamelCase_ = self.norma(A_ )
lowerCamelCase_ = nn.swish(A_ )
lowerCamelCase_ = self.conva(A_ )
lowerCamelCase_ = self.time_emb_proj(nn.swish(A_ ) )
lowerCamelCase_ = jnp.expand_dims(jnp.expand_dims(A_ , 1 ) , 1 )
lowerCamelCase_ = hidden_states + temb
lowerCamelCase_ = self.norma(A_ )
lowerCamelCase_ = nn.swish(A_ )
lowerCamelCase_ = self.dropout(A_ , A_ )
lowerCamelCase_ = self.conva(A_ )
if self.conv_shortcut is not None:
lowerCamelCase_ = self.conv_shortcut(A_ )
return hidden_states + residual
| 204 |
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
lowerCamelCase : List[str] = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE ( lowercase : Tuple , lowercase : Optional[int] , lowercase : Optional[int] ):
'''simple docstring'''
return [
int(10_00 * (box[0] / width) ),
int(10_00 * (box[1] / height) ),
int(10_00 * (box[2] / width) ),
int(10_00 * (box[3] / height) ),
]
def _SCREAMING_SNAKE_CASE ( lowercase : np.ndarray , lowercase : Optional[str] , lowercase : Optional[str] ):
'''simple docstring'''
lowerCamelCase_ = to_pil_image(lowercase )
lowerCamelCase_ , lowerCamelCase_ = pil_image.size
lowerCamelCase_ = pytesseract.image_to_data(lowercase , lang=lowercase , output_type='dict' , config=lowercase )
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = data['text'], data['left'], data['top'], data['width'], data['height']
# filter empty words and corresponding coordinates
lowerCamelCase_ = [idx for idx, word in enumerate(lowercase ) if not word.strip()]
lowerCamelCase_ = [word for idx, word in enumerate(lowercase ) if idx not in irrelevant_indices]
lowerCamelCase_ = [coord for idx, coord in enumerate(lowercase ) if idx not in irrelevant_indices]
lowerCamelCase_ = [coord for idx, coord in enumerate(lowercase ) if idx not in irrelevant_indices]
lowerCamelCase_ = [coord for idx, coord in enumerate(lowercase ) if idx not in irrelevant_indices]
lowerCamelCase_ = [coord for idx, coord in enumerate(lowercase ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
lowerCamelCase_ = []
for x, y, w, h in zip(lowercase , lowercase , lowercase , lowercase ):
lowerCamelCase_ = [x, y, x + w, y + h]
actual_boxes.append(lowercase )
# finally, normalize the bounding boxes
lowerCamelCase_ = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(lowercase , lowercase , lowercase ) )
assert len(lowercase ) == len(lowercase ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class A( UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = ['''pixel_values''']
def __init__( self : int , A_ : bool = True , A_ : Dict[str, int] = None , A_ : PILImageResampling = PILImageResampling.BILINEAR , A_ : bool = True , A_ : float = 1 / 255 , A_ : bool = True , A_ : Union[float, Iterable[float]] = None , A_ : Union[float, Iterable[float]] = None , A_ : bool = True , A_ : Optional[str] = None , A_ : Optional[str] = "" , **A_ : Optional[int] , ) -> None:
"""simple docstring"""
super().__init__(**A_ )
lowerCamelCase_ = size if size is not None else {'height': 224, 'width': 224}
lowerCamelCase_ = get_size_dict(A_ )
lowerCamelCase_ = do_resize
lowerCamelCase_ = size
lowerCamelCase_ = resample
lowerCamelCase_ = do_rescale
lowerCamelCase_ = rescale_value
lowerCamelCase_ = do_normalize
lowerCamelCase_ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCamelCase_ = image_std if image_std is not None else IMAGENET_STANDARD_STD
lowerCamelCase_ = apply_ocr
lowerCamelCase_ = ocr_lang
lowerCamelCase_ = tesseract_config
def a__ ( self : str , A_ : np.ndarray , A_ : Dict[str, int] , A_ : PILImageResampling = PILImageResampling.BILINEAR , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : str , ) -> np.ndarray:
"""simple docstring"""
lowerCamelCase_ = get_size_dict(A_ )
if "height" not in size or "width" not in size:
raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
lowerCamelCase_ = (size['height'], size['width'])
return resize(A_ , size=A_ , resample=A_ , data_format=A_ , **A_ )
def a__ ( self : Any , A_ : np.ndarray , A_ : Union[int, float] , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : Optional[Any] , ) -> np.ndarray:
"""simple docstring"""
return rescale(A_ , scale=A_ , data_format=A_ , **A_ )
def a__ ( self : Union[str, Any] , A_ : np.ndarray , A_ : Union[float, Iterable[float]] , A_ : Union[float, Iterable[float]] , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : int , ) -> np.ndarray:
"""simple docstring"""
return normalize(A_ , mean=A_ , std=A_ , data_format=A_ , **A_ )
def a__ ( self : List[Any] , A_ : ImageInput , A_ : bool = None , A_ : Dict[str, int] = None , A_ : Dict=None , A_ : bool = None , A_ : float = None , A_ : bool = None , A_ : Union[float, Iterable[float]] = None , A_ : Union[float, Iterable[float]] = None , A_ : bool = None , A_ : Optional[str] = None , A_ : Optional[str] = None , A_ : Optional[Union[str, TensorType]] = None , A_ : ChannelDimension = ChannelDimension.FIRST , **A_ : Any , ) -> PIL.Image.Image:
"""simple docstring"""
lowerCamelCase_ = do_resize if do_resize is not None else self.do_resize
lowerCamelCase_ = size if size is not None else self.size
lowerCamelCase_ = get_size_dict(A_ )
lowerCamelCase_ = resample if resample is not None else self.resample
lowerCamelCase_ = do_rescale if do_rescale is not None else self.do_rescale
lowerCamelCase_ = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCamelCase_ = do_normalize if do_normalize is not None else self.do_normalize
lowerCamelCase_ = image_mean if image_mean is not None else self.image_mean
lowerCamelCase_ = image_std if image_std is not None else self.image_std
lowerCamelCase_ = apply_ocr if apply_ocr is not None else self.apply_ocr
lowerCamelCase_ = ocr_lang if ocr_lang is not None else self.ocr_lang
lowerCamelCase_ = tesseract_config if tesseract_config is not None else self.tesseract_config
lowerCamelCase_ = make_list_of_images(A_ )
if not valid_images(A_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('If do_normalize is True, image_mean and image_std must be specified.' )
# All transformations expect numpy arrays.
lowerCamelCase_ = [to_numpy_array(A_ ) for image in images]
# Tesseract OCR to get words + normalized bounding boxes
if apply_ocr:
requires_backends(self , 'pytesseract' )
lowerCamelCase_ = []
lowerCamelCase_ = []
for image in images:
lowerCamelCase_ , lowerCamelCase_ = apply_tesseract(A_ , A_ , A_ )
words_batch.append(A_ )
boxes_batch.append(A_ )
if do_resize:
lowerCamelCase_ = [self.resize(image=A_ , size=A_ , resample=A_ ) for image in images]
if do_rescale:
lowerCamelCase_ = [self.rescale(image=A_ , scale=A_ ) for image in images]
if do_normalize:
lowerCamelCase_ = [self.normalize(image=A_ , mean=A_ , std=A_ ) for image in images]
lowerCamelCase_ = [to_channel_dimension_format(A_ , A_ ) for image in images]
lowerCamelCase_ = BatchFeature(data={'pixel_values': images} , tensor_type=A_ )
if apply_ocr:
lowerCamelCase_ = words_batch
lowerCamelCase_ = boxes_batch
return data
| 204 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ : str ) -> str:
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 353 |
import datasets
from .evaluate import evaluate
lowercase_ = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016}\n}\n"
lowercase_ = "\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n"
lowercase_ = "\nComputes SQuAD scores (F1 and EM).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - 'id': id of the question-answer pair as given in the references (see below)\n - 'prediction_text': the text of the answer\n references: List of question-answers dictionaries with the following key-values:\n - 'id': id of the question-answer pair (see above),\n - 'answers': a Dict in the SQuAD dataset format\n {\n 'text': list of possible texts for the answer, as a list of strings\n 'answer_start': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n 'exact_match': Exact match (the normalized answer exactly match the gold answer)\n 'f1': The F-score of predicted tokens versus the gold answer\nExamples:\n\n >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]\n >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n >>> squad_metric = datasets.load_metric(\"squad\")\n >>> results = squad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 100.0, 'f1': 100.0}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A ( datasets.Metric ):
"""simple docstring"""
def snake_case__ ( self : List[Any] )-> Tuple:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
{
'predictions': {'id': datasets.Value('string' ), 'prediction_text': datasets.Value('string' )},
'references': {
'id': datasets.Value('string' ),
'answers': datasets.features.Sequence(
{
'text': datasets.Value('string' ),
'answer_start': datasets.Value('int32' ),
} ),
},
} ),codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/'],reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/'],)
def snake_case__ ( self : str,lowercase_ : List[Any],lowercase_ : Dict )-> List[str]:
'''simple docstring'''
A__ = {prediction['id']: prediction['prediction_text'] for prediction in predictions}
A__ = [
{
'paragraphs': [
{
'qas': [
{
'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
'id': ref['id'],
}
for ref in references
]
}
]
}
]
A__ = evaluate(dataset=lowercase_,predictions=lowercase_ )
return score
| 282 | 0 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
_UpperCamelCase = '''base_with_context'''
def lowerCAmelCase__( lowercase : List[str] , lowercase : str ) -> Tuple:
__snake_case : Any = nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"] ) )
__snake_case : Optional[int] = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) , requires_grad=lowercase )
for lyr_num, lyr in enumerate(model.encoders ):
__snake_case : List[str] = weights[f"""layers_{lyr_num}"""]
__snake_case : int = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"] ) )
__snake_case : str = ly_weight["attention"]
__snake_case : Optional[Any] = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
__snake_case : Optional[Any] = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
__snake_case : Dict = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
__snake_case : Optional[Any] = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
__snake_case : Union[str, Any] = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
__snake_case : int = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
__snake_case : Dict = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
__snake_case : str = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
__snake_case : int = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"] ) )
return model
def lowerCAmelCase__( lowercase : Optional[Any] , lowercase : List[Any] ) -> Dict:
__snake_case : List[Any] = nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T ) )
__snake_case : Union[str, Any] = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) , requires_grad=lowercase )
for lyr_num, lyr in enumerate(model.encoders ):
__snake_case : Tuple = weights[f"""layers_{lyr_num}"""]
__snake_case : List[str] = ly_weight["attention"]
__snake_case : Tuple = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
__snake_case : List[Any] = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
__snake_case : Any = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
__snake_case : Any = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
__snake_case : Dict = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"] ) )
__snake_case : int = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
__snake_case : Tuple = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
__snake_case : Union[str, Any] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
__snake_case : Any = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
__snake_case : Optional[Any] = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"] ) )
return model
def lowerCAmelCase__( lowercase : str , lowercase : Optional[int] ) -> Dict:
__snake_case : Optional[int] = nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T ) )
__snake_case : Optional[int] = nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T ) )
__snake_case : Dict = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) , requires_grad=lowercase )
__snake_case : Tuple = nn.Parameter(
torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T ) )
for lyr_num, lyr in enumerate(model.decoders ):
__snake_case : Optional[Any] = weights[f"""layers_{lyr_num}"""]
__snake_case : Any = nn.Parameter(
torch.FloatTensor(ly_weight["pre_self_attention_layer_norm"]["scale"] ) )
__snake_case : Dict = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T ) )
__snake_case : Optional[int] = ly_weight["self_attention"]
__snake_case : Tuple = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
__snake_case : Any = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
__snake_case : Optional[Any] = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
__snake_case : Any = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
__snake_case : Tuple = ly_weight["MultiHeadDotProductAttention_0"]
__snake_case : Optional[int] = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
__snake_case : List[Any] = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
__snake_case : Optional[Any] = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
__snake_case : Optional[Any] = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
__snake_case : Optional[Any] = nn.Parameter(
torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"] ) )
__snake_case : Optional[int] = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
__snake_case : int = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T ) )
__snake_case : List[str] = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
__snake_case : int = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
__snake_case : Any = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
__snake_case : Dict = nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"] ) )
__snake_case : Any = nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T ) )
return model
def lowerCAmelCase__( lowercase : Optional[Any] ) -> Optional[Any]:
__snake_case : List[Any] = checkpoints.load_tax_checkpoint(args.checkpoint_path )
__snake_case : List[Any] = jnp.tree_util.tree_map(onp.array , lowercase )
__snake_case : Tuple = [
"from __gin__ import dynamic_registration",
"from music_spectrogram_diffusion.models.diffusion import diffusion_utils",
"diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0",
"diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()",
]
__snake_case : Union[str, Any] = os.path.join(args.checkpoint_path , ".." , "config.gin" )
__snake_case : Dict = inference.parse_training_gin_file(lowercase , lowercase )
__snake_case : Union[str, Any] = inference.InferenceModel(args.checkpoint_path , lowercase )
__snake_case : List[str] = DDPMScheduler(beta_schedule="squaredcos_cap_v2" , variance_type="fixed_large" )
__snake_case : List[Any] = SpectrogramNotesEncoder(
max_length=synth_model.sequence_length["inputs"] , vocab_size=synth_model.model.module.config.vocab_size , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="gated-gelu" , )
__snake_case : Any = SpectrogramContEncoder(
input_dims=synth_model.audio_codec.n_dims , targets_context_length=synth_model.sequence_length["targets_context"] , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="gated-gelu" , )
__snake_case : Tuple = TaFilmDecoder(
input_dims=synth_model.audio_codec.n_dims , targets_length=synth_model.sequence_length["targets_context"] , max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time , d_model=synth_model.model.module.config.emb_dim , num_layers=synth_model.model.module.config.num_decoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , dropout_rate=synth_model.model.module.config.dropout_rate , )
__snake_case : Optional[Any] = load_notes_encoder(ta_checkpoint["target"]["token_encoder"] , lowercase )
__snake_case : Tuple = load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"] , lowercase )
__snake_case : int = load_decoder(ta_checkpoint["target"]["decoder"] , lowercase )
__snake_case : List[Any] = OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder" )
__snake_case : Tuple = SpectrogramDiffusionPipeline(
notes_encoder=lowercase , continuous_encoder=lowercase , decoder=lowercase , scheduler=lowercase , melgan=lowercase , )
if args.save:
pipe.save_pretrained(args.output_path )
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser()
parser.add_argument('''--output_path''', default=None, type=str, required=True, help='''Path to the converted model.''')
parser.add_argument(
'''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
)
parser.add_argument(
'''--checkpoint_path''',
default=F'''{MODEL}/checkpoint_500000''',
type=str,
required=False,
help='''Path to the original jax model checkpoint.''',
)
_UpperCamelCase = parser.parse_args()
main(args)
| 326 |
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.modeling_flax_big_bird import (
FlaxBigBirdForCausalLM,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForPreTraining,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
FlaxBigBirdModel,
)
class _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase=2 , UpperCAmelCase=56 , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=99 , UpperCAmelCase=32 , UpperCAmelCase=2 , UpperCAmelCase=2 , UpperCAmelCase=7 , UpperCAmelCase="gelu_new" , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=512 , UpperCAmelCase=16 , UpperCAmelCase=2 , UpperCAmelCase=0.02 , UpperCAmelCase=4 , UpperCAmelCase="block_sparse" , UpperCAmelCase=True , UpperCAmelCase=False , UpperCAmelCase=2 , UpperCAmelCase=3 , ) -> Tuple:
'''simple docstring'''
__snake_case : Optional[int] = parent
__snake_case : Tuple = batch_size
__snake_case : List[str] = seq_length
__snake_case : Optional[int] = is_training
__snake_case : int = use_attention_mask
__snake_case : Union[str, Any] = use_token_type_ids
__snake_case : Any = use_labels
__snake_case : List[str] = vocab_size
__snake_case : int = hidden_size
__snake_case : List[str] = num_hidden_layers
__snake_case : List[Any] = num_attention_heads
__snake_case : Optional[int] = intermediate_size
__snake_case : Union[str, Any] = hidden_act
__snake_case : Optional[int] = hidden_dropout_prob
__snake_case : Optional[Any] = attention_probs_dropout_prob
__snake_case : str = max_position_embeddings
__snake_case : List[Any] = type_vocab_size
__snake_case : int = type_sequence_label_size
__snake_case : Dict = initializer_range
__snake_case : List[Any] = num_choices
__snake_case : Union[str, Any] = rescale_embeddings
__snake_case : List[Any] = attention_type
__snake_case : str = use_bias
__snake_case : Dict = block_size
__snake_case : Optional[Any] = num_random_blocks
def UpperCAmelCase ( self ) -> int:
'''simple docstring'''
__snake_case : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__snake_case : Any = None
if self.use_attention_mask:
__snake_case : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
__snake_case : Union[str, Any] = None
if self.use_token_type_ids:
__snake_case : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__snake_case : Optional[int] = BigBirdConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCAmelCase , initializer_range=self.initializer_range , attention_type=self.attention_type , block_size=self.block_size , num_random_blocks=self.num_random_blocks , use_bias=self.use_bias , rescale_embeddings=self.rescale_embeddings , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
__snake_case : Optional[int] = self.prepare_config_and_inputs()
__snake_case , __snake_case , __snake_case , __snake_case : Dict = config_and_inputs
__snake_case : int = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_flax
class _lowerCamelCase ( a , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] =(
(
FlaxBigBirdForCausalLM,
FlaxBigBirdModel,
FlaxBigBirdForPreTraining,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
)
if is_flax_available()
else ()
)
UpperCAmelCase_ : Dict =False
UpperCAmelCase_ : str =False
def UpperCAmelCase ( self ) -> str:
'''simple docstring'''
__snake_case : Dict = FlaxBigBirdModelTester(self )
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
super().test_from_pretrained_save_pretrained()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def UpperCAmelCase ( self ) -> Optional[int]:
'''simple docstring'''
super().test_from_pretrained_with_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
super().test_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
super().test_hidden_states_output()
@slow
def UpperCAmelCase ( self ) -> Dict:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__snake_case : Any = model_class_name.from_pretrained("google/bigbird-roberta-base" )
self.assertIsNotNone(UpperCAmelCase )
def UpperCAmelCase ( self ) -> Optional[int]:
'''simple docstring'''
if self.test_attn_probs:
super().test_attention_outputs()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def UpperCAmelCase ( self ) -> int:
'''simple docstring'''
__snake_case , __snake_case : Optional[int] = 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(UpperCAmelCase , UpperCAmelCase )
__snake_case : Tuple = model_class(UpperCAmelCase )
@jax.jit
def model_jitted(UpperCAmelCase , UpperCAmelCase=None , **UpperCAmelCase ):
return model(input_ids=UpperCAmelCase , attention_mask=UpperCAmelCase , **UpperCAmelCase )
with self.subTest("JIT Enabled" ):
__snake_case : int = model_jitted(**UpperCAmelCase ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
__snake_case : List[Any] = model_jitted(**UpperCAmelCase ).to_tuple()
self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) )
for jitted_output, output in zip(UpperCAmelCase , UpperCAmelCase ):
self.assertEqual(jitted_output.shape , output.shape )
def UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=1E-5 , UpperCAmelCase="outputs" , UpperCAmelCase=None ) -> int:
'''simple docstring'''
if name.startswith("outputs.attentions" ):
return
else:
super().check_pt_flax_outputs(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
| 326 | 1 |
import faiss # 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 requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
A : List[str] = "\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n"
A : Union[str, Any] = "\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"
A : Dict = "\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\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.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: \"c\" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric('mauve')\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase (datasets.Metric ):
"""simple docstring"""
def __A ( self : Any ) -> Dict:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="https://github.com/krishnap25/mauve" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , codebase_urls=["https://github.com/krishnap25/mauve"] , reference_urls=[
"https://arxiv.org/abs/2102.01454",
"https://github.com/krishnap25/mauve",
] , )
def __A ( self : List[str] , __magic_name__ : List[Any] , __magic_name__ : Dict , __magic_name__ : Optional[Any]=None , __magic_name__ : int=None , __magic_name__ : List[Any]=None , __magic_name__ : Optional[int]=None , __magic_name__ : Union[str, Any]="auto" , __magic_name__ : Dict=-1 , __magic_name__ : Optional[int]=0.9 , __magic_name__ : List[Any]=5 , __magic_name__ : List[Any]=500 , __magic_name__ : Tuple="gpt2-large" , __magic_name__ : List[str]=-1 , __magic_name__ : str=1_024 , __magic_name__ : Optional[Any]=25 , __magic_name__ : List[Any]=5 , __magic_name__ : Any=True , __magic_name__ : int=25 , ) -> int:
SCREAMING_SNAKE_CASE_ = compute_mauve(
p_text=__magic_name__ , q_text=__magic_name__ , p_features=__magic_name__ , q_features=__magic_name__ , p_tokens=__magic_name__ , q_tokens=__magic_name__ , num_buckets=__magic_name__ , pca_max_data=__magic_name__ , kmeans_explained_var=__magic_name__ , kmeans_num_redo=__magic_name__ , kmeans_max_iter=__magic_name__ , featurize_model_name=__magic_name__ , device_id=__magic_name__ , max_text_length=__magic_name__ , divergence_curve_discretization_size=__magic_name__ , mauve_scaling_factor=__magic_name__ , verbose=__magic_name__ , seed=__magic_name__ , )
return out
| 305 | from collections.abc import Generator
from math import sin
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def a__ ( __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
SCREAMING_SNAKE_CASE_ = format(__UpperCamelCase , "08x" )[-8:]
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8" )
return little_endian_hex
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = b""
for char in message:
bit_string += format(__UpperCamelCase , "08b" ).encode("utf-8" )
SCREAMING_SNAKE_CASE_ = format(len(__UpperCamelCase ) , "064b" ).encode("utf-8" )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(__UpperCamelCase ) % 5_1_2 != 4_4_8:
bit_string += b"0"
bit_string += to_little_endian(start_len[3_2:] ) + to_little_endian(start_len[:3_2] )
return bit_string
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) % 5_1_2 != 0:
raise ValueError("Input must have length that's a multiple of 512" )
for pos in range(0 , len(__UpperCamelCase ) , 5_1_2 ):
SCREAMING_SNAKE_CASE_ = bit_string[pos : pos + 5_1_2]
SCREAMING_SNAKE_CASE_ = []
for i in range(0 , 5_1_2 , 3_2 ):
block_words.append(int(to_little_endian(block[i : i + 3_2] ) , 2 ) )
yield block_words
def a__ ( __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
SCREAMING_SNAKE_CASE_ = format(__UpperCamelCase , "032b" )
SCREAMING_SNAKE_CASE_ = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(__UpperCamelCase , 2 )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
return (a + b) % 2**3_2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
if shift < 0:
raise ValueError("Shift must be non-negative" )
return ((i << shift) ^ (i >> (3_2 - shift))) % 2**3_2
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = preprocess(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = [int(2**3_2 * abs(sin(i + 1 ) ) ) for i in range(6_4 )]
# Starting states
SCREAMING_SNAKE_CASE_ = 0X67452301
SCREAMING_SNAKE_CASE_ = 0Xefcdab89
SCREAMING_SNAKE_CASE_ = 0X98badcfe
SCREAMING_SNAKE_CASE_ = 0X10325476
SCREAMING_SNAKE_CASE_ = [
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = aa
SCREAMING_SNAKE_CASE_ = ba
SCREAMING_SNAKE_CASE_ = ca
SCREAMING_SNAKE_CASE_ = da
# Hash current chunk
for i in range(6_4 ):
if i <= 1_5:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
SCREAMING_SNAKE_CASE_ = d ^ (b & (c ^ d))
SCREAMING_SNAKE_CASE_ = i
elif i <= 3_1:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
SCREAMING_SNAKE_CASE_ = c ^ (d & (b ^ c))
SCREAMING_SNAKE_CASE_ = (5 * i + 1) % 1_6
elif i <= 4_7:
SCREAMING_SNAKE_CASE_ = b ^ c ^ d
SCREAMING_SNAKE_CASE_ = (3 * i + 5) % 1_6
else:
SCREAMING_SNAKE_CASE_ = c ^ (b | not_aa(__UpperCamelCase ))
SCREAMING_SNAKE_CASE_ = (7 * i) % 1_6
SCREAMING_SNAKE_CASE_ = (f + a + added_consts[i] + block_words[g]) % 2**3_2
SCREAMING_SNAKE_CASE_ = d
SCREAMING_SNAKE_CASE_ = c
SCREAMING_SNAKE_CASE_ = b
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , left_rotate_aa(__UpperCamelCase , shift_amounts[i] ) )
# Add hashed chunk to running total
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 305 | 1 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _A (__a , __a , __a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (
('''layer.''', '''layer_'''),
('''word_embeddings.weight''', '''word_embeddings'''),
('''position_embeddings.weight''', '''position_embeddings'''),
('''token_type_embeddings.weight''', '''token_type_embeddings'''),
('''.''', '''/'''),
('''LayerNorm/weight''', '''LayerNorm/gamma'''),
('''LayerNorm/bias''', '''LayerNorm/beta'''),
('''weight''', '''kernel'''),
)
if not os.path.isdir(__a ):
os.makedirs(__a )
SCREAMING_SNAKE_CASE_ : Dict = model.state_dict()
def to_tf_var_name(__a ):
for patt, repl in iter(__a ):
SCREAMING_SNAKE_CASE_ : Optional[int] = name.replace(__a , __a )
return f'bert/{name}'
def create_tf_var(__a , __a , __a ):
SCREAMING_SNAKE_CASE_ : List[str] = tf.dtypes.as_dtype(tensor.dtype )
SCREAMING_SNAKE_CASE_ : Optional[int] = tf.get_variable(dtype=__a , shape=tensor.shape , name=__a , initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(__a )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
SCREAMING_SNAKE_CASE_ : Any = to_tf_var_name(__a )
SCREAMING_SNAKE_CASE_ : Any = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
SCREAMING_SNAKE_CASE_ : List[str] = torch_tensor.T
SCREAMING_SNAKE_CASE_ : List[Any] = create_tf_var(tensor=__a , name=__a , session=__a )
tf.keras.backend.set_value(__a , __a )
SCREAMING_SNAKE_CASE_ : Optional[int] = session.run(__a )
print(f'Successfully created {tf_name}: {np.allclose(__a , __a )}' )
SCREAMING_SNAKE_CASE_ : str = tf.train.Saver(tf.trainable_variables() )
saver.save(__a , os.path.join(__a , model_name.replace('''-''' , '''_''' ) + '''.ckpt''' ) )
def _A (__a=None ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = argparse.ArgumentParser()
parser.add_argument('''--model_name''' , type=__a , required=__a , help='''model name e.g. bert-base-uncased''' )
parser.add_argument(
'''--cache_dir''' , type=__a , default=__a , required=__a , help='''Directory containing pytorch model''' )
parser.add_argument('''--pytorch_model_path''' , type=__a , required=__a , help='''/path/to/<pytorch-model-name>.bin''' )
parser.add_argument('''--tf_cache_dir''' , type=__a , required=__a , help='''Directory in which to save tensorflow model''' )
SCREAMING_SNAKE_CASE_ : Dict = parser.parse_args(__a )
SCREAMING_SNAKE_CASE_ : Dict = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path ) , cache_dir=args.cache_dir , )
convert_pytorch_checkpoint_to_tf(model=__a , ckpt_dir=args.tf_cache_dir , model_name=args.model_name )
if __name__ == "__main__":
main()
| 91 | def A ( _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = len(_lowercase )
SCREAMING_SNAKE_CASE : Any = len(_lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
SCREAMING_SNAKE_CASE : Union[str, Any] = True
for i in range(_lowercase ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
SCREAMING_SNAKE_CASE : List[str] = True
if a[i].islower():
SCREAMING_SNAKE_CASE : Dict = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 182 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
A_ : Union[str, Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=False , ):
output_path.parent.mkdir(parents=_lowerCamelCase , exist_ok=_lowerCamelCase )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
_lowerCamelCase , _lowerCamelCase , f=output_path.as_posix() , input_names=_lowerCamelCase , output_names=_lowerCamelCase , dynamic_axes=_lowerCamelCase , do_constant_folding=_lowerCamelCase , use_external_data_format=_lowerCamelCase , enable_onnx_checker=_lowerCamelCase , opset_version=_lowerCamelCase , )
else:
export(
_lowerCamelCase , _lowerCamelCase , f=output_path.as_posix() , input_names=_lowerCamelCase , output_names=_lowerCamelCase , dynamic_axes=_lowerCamelCase , do_constant_folding=_lowerCamelCase , opset_version=_lowerCamelCase , )
@torch.no_grad()
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = False ):
lowerCamelCase__ : str = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
lowerCamelCase__ : Union[str, Any] = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
lowerCamelCase__ : Tuple = 'cpu'
lowerCamelCase__ : Dict = Path(_lowerCamelCase )
# VAE DECODER
lowerCamelCase__ : List[str] = AutoencoderKL.from_pretrained(model_path + '/vae' )
lowerCamelCase__ : Any = vae_decoder.config.latent_channels
# forward only through the decoder part
lowerCamelCase__ : List[str] = vae_decoder.decode
onnx_export(
_lowerCamelCase , model_args=(
torch.randn(1 , _lowerCamelCase , 25 , 25 ).to(device=_lowerCamelCase , dtype=_lowerCamelCase ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=_lowerCamelCase , )
del vae_decoder
if __name__ == "__main__":
A_ : Any = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=14,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
A_ : List[Any] = parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print("SD: Done: ONNX")
| 316 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
while second != 0:
lowerCamelCase__ : Tuple = first & second
first ^= second
lowerCamelCase__ : int = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Tuple = int(input("Enter the first number: ").strip())
A_ : Union[str, Any] = int(input("Enter the second number: ").strip())
print(f"{add(first, second) = }")
| 316 | 1 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _snake_case ( a__ , a__ , unittest.TestCase ):
lowerCAmelCase :str = StableDiffusionDiffEditPipeline
lowerCAmelCase :Optional[int] = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
lowerCAmelCase :str = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
lowerCAmelCase :Optional[Any] = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowerCAmelCase :Union[str, Any] = frozenset([] )
def snake_case__ ( self):
torch.manual_seed(0)
UpperCAmelCase__ : Union[str, Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=_lowerCamelCase , )
UpperCAmelCase__ : str = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_lowerCamelCase , set_alpha_to_one=_lowerCamelCase , )
UpperCAmelCase__ : List[str] = DDIMInverseScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_lowerCamelCase , set_alpha_to_zero=_lowerCamelCase , )
torch.manual_seed(0)
UpperCAmelCase__ : str = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0)
UpperCAmelCase__ : 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=1000 , hidden_act="""gelu""" , projection_dim=512 , )
UpperCAmelCase__ : Optional[int] = CLIPTextModel(_lowerCamelCase)
UpperCAmelCase__ : Any = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")
UpperCAmelCase__ : Optional[int] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""inverse_scheduler""": inverse_scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase=0):
UpperCAmelCase__ : List[str] = floats_tensor((1, 16, 16) , rng=random.Random(_lowerCamelCase)).to(_lowerCamelCase)
UpperCAmelCase__ : Any = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(_lowerCamelCase)).to(_lowerCamelCase)
if str(_lowerCamelCase).startswith("""mps"""):
UpperCAmelCase__ : List[str] = torch.manual_seed(_lowerCamelCase)
else:
UpperCAmelCase__ : Optional[int] = torch.Generator(device=_lowerCamelCase).manual_seed(_lowerCamelCase)
UpperCAmelCase__ : Union[str, Any] = {
"""prompt""": """a dog and a newt""",
"""mask_image""": mask,
"""image_latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase=0):
UpperCAmelCase__ : List[Any] = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowerCamelCase)).to(_lowerCamelCase)
UpperCAmelCase__ : Tuple = image.cpu().permute(0 , 2 , 3 , 1)[0]
UpperCAmelCase__ : Dict = Image.fromarray(np.uinta(_lowerCamelCase)).convert("""RGB""")
if str(_lowerCamelCase).startswith("""mps"""):
UpperCAmelCase__ : Optional[int] = torch.manual_seed(_lowerCamelCase)
else:
UpperCAmelCase__ : int = torch.Generator(device=_lowerCamelCase).manual_seed(_lowerCamelCase)
UpperCAmelCase__ : Tuple = {
"""image""": image,
"""source_prompt""": """a cat and a frog""",
"""target_prompt""": """a dog and a newt""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""num_maps_per_mask""": 2,
"""mask_encode_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase=0):
UpperCAmelCase__ : List[Any] = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowerCamelCase)).to(_lowerCamelCase)
UpperCAmelCase__ : List[Any] = image.cpu().permute(0 , 2 , 3 , 1)[0]
UpperCAmelCase__ : Optional[int] = Image.fromarray(np.uinta(_lowerCamelCase)).convert("""RGB""")
if str(_lowerCamelCase).startswith("""mps"""):
UpperCAmelCase__ : int = torch.manual_seed(_lowerCamelCase)
else:
UpperCAmelCase__ : Union[str, Any] = torch.Generator(device=_lowerCamelCase).manual_seed(_lowerCamelCase)
UpperCAmelCase__ : int = {
"""image""": image,
"""prompt""": """a cat and a frog""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""decode_latents""": True,
"""output_type""": """numpy""",
}
return inputs
def snake_case__ ( self):
if not hasattr(self.pipeline_class , """_optional_components"""):
return
UpperCAmelCase__ : Dict = self.get_dummy_components()
UpperCAmelCase__ : int = self.pipeline_class(**_lowerCamelCase)
pipe.to(_lowerCamelCase)
pipe.set_progress_bar_config(disable=_lowerCamelCase)
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components})
UpperCAmelCase__ : int = self.get_dummy_inputs(_lowerCamelCase)
UpperCAmelCase__ : Optional[Any] = pipe(**_lowerCamelCase)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_lowerCamelCase)
UpperCAmelCase__ : int = self.pipeline_class.from_pretrained(_lowerCamelCase)
pipe_loaded.to(_lowerCamelCase)
pipe_loaded.set_progress_bar_config(disable=_lowerCamelCase)
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(_lowerCamelCase , _lowerCamelCase) is None , f'''`{optional_component}` did not stay set to None after loading.''' , )
UpperCAmelCase__ : Optional[int] = self.get_dummy_inputs(_lowerCamelCase)
UpperCAmelCase__ : Dict = pipe_loaded(**_lowerCamelCase)[0]
UpperCAmelCase__ : Union[str, Any] = np.abs(output - output_loaded).max()
self.assertLess(_lowerCamelCase , 1e-4)
def snake_case__ ( self):
UpperCAmelCase__ : Tuple = """cpu"""
UpperCAmelCase__ : List[str] = self.get_dummy_components()
UpperCAmelCase__ : int = self.pipeline_class(**_lowerCamelCase)
pipe.to(_lowerCamelCase)
pipe.set_progress_bar_config(disable=_lowerCamelCase)
UpperCAmelCase__ : List[str] = self.get_dummy_mask_inputs(_lowerCamelCase)
UpperCAmelCase__ : Optional[int] = pipe.generate_mask(**_lowerCamelCase)
UpperCAmelCase__ : int = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16))
UpperCAmelCase__ : Dict = np.array([0] * 9)
UpperCAmelCase__ : str = np.abs(mask_slice.flatten() - expected_slice).max()
self.assertLessEqual(_lowerCamelCase , 1e-3)
self.assertEqual(mask[0, -3, -4] , 0)
def snake_case__ ( self):
UpperCAmelCase__ : Dict = """cpu"""
UpperCAmelCase__ : Union[str, Any] = self.get_dummy_components()
UpperCAmelCase__ : Optional[Any] = self.pipeline_class(**_lowerCamelCase)
pipe.to(_lowerCamelCase)
pipe.set_progress_bar_config(disable=_lowerCamelCase)
UpperCAmelCase__ : Optional[int] = self.get_dummy_inversion_inputs(_lowerCamelCase)
UpperCAmelCase__ : Optional[int] = pipe.invert(**_lowerCamelCase).images
UpperCAmelCase__ : Dict = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3))
UpperCAmelCase__ : List[Any] = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799] , )
UpperCAmelCase__ : Union[str, Any] = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(_lowerCamelCase , 1e-3)
def snake_case__ ( self):
super().test_inference_batch_single_identical(expected_max_diff=5e-3)
def snake_case__ ( self):
UpperCAmelCase__ : str = """cpu"""
UpperCAmelCase__ : List[str] = self.get_dummy_components()
UpperCAmelCase__ : Optional[Any] = {"""beta_start""": 0.00085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
UpperCAmelCase__ : Union[str, Any] = DPMSolverMultistepScheduler(**_lowerCamelCase)
UpperCAmelCase__ : List[str] = DPMSolverMultistepInverseScheduler(**_lowerCamelCase)
UpperCAmelCase__ : Any = self.pipeline_class(**_lowerCamelCase)
pipe.to(_lowerCamelCase)
pipe.set_progress_bar_config(disable=_lowerCamelCase)
UpperCAmelCase__ : List[Any] = self.get_dummy_inversion_inputs(_lowerCamelCase)
UpperCAmelCase__ : List[Any] = pipe.invert(**_lowerCamelCase).images
UpperCAmelCase__ : Tuple = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3))
UpperCAmelCase__ : List[str] = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799] , )
UpperCAmelCase__ : Tuple = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(_lowerCamelCase , 1e-3)
@require_torch_gpu
@slow
class _snake_case ( unittest.TestCase ):
def snake_case__ ( self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def snake_case__ ( cls):
UpperCAmelCase__ : List[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png""")
UpperCAmelCase__ : str = raw_image.convert("""RGB""").resize((768, 768))
UpperCAmelCase__ : int = raw_image
def snake_case__ ( self):
UpperCAmelCase__ : str = torch.manual_seed(0)
UpperCAmelCase__ : int = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=_lowerCamelCase , torch_dtype=torch.floataa)
UpperCAmelCase__ : Dict = DDIMScheduler.from_config(pipe.scheduler.config)
UpperCAmelCase__ : str = DDIMInverseScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_lowerCamelCase)
UpperCAmelCase__ : Any = """a bowl of fruit"""
UpperCAmelCase__ : str = """a bowl of pears"""
UpperCAmelCase__ : Optional[int] = pipe.generate_mask(
image=self.raw_image , source_prompt=_lowerCamelCase , target_prompt=_lowerCamelCase , generator=_lowerCamelCase , )
UpperCAmelCase__ : Tuple = pipe.invert(
prompt=_lowerCamelCase , image=self.raw_image , inpaint_strength=0.7 , generator=_lowerCamelCase).latents
UpperCAmelCase__ : Optional[int] = pipe(
prompt=_lowerCamelCase , mask_image=_lowerCamelCase , image_latents=_lowerCamelCase , generator=_lowerCamelCase , negative_prompt=_lowerCamelCase , inpaint_strength=0.7 , output_type="""numpy""" , ).images[0]
UpperCAmelCase__ : Any = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""").resize((768, 768)))
/ 255
)
assert np.abs((expected_image - image).max()) < 5e-1
def snake_case__ ( self):
UpperCAmelCase__ : List[Any] = torch.manual_seed(0)
UpperCAmelCase__ : int = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=_lowerCamelCase , torch_dtype=torch.floataa)
UpperCAmelCase__ : Tuple = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
UpperCAmelCase__ : Optional[Any] = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_lowerCamelCase)
UpperCAmelCase__ : str = """a bowl of fruit"""
UpperCAmelCase__ : Optional[Any] = """a bowl of pears"""
UpperCAmelCase__ : Optional[int] = pipe.generate_mask(
image=self.raw_image , source_prompt=_lowerCamelCase , target_prompt=_lowerCamelCase , generator=_lowerCamelCase , )
UpperCAmelCase__ : Optional[int] = pipe.invert(
prompt=_lowerCamelCase , image=self.raw_image , inpaint_strength=0.7 , generator=_lowerCamelCase , num_inference_steps=25 , ).latents
UpperCAmelCase__ : Optional[Any] = pipe(
prompt=_lowerCamelCase , mask_image=_lowerCamelCase , image_latents=_lowerCamelCase , generator=_lowerCamelCase , negative_prompt=_lowerCamelCase , inpaint_strength=0.7 , num_inference_steps=25 , output_type="""numpy""" , ).images[0]
UpperCAmelCase__ : List[str] = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""").resize((768, 768)))
/ 255
)
assert np.abs((expected_image - image).max()) < 5e-1 | 163 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A =get_tests_dir('fixtures/spiece.model')
@require_sentencepiece
@require_tokenizers
class _snake_case ( a__ , unittest.TestCase ):
lowerCAmelCase :int = AlbertTokenizer
lowerCAmelCase :int = AlbertTokenizerFast
lowerCAmelCase :List[str] = True
lowerCAmelCase :List[str] = True
lowerCAmelCase :str = True
def snake_case__ ( self):
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase__ : Optional[int] = AlbertTokenizer(_lowerCamelCase)
tokenizer.save_pretrained(self.tmpdirname)
def snake_case__ ( self , _lowerCamelCase):
UpperCAmelCase__ : Dict = """this is a test"""
UpperCAmelCase__ : int = """this is a test"""
return input_text, output_text
def snake_case__ ( self):
UpperCAmelCase__ : Tuple = """<pad>"""
UpperCAmelCase__ : str = 0
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):
UpperCAmelCase__ : Union[str, Any] = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , """<pad>""")
self.assertEqual(vocab_keys[1] , """<unk>""")
self.assertEqual(vocab_keys[-1] , """▁eloquent""")
self.assertEqual(len(_lowerCamelCase) , 3_0000)
def snake_case__ ( self):
self.assertEqual(self.get_tokenizer().vocab_size , 3_0000)
def snake_case__ ( self):
if not self.test_rust_tokenizer:
return
UpperCAmelCase__ : Union[str, Any] = self.get_tokenizer()
UpperCAmelCase__ : Tuple = self.get_rust_tokenizer()
UpperCAmelCase__ : List[Any] = """I was born in 92000, and this is falsé."""
UpperCAmelCase__ : Any = tokenizer.tokenize(_lowerCamelCase)
UpperCAmelCase__ : Optional[int] = rust_tokenizer.tokenize(_lowerCamelCase)
self.assertListEqual(_lowerCamelCase , _lowerCamelCase)
UpperCAmelCase__ : List[str] = tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase)
UpperCAmelCase__ : Optional[Any] = rust_tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase)
self.assertListEqual(_lowerCamelCase , _lowerCamelCase)
UpperCAmelCase__ : List[str] = self.get_rust_tokenizer()
UpperCAmelCase__ : List[Any] = tokenizer.encode(_lowerCamelCase)
UpperCAmelCase__ : Dict = rust_tokenizer.encode(_lowerCamelCase)
self.assertListEqual(_lowerCamelCase , _lowerCamelCase)
def snake_case__ ( self):
UpperCAmelCase__ : List[str] = AlbertTokenizer(_lowerCamelCase , keep_accents=_lowerCamelCase)
UpperCAmelCase__ : Optional[int] = tokenizer.tokenize("""This is a test""")
self.assertListEqual(_lowerCamelCase , ["""▁this""", """▁is""", """▁a""", """▁test"""])
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase) , [48, 25, 21, 1289])
UpperCAmelCase__ : Union[str, Any] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
self.assertListEqual(
_lowerCamelCase , ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""])
UpperCAmelCase__ : Optional[Any] = tokenizer.convert_tokens_to_ids(_lowerCamelCase)
self.assertListEqual(_lowerCamelCase , [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9])
UpperCAmelCase__ : List[Any] = tokenizer.convert_ids_to_tokens(_lowerCamelCase)
self.assertListEqual(
_lowerCamelCase , ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""] , )
def snake_case__ ( self):
UpperCAmelCase__ : Tuple = AlbertTokenizer(_lowerCamelCase)
UpperCAmelCase__ : Union[str, Any] = tokenizer.encode("""sequence builders""")
UpperCAmelCase__ : Optional[Any] = tokenizer.encode("""multi-sequence build""")
UpperCAmelCase__ : List[str] = tokenizer.build_inputs_with_special_tokens(_lowerCamelCase)
UpperCAmelCase__ : Optional[Any] = tokenizer.build_inputs_with_special_tokens(_lowerCamelCase , _lowerCamelCase)
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
@slow
def snake_case__ ( self):
# fmt: off
UpperCAmelCase__ : Union[str, Any] = {"""attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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], [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]], """input_ids""": [[2, 2_1970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 1_2051, 18, 17, 7103, 2153, 673, 8, 3515, 1_8684, 8, 4461, 6, 1927, 297, 8, 1_2060, 2607, 18, 13, 5, 4461, 15, 1_0538, 38, 8, 135, 15, 822, 58, 15, 993, 1_0363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 1_0641, 6, 29, 84, 2512, 2430, 782, 1_8684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 1_1712, 15, 7103, 2153, 673, 17, 2_4883, 9990, 9, 3], [2, 1_1502, 25, 1006, 20, 782, 8, 1_1809, 855, 1732, 1_9393, 1_8667, 37, 367, 2_1018, 69, 1854, 34, 1_1860, 1_9124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 14, 2231, 886, 2385, 1_7659, 84, 14, 1_6792, 1952, 9, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_lowerCamelCase , model_name="""albert-base-v2""" , revision="""6b6560eaf5ff2e250b00c50f380c5389a9c2d82e""" , ) | 163 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_lowerCamelCase : Any = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : str = [
'''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoForCausalLM''',
'''GPTNeoForQuestionAnswering''',
'''GPTNeoForSequenceClassification''',
'''GPTNeoForTokenClassification''',
'''GPTNeoModel''',
'''GPTNeoPreTrainedModel''',
'''load_tf_weights_in_gpt_neo''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Tuple = [
'''FlaxGPTNeoForCausalLM''',
'''FlaxGPTNeoModel''',
'''FlaxGPTNeoPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
_lowerCamelCase : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 206 | from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
class lowercase ( a ):
lowercase__ : List[str] = ["""pixel_values"""]
def __init__( self : List[str] , _UpperCamelCase : bool = True , _UpperCamelCase : Dict[str, int] = None , _UpperCamelCase : float = None , _UpperCamelCase : PILImageResampling = PILImageResampling.BILINEAR , _UpperCamelCase : bool = True , _UpperCamelCase : Union[int, float] = 1 / 255 , _UpperCamelCase : bool = True , _UpperCamelCase : Optional[Union[float, List[float]]] = None , _UpperCamelCase : Optional[Union[float, List[float]]] = None , **_UpperCamelCase : Dict , ) -> None:
'''simple docstring'''
super().__init__(**_UpperCamelCase )
SCREAMING_SNAKE_CASE = size if size is not None else {"shortest_edge": 384}
SCREAMING_SNAKE_CASE = get_size_dict(_UpperCamelCase , default_to_square=_UpperCamelCase )
SCREAMING_SNAKE_CASE = do_resize
SCREAMING_SNAKE_CASE = size
# Default value set here for backwards compatibility where the value in config is None
SCREAMING_SNAKE_CASE = crop_pct if crop_pct is not None else 224 / 256
SCREAMING_SNAKE_CASE = resample
SCREAMING_SNAKE_CASE = do_rescale
SCREAMING_SNAKE_CASE = rescale_factor
SCREAMING_SNAKE_CASE = do_normalize
SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
SCREAMING_SNAKE_CASE = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __snake_case( self : Optional[Any] , _UpperCamelCase : np.ndarray , _UpperCamelCase : Dict[str, int] , _UpperCamelCase : float , _UpperCamelCase : PILImageResampling = PILImageResampling.BICUBIC , _UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCamelCase : Optional[int] , ) -> np.ndarray:
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_size_dict(_UpperCamelCase , default_to_square=_UpperCamelCase )
if "shortest_edge" not in size:
raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}" )
SCREAMING_SNAKE_CASE = size["shortest_edge"]
if shortest_edge < 384:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
SCREAMING_SNAKE_CASE = int(shortest_edge / crop_pct )
SCREAMING_SNAKE_CASE = get_resize_output_image_size(_UpperCamelCase , size=_UpperCamelCase , default_to_square=_UpperCamelCase )
SCREAMING_SNAKE_CASE = resize(image=_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=_UpperCamelCase , size=(shortest_edge, shortest_edge) , data_format=_UpperCamelCase , **_UpperCamelCase )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
_UpperCamelCase , size=(shortest_edge, shortest_edge) , resample=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def __snake_case( self : int , _UpperCamelCase : np.ndarray , _UpperCamelCase : Union[int, float] , _UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCamelCase : Optional[Any] , ) -> List[Any]:
'''simple docstring'''
return rescale(_UpperCamelCase , scale=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def __snake_case( self : Tuple , _UpperCamelCase : np.ndarray , _UpperCamelCase : Union[float, List[float]] , _UpperCamelCase : Union[float, List[float]] , _UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCamelCase : List[Any] , ) -> np.ndarray:
'''simple docstring'''
return normalize(_UpperCamelCase , mean=_UpperCamelCase , std=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def __snake_case( self : List[str] , _UpperCamelCase : ImageInput , _UpperCamelCase : bool = None , _UpperCamelCase : Dict[str, int] = None , _UpperCamelCase : float = None , _UpperCamelCase : PILImageResampling = None , _UpperCamelCase : bool = None , _UpperCamelCase : float = None , _UpperCamelCase : bool = None , _UpperCamelCase : Optional[Union[float, List[float]]] = None , _UpperCamelCase : Optional[Union[float, List[float]]] = None , _UpperCamelCase : Optional[Union[str, TensorType]] = None , _UpperCamelCase : ChannelDimension = ChannelDimension.FIRST , **_UpperCamelCase : Any , ) -> PIL.Image.Image:
'''simple docstring'''
SCREAMING_SNAKE_CASE = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE = crop_pct if crop_pct is not None else self.crop_pct
SCREAMING_SNAKE_CASE = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE = size if size is not None else self.size
SCREAMING_SNAKE_CASE = get_size_dict(_UpperCamelCase , default_to_square=_UpperCamelCase )
SCREAMING_SNAKE_CASE = make_list_of_images(_UpperCamelCase )
if not valid_images(_UpperCamelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
raise ValueError("crop_pct must be specified if size < 384." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE = [to_numpy_array(_UpperCamelCase ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE = [self.resize(image=_UpperCamelCase , size=_UpperCamelCase , crop_pct=_UpperCamelCase , resample=_UpperCamelCase ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE = [self.rescale(image=_UpperCamelCase , scale=_UpperCamelCase ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE = [self.normalize(image=_UpperCamelCase , mean=_UpperCamelCase , std=_UpperCamelCase ) for image in images]
SCREAMING_SNAKE_CASE = [to_channel_dimension_format(_UpperCamelCase , _UpperCamelCase ) for image in images]
SCREAMING_SNAKE_CASE = {"pixel_values": images}
return BatchFeature(data=_UpperCamelCase , tensor_type=_UpperCamelCase )
| 206 | 1 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
# ===== initialization =====
_A : List[str] = Mock()
_A : Optional[int] = conn, Mock()
_A : Union[str, Any] = iter([1, None] )
_A : List[str] = lambda snake_case_ : next(snake_case_ )
# ===== invoke =====
send_file(filename="""mytext.txt""",testing=snake_case_ )
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| 26 |
'''simple docstring'''
import math
from collections.abc import Callable
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
snake_case_ = xa
snake_case_ = xa
while True:
if x_n == x_na or function(__UpperCAmelCase ) == function(__UpperCAmelCase ):
raise ZeroDivisionError('''float division by zero, could not find root''' )
snake_case_ = x_na - (
function(__UpperCAmelCase ) / ((function(__UpperCAmelCase ) - function(__UpperCAmelCase )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 10**-5:
return x_na
snake_case_ = x_na
snake_case_ = x_na
def __magic_name__ ( __UpperCAmelCase ) -> float:
'''simple docstring'''
return math.pow(__UpperCAmelCase, 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5))
| 56 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
__lowercase = random.Random()
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=1.0 , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
if rng is None:
__UpperCamelCase :List[Any] = global_rng
__UpperCamelCase :Optional[int] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase=7 , __lowercase=400 , __lowercase=2_000 , __lowercase=10 , __lowercase=160 , __lowercase=8 , __lowercase=0.0 , __lowercase=4_000 , __lowercase=False , __lowercase=True , ) -> List[str]:
__UpperCamelCase :Optional[Any] = parent
__UpperCamelCase :List[Any] = batch_size
__UpperCamelCase :Optional[Any] = min_seq_length
__UpperCamelCase :Any = max_seq_length
__UpperCamelCase :Optional[Any] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
__UpperCamelCase :List[str] = padding_value
__UpperCamelCase :Union[str, Any] = sampling_rate
__UpperCamelCase :int = return_attention_mask
__UpperCamelCase :Dict = do_normalize
__UpperCamelCase :Tuple = feature_size
__UpperCamelCase :List[Any] = chunk_length
__UpperCamelCase :str = hop_length
def UpperCamelCase__ ( self) -> int:
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def UpperCamelCase__ ( self , __lowercase=False , __lowercase=False) -> Optional[int]:
def _flatten(__lowercase):
return list(itertools.chain(*__lowercase))
if equal_length:
__UpperCamelCase :str = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
else:
# make sure that inputs increase in size
__UpperCamelCase :Optional[Any] = [
floats_list((x, self.feature_size))
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff)
]
if numpify:
__UpperCamelCase :str = [np.asarray(__lowercase) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class lowerCamelCase_ ( UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
a__ : Optional[Any] = WhisperFeatureExtractor if is_speech_available() else None
def UpperCamelCase__ ( self) -> List[Any]:
__UpperCamelCase :int = WhisperFeatureExtractionTester(self)
def UpperCamelCase__ ( self) -> Union[str, Any]:
__UpperCamelCase :str = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
__UpperCamelCase :List[Any] = feat_extract_first.save_pretrained(__lowercase)[0]
check_json_file_has_correct_format(__lowercase)
__UpperCamelCase :List[Any] = self.feature_extraction_class.from_pretrained(__lowercase)
__UpperCamelCase :str = feat_extract_first.to_dict()
__UpperCamelCase :Optional[int] = feat_extract_second.to_dict()
__UpperCamelCase :Any = feat_extract_first.mel_filters
__UpperCamelCase :int = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__lowercase , __lowercase))
self.assertEqual(__lowercase , __lowercase)
def UpperCamelCase__ ( self) -> List[str]:
__UpperCamelCase :Union[str, Any] = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
__UpperCamelCase :Optional[int] = os.path.join(__lowercase , '''feat_extract.json''')
feat_extract_first.to_json_file(__lowercase)
__UpperCamelCase :Optional[int] = self.feature_extraction_class.from_json_file(__lowercase)
__UpperCamelCase :Union[str, Any] = feat_extract_first.to_dict()
__UpperCamelCase :Optional[Any] = feat_extract_second.to_dict()
__UpperCamelCase :Dict = feat_extract_first.mel_filters
__UpperCamelCase :str = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__lowercase , __lowercase))
self.assertEqual(__lowercase , __lowercase)
def UpperCamelCase__ ( self) -> Optional[Any]:
# Tests that all call wrap to encode_plus and batch_encode_plus
__UpperCamelCase :Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
__UpperCamelCase :int = [floats_list((1, x))[0] for x in range(800 , 1_400 , 200)]
__UpperCamelCase :Union[str, Any] = [np.asarray(__lowercase) for speech_input in speech_inputs]
# Test feature size
__UpperCamelCase :Union[str, Any] = feature_extractor(__lowercase , padding='''max_length''' , return_tensors='''np''').input_features
self.assertTrue(input_features.ndim == 3)
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames)
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size)
# Test not batched input
__UpperCamelCase :Optional[int] = feature_extractor(speech_inputs[0] , return_tensors='''np''').input_features
__UpperCamelCase :int = feature_extractor(np_speech_inputs[0] , return_tensors='''np''').input_features
self.assertTrue(np.allclose(__lowercase , __lowercase , atol=1E-3))
# Test batched
__UpperCamelCase :Dict = feature_extractor(__lowercase , return_tensors='''np''').input_features
__UpperCamelCase :str = feature_extractor(__lowercase , return_tensors='''np''').input_features
for enc_seq_a, enc_seq_a in zip(__lowercase , __lowercase):
self.assertTrue(np.allclose(__lowercase , __lowercase , atol=1E-3))
# Test 2-D numpy arrays are batched.
__UpperCamelCase :Any = [floats_list((1, x))[0] for x in (800, 800, 800)]
__UpperCamelCase :str = np.asarray(__lowercase)
__UpperCamelCase :List[Any] = feature_extractor(__lowercase , return_tensors='''np''').input_features
__UpperCamelCase :int = feature_extractor(__lowercase , return_tensors='''np''').input_features
for enc_seq_a, enc_seq_a in zip(__lowercase , __lowercase):
self.assertTrue(np.allclose(__lowercase , __lowercase , atol=1E-3))
# Test truncation required
__UpperCamelCase :Optional[Any] = [floats_list((1, x))[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200)]
__UpperCamelCase :Tuple = [np.asarray(__lowercase) for speech_input in speech_inputs]
__UpperCamelCase :Optional[int] = [x[: feature_extractor.n_samples] for x in speech_inputs]
__UpperCamelCase :Any = [np.asarray(__lowercase) for speech_input in speech_inputs_truncated]
__UpperCamelCase :str = feature_extractor(__lowercase , return_tensors='''np''').input_features
__UpperCamelCase :Union[str, Any] = feature_extractor(__lowercase , return_tensors='''np''').input_features
for enc_seq_a, enc_seq_a in zip(__lowercase , __lowercase):
self.assertTrue(np.allclose(__lowercase , __lowercase , atol=1E-3))
def UpperCamelCase__ ( self) -> Union[str, Any]:
import torch
__UpperCamelCase :int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
__UpperCamelCase :List[str] = np.random.rand(100 , 32).astype(np.floataa)
__UpperCamelCase :List[Any] = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
__UpperCamelCase :Any = feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''np''')
self.assertTrue(np_processed.input_features.dtype == np.floataa)
__UpperCamelCase :str = feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''pt''')
self.assertTrue(pt_processed.input_features.dtype == torch.floataa)
def UpperCamelCase__ ( self , __lowercase) -> List[str]:
__UpperCamelCase :Optional[Any] = load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation''')
# automatic decoding with librispeech
__UpperCamelCase :Optional[Any] = ds.sort('''id''').select(range(__lowercase))[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def UpperCamelCase__ ( self) -> Any:
# fmt: off
__UpperCamelCase :str = torch.tensor(
[
0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
-0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
])
# fmt: on
__UpperCamelCase :Optional[Any] = self._load_datasamples(1)
__UpperCamelCase :Optional[Any] = WhisperFeatureExtractor()
__UpperCamelCase :Tuple = feature_extractor(__lowercase , return_tensors='''pt''').input_features
self.assertEqual(input_features.shape , (1, 80, 3_000))
self.assertTrue(torch.allclose(input_features[0, 0, :30] , __lowercase , atol=1E-4))
def UpperCamelCase__ ( self) -> Tuple:
__UpperCamelCase :int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
__UpperCamelCase :int = self._load_datasamples(1)[0]
__UpperCamelCase :List[str] = ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535 # Rescale to [0, 65535] to show issue
__UpperCamelCase :Any = feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=__lowercase)[0]
self.assertTrue(np.all(np.mean(__lowercase) < 1E-3))
self.assertTrue(np.all(np.abs(np.var(__lowercase) - 1) < 1E-3))
| 350 | import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
__lowercase = '''bert-base-cased'''
__lowercase = '''google/pegasus-xsum'''
__lowercase = [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__lowercase = ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__lowercase = '''patrickvonplaten/t5-tiny-random'''
__lowercase = '''sshleifer/bart-tiny-random'''
__lowercase = '''sshleifer/tiny-mbart'''
__lowercase = '''sshleifer/tiny-marian-en-de'''
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :str = '''\n'''.join(SCREAMING_SNAKE_CASE )
Path(SCREAMING_SNAKE_CASE ).open('''w''' ).writelines(SCREAMING_SNAKE_CASE )
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(SCREAMING_SNAKE_CASE , f"""{split}.source""" ) , SCREAMING_SNAKE_CASE )
_dump_articles(os.path.join(SCREAMING_SNAKE_CASE , f"""{split}.target""" ) , SCREAMING_SNAKE_CASE )
return tmp_dir
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def UpperCamelCase__ ( self , __lowercase) -> List[Any]:
__UpperCamelCase :Dict = AutoTokenizer.from_pretrained(__lowercase)
__UpperCamelCase :Optional[Any] = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
__UpperCamelCase :List[Any] = max(len(tokenizer.encode(__lowercase)) for a in ARTICLES)
__UpperCamelCase :Optional[int] = max(len(tokenizer.encode(__lowercase)) for a in SUMMARIES)
__UpperCamelCase :int = 4
__UpperCamelCase :Any = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
__UpperCamelCase , __UpperCamelCase :Tuple = '''ro_RO''', '''de_DE''' # ignored for all but mbart, but never causes error.
__UpperCamelCase :str = SeqaSeqDataset(
__lowercase , data_dir=__lowercase , type_path='''train''' , max_source_length=__lowercase , max_target_length=__lowercase , src_lang=__lowercase , tgt_lang=__lowercase , )
__UpperCamelCase :Any = DataLoader(__lowercase , batch_size=2 , collate_fn=train_dataset.collate_fn)
for batch in dataloader:
assert isinstance(__lowercase , __lowercase)
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
__UpperCamelCase :Optional[int] = shift_tokens_right(batch['''labels'''] , tokenizer.pad_token_id)
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED])
def UpperCamelCase__ ( self , __lowercase) -> int:
__UpperCamelCase :Union[str, Any] = AutoTokenizer.from_pretrained(__lowercase)
__UpperCamelCase :Union[str, Any] = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
__UpperCamelCase :int = max(len(tokenizer.encode(__lowercase)) for a in ARTICLES)
__UpperCamelCase :Dict = max(len(tokenizer.encode(__lowercase)) for a in SUMMARIES)
__UpperCamelCase :Union[str, Any] = 4
__UpperCamelCase :List[str] = LegacySeqaSeqDataset(
__lowercase , data_dir=__lowercase , type_path='''train''' , max_source_length=20 , max_target_length=__lowercase , )
__UpperCamelCase :Dict = DataLoader(__lowercase , batch_size=2 , collate_fn=train_dataset.collate_fn)
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 20 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def UpperCamelCase__ ( self) -> Dict:
__UpperCamelCase :List[Any] = AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25''')
__UpperCamelCase :Union[str, Any] = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
__UpperCamelCase :str = tmp_dir.joinpath('''train.source''').open().readlines()
__UpperCamelCase :int = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
pack_data_dir(__lowercase , __lowercase , 128 , __lowercase)
__UpperCamelCase :Union[str, Any] = {x.name for x in tmp_dir.iterdir()}
__UpperCamelCase :int = {x.name for x in save_dir.iterdir()}
__UpperCamelCase :Optional[int] = save_dir.joinpath('''train.source''').open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(__lowercase) < len(__lowercase)
assert len(__lowercase) == 1
assert len(packed_examples[0]) == sum(len(__lowercase) for x in orig_examples)
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason='''This test requires fairseq''')
def UpperCamelCase__ ( self) -> List[Any]:
if not FAIRSEQ_AVAILABLE:
return
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase :Any = self._get_dataset(max_len=64)
__UpperCamelCase :Union[str, Any] = 64
__UpperCamelCase :Tuple = ds.make_dynamic_sampler(__lowercase , required_batch_size_multiple=__lowercase)
__UpperCamelCase :List[str] = [len(__lowercase) for x in batch_sampler]
assert len(set(__lowercase)) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(__lowercase) == len(__lowercase) # no dropped or added examples
__UpperCamelCase :int = DataLoader(__lowercase , batch_sampler=__lowercase , collate_fn=ds.collate_fn , num_workers=2)
__UpperCamelCase :List[str] = []
__UpperCamelCase :int = []
for batch in data_loader:
__UpperCamelCase :List[Any] = batch['''input_ids'''].shape
__UpperCamelCase :Dict = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
__UpperCamelCase :Optional[int] = np.product(batch['''input_ids'''].shape)
num_src_per_batch.append(__lowercase)
if num_src_tokens > (max_tokens * 1.1):
failures.append(__lowercase)
assert num_src_per_batch[0] == max(__lowercase)
if failures:
raise AssertionError(f"""too many tokens in {len(__lowercase)} batches""")
def UpperCamelCase__ ( self) -> Tuple:
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase :Optional[Any] = self._get_dataset(max_len=512)
__UpperCamelCase :Any = 2
__UpperCamelCase :List[Any] = ds.make_sortish_sampler(__lowercase , shuffle=__lowercase)
__UpperCamelCase :List[Any] = DataLoader(__lowercase , batch_size=__lowercase , collate_fn=ds.collate_fn , num_workers=2)
__UpperCamelCase :Tuple = DataLoader(__lowercase , batch_size=__lowercase , collate_fn=ds.collate_fn , num_workers=2 , sampler=__lowercase)
__UpperCamelCase :int = tokenizer.pad_token_id
def count_pad_tokens(__lowercase , __lowercase="input_ids"):
return [batch[k].eq(__lowercase).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(__lowercase , k='''labels''')) < sum(count_pad_tokens(__lowercase , k='''labels'''))
assert sum(count_pad_tokens(__lowercase)) < sum(count_pad_tokens(__lowercase))
assert len(__lowercase) == len(__lowercase)
def UpperCamelCase__ ( self , __lowercase=1_000 , __lowercase=128) -> List[Any]:
if os.getenv('''USE_REAL_DATA''' , __lowercase):
__UpperCamelCase :Optional[Any] = '''examples/seq2seq/wmt_en_ro'''
__UpperCamelCase :Dict = max_len * 2 * 64
if not Path(__lowercase).joinpath('''train.len''').exists():
save_len_file(__lowercase , __lowercase)
else:
__UpperCamelCase :Union[str, Any] = '''examples/seq2seq/test_data/wmt_en_ro'''
__UpperCamelCase :Optional[int] = max_len * 4
save_len_file(__lowercase , __lowercase)
__UpperCamelCase :str = AutoTokenizer.from_pretrained(__lowercase)
__UpperCamelCase :List[Any] = SeqaSeqDataset(
__lowercase , data_dir=__lowercase , type_path='''train''' , max_source_length=__lowercase , max_target_length=__lowercase , n_obs=__lowercase , )
return ds, max_tokens, tokenizer
def UpperCamelCase__ ( self) -> Tuple:
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase :List[Any] = self._get_dataset()
__UpperCamelCase :List[str] = set(DistributedSortishSampler(__lowercase , 256 , num_replicas=2 , rank=0 , add_extra_examples=__lowercase))
__UpperCamelCase :Tuple = set(DistributedSortishSampler(__lowercase , 256 , num_replicas=2 , rank=1 , add_extra_examples=__lowercase))
assert idsa.intersection(__lowercase) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def UpperCamelCase__ ( self , __lowercase) -> List[Any]:
__UpperCamelCase :List[Any] = AutoTokenizer.from_pretrained(__lowercase , use_fast=__lowercase)
if tok_name == MBART_TINY:
__UpperCamelCase :Optional[Any] = SeqaSeqDataset(
__lowercase , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()) , type_path='''train''' , max_source_length=4 , max_target_length=8 , src_lang='''EN''' , tgt_lang='''FR''' , )
__UpperCamelCase :Tuple = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
__UpperCamelCase :Tuple = SeqaSeqDataset(
__lowercase , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()) , type_path='''train''' , max_source_length=4 , max_target_length=8 , )
__UpperCamelCase :Optional[int] = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(__lowercase) == 1 if tok_name == BART_TINY else len(__lowercase) == 0
| 105 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : int ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def lowerCAmelCase_ ( ) -> None:
'''simple docstring'''
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) == 0
assert and_gate(1 , 1 ) == 1
if __name__ == "__main__":
test_and_gate()
print(and_gate(1, 0))
print(and_gate(0, 0))
print(and_gate(0, 1))
print(and_gate(1, 1))
| 1 |
"""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 lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """new-model"""
if is_tf_available():
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = NewModelConfig
@require_tf
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def A_ ( self ):
_lowerCamelCase : List[str] = 'bert-base-cased'
_lowerCamelCase : Union[str, Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Union[str, Any] = TFAutoModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
_lowerCamelCase : List[str] = 'bert-base-cased'
_lowerCamelCase : int = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : int = TFAutoModelForPreTraining.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : int = TFAutoModelForCausalLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : str = TFAutoModelForCausalLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : List[Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : str = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Tuple = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : Tuple = TFAutoModelForMaskedLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Optional[int] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_lowerCamelCase : str = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Union[str, Any] = TFAutoModelForSequenceClassification.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_lowerCamelCase : Optional[Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : List[str] = TFAutoModelForQuestionAnswering.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
@require_tensorflow_probability
def A_ ( self ):
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
_lowerCamelCase : Dict = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Dict = TFAutoModelForTableQuestionAnswering.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : List[Any] = TFAutoModelForTableQuestionAnswering.from_pretrained(
lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A_ ( self ):
_lowerCamelCase : int = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=lowercase ) , 14410 )
def A_ ( self ):
_lowerCamelCase : Any = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=lowercase ) , 14410 )
def A_ ( self ):
# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
_lowerCamelCase : List[str] = TFAutoModel.from_pretrained('sgugger/funnel-random-tiny' )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Optional[int] = copy.deepcopy(model.config )
_lowerCamelCase : Dict = ['FunnelBaseModel']
_lowerCamelCase : List[Any] = TFAutoModel.from_config(lowercase )
self.assertIsInstance(lowercase , lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowercase )
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A_ ( self ):
try:
AutoConfig.register('new-model' , lowercase )
_lowerCamelCase : Tuple = [
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(lowercase ):
auto_class.register(lowercase , lowercase )
auto_class.register(lowercase , lowercase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowercase ):
auto_class.register(lowercase , lowercase )
# Now that the config is registered, it can be used as any other config with the auto-API
_lowerCamelCase : Optional[Any] = BertModelTester(self ).get_config()
_lowerCamelCase : Dict = NewModelConfig(**tiny_config.to_dict() )
_lowerCamelCase : int = auto_class.from_config(lowercase )
self.assertIsInstance(lowercase , lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowercase )
_lowerCamelCase : List[Any] = auto_class.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
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 A_ ( self ):
with self.assertRaisesRegex(
lowercase , 'bert-base is not a local folder and is not a valid model identifier' ):
_lowerCamelCase : Union[str, Any] = TFAutoModel.from_pretrained('bert-base' )
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
_lowerCamelCase : str = TFAutoModel.from_pretrained(lowercase , revision='aaaaaa' )
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , 'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin' , ):
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained('hf-internal-testing/config-no-model' )
def A_ ( self ):
with self.assertRaisesRegex(lowercase , 'Use `from_pt=True` to load this model' ):
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only' )
def A_ ( self ):
# Make sure we have cached the model.
_lowerCamelCase : Optional[int] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert' )
with RequestCounter() as counter:
_lowerCamelCase : Optional[int] = 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
_lowerCamelCase : int = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded' )
with RequestCounter() as counter:
_lowerCamelCase : List[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 ) | 96 | 0 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
__snake_case : List[str] = logging.get_logger(__name__)
__snake_case : Dict = r"\n Args:\n input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n Indices of input sequence tokens in the vocabulary.\n\n Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n [What are input IDs?](../glossary#input-ids)\n scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n or scores for each vocabulary token after SoftMax.\n kwargs (`Dict[str, Any]`, *optional*):\n Additional stopping criteria specific kwargs.\n\n Return:\n `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n"
class __SCREAMING_SNAKE_CASE ( a_):
@add_start_docstrings(_UpperCamelCase )
def __call__( self , _UpperCamelCase , _UpperCamelCase , **_UpperCamelCase ):
"""simple docstring"""
raise NotImplementedError('StoppingCriteria needs to be subclassed' )
class __SCREAMING_SNAKE_CASE ( a_):
def __init__( self , _UpperCamelCase , _UpperCamelCase = None ):
"""simple docstring"""
lowerCAmelCase__ = max_length
lowerCAmelCase__ = max_position_embeddings
@add_start_docstrings(_UpperCamelCase )
def __call__( self , _UpperCamelCase , _UpperCamelCase , **_UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ = input_ids.shape[-1]
lowerCAmelCase__ = cur_len >= self.max_length
if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
logger.warning_once(
'This is a friendly reminder - the current text generation call will exceed the model\'s predefined '
F"maximum length ({self.max_position_embeddings}). Depending on the model, you may observe "
'exceptions, performance degradation, or nothing at all.' )
return is_done
class __SCREAMING_SNAKE_CASE ( a_):
def __init__( self , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
warnings.warn(
'The class `MaxNewTokensCriteria` is deprecated. '
F"Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` "
'with `max_length = start_length + max_new_tokens` instead.' , _UpperCamelCase , )
lowerCAmelCase__ = start_length
lowerCAmelCase__ = max_new_tokens
lowerCAmelCase__ = start_length + max_new_tokens
@add_start_docstrings(_UpperCamelCase )
def __call__( self , _UpperCamelCase , _UpperCamelCase , **_UpperCamelCase ):
"""simple docstring"""
return input_ids.shape[-1] >= self.max_length
class __SCREAMING_SNAKE_CASE ( a_):
def __init__( self , _UpperCamelCase , _UpperCamelCase = None ):
"""simple docstring"""
lowerCAmelCase__ = max_time
lowerCAmelCase__ = time.time() if initial_timestamp is None else initial_timestamp
@add_start_docstrings(_UpperCamelCase )
def __call__( self , _UpperCamelCase , _UpperCamelCase , **_UpperCamelCase ):
"""simple docstring"""
return time.time() - self.initial_timestamp > self.max_time
class __SCREAMING_SNAKE_CASE ( a_):
@add_start_docstrings(_UpperCamelCase )
def __call__( self , _UpperCamelCase , _UpperCamelCase , **_UpperCamelCase ):
"""simple docstring"""
return any(criteria(_UpperCamelCase , _UpperCamelCase ) for criteria in self )
@property
def UpperCamelCase__ ( self ):
"""simple docstring"""
for stopping_criterium in self:
if isinstance(_UpperCamelCase , _UpperCamelCase ):
return stopping_criterium.max_length
elif isinstance(_UpperCamelCase , _UpperCamelCase ):
return stopping_criterium.max_length
return None
def _UpperCamelCase ( UpperCamelCase_ : StoppingCriteriaList , UpperCamelCase_ : int ) -> Tuple:
"""simple docstring"""
lowerCAmelCase__ = stopping_criteria.max_length
lowerCAmelCase__ = deepcopy(lowerCamelCase__ )
if stopping_max_length is not None and stopping_max_length != max_length:
warnings.warn('You set different `max_length` for stopping criteria and `max_length` parameter' , lowerCamelCase__ )
elif stopping_max_length is None:
new_stopping_criteria.append(MaxLengthCriteria(max_length=lowerCamelCase__ ) )
return new_stopping_criteria
| 350 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
__snake_case : Any = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( __lowercase):
_SCREAMING_SNAKE_CASE : List[Any] = ['''pixel_values''']
def __init__( self , _UpperCamelCase = True , _UpperCamelCase = None , _UpperCamelCase = PILImageResampling.BILINEAR , _UpperCamelCase = True , _UpperCamelCase = None , _UpperCamelCase = True , _UpperCamelCase = 1 / 2_55 , _UpperCamelCase = True , _UpperCamelCase = None , _UpperCamelCase = None , **_UpperCamelCase , ):
"""simple docstring"""
super().__init__(**_UpperCamelCase )
lowerCAmelCase__ = size if size is not None else {'shortest_edge': 2_56}
lowerCAmelCase__ = get_size_dict(_UpperCamelCase , default_to_square=_UpperCamelCase )
lowerCAmelCase__ = crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
lowerCAmelCase__ = get_size_dict(_UpperCamelCase )
lowerCAmelCase__ = do_resize
lowerCAmelCase__ = size
lowerCAmelCase__ = resample
lowerCAmelCase__ = do_center_crop
lowerCAmelCase__ = crop_size
lowerCAmelCase__ = do_rescale
lowerCAmelCase__ = rescale_factor
lowerCAmelCase__ = do_normalize
lowerCAmelCase__ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCAmelCase__ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = PILImageResampling.BICUBIC , _UpperCamelCase = None , **_UpperCamelCase , ):
"""simple docstring"""
lowerCAmelCase__ = get_size_dict(_UpperCamelCase , default_to_square=_UpperCamelCase )
if "shortest_edge" not in size:
raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
lowerCAmelCase__ = get_resize_output_image_size(_UpperCamelCase , size=size['shortest_edge'] , default_to_square=_UpperCamelCase )
return resize(_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase , ):
"""simple docstring"""
lowerCAmelCase__ = get_size_dict(_UpperCamelCase )
return center_crop(_UpperCamelCase , size=(size['height'], size['width']) , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase ):
"""simple docstring"""
return rescale(_UpperCamelCase , scale=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase , ):
"""simple docstring"""
return normalize(_UpperCamelCase , mean=_UpperCamelCase , std=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = ChannelDimension.FIRST , **_UpperCamelCase , ):
"""simple docstring"""
lowerCAmelCase__ = do_resize if do_resize is not None else self.do_resize
lowerCAmelCase__ = size if size is not None else self.size
lowerCAmelCase__ = get_size_dict(_UpperCamelCase , default_to_square=_UpperCamelCase )
lowerCAmelCase__ = resample if resample is not None else self.resample
lowerCAmelCase__ = do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCAmelCase__ = crop_size if crop_size is not None else self.crop_size
lowerCAmelCase__ = get_size_dict(_UpperCamelCase )
lowerCAmelCase__ = do_rescale if do_rescale is not None else self.do_rescale
lowerCAmelCase__ = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCAmelCase__ = do_normalize if do_normalize is not None else self.do_normalize
lowerCAmelCase__ = image_mean if image_mean is not None else self.image_mean
lowerCAmelCase__ = image_std if image_std is not None else self.image_std
lowerCAmelCase__ = make_list_of_images(_UpperCamelCase )
if not valid_images(_UpperCamelCase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
lowerCAmelCase__ = [to_numpy_array(_UpperCamelCase ) for image in images]
if do_resize:
lowerCAmelCase__ = [self.resize(image=_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase ) for image in images]
if do_center_crop:
lowerCAmelCase__ = [self.center_crop(image=_UpperCamelCase , size=_UpperCamelCase ) for image in images]
if do_rescale:
lowerCAmelCase__ = [self.rescale(image=_UpperCamelCase , scale=_UpperCamelCase ) for image in images]
if do_normalize:
lowerCAmelCase__ = [self.normalize(image=_UpperCamelCase , mean=_UpperCamelCase , std=_UpperCamelCase ) for image in images]
lowerCAmelCase__ = [to_channel_dimension_format(_UpperCamelCase , _UpperCamelCase ) for image in images]
lowerCAmelCase__ = {'pixel_values': images}
return BatchFeature(data=_UpperCamelCase , tensor_type=_UpperCamelCase )
| 122 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
"""SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Swinv2ForImageClassification""",
"""Swinv2ForMaskedImageModeling""",
"""Swinv2Model""",
"""Swinv2PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 78 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
snake_case_ = logging.get_logger(__name__)
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
__UpperCamelCase = ["""pixel_values"""]
def __init__( self :int , lowercase_ :bool = True , lowercase_ :Dict[str, int] = None , lowercase_ :PILImageResampling = PILImageResampling.BICUBIC , lowercase_ :bool = True , lowercase_ :Union[int, float] = 1 / 2_55 , lowercase_ :bool = True , lowercase_ :Optional[Union[float, List[float]]] = None , lowercase_ :Optional[Union[float, List[float]]] = None , lowercase_ :bool = True , **lowercase_ :Union[str, Any] , ) -> None:
super().__init__(**lowercase_ )
UpperCAmelCase = size if size is not None else {'height': 3_84, 'width': 3_84}
UpperCAmelCase = get_size_dict(lowercase_ , default_to_square=lowercase_ )
UpperCAmelCase = do_resize
UpperCAmelCase = size
UpperCAmelCase = resample
UpperCAmelCase = do_rescale
UpperCAmelCase = rescale_factor
UpperCAmelCase = do_normalize
UpperCAmelCase = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
UpperCAmelCase = image_std if image_std is not None else OPENAI_CLIP_STD
UpperCAmelCase = do_convert_rgb
def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :np.ndarray , lowercase_ :Dict[str, int] , lowercase_ :PILImageResampling = PILImageResampling.BICUBIC , lowercase_ :Optional[Union[str, ChannelDimension]] = None , **lowercase_ :Any , ) -> np.ndarray:
UpperCAmelCase = get_size_dict(lowercase_ , default_to_square=lowercase_ )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" )
UpperCAmelCase = (size['height'], size['width'])
return resize(lowercase_ , size=lowercase_ , resample=lowercase_ , data_format=lowercase_ , **lowercase_ )
def UpperCAmelCase__ ( self :Union[str, Any] , lowercase_ :np.ndarray , lowercase_ :Union[int, float] , lowercase_ :Optional[Union[str, ChannelDimension]] = None , **lowercase_ :Optional[int] , ) -> int:
return rescale(lowercase_ , scale=lowercase_ , data_format=lowercase_ , **lowercase_ )
def UpperCAmelCase__ ( self :Any , lowercase_ :np.ndarray , lowercase_ :Union[float, List[float]] , lowercase_ :Union[float, List[float]] , lowercase_ :Optional[Union[str, ChannelDimension]] = None , **lowercase_ :Optional[Any] , ) -> np.ndarray:
return normalize(lowercase_ , mean=lowercase_ , std=lowercase_ , data_format=lowercase_ , **lowercase_ )
def UpperCAmelCase__ ( self :List[Any] , lowercase_ :ImageInput , lowercase_ :Optional[bool] = None , lowercase_ :Optional[Dict[str, int]] = None , lowercase_ :PILImageResampling = None , lowercase_ :Optional[bool] = None , lowercase_ :Optional[float] = None , lowercase_ :Optional[bool] = None , lowercase_ :Optional[Union[float, List[float]]] = None , lowercase_ :Optional[Union[float, List[float]]] = None , lowercase_ :Optional[Union[str, TensorType]] = None , lowercase_ :bool = None , lowercase_ :ChannelDimension = ChannelDimension.FIRST , **lowercase_ :Tuple , ) -> PIL.Image.Image:
UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase = resample if resample is not None else self.resample
UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase = image_std if image_std is not None else self.image_std
UpperCAmelCase = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
UpperCAmelCase = size if size is not None else self.size
UpperCAmelCase = get_size_dict(lowercase_ , default_to_square=lowercase_ )
UpperCAmelCase = make_list_of_images(lowercase_ )
if not valid_images(lowercase_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
UpperCAmelCase = [convert_to_rgb(lowercase_ ) for image in images]
# All transformations expect numpy arrays.
UpperCAmelCase = [to_numpy_array(lowercase_ ) for image in images]
if do_resize:
UpperCAmelCase = [self.resize(image=lowercase_ , size=lowercase_ , resample=lowercase_ ) for image in images]
if do_rescale:
UpperCAmelCase = [self.rescale(image=lowercase_ , scale=lowercase_ ) for image in images]
if do_normalize:
UpperCAmelCase = [self.normalize(image=lowercase_ , mean=lowercase_ , std=lowercase_ ) for image in images]
UpperCAmelCase = [to_channel_dimension_format(lowercase_ , lowercase_ ) for image in images]
UpperCAmelCase = BatchFeature(data={'pixel_values': images} , tensor_type=lowercase_ )
return encoded_outputs
| 78 | 1 |
from __future__ import annotations
__SCREAMING_SNAKE_CASE : Optional[int] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class lowercase_ :
def __init__( self , lowercase_ , lowercase_ ):
_snake_case : str = graph
# mapping node to its parent in resulting breadth first tree
_snake_case : dict[str, str | None] = {}
_snake_case : str = source_vertex
def UpperCamelCase ( self ):
_snake_case : List[Any] = {self.source_vertex}
_snake_case : Optional[int] = None
_snake_case : Optional[Any] = [self.source_vertex] # first in first out queue
while queue:
_snake_case : Any = queue.pop(0 )
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(lowercase_ )
_snake_case : Dict = vertex
queue.append(lowercase_ )
def UpperCamelCase ( self , lowercase_ ):
if target_vertex == self.source_vertex:
return self.source_vertex
_snake_case : int = self.parent.get(lowercase_ )
if target_vertex_parent is None:
_snake_case : List[str] = (
f"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
)
raise ValueError(lowercase_ )
return self.shortest_path(lowercase_ ) + f"""->{target_vertex}"""
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : List[Any] = Graph(graph, 'G')
g.breath_first_search()
print(g.shortest_path('D'))
print(g.shortest_path('G'))
print(g.shortest_path('Foo')) | 284 | import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowercase_ ( __snake_case ):
_lowerCamelCase = 'M-CLIP'
def __init__( self , lowercase_=1_024 , lowercase_=768 , **lowercase_ ):
_snake_case : str = transformerDimSize
_snake_case : Union[str, Any] = imageDimSize
super().__init__(**lowercase_ )
class lowercase_ ( __snake_case ):
_lowerCamelCase = MCLIPConfig
def __init__( self , lowercase_ , *lowercase_ , **lowercase_ ):
super().__init__(lowercase_ , *lowercase_ , **lowercase_ )
_snake_case : List[Any] = XLMRobertaModel(lowercase_ )
_snake_case : int = torch.nn.Linear(
in_features=config.transformerDimensions , out_features=config.numDims )
def UpperCamelCase ( self , lowercase_ , lowercase_ ):
_snake_case : Tuple = self.transformer(input_ids=lowercase_ , attention_mask=lowercase_ )[0]
_snake_case : Tuple = (embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
return self.LinearTransformation(lowercase_ ), embs | 284 | 1 |
import pprint
import requests
__UpperCamelCase : Union[str, Any] = """https://zenquotes.io/api"""
def a_ ( ) -> Tuple:
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def a_ ( ) -> List[str]:
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + '/random' ).json()
if __name__ == "__main__":
__UpperCamelCase : Optional[int] = random_quotes()
pprint.pprint(response)
| 307 |
'''simple docstring'''
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : Dict ):
'''simple docstring'''
_a : Dict = {}
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
print(self.vertex )
for i in self.vertex:
print(_a ,' -> ' ,' -> '.join([str(_a ) for j in self.vertex[i]] ) )
def __lowercase ( self : Dict ,_a : int ,_a : int ):
'''simple docstring'''
if from_vertex in self.vertex:
self.vertex[from_vertex].append(_a )
else:
# else make a new vertex
_a : int = [to_vertex]
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
_a : Tuple = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(_a ,_a )
def __lowercase ( self : Union[str, Any] ,_a : int ,_a : list ):
'''simple docstring'''
_a : List[Any] = True
print(_a ,end=' ' )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(_a ,_a )
if __name__ == "__main__":
__lowerCAmelCase = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print("""DFS:""")
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 271 | 0 |
"""simple docstring"""
def lowercase (SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : int=None ) -> Tuple:
SCREAMING_SNAKE_CASE = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = True, True
SCREAMING_SNAKE_CASE = dfs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return path
def lowercase (SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : List[str] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = -1
for i in range(SCREAMING_SNAKE_CASE_ ):
if i not in graph.keys():
continue
if len(graph[i] ) % 2 == 1:
odd_degree_nodes += 1
SCREAMING_SNAKE_CASE = i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def lowercase (SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Any ) -> int:
SCREAMING_SNAKE_CASE = [[False for _ in range(max_node + 1 )] for _ in range(max_node + 1 )]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = check_circuit_or_path(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if check == 3:
print('graph is not Eulerian' )
print('no path' )
return
SCREAMING_SNAKE_CASE = 1
if check == 2:
SCREAMING_SNAKE_CASE = odd_node
print('graph has a Euler path' )
if check == 1:
print('graph has a Euler cycle' )
SCREAMING_SNAKE_CASE = dfs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
print(SCREAMING_SNAKE_CASE_ )
def lowercase () -> List[str]:
SCREAMING_SNAKE_CASE = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
SCREAMING_SNAKE_CASE = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
SCREAMING_SNAKE_CASE = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
SCREAMING_SNAKE_CASE = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
SCREAMING_SNAKE_CASE = {
1: [],
2: []
# all degree is zero
}
SCREAMING_SNAKE_CASE = 10
check_euler(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
check_euler(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
check_euler(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
check_euler(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
check_euler(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main()
| 353 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowercase (SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : str ) -> Any:
# Load configuration defined in the metadata file
with open(SCREAMING_SNAKE_CASE_ ) as metadata_file:
SCREAMING_SNAKE_CASE = json.load(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE_ , **metadata['model_config'] )
# Load in the weights from the checkpoint_path
SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location='cpu' )['module']
# Load the entity vocab file
SCREAMING_SNAKE_CASE = load_original_entity_vocab(SCREAMING_SNAKE_CASE_ )
# add an entry for [MASK2]
SCREAMING_SNAKE_CASE = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
SCREAMING_SNAKE_CASE = XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name'] )
# Add special tokens to the token vocabulary for downstream tasks
SCREAMING_SNAKE_CASE = AddedToken('<ent>' , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = AddedToken('<ent2>' , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ )
tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(F'Saving tokenizer to {pytorch_dump_folder_path}' )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
with open(os.path.join(SCREAMING_SNAKE_CASE_ , 'tokenizer_config.json' ) , 'r' ) as f:
SCREAMING_SNAKE_CASE = json.load(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = 'MLukeTokenizer'
with open(os.path.join(SCREAMING_SNAKE_CASE_ , 'tokenizer_config.json' ) , 'w' ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
with open(os.path.join(SCREAMING_SNAKE_CASE_ , MLukeTokenizer.vocab_files_names['entity_vocab_file'] ) , 'w' ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Initialize the embeddings of the special tokens
SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(['@'] )[0]
SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(['#'] )[0]
SCREAMING_SNAKE_CASE = state_dict['embeddings.word_embeddings.weight']
SCREAMING_SNAKE_CASE = word_emb[ent_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE = word_emb[enta_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
SCREAMING_SNAKE_CASE = state_dict[bias_name]
SCREAMING_SNAKE_CASE = decoder_bias[ent_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE = decoder_bias[enta_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
SCREAMING_SNAKE_CASE = F'encoder.layer.{layer_index}.attention.self.'
SCREAMING_SNAKE_CASE = state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE = state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
SCREAMING_SNAKE_CASE = state_dict['entity_embeddings.entity_embeddings.weight']
SCREAMING_SNAKE_CASE = entity_emb[entity_vocab['[MASK]']].unsqueeze(0 )
SCREAMING_SNAKE_CASE = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
SCREAMING_SNAKE_CASE = state_dict['entity_predictions.bias']
SCREAMING_SNAKE_CASE = entity_prediction_bias[entity_vocab['[MASK]']].unsqueeze(0 )
SCREAMING_SNAKE_CASE = torch.cat([entity_prediction_bias, entity_mask_bias] )
SCREAMING_SNAKE_CASE = LukeForMaskedLM(config=SCREAMING_SNAKE_CASE_ ).eval()
state_dict.pop('entity_predictions.decoder.weight' )
state_dict.pop('lm_head.decoder.weight' )
state_dict.pop('lm_head.decoder.bias' )
SCREAMING_SNAKE_CASE = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('lm_head' ) or key.startswith('entity_predictions' )):
SCREAMING_SNAKE_CASE = state_dict[key]
else:
SCREAMING_SNAKE_CASE = state_dict[key]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
if set(SCREAMING_SNAKE_CASE_ ) != {"luke.embeddings.position_ids"}:
raise ValueError(F'Unexpected unexpected_keys: {unexpected_keys}' )
if set(SCREAMING_SNAKE_CASE_ ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(F'Unexpected missing_keys: {missing_keys}' )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
SCREAMING_SNAKE_CASE = MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ , task='entity_classification' )
SCREAMING_SNAKE_CASE = 'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
SCREAMING_SNAKE_CASE = (0, 9)
SCREAMING_SNAKE_CASE = tokenizer(SCREAMING_SNAKE_CASE_ , entity_spans=[span] , return_tensors='pt' )
SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
SCREAMING_SNAKE_CASE = torch.Size((1, 33, 7_68) )
SCREAMING_SNAKE_CASE = torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
F'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
SCREAMING_SNAKE_CASE = torch.Size((1, 1, 7_68) )
SCREAMING_SNAKE_CASE = torch.tensor([[-0.14_82, 0.06_09, 0.03_22]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
F'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
F' {expected_shape}' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ):
raise ValueError
# Verify masked word/entity prediction
SCREAMING_SNAKE_CASE = MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = 'Tokyo is the capital of <mask>.'
SCREAMING_SNAKE_CASE = (24, 30)
SCREAMING_SNAKE_CASE = tokenizer(SCREAMING_SNAKE_CASE_ , entity_spans=[span] , return_tensors='pt' )
SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = encoding['input_ids'][0].tolist()
SCREAMING_SNAKE_CASE = input_ids.index(tokenizer.convert_tokens_to_ids('<mask>' ) )
SCREAMING_SNAKE_CASE = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = outputs.entity_logits[0][0].argmax().item()
SCREAMING_SNAKE_CASE = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('en:' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('Saving PyTorch model to {}'.format(SCREAMING_SNAKE_CASE_ ) )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> int:
SCREAMING_SNAKE_CASE = ['[MASK]', '[PAD]', '[UNK]']
SCREAMING_SNAKE_CASE = [json.loads(SCREAMING_SNAKE_CASE_ ) for line in open(SCREAMING_SNAKE_CASE_ )]
SCREAMING_SNAKE_CASE = {}
for entry in data:
SCREAMING_SNAKE_CASE = entry['id']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
SCREAMING_SNAKE_CASE = entity_id
break
SCREAMING_SNAKE_CASE = F'{language}:{entity_name}'
SCREAMING_SNAKE_CASE = entity_id
return new_mapping
if __name__ == "__main__":
__UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
parser.add_argument(
'''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
)
parser.add_argument(
'''--entity_vocab_path''',
default=None,
type=str,
help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
)
parser.add_argument(
'''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
)
__UpperCamelCase = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 38 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = ['''image_processor''', '''tokenizer''']
snake_case_ = '''AutoImageProcessor'''
snake_case_ = '''AutoTokenizer'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
super().__init__(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = self.image_processor
def __call__( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , **lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
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 = self.tokenizer(lowerCamelCase__ , return_tensors=lowerCamelCase__ , **lowerCamelCase__ )
if images is not None:
__lowerCamelCase = self.image_processor(lowerCamelCase__ , return_tensors=lowerCamelCase__ , **lowerCamelCase__ )
if text is not None and images is not None:
__lowerCamelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowerCamelCase__ ) , tensor_type=lowerCamelCase__ )
def lowercase_ ( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCamelCase__ , **lowerCamelCase__ )
def lowercase_ ( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
return self.tokenizer.decode(*lowerCamelCase__ , **lowerCamelCase__ )
@property
def lowercase_ ( self ) -> str:
'''simple docstring'''
return ["input_ids", "attention_mask", "pixel_values"]
| 90 |
import baseaa
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> bytes:
"""simple docstring"""
return baseaa.aaaencode(string.encode('utf-8' ) )
def lowerCamelCase_ ( UpperCamelCase__ : bytes ) -> str:
"""simple docstring"""
return baseaa.aaadecode(UpperCamelCase__ ).decode('utf-8' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 90 | 1 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
lowercase_ = sys.version_info >= (3, 10)
def __lowerCAmelCase ( _A : str=None , _A : Optional[int]=None ):
'''simple docstring'''
return field(default_factory=lambda: default , metadata=__SCREAMING_SNAKE_CASE )
@dataclass
class SCREAMING_SNAKE_CASE__ :
A : int
A : float
A : str
A : bool
@dataclass
class SCREAMING_SNAKE_CASE__ :
A : int = 42
A : str = field(default="toto" , metadata={"help": "help message"} )
@dataclass
class SCREAMING_SNAKE_CASE__ :
A : bool = False
A : bool = True
A : Optional[bool] = None
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
A : Tuple = "titi"
A : List[str] = "toto"
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
A : List[Any] = "titi"
A : Tuple = "toto"
A : Optional[int] = 42
@dataclass
class SCREAMING_SNAKE_CASE__ :
A : BasicEnum = "toto"
def snake_case__ ( self : Any ):
__snake_case : Tuple = BasicEnum(self.foo )
@dataclass
class SCREAMING_SNAKE_CASE__ :
A : MixedTypeEnum = "toto"
def snake_case__ ( self : Optional[Any] ):
__snake_case : Optional[int] = MixedTypeEnum(self.foo )
@dataclass
class SCREAMING_SNAKE_CASE__ :
A : Optional[int] = None
A : Optional[float] = field(default=__UpperCamelCase , metadata={"help": "help message"} )
A : Optional[str] = None
A : Optional[List[str]] = list_field(default=[] )
A : Optional[List[int]] = list_field(default=[] )
@dataclass
class SCREAMING_SNAKE_CASE__ :
A : List[int] = list_field(default=[] )
A : List[int] = list_field(default=[1, 2, 3] )
A : List[str] = list_field(default=["Hallo", "Bonjour", "Hello"] )
A : List[float] = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class SCREAMING_SNAKE_CASE__ :
A : List[int] = field()
A : str = field()
A : BasicEnum = field()
def snake_case__ ( self : Optional[Any] ):
__snake_case : Union[str, Any] = BasicEnum(self.required_enum )
@dataclass
class SCREAMING_SNAKE_CASE__ :
A : int
A : "BasicEnum" = field()
A : "Optional[bool]" = None
A : "str" = field(default="toto" , metadata={"help": "help message"} )
A : "List[str]" = list_field(default=["Hallo", "Bonjour", "Hello"] )
if is_python_no_less_than_3_10:
@dataclass
class SCREAMING_SNAKE_CASE__ :
A : bool = False
A : bool = True
A : bool | None = None
@dataclass
class SCREAMING_SNAKE_CASE__ :
A : int | None = None
A : float | None = field(default=__UpperCamelCase , metadata={"help": "help message"} )
A : str | None = None
A : list[str] | None = list_field(default=[] )
A : list[int] | None = list_field(default=[] )
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def snake_case__ ( self : Dict , _lowerCAmelCase : argparse.ArgumentParser , _lowerCAmelCase : argparse.ArgumentParser ):
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
__snake_case : Optional[Any] = {k: v for k, v in vars(_lowerCAmelCase ).items() if k != """container"""}
__snake_case : Tuple = {k: v for k, v in vars(_lowerCAmelCase ).items() if k != """container"""}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get("""choices""" , _lowerCAmelCase ) and yy.get("""choices""" , _lowerCAmelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx["""type"""](_lowerCAmelCase ) , yy["""type"""](_lowerCAmelCase ) )
del xx["type"], yy["type"]
self.assertEqual(_lowerCAmelCase , _lowerCAmelCase )
def snake_case__ ( self : Dict ):
__snake_case : int = HfArgumentParser(_lowerCAmelCase )
__snake_case : Tuple = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=_lowerCAmelCase , required=_lowerCAmelCase )
expected.add_argument("""--bar""" , type=_lowerCAmelCase , required=_lowerCAmelCase )
expected.add_argument("""--baz""" , type=_lowerCAmelCase , required=_lowerCAmelCase )
expected.add_argument("""--flag""" , type=_lowerCAmelCase , default=_lowerCAmelCase , const=_lowerCAmelCase , nargs="""?""" )
self.argparsersEqual(_lowerCAmelCase , _lowerCAmelCase )
__snake_case : Dict = ["""--foo""", """1""", """--baz""", """quux""", """--bar""", """0.5"""]
(__snake_case ) : str = parser.parse_args_into_dataclasses(_lowerCAmelCase , look_for_args_file=_lowerCAmelCase )
self.assertFalse(example.flag )
def snake_case__ ( self : List[str] ):
__snake_case : str = HfArgumentParser(_lowerCAmelCase )
__snake_case : Optional[int] = argparse.ArgumentParser()
expected.add_argument("""--foo""" , default=42 , type=_lowerCAmelCase )
expected.add_argument("""--baz""" , default="""toto""" , type=_lowerCAmelCase , help="""help message""" )
self.argparsersEqual(_lowerCAmelCase , _lowerCAmelCase )
def snake_case__ ( self : Optional[Any] ):
__snake_case : Optional[int] = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=_lowerCAmelCase , default=_lowerCAmelCase , const=_lowerCAmelCase , nargs="""?""" )
expected.add_argument("""--baz""" , type=_lowerCAmelCase , default=_lowerCAmelCase , const=_lowerCAmelCase , nargs="""?""" )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument("""--no_baz""" , action="""store_false""" , default=_lowerCAmelCase , dest="""baz""" )
expected.add_argument("""--opt""" , type=_lowerCAmelCase , default=_lowerCAmelCase )
__snake_case : List[str] = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_lowerCAmelCase )
for dataclass_type in dataclass_types:
__snake_case : str = HfArgumentParser(_lowerCAmelCase )
self.argparsersEqual(_lowerCAmelCase , _lowerCAmelCase )
__snake_case : Any = parser.parse_args([] )
self.assertEqual(_lowerCAmelCase , Namespace(foo=_lowerCAmelCase , baz=_lowerCAmelCase , opt=_lowerCAmelCase ) )
__snake_case : List[Any] = parser.parse_args(["""--foo""", """--no_baz"""] )
self.assertEqual(_lowerCAmelCase , Namespace(foo=_lowerCAmelCase , baz=_lowerCAmelCase , opt=_lowerCAmelCase ) )
__snake_case : Optional[int] = parser.parse_args(["""--foo""", """--baz"""] )
self.assertEqual(_lowerCAmelCase , Namespace(foo=_lowerCAmelCase , baz=_lowerCAmelCase , opt=_lowerCAmelCase ) )
__snake_case : int = parser.parse_args(["""--foo""", """True""", """--baz""", """True""", """--opt""", """True"""] )
self.assertEqual(_lowerCAmelCase , Namespace(foo=_lowerCAmelCase , baz=_lowerCAmelCase , opt=_lowerCAmelCase ) )
__snake_case : Tuple = parser.parse_args(["""--foo""", """False""", """--baz""", """False""", """--opt""", """False"""] )
self.assertEqual(_lowerCAmelCase , Namespace(foo=_lowerCAmelCase , baz=_lowerCAmelCase , opt=_lowerCAmelCase ) )
def snake_case__ ( self : List[str] ):
__snake_case : Optional[Any] = HfArgumentParser(_lowerCAmelCase )
__snake_case : Optional[int] = argparse.ArgumentParser()
expected.add_argument(
"""--foo""" , default="""toto""" , choices=["""titi""", """toto""", 42] , type=make_choice_type_function(["""titi""", """toto""", 42] ) , )
self.argparsersEqual(_lowerCAmelCase , _lowerCAmelCase )
__snake_case : str = parser.parse_args([] )
self.assertEqual(args.foo , """toto""" )
__snake_case : Optional[Any] = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
__snake_case : Tuple = parser.parse_args(["""--foo""", """titi"""] )
self.assertEqual(args.foo , """titi""" )
__snake_case : str = parser.parse_args_into_dataclasses(["""--foo""", """titi"""] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
__snake_case : List[str] = parser.parse_args(["""--foo""", """42"""] )
self.assertEqual(args.foo , 42 )
__snake_case : List[Any] = parser.parse_args_into_dataclasses(["""--foo""", """42"""] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def snake_case__ ( self : Dict ):
@dataclass
class SCREAMING_SNAKE_CASE__ :
A : Literal["titi", "toto", 42] = "toto"
__snake_case : Any = HfArgumentParser(_lowerCAmelCase )
__snake_case : int = argparse.ArgumentParser()
expected.add_argument(
"""--foo""" , default="""toto""" , choices=("""titi""", """toto""", 42) , type=make_choice_type_function(["""titi""", """toto""", 42] ) , )
self.argparsersEqual(_lowerCAmelCase , _lowerCAmelCase )
__snake_case : Tuple = parser.parse_args([] )
self.assertEqual(args.foo , """toto""" )
__snake_case : List[Any] = parser.parse_args(["""--foo""", """titi"""] )
self.assertEqual(args.foo , """titi""" )
__snake_case : Tuple = parser.parse_args(["""--foo""", """42"""] )
self.assertEqual(args.foo , 42 )
def snake_case__ ( self : Dict ):
__snake_case : Optional[int] = HfArgumentParser(_lowerCAmelCase )
__snake_case : List[Any] = argparse.ArgumentParser()
expected.add_argument("""--foo_int""" , nargs="""+""" , default=[] , type=_lowerCAmelCase )
expected.add_argument("""--bar_int""" , nargs="""+""" , default=[1, 2, 3] , type=_lowerCAmelCase )
expected.add_argument("""--foo_str""" , nargs="""+""" , default=["""Hallo""", """Bonjour""", """Hello"""] , type=_lowerCAmelCase )
expected.add_argument("""--foo_float""" , nargs="""+""" , default=[0.1, 0.2, 0.3] , type=_lowerCAmelCase )
self.argparsersEqual(_lowerCAmelCase , _lowerCAmelCase )
__snake_case : int = parser.parse_args([] )
self.assertEqual(
_lowerCAmelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=["""Hallo""", """Bonjour""", """Hello"""] , foo_float=[0.1, 0.2, 0.3] ) , )
__snake_case : Tuple = parser.parse_args("""--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7""".split() )
self.assertEqual(_lowerCAmelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=["""a""", """b""", """c"""] , foo_float=[0.1, 0.7] ) )
def snake_case__ ( self : str ):
__snake_case : List[Any] = argparse.ArgumentParser()
expected.add_argument("""--foo""" , default=_lowerCAmelCase , type=_lowerCAmelCase )
expected.add_argument("""--bar""" , default=_lowerCAmelCase , type=_lowerCAmelCase , help="""help message""" )
expected.add_argument("""--baz""" , default=_lowerCAmelCase , type=_lowerCAmelCase )
expected.add_argument("""--ces""" , nargs="""+""" , default=[] , type=_lowerCAmelCase )
expected.add_argument("""--des""" , nargs="""+""" , default=[] , type=_lowerCAmelCase )
__snake_case : Optional[Any] = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_lowerCAmelCase )
for dataclass_type in dataclass_types:
__snake_case : Optional[Any] = HfArgumentParser(_lowerCAmelCase )
self.argparsersEqual(_lowerCAmelCase , _lowerCAmelCase )
__snake_case : List[Any] = parser.parse_args([] )
self.assertEqual(_lowerCAmelCase , Namespace(foo=_lowerCAmelCase , bar=_lowerCAmelCase , baz=_lowerCAmelCase , ces=[] , des=[] ) )
__snake_case : List[Any] = parser.parse_args("""--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3""".split() )
self.assertEqual(_lowerCAmelCase , Namespace(foo=12 , bar=3.14 , baz="""42""" , ces=["""a""", """b""", """c"""] , des=[1, 2, 3] ) )
def snake_case__ ( self : Union[str, Any] ):
__snake_case : List[str] = HfArgumentParser(_lowerCAmelCase )
__snake_case : Any = argparse.ArgumentParser()
expected.add_argument("""--required_list""" , nargs="""+""" , type=_lowerCAmelCase , required=_lowerCAmelCase )
expected.add_argument("""--required_str""" , type=_lowerCAmelCase , required=_lowerCAmelCase )
expected.add_argument(
"""--required_enum""" , type=make_choice_type_function(["""titi""", """toto"""] ) , choices=["""titi""", """toto"""] , required=_lowerCAmelCase , )
self.argparsersEqual(_lowerCAmelCase , _lowerCAmelCase )
def snake_case__ ( self : str ):
__snake_case : Optional[Any] = HfArgumentParser(_lowerCAmelCase )
__snake_case : Dict = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=_lowerCAmelCase , required=_lowerCAmelCase )
expected.add_argument(
"""--required_enum""" , type=make_choice_type_function(["""titi""", """toto"""] ) , choices=["""titi""", """toto"""] , required=_lowerCAmelCase , )
expected.add_argument("""--opt""" , type=_lowerCAmelCase , default=_lowerCAmelCase )
expected.add_argument("""--baz""" , default="""toto""" , type=_lowerCAmelCase , help="""help message""" )
expected.add_argument("""--foo_str""" , nargs="""+""" , default=["""Hallo""", """Bonjour""", """Hello"""] , type=_lowerCAmelCase )
self.argparsersEqual(_lowerCAmelCase , _lowerCAmelCase )
def snake_case__ ( self : Optional[int] ):
__snake_case : str = HfArgumentParser(_lowerCAmelCase )
__snake_case : int = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
__snake_case : Tuple = parser.parse_dict(_lowerCAmelCase )[0]
__snake_case : int = BasicExample(**_lowerCAmelCase )
self.assertEqual(_lowerCAmelCase , _lowerCAmelCase )
def snake_case__ ( self : Optional[int] ):
__snake_case : Optional[Any] = HfArgumentParser(_lowerCAmelCase )
__snake_case : str = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
"""extra""": 42,
}
self.assertRaises(_lowerCAmelCase , parser.parse_dict , _lowerCAmelCase , allow_extra_keys=_lowerCAmelCase )
def snake_case__ ( self : Any ):
__snake_case : Optional[int] = HfArgumentParser(_lowerCAmelCase )
__snake_case : int = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
__snake_case : Tuple = os.path.join(_lowerCAmelCase , """temp_json""" )
os.mkdir(_lowerCAmelCase )
with open(temp_local_path + """.json""" , """w+""" ) as f:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
__snake_case : List[str] = parser.parse_yaml_file(Path(temp_local_path + """.json""" ) )[0]
__snake_case : int = BasicExample(**_lowerCAmelCase )
self.assertEqual(_lowerCAmelCase , _lowerCAmelCase )
def snake_case__ ( self : Tuple ):
__snake_case : List[Any] = HfArgumentParser(_lowerCAmelCase )
__snake_case : Any = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
__snake_case : int = os.path.join(_lowerCAmelCase , """temp_yaml""" )
os.mkdir(_lowerCAmelCase )
with open(temp_local_path + """.yaml""" , """w+""" ) as f:
yaml.dump(_lowerCAmelCase , _lowerCAmelCase )
__snake_case : str = parser.parse_yaml_file(Path(temp_local_path + """.yaml""" ) )[0]
__snake_case : Tuple = BasicExample(**_lowerCAmelCase )
self.assertEqual(_lowerCAmelCase , _lowerCAmelCase )
def snake_case__ ( self : Tuple ):
__snake_case : List[str] = HfArgumentParser(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
| 353 | import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
lowercase_ = None
lowercase_ = "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
lowercase_ = [
np.dtype("|b1"),
np.dtype("|u1"),
np.dtype("<u2"),
np.dtype(">u2"),
np.dtype("<i2"),
np.dtype(">i2"),
np.dtype("<u4"),
np.dtype(">u4"),
np.dtype("<i4"),
np.dtype(">i4"),
np.dtype("<f4"),
np.dtype(">f4"),
np.dtype("<f8"),
np.dtype(">f8"),
]
@dataclass
class SCREAMING_SNAKE_CASE__ :
A : bool = True
A : Optional[str] = None
# Automatically constructed
A : ClassVar[str] = "PIL.Image.Image"
A : ClassVar[Any] = pa.struct({"bytes": pa.binary(), "path": pa.string()} )
A : str = field(default="Image" , init=__UpperCamelCase , repr=__UpperCamelCase )
def __call__( self : Any ):
return self.pa_type
def snake_case__ ( self : List[Any] , _lowerCAmelCase : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
__snake_case : str = np.array(_lowerCAmelCase )
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return {"path": value, "bytes": None}
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return {"path": None, "bytes": value}
elif isinstance(_lowerCAmelCase , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(_lowerCAmelCase )
elif isinstance(_lowerCAmelCase , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(_lowerCAmelCase )
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def snake_case__ ( self : List[str] , _lowerCAmelCase : dict , _lowerCAmelCase : Dict=None ):
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead.""" )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support decoding images, please install 'Pillow'.""" )
if token_per_repo_id is None:
__snake_case : Tuple = {}
__snake_case , __snake_case : str = value["""path"""], value["""bytes"""]
if bytes_ is None:
if path is None:
raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
else:
if is_local_path(_lowerCAmelCase ):
__snake_case : str = PIL.Image.open(_lowerCAmelCase )
else:
__snake_case : List[str] = path.split("""::""" )[-1]
try:
__snake_case : Dict = string_to_dict(_lowerCAmelCase , config.HUB_DATASETS_URL )["""repo_id"""]
__snake_case : int = token_per_repo_id.get(_lowerCAmelCase )
except ValueError:
__snake_case : List[Any] = None
with xopen(_lowerCAmelCase , """rb""" , use_auth_token=_lowerCAmelCase ) as f:
__snake_case : Union[str, Any] = BytesIO(f.read() )
__snake_case : Dict = PIL.Image.open(bytes_ )
else:
__snake_case : Optional[Any] = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def snake_case__ ( self : Union[str, Any] ):
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
)
def snake_case__ ( self : Optional[int] , _lowerCAmelCase : Union[pa.StringArray, pa.StructArray, pa.ListArray] ):
if pa.types.is_string(storage.type ):
__snake_case : Optional[Any] = pa.array([None] * len(_lowerCAmelCase ) , type=pa.binary() )
__snake_case : Any = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
__snake_case : Optional[Any] = pa.array([None] * len(_lowerCAmelCase ) , type=pa.string() )
__snake_case : List[str] = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
__snake_case : List[str] = storage.field("""bytes""" )
else:
__snake_case : List[Any] = pa.array([None] * len(_lowerCAmelCase ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
__snake_case : Optional[int] = storage.field("""path""" )
else:
__snake_case : int = pa.array([None] * len(_lowerCAmelCase ) , type=pa.string() )
__snake_case : Tuple = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
__snake_case : Optional[Any] = pa.array(
[encode_np_array(np.array(_lowerCAmelCase ) )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
__snake_case : Optional[int] = pa.array([None] * len(_lowerCAmelCase ) , type=pa.string() )
__snake_case : List[str] = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(_lowerCAmelCase , self.pa_type )
def snake_case__ ( self : Union[str, Any] , _lowerCAmelCase : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(_lowerCAmelCase : Tuple ):
with xopen(_lowerCAmelCase , """rb""" ) as f:
__snake_case : Optional[int] = f.read()
return bytes_
__snake_case : Tuple = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
__snake_case : Optional[Any] = pa.array(
[os.path.basename(_lowerCAmelCase ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
__snake_case : Any = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(_lowerCAmelCase , self.pa_type )
def __lowerCAmelCase ( ):
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
__snake_case : Optional[Any] = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : "PIL.Image.Image" ):
'''simple docstring'''
__snake_case : List[Any] = BytesIO()
if image.format in list_image_compression_formats():
__snake_case : Union[str, Any] = image.format
else:
__snake_case : List[Any] = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
image.save(__SCREAMING_SNAKE_CASE , format=__SCREAMING_SNAKE_CASE )
return buffer.getvalue()
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : "PIL.Image.Image" ):
'''simple docstring'''
if hasattr(__SCREAMING_SNAKE_CASE , """filename""" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(__SCREAMING_SNAKE_CASE )}
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
__snake_case : List[Any] = array.dtype
__snake_case : List[Any] = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
__snake_case : Dict = dtype.kind
__snake_case : Union[str, Any] = dtype.itemsize
__snake_case : Tuple = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
__snake_case : int = np.dtype("""|u1""" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
if dtype is not dest_dtype:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
__snake_case : List[str] = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
__snake_case : int = dtype_byteorder + dtype_kind + str(__SCREAMING_SNAKE_CASE )
__snake_case : Any = np.dtype(__SCREAMING_SNAKE_CASE )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
__snake_case : Optional[int] = PIL.Image.fromarray(array.astype(__SCREAMING_SNAKE_CASE ) )
return {"path": None, "bytes": image_to_bytes(__SCREAMING_SNAKE_CASE )}
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ):
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if objs:
__snake_case , __snake_case : Any = first_non_null_value(__SCREAMING_SNAKE_CASE )
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(__SCREAMING_SNAKE_CASE , np.ndarray ):
__snake_case : int = no_op_if_value_is_null(__SCREAMING_SNAKE_CASE )
return [obj_to_image_dict_func(__SCREAMING_SNAKE_CASE ) for obj in objs]
elif isinstance(__SCREAMING_SNAKE_CASE , PIL.Image.Image ):
__snake_case : List[str] = no_op_if_value_is_null(__SCREAMING_SNAKE_CASE )
return [obj_to_image_dict_func(__SCREAMING_SNAKE_CASE ) for obj in objs]
else:
return objs
else:
return objs
| 20 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'''configuration_time_series_transformer''': [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TimeSeriesTransformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TimeSeriesTransformerForPrediction''',
'''TimeSeriesTransformerModel''',
'''TimeSeriesTransformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TimeSeriesTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimeSeriesTransformerForPrediction,
TimeSeriesTransformerModel,
TimeSeriesTransformerPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 72 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__A = abspath(join(dirname(dirname(dirname(__file__))), "src"))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def UpperCamelCase__ ( lowercase__ : Any ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(lowercase__ )
def UpperCamelCase__ ( lowercase__ : Optional[int] ):
from transformers.testing_utils import pytest_terminal_summary_main
snake_case : Any = terminalreporter.config.getoption("--make-reports" )
if make_reports:
pytest_terminal_summary_main(lowercase__ , id=lowercase__ )
| 148 | 0 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
a_ = logging.getLogger(__name__)
@dataclass(frozen=snake_case_ )
class _lowercase :
lowercase = 42
lowercase = 42
lowercase = None
lowercase = None
lowercase = None
@dataclass(frozen=snake_case_ )
class _lowercase :
lowercase = 42
lowercase = None
lowercase = None
lowercase = None
lowercase = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class _lowercase ( snake_case_ ):
lowercase = 42
def __init__( self : int , snake_case : str , snake_case : PreTrainedTokenizer , snake_case : str , snake_case : Optional[int] = None , snake_case : Tuple=False , snake_case : bool = False , ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = hans_processors[task]()
UpperCamelCase_ : Any = os.path.join(
snake_case , 'cached_{}_{}_{}_{}'.format(
'dev' if evaluate else 'train' , tokenizer.__class__.__name__ , str(snake_case ) , snake_case , ) , )
UpperCamelCase_ : List[str] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
UpperCamelCase_, UpperCamelCase_ : int = label_list[2], label_list[1]
UpperCamelCase_ : List[str] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
UpperCamelCase_ : Tuple = cached_features_file + '.lock'
with FileLock(snake_case ):
if os.path.exists(snake_case ) and not overwrite_cache:
logger.info(f"Loading features from cached file {cached_features_file}" )
UpperCamelCase_ : Union[str, Any] = torch.load(snake_case )
else:
logger.info(f"Creating features from dataset file at {data_dir}" )
UpperCamelCase_ : List[str] = (
processor.get_dev_examples(snake_case ) if evaluate else processor.get_train_examples(snake_case )
)
logger.info('Training examples: %s' , len(snake_case ) )
UpperCamelCase_ : Optional[Any] = hans_convert_examples_to_features(snake_case , snake_case , snake_case , snake_case )
logger.info('Saving features into cached file %s' , snake_case )
torch.save(self.features , snake_case )
def __len__( self : List[str] ) -> Tuple:
"""simple docstring"""
return len(self.features )
def __getitem__( self : str , snake_case : Any ) -> InputFeatures:
"""simple docstring"""
return self.features[i]
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Any:
"""simple docstring"""
return self.label_list
if is_tf_available():
import tensorflow as tf
class _lowercase :
lowercase = 42
def __init__( self : Optional[Any] , snake_case : str , snake_case : PreTrainedTokenizer , snake_case : str , snake_case : Optional[int] = 1_2_8 , snake_case : Optional[int]=False , snake_case : bool = False , ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ : Any = hans_processors[task]()
UpperCamelCase_ : List[Any] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
UpperCamelCase_, UpperCamelCase_ : Dict = label_list[2], label_list[1]
UpperCamelCase_ : int = label_list
UpperCamelCase_ : Dict = processor.get_dev_examples(snake_case ) if evaluate else processor.get_train_examples(snake_case )
UpperCamelCase_ : Optional[int] = hans_convert_examples_to_features(snake_case , snake_case , snake_case , snake_case )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc='convert examples to features' ):
if ex_index % 1_0_0_0_0 == 0:
logger.info('Writing example %d of %d' % (ex_index, len(snake_case )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
UpperCamelCase_ : Tuple = tf.data.Dataset.from_generator(
snake_case , (
{
'example_id': tf.intaa,
'input_ids': tf.intaa,
'attention_mask': tf.intaa,
'token_type_ids': tf.intaa,
},
tf.intaa,
) , (
{
'example_id': tf.TensorShape([] ),
'input_ids': tf.TensorShape([None, None] ),
'attention_mask': tf.TensorShape([None, None] ),
'token_type_ids': tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Any:
"""simple docstring"""
return self.dataset
def __len__( self : List[str] ) -> Dict:
"""simple docstring"""
return len(self.features )
def __getitem__( self : List[str] , snake_case : int ) -> InputFeatures:
"""simple docstring"""
return self.features[i]
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
return self.label_list
class _lowercase ( snake_case_ ):
def SCREAMING_SNAKE_CASE__ ( self : List[str] , snake_case : List[str] ) -> List[str]:
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(snake_case , 'heuristics_train_set.txt' ) ) , 'train' )
def SCREAMING_SNAKE_CASE__ ( self : Tuple , snake_case : Optional[Any] ) -> str:
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(snake_case , 'heuristics_evaluation_set.txt' ) ) , 'dev' )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Union[str, Any]:
"""simple docstring"""
return ["contradiction", "entailment", "neutral"]
def SCREAMING_SNAKE_CASE__ ( self : Any , snake_case : Tuple , snake_case : Optional[int] ) -> str:
"""simple docstring"""
UpperCamelCase_ : List[str] = []
for i, line in enumerate(snake_case ):
if i == 0:
continue
UpperCamelCase_ : Any = '%s-%s' % (set_type, line[0])
UpperCamelCase_ : Any = line[5]
UpperCamelCase_ : Any = line[6]
UpperCamelCase_ : str = line[7][2:] if line[7].startswith('ex' ) else line[7]
UpperCamelCase_ : Dict = line[0]
examples.append(InputExample(guid=snake_case , text_a=snake_case , text_b=snake_case , label=snake_case , pairID=snake_case ) )
return examples
def __lowercase ( lowerCamelCase : List[InputExample] , lowerCamelCase : List[str] , lowerCamelCase : int , lowerCamelCase : PreTrainedTokenizer , ):
UpperCamelCase_ : Tuple = {label: i for i, label in enumerate(lowerCamelCase )}
UpperCamelCase_ : int = []
for ex_index, example in tqdm.tqdm(enumerate(lowerCamelCase ) , desc='convert examples to features' ):
if ex_index % 10000 == 0:
logger.info('Writing example %d' % (ex_index) )
UpperCamelCase_ : Any = tokenizer(
example.text_a , example.text_b , add_special_tokens=lowerCamelCase , max_length=lowerCamelCase , padding='max_length' , truncation=lowerCamelCase , return_overflowing_tokens=lowerCamelCase , )
UpperCamelCase_ : Tuple = label_map[example.label] if example.label in label_map else 0
UpperCamelCase_ : Tuple = int(example.pairID )
features.append(InputFeatures(**lowerCamelCase , label=lowerCamelCase , pairID=lowerCamelCase ) )
for i, example in enumerate(examples[:5] ):
logger.info('*** Example ***' )
logger.info(F"guid: {example}" )
logger.info(F"features: {features[i]}" )
return features
a_ = {
'hans': 3,
}
a_ = {
'hans': HansProcessor,
}
| 50 | from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
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 (
TF_MODEL_FOR_PRETRAINING_MAPPING,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
@require_tf
class _lowercase ( snake_case_ , snake_case_ , unittest.TestCase ):
lowercase = (
(
TFMobileBertModel,
TFMobileBertForMaskedLM,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertForMultipleChoice,
)
if is_tf_available()
else ()
)
lowercase = (
{
'feature-extraction': TFMobileBertModel,
'fill-mask': TFMobileBertForMaskedLM,
'question-answering': TFMobileBertForQuestionAnswering,
'text-classification': TFMobileBertForSequenceClassification,
'token-classification': TFMobileBertForTokenClassification,
'zero-shot': TFMobileBertForSequenceClassification,
}
if is_tf_available()
else {}
)
lowercase = False
lowercase = False
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , snake_case : Tuple , snake_case : Optional[Any] , snake_case : Dict=False ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ : List[Any] = super()._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
if return_labels:
if model_class in get_values(snake_case ):
UpperCamelCase_ : Optional[int] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
return inputs_dict
class _lowercase ( snake_case_ ):
def __init__( self : Tuple , snake_case : Optional[int] , snake_case : Optional[Any]=1_3 , snake_case : Optional[Any]=7 , snake_case : Any=True , snake_case : Optional[int]=True , snake_case : Union[str, Any]=True , snake_case : Optional[Any]=True , snake_case : List[Any]=9_9 , snake_case : int=3_2 , snake_case : str=3_2 , snake_case : str=2 , snake_case : List[Any]=4 , snake_case : Tuple=3_7 , snake_case : Any="gelu" , snake_case : str=0.1 , snake_case : Tuple=0.1 , snake_case : Optional[Any]=5_1_2 , snake_case : Optional[int]=1_6 , snake_case : List[Any]=2 , snake_case : Dict=0.02 , snake_case : List[str]=3 , snake_case : Any=4 , snake_case : Any=None , ) -> int:
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = parent
UpperCamelCase_ : Any = batch_size
UpperCamelCase_ : List[str] = seq_length
UpperCamelCase_ : List[Any] = is_training
UpperCamelCase_ : Optional[Any] = use_input_mask
UpperCamelCase_ : Tuple = use_token_type_ids
UpperCamelCase_ : Optional[int] = use_labels
UpperCamelCase_ : Dict = vocab_size
UpperCamelCase_ : Dict = hidden_size
UpperCamelCase_ : List[str] = num_hidden_layers
UpperCamelCase_ : Tuple = num_attention_heads
UpperCamelCase_ : Optional[int] = intermediate_size
UpperCamelCase_ : int = hidden_act
UpperCamelCase_ : List[str] = hidden_dropout_prob
UpperCamelCase_ : Optional[Any] = attention_probs_dropout_prob
UpperCamelCase_ : Tuple = max_position_embeddings
UpperCamelCase_ : Tuple = type_vocab_size
UpperCamelCase_ : Optional[Any] = type_sequence_label_size
UpperCamelCase_ : Any = initializer_range
UpperCamelCase_ : Tuple = num_labels
UpperCamelCase_ : Tuple = num_choices
UpperCamelCase_ : Tuple = scope
UpperCamelCase_ : Dict = embedding_size
def SCREAMING_SNAKE_CASE__ ( self : str ) -> str:
"""simple docstring"""
UpperCamelCase_ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase_ : Optional[Any] = None
if self.use_input_mask:
UpperCamelCase_ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase_ : Union[str, Any] = None
if self.use_token_type_ids:
UpperCamelCase_ : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase_ : Optional[int] = None
UpperCamelCase_ : Tuple = None
UpperCamelCase_ : Dict = None
if self.use_labels:
UpperCamelCase_ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase_ : Tuple = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase_ : Union[str, Any] = MobileBertConfig(
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 , embedding_size=self.embedding_size , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__ ( self : Dict , snake_case : str , snake_case : Optional[int] , snake_case : Optional[Any] , snake_case : Tuple , snake_case : str , snake_case : Optional[Any] , snake_case : List[str] ) -> int:
"""simple docstring"""
UpperCamelCase_ : str = TFMobileBertModel(config=snake_case )
UpperCamelCase_ : Optional[Any] = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
UpperCamelCase_ : Union[str, Any] = model(snake_case )
UpperCamelCase_ : Optional[Any] = [input_ids, input_mask]
UpperCamelCase_ : List[Any] = model(snake_case )
UpperCamelCase_ : Union[str, Any] = model(snake_case )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : int , snake_case : Union[str, Any] , snake_case : Optional[int] , snake_case : Union[str, Any] , snake_case : Dict , snake_case : Any , snake_case : Dict , snake_case : int ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ : List[str] = TFMobileBertForMaskedLM(config=snake_case )
UpperCamelCase_ : Optional[int] = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
UpperCamelCase_ : int = model(snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self : Tuple , snake_case : Any , snake_case : int , snake_case : int , snake_case : Union[str, Any] , snake_case : Optional[int] , snake_case : Optional[Any] , snake_case : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ : Optional[int] = TFMobileBertForNextSentencePrediction(config=snake_case )
UpperCamelCase_ : Any = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
UpperCamelCase_ : List[Any] = model(snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , snake_case : Optional[Any] , snake_case : List[Any] , snake_case : int , snake_case : str , snake_case : str , snake_case : Any , snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ : List[str] = TFMobileBertForPreTraining(config=snake_case )
UpperCamelCase_ : List[Any] = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
UpperCamelCase_ : Any = model(snake_case )
self.parent.assertEqual(
result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def SCREAMING_SNAKE_CASE__ ( self : Any , snake_case : Optional[int] , snake_case : Optional[Any] , snake_case : Dict , snake_case : List[str] , snake_case : str , snake_case : List[str] , snake_case : List[Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ : List[Any] = self.num_labels
UpperCamelCase_ : Dict = TFMobileBertForSequenceClassification(config=snake_case )
UpperCamelCase_ : List[Any] = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
UpperCamelCase_ : List[Any] = model(snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , snake_case : Tuple , snake_case : Any , snake_case : Optional[int] , snake_case : Optional[Any] , snake_case : str , snake_case : List[str] , snake_case : Any ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ : Optional[int] = self.num_choices
UpperCamelCase_ : Dict = TFMobileBertForMultipleChoice(config=snake_case )
UpperCamelCase_ : int = tf.tile(tf.expand_dims(snake_case , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase_ : int = tf.tile(tf.expand_dims(snake_case , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase_ : List[str] = tf.tile(tf.expand_dims(snake_case , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase_ : Optional[Any] = {
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
UpperCamelCase_ : int = model(snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , snake_case : Optional[int] , snake_case : Tuple , snake_case : str , snake_case : str , snake_case : Optional[int] , snake_case : str , snake_case : List[str] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ : Any = self.num_labels
UpperCamelCase_ : Optional[Any] = TFMobileBertForTokenClassification(config=snake_case )
UpperCamelCase_ : str = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
UpperCamelCase_ : Tuple = model(snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : int , snake_case : Tuple , snake_case : Tuple , snake_case : Union[str, Any] , snake_case : Optional[int] , snake_case : Union[str, Any] , snake_case : Dict , snake_case : List[str] ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = TFMobileBertForQuestionAnswering(config=snake_case )
UpperCamelCase_ : int = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
UpperCamelCase_ : Tuple = model(snake_case )
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 SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Dict:
"""simple docstring"""
UpperCamelCase_ : Tuple = self.prepare_config_and_inputs()
(
(
UpperCamelCase_
), (
UpperCamelCase_
), (
UpperCamelCase_
), (
UpperCamelCase_
), (
UpperCamelCase_
), (
UpperCamelCase_
), (
UpperCamelCase_
),
) : Union[str, Any] = config_and_inputs
UpperCamelCase_ : Tuple = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> int:
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = TFMobileBertModelTest.TFMobileBertModelTester(self )
UpperCamelCase_ : str = ConfigTester(self , config_class=snake_case , hidden_size=3_7 )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self : int ) -> str:
"""simple docstring"""
UpperCamelCase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> str:
"""simple docstring"""
UpperCamelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Dict:
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> str:
"""simple docstring"""
UpperCamelCase_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> str:
"""simple docstring"""
UpperCamelCase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Tuple:
"""simple docstring"""
for model_name in ["google/mobilebert-uncased"]:
UpperCamelCase_ : Optional[Any] = TFMobileBertModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@require_tf
class _lowercase ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Dict:
"""simple docstring"""
UpperCamelCase_ : Any = TFMobileBertForPreTraining.from_pretrained('google/mobilebert-uncased' )
UpperCamelCase_ : List[Any] = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCamelCase_ : List[str] = model(snake_case )[0]
UpperCamelCase_ : Any = [1, 6, 3_0_5_2_2]
self.assertEqual(output.shape , snake_case )
UpperCamelCase_ : Dict = tf.constant(
[
[
[-4.5919547, -9.248295, -9.645256],
[-6.7306175, -6.440284, -6.6052837],
[-7.2743506, -6.7847915, -6.024673],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , snake_case , atol=1e-4 )
| 50 | 1 |
"""simple docstring"""
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 snake_case ( unittest.TestCase ):
"""simple docstring"""
@property
def __lowerCAmelCase ( self : List[str] ):
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 __lowerCAmelCase ( self : Dict ):
UpperCAmelCase__ = self.dummy_uncond_unet
UpperCAmelCase__ = KarrasVeScheduler()
UpperCAmelCase__ = KarrasVePipeline(unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
UpperCAmelCase__ = torch.manual_seed(0 )
UpperCAmelCase__ = pipe(num_inference_steps=2 ,generator=lowerCamelCase__ ,output_type='numpy' ).images
UpperCAmelCase__ = torch.manual_seed(0 )
UpperCAmelCase__ = pipe(num_inference_steps=2 ,generator=lowerCamelCase__ ,output_type='numpy' ,return_dict=lowerCamelCase__ )[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 snake_case ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : List[Any] ):
UpperCAmelCase__ = 'google/ncsnpp-celebahq-256'
UpperCAmelCase__ = UNetaDModel.from_pretrained(lowerCamelCase__ )
UpperCAmelCase__ = KarrasVeScheduler()
UpperCAmelCase__ = KarrasVePipeline(unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
UpperCAmelCase__ = torch.manual_seed(0 )
UpperCAmelCase__ = pipe(num_inference_steps=20 ,generator=lowerCamelCase__ ,output_type='numpy' ).images
UpperCAmelCase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase__ = np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 98 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : int = logging.get_logger(__name__)
lowerCAmelCase__ : str = {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
# See all XGLM models at https://huggingface.co/models?filter=xglm
}
class snake_case ( __UpperCAmelCase ):
"""simple docstring"""
snake_case__ = "xglm"
snake_case__ = ["past_key_values"]
snake_case__ = {
"num_attention_heads": "attention_heads",
"hidden_size": "d_model",
"num_hidden_layers": "num_layers",
}
def __init__( self : Any ,lowerCamelCase__ : Any=256_008 ,lowerCamelCase__ : Optional[Any]=2_048 ,lowerCamelCase__ : List[str]=1_024 ,lowerCamelCase__ : List[str]=4_096 ,lowerCamelCase__ : Tuple=24 ,lowerCamelCase__ : Optional[int]=16 ,lowerCamelCase__ : int="gelu" ,lowerCamelCase__ : Dict=0.1 ,lowerCamelCase__ : int=0.1 ,lowerCamelCase__ : List[Any]=0.0 ,lowerCamelCase__ : List[str]=0.0 ,lowerCamelCase__ : Optional[Any]=0.0_2 ,lowerCamelCase__ : List[str]=True ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : str=2 ,lowerCamelCase__ : Dict=1 ,lowerCamelCase__ : Optional[int]=0 ,lowerCamelCase__ : Tuple=2 ,**lowerCamelCase__ : List[Any] ,):
UpperCAmelCase__ = vocab_size
UpperCAmelCase__ = max_position_embeddings
UpperCAmelCase__ = d_model
UpperCAmelCase__ = ffn_dim
UpperCAmelCase__ = num_layers
UpperCAmelCase__ = attention_heads
UpperCAmelCase__ = activation_function
UpperCAmelCase__ = dropout
UpperCAmelCase__ = attention_dropout
UpperCAmelCase__ = activation_dropout
UpperCAmelCase__ = layerdrop
UpperCAmelCase__ = init_std
UpperCAmelCase__ = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase__ = use_cache
super().__init__(
pad_token_id=lowerCamelCase__ ,bos_token_id=lowerCamelCase__ ,eos_token_id=lowerCamelCase__ ,decoder_start_token_id=lowerCamelCase__ ,**lowerCamelCase__ ,)
| 98 | 1 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowercase__ : Union[str, Any] = get_logger(__name__)
class _UpperCAmelCase ( enum.Enum):
_lowerCAmelCase : Dict = """all_checks"""
_lowerCAmelCase : List[str] = """basic_checks"""
_lowerCAmelCase : List[str] = """no_checks"""
class _UpperCAmelCase ( _A):
pass
class _UpperCAmelCase ( _A):
pass
class _UpperCAmelCase ( _A):
pass
class _UpperCAmelCase ( _A):
pass
def __lowercase ( _a , _a , _a=None ):
"""simple docstring"""
if expected_checksums is None:
logger.info('''Unable to verify checksums.''' )
return
if len(set(lowercase__ ) - set(lowercase__ ) ) > 0:
raise ExpectedMoreDownloadedFiles(str(set(lowercase__ ) - set(lowercase__ ) ) )
if len(set(lowercase__ ) - set(lowercase__ ) ) > 0:
raise UnexpectedDownloadedFile(str(set(lowercase__ ) - set(lowercase__ ) ) )
snake_case_ : Optional[Any] = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
snake_case_ : str = ''' for ''' + verification_name if verification_name is not None else ''''''
if len(lowercase__ ) > 0:
raise NonMatchingChecksumError(
f"Checksums didn't match{for_verification_name}:\n"
f"{bad_urls}\n"
'''Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error''' )
logger.info('''All the checksums matched successfully''' + for_verification_name )
class _UpperCAmelCase ( _A):
pass
class _UpperCAmelCase ( _A):
pass
class _UpperCAmelCase ( _A):
pass
class _UpperCAmelCase ( _A):
pass
def __lowercase ( _a , _a ):
"""simple docstring"""
if expected_splits is None:
logger.info('''Unable to verify splits sizes.''' )
return
if len(set(lowercase__ ) - set(lowercase__ ) ) > 0:
raise ExpectedMoreSplits(str(set(lowercase__ ) - set(lowercase__ ) ) )
if len(set(lowercase__ ) - set(lowercase__ ) ) > 0:
raise UnexpectedSplits(str(set(lowercase__ ) - set(lowercase__ ) ) )
snake_case_ : List[str] = [
{'''expected''': expected_splits[name], '''recorded''': recorded_splits[name]}
for name in expected_splits
if expected_splits[name].num_examples != recorded_splits[name].num_examples
]
if len(lowercase__ ) > 0:
raise NonMatchingSplitsSizesError(str(lowercase__ ) )
logger.info('''All the splits matched successfully.''' )
def __lowercase ( _a , _a = True ):
"""simple docstring"""
if record_checksum:
snake_case_ : str = shaaaa()
with open(lowercase__ , '''rb''' ) as f:
for chunk in iter(lambda: f.read(1 << 20 ) , b'''''' ):
m.update(lowercase__ )
snake_case_ : List[Any] = m.hexdigest()
else:
snake_case_ : str = None
return {"num_bytes": os.path.getsize(lowercase__ ), "checksum": checksum}
def __lowercase ( _a ):
"""simple docstring"""
if dataset_size and config.IN_MEMORY_MAX_SIZE:
return dataset_size < config.IN_MEMORY_MAX_SIZE
else:
return False
| 363 |
"""simple docstring"""
from math import pow
def __lowercase ( _a , _a , _a , _a , _a , ):
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_count += 1
return current_sum, solutions_count
snake_case_ : int = int(pow(_a , _a ) )
if current_sum + i_to_n <= needed_sum:
# If the sum of the powers is less than needed_sum, then continue adding powers.
current_sum += i_to_n
snake_case_, snake_case_ : List[Any] = backtrack(
_a , _a , current_number + 1 , _a , _a )
current_sum -= i_to_n
if i_to_n < needed_sum:
# If the power of i is less than needed_sum, then try with the next power.
snake_case_, snake_case_ : str = backtrack(
_a , _a , current_number + 1 , _a , _a )
return current_sum, solutions_count
def __lowercase ( _a , _a ):
if not (1 <= needed_sum <= 1_000 and 2 <= power <= 10):
raise ValueError(
'''Invalid input\n'''
'''needed_sum must be between 1 and 1000, power between 2 and 10.''' )
return backtrack(_a , _a , 1 , 0 , 0 )[1] # Return the solutions_count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 155 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, 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 numpy as np
import tensorflow as tf
from transformers import (
TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaubertConfig,
TFFlaubertForMultipleChoice,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForSequenceClassification,
TFFlaubertForTokenClassification,
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
)
class _a :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase : Tuple , ):
A_ = parent
A_ = 13
A_ = 7
A_ = True
A_ = True
A_ = True
A_ = True
A_ = True
A_ = False
A_ = False
A_ = False
A_ = 2
A_ = 99
A_ = 0
A_ = 32
A_ = 2
A_ = 4
A_ = 0.1
A_ = 0.1
A_ = 512
A_ = 16
A_ = 2
A_ = 0.02
A_ = 3
A_ = 4
A_ = "last"
A_ = True
A_ = None
A_ = 0
def __A ( self : Tuple ):
A_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ = random_attention_mask([self.batch_size, self.seq_length] , dtype=tf.floataa )
A_ = None
if self.use_input_lengths:
A_ = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
A_ = None
if self.use_token_type_ids:
A_ = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
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] , 2 , dtype=tf.floataa )
A_ = ids_tensor([self.batch_size] , self.num_choices )
A_ = FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , bos_token_id=self.bos_token_id , )
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def __A ( self : int , UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : List[str] , UpperCAmelCase : Tuple , UpperCAmelCase : Any , UpperCAmelCase : Any , UpperCAmelCase : Tuple , UpperCAmelCase : Dict , UpperCAmelCase : Dict , ):
A_ = TFFlaubertModel(config=UpperCAmelCase )
A_ = {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
A_ = model(UpperCAmelCase )
A_ = [input_ids, input_mask]
A_ = model(UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __A ( self : int , UpperCAmelCase : str , UpperCAmelCase : Dict , UpperCAmelCase : str , UpperCAmelCase : Tuple , UpperCAmelCase : List[Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : Dict , UpperCAmelCase : int , ):
A_ = TFFlaubertWithLMHeadModel(UpperCAmelCase )
A_ = {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
A_ = model(UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self : Dict , UpperCAmelCase : List[str] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple , ):
A_ = TFFlaubertForQuestionAnsweringSimple(UpperCAmelCase )
A_ = {"input_ids": input_ids, "lengths": input_lengths}
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 __A ( self : List[str] , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Dict , UpperCAmelCase : List[str] , UpperCAmelCase : str , UpperCAmelCase : Tuple , ):
A_ = TFFlaubertForSequenceClassification(UpperCAmelCase )
A_ = {"input_ids": input_ids, "lengths": input_lengths}
A_ = model(UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __A ( self : Any , UpperCAmelCase : Dict , UpperCAmelCase : Any , UpperCAmelCase : Any , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple , ):
A_ = self.num_labels
A_ = TFFlaubertForTokenClassification(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 __A ( self : Any , UpperCAmelCase : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : str , UpperCAmelCase : Dict , UpperCAmelCase : List[Any] , ):
A_ = self.num_choices
A_ = TFFlaubertForMultipleChoice(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 __A ( self : Optional[int] ):
A_ = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) = config_and_inputs
A_ = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"langs": token_type_ids,
"lengths": input_lengths,
}
return config, inputs_dict
@require_tf
class _a ( snake_case_ , snake_case_ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase : int = (
(
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
TFFlaubertForSequenceClassification,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForTokenClassification,
TFFlaubertForMultipleChoice,
)
if is_tf_available()
else ()
)
_lowerCamelCase : int = (
(TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
_lowerCamelCase : int = (
{
'feature-extraction': TFFlaubertModel,
'fill-mask': TFFlaubertWithLMHeadModel,
'question-answering': TFFlaubertForQuestionAnsweringSimple,
'text-classification': TFFlaubertForSequenceClassification,
'token-classification': TFFlaubertForTokenClassification,
'zero-shot': TFFlaubertForSequenceClassification,
}
if is_tf_available()
else {}
)
_lowerCamelCase : List[Any] = False
_lowerCamelCase : Dict = False
def __A ( self : Any , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Any , UpperCAmelCase : int , UpperCAmelCase : Tuple ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def __A ( self : Union[str, Any] ):
A_ = TFFlaubertModelTester(self )
A_ = ConfigTester(self , config_class=UpperCAmelCase , emb_dim=37 )
def __A ( self : List[str] ):
self.config_tester.run_common_tests()
def __A ( self : int ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*UpperCAmelCase )
def __A ( self : Dict ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*UpperCAmelCase )
def __A ( self : Dict ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*UpperCAmelCase )
def __A ( self : Optional[Any] ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*UpperCAmelCase )
def __A ( self : Tuple ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_for_token_classification(*UpperCAmelCase )
def __A ( self : List[Any] ):
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_for_multiple_choice(*UpperCAmelCase )
@slow
def __A ( self : str ):
for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ = TFFlaubertModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
@require_tf
@require_sentencepiece
@require_tokenizers
class _a ( unittest.TestCase ):
"""simple docstring"""
@slow
def __A ( self : Any ):
A_ = TFFlaubertModel.from_pretrained("jplu/tf-flaubert-small-cased" )
A_ = tf.convert_to_tensor(
[[0, 158, 735, 2592, 1424, 6727, 82, 1]] , dtype=tf.intaa , ) # "J'aime flaubert !"
A_ = model(UpperCAmelCase )[0]
A_ = tf.TensorShape((1, 8, 512) )
self.assertEqual(output.shape , UpperCAmelCase )
# compare the actual values for a slice.
A_ = tf.convert_to_tensor(
[
[
[-1.8_768_773, -1.566_555, 0.27_072_418],
[-1.6_920_038, -0.5_873_505, 1.9_329_599],
[-2.9_563_985, -1.6_993_835, 1.7_972_052],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) ) | 312 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging
__a :int = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
from transformers import Trainer
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : Tuple , UpperCAmelCase : int = 101 ):
A_ = length
def __len__( self : int ):
return self.length
def __getitem__( self : Optional[int] , UpperCAmelCase : Optional[int] ):
return i
class _a :
"""simple docstring"""
def __call__( self : Any , UpperCAmelCase : Optional[Any] ):
return {"input_ids": torch.tensor(UpperCAmelCase ), "labels": torch.tensor(UpperCAmelCase )}
class _a ( nn.Module ):
"""simple docstring"""
def __init__( self : int ):
super().__init__()
# Add some (unused) params otherwise DDP will complain.
A_ = nn.Linear(120 , 80 )
def __A ( self : Tuple , UpperCAmelCase : Dict , UpperCAmelCase : Tuple=None ):
if labels is not None:
return torch.tensor(0.0 , device=input_ids.device ), input_ids
else:
return input_ids
class _a ( snake_case_ ):
"""simple docstring"""
@require_torch_neuroncore
def __A ( self : List[str] ):
A_ = f'''--nproc_per_node=2
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
'''.split()
A_ = self.get_auto_remove_tmp_dir()
A_ = f'''--output_dir {output_dir}'''.split()
A_ = ["torchrun"] + distributed_args + args
execute_subprocess_async(UpperCAmelCase , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
class _a ( snake_case_ ):
"""simple docstring"""
@require_torch_multi_gpu
def __A ( self : List[str] ):
A_ = f'''--nproc_per_node={torch.cuda.device_count()}
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
'''.split()
A_ = self.get_auto_remove_tmp_dir()
A_ = f'''--output_dir {output_dir}'''.split()
A_ = ["torchrun"] + distributed_args + args
execute_subprocess_async(UpperCAmelCase , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
__a :Union[str, Any] = HfArgumentParser((TrainingArguments,))
__a :Tuple = parser.parse_args_into_dataclasses()[0]
logger.warning(
F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
F"distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"
)
# Essentially, what we want to verify in the distributed case is that we get all samples back,
# in the right order. (this is crucial for prediction for instance)
for dataset_length in [101, 40, 7]:
__a :int = DummyDataset(dataset_length)
def __snake_case ( __UpperCamelCase : EvalPrediction ):
"""simple docstring"""
A_ = list(range(len(__UpperCamelCase ) ) )
A_ = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
if not success and training_args.local_rank == 0:
logger.warning(
"Predictions and/or labels do not match expected results:\n - predictions: "
f'''{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}''' )
return {"success": success}
__a :str = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
__a :str = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
__a :str = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
__a :Optional[int] = 2
__a :List[Any] = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
__a :str = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
__a :Union[str, Any] = None | 312 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
from transformers import TvltProcessor
@require_torch
class lowerCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : int ):
'''simple docstring'''
__UpperCAmelCase : Optional[int] = """ZinengTang/tvlt-base"""
__UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
def lowerCamelCase__ ( self : Union[str, Any] , **UpperCamelCase : int ):
'''simple docstring'''
return TvltImageProcessor.from_pretrained(self.checkpoint , **UpperCamelCase )
def lowerCamelCase__ ( self : int , **UpperCamelCase : Any ):
'''simple docstring'''
return TvltFeatureExtractor.from_pretrained(self.checkpoint , **UpperCamelCase )
def lowerCamelCase__ ( self : Optional[int] ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowerCamelCase__ ( self : Optional[int] ):
'''simple docstring'''
__UpperCAmelCase : Tuple = self.get_image_processor()
__UpperCAmelCase : int = self.get_feature_extractor()
__UpperCAmelCase : int = TvltProcessor(image_processor=UpperCamelCase , feature_extractor=UpperCamelCase )
processor.save_pretrained(self.tmpdirname )
__UpperCAmelCase : Optional[Any] = TvltProcessor.from_pretrained(self.tmpdirname )
self.assertIsInstance(processor.feature_extractor , UpperCamelCase )
self.assertIsInstance(processor.image_processor , UpperCamelCase )
def lowerCamelCase__ ( self : Optional[Any] ):
'''simple docstring'''
__UpperCAmelCase : int = self.get_image_processor()
__UpperCAmelCase : int = self.get_feature_extractor()
__UpperCAmelCase : Tuple = TvltProcessor(image_processor=UpperCamelCase , feature_extractor=UpperCamelCase )
__UpperCAmelCase : Tuple = np.ones([12_000] )
__UpperCAmelCase : List[Any] = feature_extractor(UpperCamelCase , return_tensors="""np""" )
__UpperCAmelCase : Optional[Any] = processor(audio=UpperCamelCase , return_tensors="""np""" )
for key in audio_dict.keys():
self.assertAlmostEqual(audio_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def lowerCamelCase__ ( self : Optional[Any] ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = self.get_image_processor()
__UpperCAmelCase : Optional[int] = self.get_feature_extractor()
__UpperCAmelCase : List[Any] = TvltProcessor(image_processor=UpperCamelCase , feature_extractor=UpperCamelCase )
__UpperCAmelCase : str = np.ones([3, 224, 224] )
__UpperCAmelCase : Any = image_processor(UpperCamelCase , return_tensors="""np""" )
__UpperCAmelCase : List[Any] = processor(images=UpperCamelCase , return_tensors="""np""" )
for key in image_dict.keys():
self.assertAlmostEqual(image_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def lowerCamelCase__ ( self : Optional[Any] ):
'''simple docstring'''
__UpperCAmelCase : List[str] = self.get_image_processor()
__UpperCAmelCase : Dict = self.get_feature_extractor()
__UpperCAmelCase : Tuple = TvltProcessor(image_processor=UpperCamelCase , feature_extractor=UpperCamelCase )
__UpperCAmelCase : Dict = np.ones([12_000] )
__UpperCAmelCase : Union[str, Any] = np.ones([3, 224, 224] )
__UpperCAmelCase : Dict = processor(audio=UpperCamelCase , images=UpperCamelCase )
self.assertListEqual(list(inputs.keys() ) , ["""audio_values""", """audio_mask""", """pixel_values""", """pixel_mask"""] )
# test if it raises when no input is passed
with pytest.raises(UpperCamelCase ):
processor()
def lowerCamelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
__UpperCAmelCase : str = self.get_image_processor()
__UpperCAmelCase : str = self.get_feature_extractor()
__UpperCAmelCase : Tuple = TvltProcessor(image_processor=UpperCamelCase , feature_extractor=UpperCamelCase )
self.assertListEqual(
processor.model_input_names , image_processor.model_input_names + feature_extractor.model_input_names , msg="""`processor` and `image_processor`+`feature_extractor` model input names do not match""" , )
| 360 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .scheduling_lms_discrete import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 320 | 0 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : int = 10_00 ) -> str:
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 185 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def UpperCamelCase( __UpperCamelCase : List[str] ):
lowerCAmelCase_ : List[str] = SwinvaConfig()
lowerCAmelCase_ : List[str] = swinva_name.split('''_''' )
lowerCAmelCase_ : str = name_split[1]
if "to" in name_split[3]:
lowerCAmelCase_ : List[Any] = int(name_split[3][-3:] )
else:
lowerCAmelCase_ : List[Any] = int(name_split[3] )
if "to" in name_split[2]:
lowerCAmelCase_ : List[str] = int(name_split[2][-2:] )
else:
lowerCAmelCase_ : int = int(name_split[2][6:] )
if model_size == "tiny":
lowerCAmelCase_ : Any = 96
lowerCAmelCase_ : List[str] = (2, 2, 6, 2)
lowerCAmelCase_ : Union[str, Any] = (3, 6, 12, 24)
elif model_size == "small":
lowerCAmelCase_ : List[str] = 96
lowerCAmelCase_ : Any = (2, 2, 18, 2)
lowerCAmelCase_ : Dict = (3, 6, 12, 24)
elif model_size == "base":
lowerCAmelCase_ : Union[str, Any] = 128
lowerCAmelCase_ : List[Any] = (2, 2, 18, 2)
lowerCAmelCase_ : Tuple = (4, 8, 16, 32)
else:
lowerCAmelCase_ : Optional[Any] = 192
lowerCAmelCase_ : List[Any] = (2, 2, 18, 2)
lowerCAmelCase_ : List[Any] = (6, 12, 24, 48)
if "to" in swinva_name:
lowerCAmelCase_ : Union[str, Any] = (12, 12, 12, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
lowerCAmelCase_ : Optional[int] = 21841
lowerCAmelCase_ : Any = '''huggingface/label-files'''
lowerCAmelCase_ : Tuple = '''imagenet-22k-id2label.json'''
lowerCAmelCase_ : Any = json.load(open(hf_hub_download(__UpperCamelCase ,__UpperCamelCase ,repo_type='''dataset''' ) ,'''r''' ) )
lowerCAmelCase_ : Optional[Any] = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
lowerCAmelCase_ : str = idalabel
lowerCAmelCase_ : List[str] = {v: k for k, v in idalabel.items()}
else:
lowerCAmelCase_ : Optional[int] = 1000
lowerCAmelCase_ : Tuple = '''huggingface/label-files'''
lowerCAmelCase_ : Union[str, Any] = '''imagenet-1k-id2label.json'''
lowerCAmelCase_ : Dict = json.load(open(hf_hub_download(__UpperCamelCase ,__UpperCamelCase ,repo_type='''dataset''' ) ,'''r''' ) )
lowerCAmelCase_ : int = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
lowerCAmelCase_ : List[str] = idalabel
lowerCAmelCase_ : Optional[int] = {v: k for k, v in idalabel.items()}
lowerCAmelCase_ : Optional[int] = img_size
lowerCAmelCase_ : Dict = num_classes
lowerCAmelCase_ : Dict = embed_dim
lowerCAmelCase_ : Optional[Any] = depths
lowerCAmelCase_ : Optional[int] = num_heads
lowerCAmelCase_ : Dict = window_size
return config
def UpperCamelCase( __UpperCamelCase : List[str] ):
if "patch_embed.proj" in name:
lowerCAmelCase_ : Dict = name.replace('''patch_embed.proj''' ,'''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
lowerCAmelCase_ : List[Any] = name.replace('''patch_embed.norm''' ,'''embeddings.norm''' )
if "layers" in name:
lowerCAmelCase_ : int = '''encoder.''' + name
if "attn.proj" in name:
lowerCAmelCase_ : Union[str, Any] = name.replace('''attn.proj''' ,'''attention.output.dense''' )
if "attn" in name:
lowerCAmelCase_ : Optional[Any] = name.replace('''attn''' ,'''attention.self''' )
if "norm1" in name:
lowerCAmelCase_ : Union[str, Any] = name.replace('''norm1''' ,'''layernorm_before''' )
if "norm2" in name:
lowerCAmelCase_ : Tuple = name.replace('''norm2''' ,'''layernorm_after''' )
if "mlp.fc1" in name:
lowerCAmelCase_ : Optional[Any] = name.replace('''mlp.fc1''' ,'''intermediate.dense''' )
if "mlp.fc2" in name:
lowerCAmelCase_ : Tuple = name.replace('''mlp.fc2''' ,'''output.dense''' )
if "q_bias" in name:
lowerCAmelCase_ : Tuple = name.replace('''q_bias''' ,'''query.bias''' )
if "k_bias" in name:
lowerCAmelCase_ : Tuple = name.replace('''k_bias''' ,'''key.bias''' )
if "v_bias" in name:
lowerCAmelCase_ : int = name.replace('''v_bias''' ,'''value.bias''' )
if "cpb_mlp" in name:
lowerCAmelCase_ : Any = name.replace('''cpb_mlp''' ,'''continuous_position_bias_mlp''' )
if name == "norm.weight":
lowerCAmelCase_ : Dict = '''layernorm.weight'''
if name == "norm.bias":
lowerCAmelCase_ : Any = '''layernorm.bias'''
if "head" in name:
lowerCAmelCase_ : int = name.replace('''head''' ,'''classifier''' )
else:
lowerCAmelCase_ : Union[str, Any] = '''swinv2.''' + name
return name
def UpperCamelCase( __UpperCamelCase : Any ,__UpperCamelCase : Optional[int] ):
for key in orig_state_dict.copy().keys():
lowerCAmelCase_ : Optional[int] = orig_state_dict.pop(__UpperCamelCase )
if "mask" in key:
continue
elif "qkv" in key:
lowerCAmelCase_ : Dict = key.split('''.''' )
lowerCAmelCase_ : Any = int(key_split[1] )
lowerCAmelCase_ : Optional[int] = int(key_split[3] )
lowerCAmelCase_ : Dict = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
lowerCAmelCase_ : Optional[Any] = val[:dim, :]
lowerCAmelCase_ : Any = val[dim : dim * 2, :]
lowerCAmelCase_ : List[Any] = val[-dim:, :]
else:
lowerCAmelCase_ : Dict = val[:dim]
lowerCAmelCase_ : Union[str, Any] = val[
dim : dim * 2
]
lowerCAmelCase_ : Dict = val[-dim:]
else:
lowerCAmelCase_ : Optional[Any] = val
return orig_state_dict
def UpperCamelCase( __UpperCamelCase : int ,__UpperCamelCase : Dict ):
lowerCAmelCase_ : Optional[Any] = timm.create_model(__UpperCamelCase ,pretrained=__UpperCamelCase )
timm_model.eval()
lowerCAmelCase_ : List[str] = get_swinva_config(__UpperCamelCase )
lowerCAmelCase_ : Union[str, Any] = SwinvaForImageClassification(__UpperCamelCase )
model.eval()
lowerCAmelCase_ : str = convert_state_dict(timm_model.state_dict() ,__UpperCamelCase )
model.load_state_dict(__UpperCamelCase )
lowerCAmelCase_ : List[Any] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowerCAmelCase_ : Tuple = AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_''' ,'''-''' ) ) )
lowerCAmelCase_ : Union[str, Any] = Image.open(requests.get(__UpperCamelCase ,stream=__UpperCamelCase ).raw )
lowerCAmelCase_ : Optional[Any] = image_processor(images=__UpperCamelCase ,return_tensors='''pt''' )
lowerCAmelCase_ : List[str] = timm_model(inputs['''pixel_values'''] )
lowerCAmelCase_ : Union[str, Any] = model(**__UpperCamelCase ).logits
assert torch.allclose(__UpperCamelCase ,__UpperCamelCase ,atol=1e-3 )
print(f"""Saving model {swinva_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__UpperCamelCase )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__UpperCamelCase )
model.push_to_hub(
repo_path_or_name=Path(__UpperCamelCase ,__UpperCamelCase ) ,organization='''nandwalritik''' ,commit_message='''Add model''' ,)
if __name__ == "__main__":
A__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--swinv2_name''',
default='''swinv2_tiny_patch4_window8_256''',
type=str,
help='''Name of the Swinv2 timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
A__ : Optional[Any] = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
| 103 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = KandinskyVaaControlnetPipeline
_SCREAMING_SNAKE_CASE :Optional[int] = ["""image_embeds""", """negative_image_embeds""", """hint"""]
_SCREAMING_SNAKE_CASE :int = ["""image_embeds""", """negative_image_embeds""", """hint"""]
_SCREAMING_SNAKE_CASE :Dict = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
_SCREAMING_SNAKE_CASE :Tuple = False
@property
def _a ( self ) -> Any:
"""simple docstring"""
return 32
@property
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
return 32
@property
def _a ( self ) -> List[str]:
"""simple docstring"""
return self.time_input_dim
@property
def _a ( self ) -> str:
"""simple docstring"""
return self.time_input_dim * 4
@property
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return 100
@property
def _a ( self ) -> Any:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Any = {
"""in_channels""": 8,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """image_hint""",
"""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,
}
SCREAMING_SNAKE_CASE__ : Optional[int] = UNetaDConditionModel(**_a )
return model
@property
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def _a ( self ) -> List[Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : List[str] = VQModel(**self.dummy_movq_kwargs )
return model
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.dummy_unet
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.dummy_movq
SCREAMING_SNAKE_CASE__ : Any = DDIMScheduler(
num_train_timesteps=1_000 , beta_schedule="""linear""" , beta_start=0.00_085 , beta_end=0.012 , clip_sample=_a , set_alpha_to_one=_a , steps_offset=1 , prediction_type="""epsilon""" , thresholding=_a , )
SCREAMING_SNAKE_CASE__ : str = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def _a ( self , _a , _a=0 ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(_a ) ).to(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
_a )
# create hint
SCREAMING_SNAKE_CASE__ : Dict = floats_tensor((1, 3, 64, 64) , rng=random.Random(_a ) ).to(_a )
if str(_a ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(_a )
else:
SCREAMING_SNAKE_CASE__ : Tuple = torch.Generator(device=_a ).manual_seed(_a )
SCREAMING_SNAKE_CASE__ : Tuple = {
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""hint""": hint,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""guidance_scale""": 4.0,
"""num_inference_steps""": 2,
"""output_type""": """np""",
}
return inputs
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = """cpu"""
SCREAMING_SNAKE_CASE__ : Any = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : str = self.pipeline_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : Dict = pipe(**self.get_dummy_inputs(_a ) )
SCREAMING_SNAKE_CASE__ : Optional[int] = output.images
SCREAMING_SNAKE_CASE__ : Any = pipe(
**self.get_dummy_inputs(_a ) , return_dict=_a , )[0]
SCREAMING_SNAKE_CASE__ : int = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : Optional[int] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE__ : Tuple = np.array(
[0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> Any:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy""" )
SCREAMING_SNAKE_CASE__ : List[str] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/hint_image_cat.png""" )
SCREAMING_SNAKE_CASE__ : int = torch.from_numpy(np.array(_a ) ).float() / 255.0
SCREAMING_SNAKE_CASE__ : List[Any] = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = KandinskyVaaControlnetPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-controlnet-depth""" , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : Dict = pipeline.to(_a )
pipeline.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : Tuple = """A robot, 4k photo"""
SCREAMING_SNAKE_CASE__ : Dict = torch.Generator(device="""cuda""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : List[Any] = pipe_prior(
_a , generator=_a , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
SCREAMING_SNAKE_CASE__ : int = torch.Generator(device="""cuda""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = pipeline(
image_embeds=_a , negative_image_embeds=_a , hint=_a , generator=_a , num_inference_steps=100 , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : List[Any] = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(_a , _a )
| 352 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
while b:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = b, a % b
return a
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
return a if b == 0 else euclidean_gcd_recursive(__lowerCAmelCase , a % b )
def _lowercase ( ) -> Union[str, Any]:
print(F'''euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}''' )
print(F'''euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}''' )
print(F'''euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}''' )
print(F'''euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}''' )
print(F'''euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}''' )
print(F'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}''' )
print(F'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}''' )
print(F'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}''' )
print(F'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}''' )
print(F'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}''' )
if __name__ == "__main__":
main()
| 56 | 0 |
'''simple docstring'''
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path import join as renamed_join # noqa: this is just for tests
__lowerCAmelCase : Optional[Any] =open # noqa: we just need to have a builtin inside this module to test it properly
| 237 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging
if is_vision_available():
import PIL
if is_torch_available():
import torch
_A : List[Any] = logging.get_logger(__name__)
class a__ ( a_ ):
__lowerCAmelCase = ["""pixel_values"""]
def __init__( self , _a = True , _a = None , _a = PILImageResampling.BILINEAR , _a = True , _a = 1 / 255 , _a = True , _a = None , _a = True , **_a , ):
super().__init__(**_a )
lowercase : Optional[Any] = size if size is not None else {"shortest_edge": 224}
lowercase : List[Any] = get_size_dict(_a , default_to_square=_a )
lowercase : str = crop_size if crop_size is not None else {"height": 256, "width": 256}
lowercase : List[str] = get_size_dict(_a , param_name="crop_size" )
lowercase : int = do_resize
lowercase : Optional[int] = size
lowercase : str = resample
lowercase : List[Any] = do_rescale
lowercase : Union[str, Any] = rescale_factor
lowercase : Optional[int] = do_center_crop
lowercase : Union[str, Any] = crop_size
lowercase : Optional[Any] = do_flip_channel_order
def __magic_name__ ( self , _a , _a , _a = PIL.Image.BILINEAR , _a = None , **_a , ):
lowercase : List[Any] = get_size_dict(_a , default_to_square=_a )
if "shortest_edge" not in size:
raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}""" )
lowercase : Union[str, Any] = get_resize_output_image_size(_a , size=size["shortest_edge"] , default_to_square=_a )
return resize(_a , size=_a , resample=_a , data_format=_a , **_a )
def __magic_name__ ( self , _a , _a , _a = None , **_a , ):
lowercase : str = get_size_dict(_a )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" )
return center_crop(_a , size=(size["height"], size["width"]) , data_format=_a , **_a )
def __magic_name__ ( self , _a , _a , _a = None , **_a , ):
return rescale(_a , scale=_a , data_format=_a , **_a )
def __magic_name__ ( self , _a , _a = None ):
return flip_channel_order(_a , data_format=_a )
def __magic_name__ ( self , _a , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = ChannelDimension.FIRST , **_a , ):
lowercase : Optional[Any] = do_resize if do_resize is not None else self.do_resize
lowercase : Tuple = resample if resample is not None else self.resample
lowercase : List[str] = do_rescale if do_rescale is not None else self.do_rescale
lowercase : List[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase : Union[str, Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase : Optional[int] = (
do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
)
lowercase : str = size if size is not None else self.size
lowercase : Any = get_size_dict(_a , default_to_square=_a )
lowercase : int = crop_size if crop_size is not None else self.crop_size
lowercase : Any = get_size_dict(_a , param_name="crop_size" )
lowercase : int = make_list_of_images(_a )
if not valid_images(_a ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
# All transformations expect numpy arrays.
lowercase : Any = [to_numpy_array(_a ) for image in images]
if do_resize:
lowercase : Optional[int] = [self.resize(image=_a , size=_a , resample=_a ) for image in images]
if do_center_crop:
lowercase : str = [self.center_crop(image=_a , size=_a ) for image in images]
if do_rescale:
lowercase : Union[str, Any] = [self.rescale(image=_a , scale=_a ) for image in images]
# the pretrained checkpoints assume images are BGR, not RGB
if do_flip_channel_order:
lowercase : int = [self.flip_channel_order(image=_a ) for image in images]
lowercase : int = [to_channel_dimension_format(_a , _a ) for image in images]
lowercase : Optional[Any] = {"pixel_values": images}
return BatchFeature(data=_a , tensor_type=_a )
def __magic_name__ ( self , _a , _a = None ):
lowercase : Optional[int] = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_a ) != len(_a ):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits" )
if is_torch_tensor(_a ):
lowercase : Tuple = target_sizes.numpy()
lowercase : List[Any] = []
for idx in range(len(_a ) ):
lowercase : Dict = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="bilinear" , align_corners=_a )
lowercase : Tuple = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_a )
else:
lowercase : str = logits.argmax(dim=1 )
lowercase : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 202 | 0 |
'''simple docstring'''
import pytest
a_ = '__dummy_dataset1__'
a_ = '\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( ):
'''simple docstring'''
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def _a( ):
'''simple docstring'''
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def _a( UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : List[str], UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =dataset_loading_script_name
SCREAMING_SNAKE_CASE__ : str =tmp_path / '''datasets''' / script_name
script_dir.mkdir(parents=UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : int =script_dir / f"{script_name}.py"
with open(UpperCamelCase__, '''w''' ) as f:
f.write(UpperCamelCase__ )
return str(UpperCamelCase__ ) | 222 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVisionConfig',
],
'processing_instructblip': ['InstructBlipProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'InstructBlipQFormerModel',
'InstructBlipPreTrainedModel',
'InstructBlipForConditionalGeneration',
'InstructBlipVisionModel',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 222 | 1 |
from __future__ import annotations
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
# Checks if the entire collection has been sorted
if len(lowerCamelCase ) <= 1 or n <= 1:
return
insert_next(lowerCamelCase, n - 1 )
rec_insertion_sort(lowerCamelCase, n - 1 )
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
# Checks order between adjacent elements
if index >= len(lowerCamelCase ) or collection[index - 1] <= collection[index]:
return
# Swaps adjacent elements since they are not in ascending order
lowercase , lowercase :Union[str, Any] = (
collection[index],
collection[index - 1],
)
insert_next(lowerCamelCase, index + 1 )
if __name__ == "__main__":
_UpperCAmelCase : int = input("Enter integers separated by spaces: ")
_UpperCAmelCase : list[int] = [int(num) for num in numbers.split()]
rec_insertion_sort(number_list, len(number_list))
print(number_list)
| 236 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
_UpperCAmelCase : Tuple = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
"openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}
# fmt: off
_UpperCAmelCase : Union[str, Any] = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
_UpperCAmelCase : List[Any] = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]
class __lowerCAmelCase ( lowerCAmelCase):
_a = '''whisper'''
_a = ['''past_key_values''']
_a = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self: int , _lowerCAmelCase: str=5_18_65 , _lowerCAmelCase: str=80 , _lowerCAmelCase: int=6 , _lowerCAmelCase: Tuple=4 , _lowerCAmelCase: Union[str, Any]=6 , _lowerCAmelCase: List[Any]=4 , _lowerCAmelCase: Any=15_36 , _lowerCAmelCase: Union[str, Any]=15_36 , _lowerCAmelCase: str=0.0 , _lowerCAmelCase: str=0.0 , _lowerCAmelCase: List[Any]=5_02_57 , _lowerCAmelCase: Optional[Any]=True , _lowerCAmelCase: Tuple=True , _lowerCAmelCase: str="gelu" , _lowerCAmelCase: Dict=2_56 , _lowerCAmelCase: Union[str, Any]=0.0 , _lowerCAmelCase: Any=0.0 , _lowerCAmelCase: Dict=0.0 , _lowerCAmelCase: Union[str, Any]=0.02 , _lowerCAmelCase: Any=False , _lowerCAmelCase: List[str]=15_00 , _lowerCAmelCase: Tuple=4_48 , _lowerCAmelCase: Optional[Any]=5_02_56 , _lowerCAmelCase: Dict=5_02_56 , _lowerCAmelCase: List[Any]=5_02_56 , _lowerCAmelCase: Union[str, Any]=None , _lowerCAmelCase: str=[2_20, 5_02_56] , _lowerCAmelCase: Optional[int]=False , _lowerCAmelCase: Optional[int]=2_56 , _lowerCAmelCase: int=False , _lowerCAmelCase: Dict=0.05 , _lowerCAmelCase: Optional[Any]=10 , _lowerCAmelCase: List[str]=2 , _lowerCAmelCase: Tuple=0.0 , _lowerCAmelCase: str=10 , _lowerCAmelCase: Union[str, Any]=0 , _lowerCAmelCase: List[Any]=7 , **_lowerCAmelCase: Union[str, Any] , ):
lowercase :Optional[Any] = vocab_size
lowercase :Optional[int] = num_mel_bins
lowercase :Union[str, Any] = d_model
lowercase :List[Any] = encoder_layers
lowercase :Optional[Any] = encoder_attention_heads
lowercase :Union[str, Any] = decoder_layers
lowercase :List[str] = decoder_attention_heads
lowercase :Optional[int] = decoder_ffn_dim
lowercase :List[Any] = encoder_ffn_dim
lowercase :Optional[Any] = dropout
lowercase :Tuple = attention_dropout
lowercase :Tuple = activation_dropout
lowercase :Optional[Any] = activation_function
lowercase :Any = init_std
lowercase :Optional[int] = encoder_layerdrop
lowercase :Optional[int] = decoder_layerdrop
lowercase :str = use_cache
lowercase :Optional[Any] = encoder_layers
lowercase :List[Any] = scale_embedding # scale factor will be sqrt(d_model) if True
lowercase :Any = max_source_positions
lowercase :Optional[Any] = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
lowercase :int = classifier_proj_size
lowercase :List[str] = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowercase :Tuple = apply_spec_augment
lowercase :int = mask_time_prob
lowercase :Union[str, Any] = mask_time_length
lowercase :Dict = mask_time_min_masks
lowercase :Tuple = mask_feature_prob
lowercase :List[Any] = mask_feature_length
lowercase :List[Any] = mask_feature_min_masks
lowercase :Any = median_filter_width
super().__init__(
pad_token_id=_lowerCAmelCase , bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , is_encoder_decoder=_lowerCAmelCase , decoder_start_token_id=_lowerCAmelCase , suppress_tokens=_lowerCAmelCase , begin_suppress_tokens=_lowerCAmelCase , **_lowerCAmelCase , )
class __lowerCAmelCase ( lowerCAmelCase):
@property
def SCREAMING_SNAKE_CASE ( self: str ):
lowercase :Tuple = OrderedDict(
[
("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
] )
if self.use_past:
lowercase :List[Any] = {0: "batch"}
else:
lowercase :str = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(_lowerCAmelCase , direction="inputs" )
return common_inputs
def SCREAMING_SNAKE_CASE ( self: Dict , _lowerCAmelCase: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , _lowerCAmelCase: int = -1 , _lowerCAmelCase: int = -1 , _lowerCAmelCase: bool = False , _lowerCAmelCase: Optional["TensorType"] = None , _lowerCAmelCase: int = 2_20_50 , _lowerCAmelCase: float = 5.0 , _lowerCAmelCase: int = 2_20 , ):
lowercase :List[str] = OrderedDict()
lowercase :str = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=_lowerCAmelCase , framework=_lowerCAmelCase , sampling_rate=_lowerCAmelCase , time_duration=_lowerCAmelCase , frequency=_lowerCAmelCase , )
lowercase :Optional[Any] = encoder_inputs["input_features"].shape[2]
lowercase :List[str] = encoder_sequence_length // 2 if self.use_past else seq_length
lowercase :Dict = super().generate_dummy_inputs(
preprocessor.tokenizer , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
lowercase :str = encoder_inputs.pop("input_features" )
lowercase :Optional[int] = decoder_inputs.pop("decoder_input_ids" )
if "past_key_values" in decoder_inputs:
lowercase :List[str] = decoder_inputs.pop("past_key_values" )
return dummy_inputs
@property
def SCREAMING_SNAKE_CASE ( self: str ):
return 1e-3
| 236 | 1 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase__( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : List[str] )->List[Any]:
# Initialise PyTorch model
A__ = FunnelConfig.from_json_file(UpperCAmelCase__ )
print(f"Building PyTorch model from configuration: {config}" )
A__ = FunnelBaseModel(UpperCAmelCase__ ) if base_model else FunnelModel(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(f"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict() , UpperCAmelCase__ )
if __name__ == "__main__":
a__: Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--base_model', action='store_true', help='Whether you want just the base model (no decoder) or not.'
)
a__: Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 371 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
a__: int = {
'gwf-440k': {
'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
'sample_rate': 48_000,
'sample_size': 65_536,
},
'jmann-small-190k': {
'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
'sample_rate': 48_000,
'sample_size': 65_536,
},
'jmann-large-580k': {
'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
'sample_rate': 48_000,
'sample_size': 131_072,
},
'maestro-uncond-150k': {
'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
'sample_rate': 16_000,
'sample_size': 65_536,
},
'unlocked-uncond-250k': {
'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
'sample_rate': 16_000,
'sample_size': 65_536,
},
'honk-140k': {
'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
'sample_rate': 16_000,
'sample_size': 65_536,
},
}
def UpperCamelCase__( UpperCamelCase__ : str , UpperCamelCase__ : Union[str, Any] )->List[str]:
return torch.atana(UpperCamelCase__ , UpperCamelCase__ ) / math.pi * 2
def UpperCamelCase__( UpperCamelCase__ : str )->List[Any]:
A__ = torch.sin(t * math.pi / 2 ) ** 2
A__ = (1 - sigma**2) ** 0.5
return alpha_sigma_to_t(UpperCamelCase__ , UpperCamelCase__ )
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
pass
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self,__lowerCamelCase ):
super().__init__()
A__ = DiffusionAttnUnetaD(__lowerCamelCase,n_attn_layers=4 )
A__ = deepcopy(self.diffusion )
A__ = torch.quasirandom.SobolEngine(1,scramble=__lowerCamelCase )
def UpperCamelCase__( UpperCamelCase__ : Optional[Any] )->List[Any]:
A__ = MODELS_MAP[model_name]['''url''']
os.system(f"wget {url} ./" )
return f"./{model_name}.ckpt"
a__: Union[str, Any] = {
'1': 'resnets.0',
'2': 'attentions.0',
'3': 'resnets.1',
'4': 'attentions.1',
'5': 'resnets.2',
'6': 'attentions.2',
}
a__: Union[str, Any] = {
'8': 'resnets.0',
'9': 'attentions.0',
'10': 'resnets.1',
'11': 'attentions.1',
'12': 'resnets.2',
'13': 'attentions.2',
}
a__: str = {
'1': 'resnets.0',
'2': 'attentions.0',
'3': 'resnets.1',
'4': 'attentions.1',
'5': 'resnets.2',
'6': 'attentions.2',
'8': 'resnets.3',
'9': 'attentions.3',
'10': 'resnets.4',
'11': 'attentions.4',
'12': 'resnets.5',
'13': 'attentions.5',
}
a__: List[str] = {
'0': 'resnets.0',
'1': 'resnets.1',
'2': 'resnets.2',
'4': 'resnets.0',
'5': 'resnets.1',
'6': 'resnets.2',
}
a__: Dict = {
'skip': 'conv_skip',
'main.0': 'conv_1',
'main.1': 'group_norm_1',
'main.3': 'conv_2',
'main.4': 'group_norm_2',
}
a__: List[str] = {
'norm': 'group_norm',
'qkv_proj': ['query', 'key', 'value'],
'out_proj': ['proj_attn'],
}
def UpperCamelCase__( UpperCamelCase__ : Optional[Any] )->Optional[Any]:
if name.startswith('''skip''' ):
return name.replace('''skip''' , RES_CONV_MAP['''skip'''] )
# name has to be of format main.{digit}
if not name.startswith('''main.''' ):
raise ValueError(f"ResConvBlock error with {name}" )
return name.replace(name[:6] , RES_CONV_MAP[name[:6]] )
def UpperCamelCase__( UpperCamelCase__ : str )->Any:
for key, value in ATTN_MAP.items():
if name.startswith(UpperCamelCase__ ) and not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
return name.replace(UpperCamelCase__ , UpperCamelCase__ )
elif name.startswith(UpperCamelCase__ ):
return [name.replace(UpperCamelCase__ , UpperCamelCase__ ) for v in value]
raise ValueError(f"Attn error with {name}" )
def UpperCamelCase__( UpperCamelCase__ : Any , UpperCamelCase__ : List[Any]=13 )->Optional[Any]:
A__ = input_string
if string.split('''.''' )[0] == "timestep_embed":
return string.replace('''timestep_embed''' , '''time_proj''' )
A__ = 0
if string.startswith('''net.3.''' ):
depth += 1
A__ = string[6:]
elif string.startswith('''net.''' ):
A__ = string[4:]
while string.startswith('''main.7.''' ):
depth += 1
A__ = string[7:]
if string.startswith('''main.''' ):
A__ = string[5:]
# mid block
if string[:2].isdigit():
A__ = string[:2]
A__ = string[2:]
else:
A__ = string[0]
A__ = string[1:]
if depth == max_depth:
A__ = MID_NUM_TO_LAYER[layer_num]
A__ = '''mid_block'''
elif depth > 0 and int(UpperCamelCase__ ) < 7:
A__ = DOWN_NUM_TO_LAYER[layer_num]
A__ = f"down_blocks.{depth}"
elif depth > 0 and int(UpperCamelCase__ ) > 7:
A__ = UP_NUM_TO_LAYER[layer_num]
A__ = f"up_blocks.{max_depth - depth - 1}"
elif depth == 0:
A__ = DEPTH_0_TO_LAYER[layer_num]
A__ = f"up_blocks.{max_depth - 1}" if int(UpperCamelCase__ ) > 3 else '''down_blocks.0'''
if not string_left.startswith('''.''' ):
raise ValueError(f"Naming error with {input_string} and string_left: {string_left}." )
A__ = string_left[1:]
if "resnets" in new_layer:
A__ = convert_resconv_naming(UpperCamelCase__ )
elif "attentions" in new_layer:
A__ = convert_attn_naming(UpperCamelCase__ )
A__ = new_string_left
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
A__ = prefix + '''.''' + new_layer + '''.''' + string_left
else:
A__ = [prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
return new_string
def UpperCamelCase__( UpperCamelCase__ : int )->int:
A__ = {}
for k, v in state_dict.items():
if k.endswith('''kernel''' ):
# up- and downsample layers, don't have trainable weights
continue
A__ = rename(UpperCamelCase__ )
# check if we need to transform from Conv => Linear for attention
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
A__ = transform_conv_attns(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
else:
A__ = v
return new_state_dict
def UpperCamelCase__( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Optional[Any] )->Optional[int]:
if len(UpperCamelCase__ ) == 1:
if len(v.shape ) == 3:
# weight
A__ = v[:, :, 0]
else:
# bias
A__ = v
else:
# qkv matrices
A__ = v.shape[0]
A__ = trippled_shape // 3
for i in range(3 ):
if len(v.shape ) == 3:
A__ = v[i * single_shape : (i + 1) * single_shape, :, 0]
else:
A__ = v[i * single_shape : (i + 1) * single_shape]
return new_state_dict
def UpperCamelCase__( UpperCamelCase__ : Tuple )->List[str]:
A__ = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
A__ = args.model_path.split('''/''' )[-1].split('''.''' )[0]
if not os.path.isfile(args.model_path ):
assert (
model_name == args.model_path
), f"Make sure to provide one of the official model names {MODELS_MAP.keys()}"
A__ = download(UpperCamelCase__ )
A__ = MODELS_MAP[model_name]['''sample_rate''']
A__ = MODELS_MAP[model_name]['''sample_size''']
A__ = Object()
A__ = sample_size
A__ = sample_rate
A__ = 0
A__ = UNetaDModel(sample_size=UpperCamelCase__ , sample_rate=UpperCamelCase__ )
A__ = diffusers_model.state_dict()
A__ = DiffusionUncond(UpperCamelCase__ )
orig_model.load_state_dict(torch.load(args.model_path , map_location=UpperCamelCase__ )['''state_dict'''] )
A__ = orig_model.diffusion_ema.eval()
A__ = orig_model.state_dict()
A__ = rename_orig_weights(UpperCamelCase__ )
A__ = set(renamed_state_dict.keys() ) - set(diffusers_state_dict.keys() )
A__ = set(diffusers_state_dict.keys() ) - set(renamed_state_dict.keys() )
assert len(UpperCamelCase__ ) == 0, f"Problem with {renamed_minus_diffusers}"
assert all(k.endswith('''kernel''' ) for k in list(UpperCamelCase__ ) ), f"Problem with {diffusers_minus_renamed}"
for key, value in renamed_state_dict.items():
assert (
diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
), f"Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"
if key == "time_proj.weight":
A__ = value.squeeze()
A__ = value
diffusers_model.load_state_dict(UpperCamelCase__ )
A__ = 1_00
A__ = 33
A__ = IPNDMScheduler(num_train_timesteps=UpperCamelCase__ )
A__ = torch.manual_seed(UpperCamelCase__ )
A__ = torch.randn([1, 2, config.sample_size] , generator=UpperCamelCase__ ).to(UpperCamelCase__ )
A__ = torch.linspace(1 , 0 , steps + 1 , device=UpperCamelCase__ )[:-1]
A__ = get_crash_schedule(UpperCamelCase__ )
A__ = DanceDiffusionPipeline(unet=UpperCamelCase__ , scheduler=UpperCamelCase__ )
A__ = torch.manual_seed(33 )
A__ = pipe(num_inference_steps=UpperCamelCase__ , generator=UpperCamelCase__ ).audios
A__ = sampling.iplms_sample(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , {} )
A__ = generated.clamp(-1 , 1 )
A__ = (generated - audio).abs().sum()
A__ = (generated - audio).abs().max()
if args.save:
pipe.save_pretrained(args.checkpoint_path )
print('''Diff sum''' , UpperCamelCase__ )
print('''Diff max''' , UpperCamelCase__ )
assert diff_max < 1e-3, f"Diff max: {diff_max} is too much :-/"
print(f"Conversion for {model_name} successful!" )
if __name__ == "__main__":
a__: Optional[Any] = argparse.ArgumentParser()
parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
parser.add_argument(
'--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
)
parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
a__: Tuple = parser.parse_args()
main(args)
| 39 | 0 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase = get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')
@require_sentencepiece
@require_tokenizers
class __a ( __UpperCamelCase , unittest.TestCase ):
__lowercase : int = PegasusTokenizer
__lowercase : Any = PegasusTokenizerFast
__lowercase : Optional[int] = True
__lowercase : Tuple = True
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowercase__: List[str] = PegasusTokenizer(lowerCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return PegasusTokenizer.from_pretrained('google/pegasus-large' )
def SCREAMING_SNAKE_CASE__ ( self , **lowerCAmelCase__ ) -> PegasusTokenizer:
'''simple docstring'''
return PegasusTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> List[str]:
'''simple docstring'''
return ("This is a test", "This is a test")
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
lowercase__: Optional[Any] = '</s>'
lowercase__: Union[str, Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__ ) , lowerCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__ ) , lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
lowercase__: Tuple = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<pad>' )
self.assertEqual(vocab_keys[1] , '</s>' )
self.assertEqual(vocab_keys[-1] , 'v' )
self.assertEqual(len(lowerCAmelCase__ ) , 1_103 )
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1_103 )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
lowercase__: Dict = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowercase__: Optional[int] = self.tokenizer_class.from_pretrained(self.tmpdirname )
lowercase__: Optional[Any] = (
'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
' </s> <pad> <pad> <pad>'
)
lowercase__: Dict = rust_tokenizer([raw_input_str] , return_tensors=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ ).input_ids[0]
lowercase__: Tuple = py_tokenizer([raw_input_str] , return_tensors=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ ).input_ids[0]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[Any]:
'''simple docstring'''
lowercase__: int = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
lowercase__: Any = '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
lowercase__: Union[str, Any] = [2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
lowercase__: int = tokenizer([raw_input_str] , return_tensors=lowerCAmelCase__ ).input_ids[0]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
lowercase__: Optional[int] = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 96_103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1_024
lowercase__: int = 'To ensure a smooth flow of bank resolutions.'
lowercase__: Any = [413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
lowercase__: str = tokenizer([raw_input_str] , return_tensors=lowerCAmelCase__ ).input_ids[0]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
lowercase__: Any = ['This is going to be way too long.' * 150, 'short example']
lowercase__: Tuple = ['not super long but more than 5 tokens', 'tiny']
lowercase__: Dict = self._large_tokenizer(lowerCAmelCase__ , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , return_tensors='pt' )
lowercase__: Any = self._large_tokenizer(
text_target=lowerCAmelCase__ , max_length=5 , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , return_tensors='pt' )
assert batch.input_ids.shape == (2, 1_024)
assert batch.attention_mask.shape == (2, 1_024)
assert targets["input_ids"].shape == (2, 5)
assert len(lowerCAmelCase__ ) == 2 # input_ids, attention_mask.
@slow
def SCREAMING_SNAKE_CASE__ ( self ) -> List[Any]:
'''simple docstring'''
# fmt: off
lowercase__: List[str] = {'input_ids': [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 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], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 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]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase__ , model_name='google/bigbird-pegasus-large-arxiv' , revision='ba85d0851d708441f91440d509690f1ab6353415' , )
@require_sentencepiece
@require_tokenizers
class __a ( __UpperCamelCase , unittest.TestCase ):
__lowercase : int = PegasusTokenizer
__lowercase : Any = PegasusTokenizerFast
__lowercase : Any = True
__lowercase : Dict = True
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowercase__: Union[str, Any] = PegasusTokenizer(lowerCAmelCase__ , offset=0 , mask_token_sent=lowerCAmelCase__ , mask_token='[MASK]' )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
'''simple docstring'''
return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv' )
def SCREAMING_SNAKE_CASE__ ( self , **lowerCAmelCase__ ) -> PegasusTokenizer:
'''simple docstring'''
return PegasusTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
return ("This is a test", "This is a test")
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
lowercase__: str = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowercase__: str = self.tokenizer_class.from_pretrained(self.tmpdirname )
lowercase__: Tuple = (
'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
' <pad> <pad> <pad>'
)
lowercase__: List[Any] = rust_tokenizer([raw_input_str] , return_tensors=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ ).input_ids[0]
lowercase__: Any = py_tokenizer([raw_input_str] , return_tensors=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ ).input_ids[0]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
@require_torch
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
'''simple docstring'''
lowercase__: List[Any] = ['This is going to be way too long.' * 1_000, 'short example']
lowercase__: str = ['not super long but more than 5 tokens', 'tiny']
lowercase__: Tuple = self._large_tokenizer(lowerCAmelCase__ , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , return_tensors='pt' )
lowercase__: Dict = self._large_tokenizer(
text_target=lowerCAmelCase__ , max_length=5 , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , return_tensors='pt' )
assert batch.input_ids.shape == (2, 4_096)
assert batch.attention_mask.shape == (2, 4_096)
assert targets["input_ids"].shape == (2, 5)
assert len(lowerCAmelCase__ ) == 2 # input_ids, attention_mask.
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
lowercase__: str = (
'This is an example string that is used to test the original TF implementation against the HF'
' implementation'
)
lowercase__: Optional[int] = self._large_tokenizer(lowerCAmelCase__ ).input_ids
self.assertListEqual(
lowerCAmelCase__ , [182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1] , )
| 196 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__lowerCAmelCase = logging.get_logger(__name__)
def snake_case_ ( snake_case , snake_case ) -> List[str]:
lowercase__: List[str] = set()
lowercase__: List[Any] = []
def parse_line(snake_case ):
for line in fp:
if isinstance(snake_case , snake_case ):
lowercase__: Optional[Any] = line.decode('UTF-8' )
if "warnings summary (final)" in line:
continue
# This means we are outside the body of a warning
elif not line.startswith(' ' ):
# process a single warning and move it to `selected_warnings`.
if len(snake_case ) > 0:
lowercase__: List[str] = '\n'.join(snake_case )
# Only keep the warnings specified in `targets`
if any(f': {x}: ' in warning for x in targets ):
selected_warnings.add(snake_case )
buffer.clear()
continue
else:
lowercase__: Union[str, Any] = line.strip()
buffer.append(snake_case )
if from_gh:
for filename in os.listdir(snake_case ):
lowercase__: Dict = os.path.join(snake_case , snake_case )
if not os.path.isdir(snake_case ):
# read the file
if filename != "warnings.txt":
continue
with open(snake_case ) as fp:
parse_line(snake_case )
else:
try:
with zipfile.ZipFile(snake_case ) as z:
for filename in z.namelist():
if not os.path.isdir(snake_case ):
# read the file
if filename != "warnings.txt":
continue
with z.open(snake_case ) as fp:
parse_line(snake_case )
except Exception:
logger.warning(
f'{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.' )
return selected_warnings
def snake_case_ ( snake_case , snake_case ) -> Any:
lowercase__: Optional[Any] = set()
lowercase__: int = [os.path.join(snake_case , snake_case ) for p in os.listdir(snake_case ) if (p.endswith('.zip' ) or from_gh)]
for p in paths:
selected_warnings.update(extract_warnings_from_single_artifact(snake_case , snake_case ) )
return selected_warnings
if __name__ == "__main__":
def snake_case_ ( snake_case ) -> str:
return values.split(',' )
__lowerCAmelCase = 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.''')
# optional parameters
parser.add_argument(
'''--targets''',
default='''DeprecationWarning,UserWarning,FutureWarning''',
type=list_str,
help='''Comma-separated list of target warning(s) which we want to extract.''',
)
parser.add_argument(
'''--from_gh''',
action='''store_true''',
help='''If running from a GitHub action workflow and collecting warnings from its artifacts.''',
)
__lowerCAmelCase = parser.parse_args()
__lowerCAmelCase = args.from_gh
if from_gh:
# The artifacts have to be downloaded using `actions/download-artifact@v3`
pass
else:
os.makedirs(args.output_dir, exist_ok=True)
# get download links
__lowerCAmelCase = 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)
# download artifacts
for idx, (name, url) in enumerate(artifacts.items()):
print(name)
print(url)
print('''=''' * 80)
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
# extract warnings from artifacts
__lowerCAmelCase = extract_warnings(args.output_dir, args.targets)
__lowerCAmelCase = sorted(selected_warnings)
with open(os.path.join(args.output_dir, '''selected_warnings.json'''), '''w''', encoding='''UTF-8''') as fp:
json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)
| 196 | 1 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def _a ( ):
__lowerCAmelCase = 10
__lowerCAmelCase = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string" ) ),
"labels": datasets.Sequence(datasets.ClassLabel(names=["negative", "positive"] ) ),
"answers": datasets.Sequence(
{
"text": datasets.Value("string" ),
"answer_start": datasets.Value("int32" ),
} ),
"id": datasets.Value("int64" ),
} )
__lowerCAmelCase = datasets.Dataset.from_dict(
{
"tokens": [["foo"] * 5] * n,
"labels": [[1] * 5] * n,
"answers": [{"answer_start": [97], "text": ["1976"]}] * 10,
"id": list(range(_UpperCAmelCase ) ),
} , features=_UpperCAmelCase , )
return dataset
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Optional[int] ):
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "file.arrow" )
dataset.map(cache_file_name=_UpperCAmelCase )
return filename
# FILE_CONTENT + files
UpperCamelCase__ = '''\
Text data.
Second line of data.'''
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Any ):
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'file.txt'
__lowerCAmelCase = FILE_CONTENT
with open(_UpperCAmelCase , "w" ) as f:
f.write(_UpperCAmelCase )
return filename
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Dict ):
import bza
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'file.txt.bz2'
__lowerCAmelCase = bytes(_UpperCAmelCase , "utf-8" )
with bza.open(_UpperCAmelCase , "wb" ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : int ):
import gzip
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "file.txt.gz" )
__lowerCAmelCase = bytes(_UpperCAmelCase , "utf-8" )
with gzip.open(_UpperCAmelCase , "wb" ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] ):
if datasets.config.LZ4_AVAILABLE:
import lza.frame
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'file.txt.lz4'
__lowerCAmelCase = bytes(_UpperCAmelCase , "utf-8" )
with lza.frame.open(_UpperCAmelCase , "wb" ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[Any] ):
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'file.txt.7z'
with pyazr.SevenZipFile(_UpperCAmelCase , "w" ) as archive:
archive.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Dict ):
import tarfile
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'file.txt.tar'
with tarfile.TarFile(_UpperCAmelCase , "w" ) as f:
f.add(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Any ):
import lzma
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'file.txt.xz'
__lowerCAmelCase = bytes(_UpperCAmelCase , "utf-8" )
with lzma.open(_UpperCAmelCase , "wb" ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Tuple ):
import zipfile
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'file.txt.zip'
with zipfile.ZipFile(_UpperCAmelCase , "w" ) as f:
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] ):
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'file.txt.zst'
__lowerCAmelCase = bytes(_UpperCAmelCase , "utf-8" )
with zstd.open(_UpperCAmelCase , "wb" ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Optional[Any] ):
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'file.xml'
__lowerCAmelCase = textwrap.dedent(
"\\n <?xml version=\"1.0\" encoding=\"UTF-8\" ?>\n <tmx version=\"1.4\">\n <header segtype=\"sentence\" srclang=\"ca\" />\n <body>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>\n </tu>\n </body>\n </tmx>" )
with open(_UpperCAmelCase , "w" ) as f:
f.write(_UpperCAmelCase )
return filename
UpperCamelCase__ = [
{'''col_1''': '''0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''3''', '''col_2''': 3, '''col_3''': 3.0},
]
UpperCamelCase__ = [
{'''col_1''': '''4''', '''col_2''': 4, '''col_3''': 4.0},
{'''col_1''': '''5''', '''col_2''': 5, '''col_3''': 5.0},
]
UpperCamelCase__ = {
'''col_1''': ['''0''', '''1''', '''2''', '''3'''],
'''col_2''': [0, 1, 2, 3],
'''col_3''': [0.0, 1.0, 2.0, 3.0],
}
UpperCamelCase__ = [
{'''col_3''': 0.0, '''col_1''': '''0''', '''col_2''': 0},
{'''col_3''': 1.0, '''col_1''': '''1''', '''col_2''': 1},
]
UpperCamelCase__ = [
{'''col_1''': '''s0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''s1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''s2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''s3''', '''col_2''': 3, '''col_3''': 3.0},
]
@pytest.fixture(scope="session" )
def _a ( ):
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : List[str] ):
__lowerCAmelCase = datasets.Dataset.from_dict(_UpperCAmelCase )
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.arrow" )
dataset.map(cache_file_name=_UpperCAmelCase )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.sqlite" )
with contextlib.closing(sqlitea.connect(_UpperCAmelCase ) ) as con:
__lowerCAmelCase = con.cursor()
cur.execute("CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)" )
for item in DATA:
cur.execute("INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)" , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] ):
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.csv" )
with open(_UpperCAmelCase , "w" , newline="" ) as f:
__lowerCAmelCase = csv.DictWriter(_UpperCAmelCase , fieldnames=["col_1", "col_2", "col_3"] )
writer.writeheader()
for item in DATA:
writer.writerow(_UpperCAmelCase )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Tuple ):
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "dataset2.csv" )
with open(_UpperCAmelCase , "w" , newline="" ) as f:
__lowerCAmelCase = csv.DictWriter(_UpperCAmelCase , fieldnames=["col_1", "col_2", "col_3"] )
writer.writeheader()
for item in DATA:
writer.writerow(_UpperCAmelCase )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
import bza
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'dataset.csv.bz2'
with open(_UpperCAmelCase , "rb" ) as f:
__lowerCAmelCase = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(_UpperCAmelCase , "wb" ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Dict ):
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'dataset.csv.zip'
with zipfile.ZipFile(_UpperCAmelCase , "w" ) as f:
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase ) )
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'dataset.csv.zip'
with zipfile.ZipFile(_UpperCAmelCase , "w" ) as f:
f.write(_UpperCAmelCase , arcname=os.path.basename(csv_path.replace(".csv" , ".CSV" ) ) )
f.write(_UpperCAmelCase , arcname=os.path.basename(csva_path.replace(".csv" , ".CSV" ) ) )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Tuple ):
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'dataset_with_dir.csv.zip'
with zipfile.ZipFile(_UpperCAmelCase , "w" ) as f:
f.write(_UpperCAmelCase , arcname=os.path.join("main_dir" , os.path.basename(_UpperCAmelCase ) ) )
f.write(_UpperCAmelCase , arcname=os.path.join("main_dir" , os.path.basename(_UpperCAmelCase ) ) )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : str ):
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.parquet" )
__lowerCAmelCase = pa.schema(
{
"col_1": pa.string(),
"col_2": pa.intaa(),
"col_3": pa.floataa(),
} )
with open(_UpperCAmelCase , "wb" ) as f:
__lowerCAmelCase = pq.ParquetWriter(_UpperCAmelCase , schema=_UpperCAmelCase )
__lowerCAmelCase = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(_UpperCAmelCase ) )] for k in DATA[0]} , schema=_UpperCAmelCase )
writer.write_table(_UpperCAmelCase )
writer.close()
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.json" )
__lowerCAmelCase = {'data': DATA}
with open(_UpperCAmelCase , "w" ) as f:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Tuple ):
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.json" )
__lowerCAmelCase = {'data': DATA_DICT_OF_LISTS}
with open(_UpperCAmelCase , "w" ) as f:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Tuple ):
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl" )
with open(_UpperCAmelCase , "w" ) as f:
for item in DATA:
f.write(json.dumps(_UpperCAmelCase ) + "\n" )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Any ):
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "dataset2.jsonl" )
with open(_UpperCAmelCase , "w" ) as f:
for item in DATA:
f.write(json.dumps(_UpperCAmelCase ) + "\n" )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] ):
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "dataset_312.jsonl" )
with open(_UpperCAmelCase , "w" ) as f:
for item in DATA_312:
f.write(json.dumps(_UpperCAmelCase ) + "\n" )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Any ):
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "dataset-str.jsonl" )
with open(_UpperCAmelCase , "w" ) as f:
for item in DATA_STR:
f.write(json.dumps(_UpperCAmelCase ) + "\n" )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Tuple ):
import gzip
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.txt.gz" )
with open(_UpperCAmelCase , "rb" ) as orig_file:
with gzip.open(_UpperCAmelCase , "wb" ) as zipped_file:
zipped_file.writelines(_UpperCAmelCase )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[Any] ):
import gzip
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl.gz" )
with open(_UpperCAmelCase , "rb" ) as orig_file:
with gzip.open(_UpperCAmelCase , "wb" ) as zipped_file:
zipped_file.writelines(_UpperCAmelCase )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : int ):
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'dataset.jsonl.zip'
with zipfile.ZipFile(_UpperCAmelCase , "w" ) as f:
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase ) )
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : int ):
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'dataset_nested.jsonl.zip'
with zipfile.ZipFile(_UpperCAmelCase , "w" ) as f:
f.write(_UpperCAmelCase , arcname=os.path.join("nested" , os.path.basename(_UpperCAmelCase ) ) )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'dataset_with_dir.jsonl.zip'
with zipfile.ZipFile(_UpperCAmelCase , "w" ) as f:
f.write(_UpperCAmelCase , arcname=os.path.join("main_dir" , os.path.basename(_UpperCAmelCase ) ) )
f.write(_UpperCAmelCase , arcname=os.path.join("main_dir" , os.path.basename(_UpperCAmelCase ) ) )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Any ):
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'dataset.jsonl.tar'
with tarfile.TarFile(_UpperCAmelCase , "w" ) as f:
f.add(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase ) )
f.add(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'dataset_nested.jsonl.tar'
with tarfile.TarFile(_UpperCAmelCase , "w" ) as f:
f.add(_UpperCAmelCase , arcname=os.path.join("nested" , os.path.basename(_UpperCAmelCase ) ) )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
__lowerCAmelCase = ['0', '1', '2', '3']
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "dataset.txt" )
with open(_UpperCAmelCase , "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : List[str] ):
__lowerCAmelCase = ['0', '1', '2', '3']
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "dataset2.txt" )
with open(_UpperCAmelCase , "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Optional[Any] ):
__lowerCAmelCase = ['0', '1', '2', '3']
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'dataset.abc'
with open(_UpperCAmelCase , "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Optional[int] ):
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'dataset.text.zip'
with zipfile.ZipFile(_UpperCAmelCase , "w" ) as f:
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase ) )
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Any ):
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'dataset_with_dir.text.zip'
with zipfile.ZipFile(_UpperCAmelCase , "w" ) as f:
f.write(_UpperCAmelCase , arcname=os.path.join("main_dir" , os.path.basename(_UpperCAmelCase ) ) )
f.write(_UpperCAmelCase , arcname=os.path.join("main_dir" , os.path.basename(_UpperCAmelCase ) ) )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : int ):
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'dataset.ext.zip'
with zipfile.ZipFile(_UpperCAmelCase , "w" ) as f:
f.write(_UpperCAmelCase , arcname=os.path.basename("unsupported.ext" ) )
f.write(_UpperCAmelCase , arcname=os.path.basename("unsupported_2.ext" ) )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] ):
__lowerCAmelCase = '\n'.join(["First", "Second\u2029with Unicode new line", "Third"] )
__lowerCAmelCase = str(tmp_path_factory.mktemp("data" ) / "dataset_with_unicode_new_lines.txt" )
with open(_UpperCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope="session" )
def _a ( ):
return os.path.join("tests" , "features" , "data" , "test_image_rgb.jpg" )
@pytest.fixture(scope="session" )
def _a ( ):
return os.path.join("tests" , "features" , "data" , "test_audio_44100.wav" )
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
__lowerCAmelCase = tmp_path_factory.mktemp("data" ) / 'dataset.img.zip'
with zipfile.ZipFile(_UpperCAmelCase , "w" ) as f:
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase ) )
f.write(_UpperCAmelCase , arcname=os.path.basename(_UpperCAmelCase ).replace(".jpg" , "2.jpg" ) )
return path
@pytest.fixture(scope="session" )
def _a ( SCREAMING_SNAKE_CASE_ : int ):
__lowerCAmelCase = tmp_path_factory.mktemp("data_dir" )
(data_dir / "subdir").mkdir()
with open(data_dir / "subdir" / "train.txt" , "w" ) as f:
f.write("foo\n" * 10 )
with open(data_dir / "subdir" / "test.txt" , "w" ) as f:
f.write("bar\n" * 10 )
# hidden file
with open(data_dir / "subdir" / ".test.txt" , "w" ) as f:
f.write("bar\n" * 10 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / ".subdir" / "train.txt" , "w" ) as f:
f.write("foo\n" * 10 )
with open(data_dir / ".subdir" / "test.txt" , "w" ) as f:
f.write("bar\n" * 10 )
return data_dir
| 350 |
import math
def _a ( SCREAMING_SNAKE_CASE_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _a ( SCREAMING_SNAKE_CASE_ : float = 0.1 ):
__lowerCAmelCase = 3
__lowerCAmelCase = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(SCREAMING_SNAKE_CASE_ )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 102 | 0 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
A_ = pytest.mark.integration
@require_faiss
class lowercase( __a ):
'''simple docstring'''
def UpperCamelCase_ ( self: Optional[Any] ):
'''simple docstring'''
_snake_case : str = Dataset.from_dict({"""filename""": ["""my_name-train""" + """_""" + str(a_ ) for x in np.arange(30 ).tolist()]} )
return dset
def UpperCamelCase_ ( self: List[str] ):
'''simple docstring'''
import faiss
_snake_case : Dataset = self._create_dummy_dataset()
_snake_case : Any = dset.map(
lambda a_, a_ : {"vecs": i * np.ones(5, dtype=np.floataa )}, with_indices=a_, keep_in_memory=a_ )
_snake_case : List[Any] = dset.add_faiss_index("""vecs""", batch_size=100, metric_type=faiss.METRIC_INNER_PRODUCT )
_snake_case , _snake_case : Optional[Any] = dset.get_nearest_examples("""vecs""", np.ones(5, dtype=np.floataa ) )
self.assertEqual(examples["""filename"""][0], """my_name-train_29""" )
dset.drop_index("""vecs""" )
def UpperCamelCase_ ( self: str ):
'''simple docstring'''
import faiss
_snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1, 1 ), index_name="""vecs""", batch_size=100, metric_type=faiss.METRIC_INNER_PRODUCT, )
_snake_case , _snake_case : Any = dset.get_nearest_examples("""vecs""", np.ones(5, dtype=np.floataa ) )
self.assertEqual(examples["""filename"""][0], """my_name-train_29""" )
def UpperCamelCase_ ( self: int ):
'''simple docstring'''
import faiss
_snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1, 1 ), index_name="""vecs""", metric_type=faiss.METRIC_INNER_PRODUCT, )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=a_ ) as tmp_file:
dset.save_faiss_index("""vecs""", tmp_file.name )
dset.load_faiss_index("""vecs2""", tmp_file.name )
os.unlink(tmp_file.name )
_snake_case , _snake_case : List[str] = dset.get_nearest_examples("""vecs2""", np.ones(5, dtype=np.floataa ) )
self.assertEqual(examples["""filename"""][0], """my_name-train_29""" )
def UpperCamelCase_ ( self: List[str] ):
'''simple docstring'''
_snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1, 1 ), index_name="""vecs""" )
dset.drop_index("""vecs""" )
self.assertRaises(a_, partial(dset.get_nearest_examples, """vecs2""", np.ones(5, dtype=np.floataa ) ) )
def UpperCamelCase_ ( self: Optional[int] ):
'''simple docstring'''
from elasticsearch import Elasticsearch
_snake_case : Dataset = self._create_dummy_dataset()
with patch("""elasticsearch.Elasticsearch.search""" ) as mocked_search, patch(
"""elasticsearch.client.IndicesClient.create""" ) as mocked_index_create, patch("""elasticsearch.helpers.streaming_bulk""" ) as mocked_bulk:
_snake_case : Tuple = {"""acknowledged""": True}
mocked_bulk.return_value([(True, None)] * 30 )
_snake_case : List[str] = {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 29}]}}
_snake_case : Dict = Elasticsearch()
dset.add_elasticsearch_index("""filename""", es_client=a_ )
_snake_case , _snake_case : int = dset.get_nearest_examples("""filename""", """my_name-train_29""" )
self.assertEqual(examples["""filename"""][0], """my_name-train_29""" )
@require_faiss
class lowercase( __a ):
'''simple docstring'''
def UpperCamelCase_ ( self: str ):
'''simple docstring'''
import faiss
_snake_case : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5, dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal, 5 )
index.add_vectors(np.zeros((5, 5), dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal, 10 )
# single query
_snake_case : List[Any] = np.zeros(5, dtype=np.floataa )
_snake_case : List[str] = 1
_snake_case , _snake_case : str = index.search(a_ )
self.assertRaises(a_, index.search, query.reshape(-1, 1 ) )
self.assertGreater(scores[0], 0 )
self.assertEqual(indices[0], 1 )
# batched queries
_snake_case : Union[str, Any] = np.eye(5, dtype=np.floataa )[::-1]
_snake_case , _snake_case : Optional[Any] = index.search_batch(a_ )
self.assertRaises(a_, index.search_batch, queries[0] )
_snake_case : Optional[Any] = [scores[0] for scores in total_scores]
_snake_case : Dict = [indices[0] for indices in total_indices]
self.assertGreater(np.min(a_ ), 0 )
self.assertListEqual([4, 3, 2, 1, 0], a_ )
def UpperCamelCase_ ( self: Tuple ):
'''simple docstring'''
import faiss
_snake_case : List[str] = FaissIndex(string_factory="""Flat""" )
index.add_vectors(np.eye(5, dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index, faiss.IndexFlat )
_snake_case : Any = FaissIndex(string_factory="""LSH""" )
index.add_vectors(np.eye(5, dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index, faiss.IndexLSH )
with self.assertRaises(a_ ):
_snake_case : Dict = FaissIndex(string_factory="""Flat""", custom_index=faiss.IndexFlat(5 ) )
def UpperCamelCase_ ( self: Tuple ):
'''simple docstring'''
import faiss
_snake_case : Any = faiss.IndexFlat(5 )
_snake_case : Tuple = FaissIndex(custom_index=a_ )
index.add_vectors(np.eye(5, dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index, faiss.IndexFlat )
def UpperCamelCase_ ( self: Union[str, Any] ):
'''simple docstring'''
import faiss
_snake_case : List[str] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5, dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=a_ ) as tmp_file:
index.save(tmp_file.name )
_snake_case : str = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
_snake_case : int = np.zeros(5, dtype=np.floataa )
_snake_case : List[str] = 1
_snake_case , _snake_case : List[Any] = index.search(a_ )
self.assertGreater(scores[0], 0 )
self.assertEqual(indices[0], 1 )
@require_faiss
def UpperCAmelCase__ (snake_case__ : List[str] ):
"""simple docstring"""
import faiss
_snake_case : Dict = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
_snake_case : Any = """index.faiss"""
_snake_case : int = F"mock://{index_name}"
index.save(snake_case__ , storage_options=mockfs.storage_options )
_snake_case : Optional[Any] = FaissIndex.load(snake_case__ , storage_options=mockfs.storage_options )
_snake_case : Union[str, Any] = np.zeros(5 , dtype=np.floataa )
_snake_case : List[str] = 1
_snake_case , _snake_case : str = index.search(snake_case__ )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class lowercase( __a ):
'''simple docstring'''
def UpperCamelCase_ ( self: Optional[Any] ):
'''simple docstring'''
from elasticsearch import Elasticsearch
with patch("""elasticsearch.Elasticsearch.search""" ) as mocked_search, patch(
"""elasticsearch.client.IndicesClient.create""" ) as mocked_index_create, patch("""elasticsearch.helpers.streaming_bulk""" ) as mocked_bulk:
_snake_case : Tuple = Elasticsearch()
_snake_case : Union[str, Any] = {"""acknowledged""": True}
_snake_case : List[str] = ElasticSearchIndex(es_client=a_ )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(["""foo""", """bar""", """foobar"""] )
# single query
_snake_case : str = """foo"""
_snake_case : Dict = {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
_snake_case , _snake_case : Tuple = index.search(a_ )
self.assertEqual(scores[0], 1 )
self.assertEqual(indices[0], 0 )
# single query with timeout
_snake_case : Union[str, Any] = """foo"""
_snake_case : Tuple = {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
_snake_case , _snake_case : int = index.search(a_, request_timeout=30 )
self.assertEqual(scores[0], 1 )
self.assertEqual(indices[0], 0 )
# batched queries
_snake_case : str = ["""foo""", """bar""", """foobar"""]
_snake_case : Dict = {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
_snake_case , _snake_case : Tuple = index.search_batch(a_ )
_snake_case : List[Any] = [scores[0] for scores in total_scores]
_snake_case : Dict = [indices[0] for indices in total_indices]
self.assertGreater(np.min(a_ ), 0 )
self.assertListEqual([1, 1, 1], a_ )
# batched queries with timeout
_snake_case : Optional[int] = ["""foo""", """bar""", """foobar"""]
_snake_case : List[Any] = {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
_snake_case , _snake_case : Tuple = index.search_batch(a_, request_timeout=30 )
_snake_case : Union[str, Any] = [scores[0] for scores in total_scores]
_snake_case : Dict = [indices[0] for indices in total_indices]
self.assertGreater(np.min(a_ ), 0 )
self.assertListEqual([1, 1, 1], a_ )
| 64 |
'''simple docstring'''
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _snake_case ( a__ , a__ , a__ ):
lowerCAmelCase :List[str] = [R'''h\.\d+\.attn\.bias''', R'''h\.\d+\.attn\.masked_bias''']
@register_to_config
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = 5_0257 , _lowerCamelCase = 1024 , _lowerCamelCase = 768 , _lowerCamelCase = 12 , _lowerCamelCase = 12 , _lowerCamelCase = None , _lowerCamelCase = "gelu_new" , _lowerCamelCase = 0.1 , _lowerCamelCase = 0.1 , _lowerCamelCase = 0.1 , _lowerCamelCase = 1e-5 , _lowerCamelCase = 0.02 , _lowerCamelCase = True , _lowerCamelCase = True , _lowerCamelCase = False , _lowerCamelCase = False , ):
super().__init__()
UpperCAmelCase__ : Optional[Any] = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
f'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
f''' `n_embd`: {n_embd} are not equal.''')
UpperCAmelCase__ : str = prefix_inner_dim
UpperCAmelCase__ : Tuple = prefix_hidden_dim
UpperCAmelCase__ : Any = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim)
if self.prefix_hidden_dim is not None
else nn.Identity()
)
UpperCAmelCase__ : Optional[Any] = (
nn.Linear(self.prefix_hidden_dim , _lowerCamelCase) if self.prefix_hidden_dim is not None else nn.Identity()
)
UpperCAmelCase__ : Optional[Any] = GPTaConfig(
vocab_size=_lowerCamelCase , n_positions=_lowerCamelCase , n_embd=_lowerCamelCase , n_layer=_lowerCamelCase , n_head=_lowerCamelCase , n_inner=_lowerCamelCase , activation_function=_lowerCamelCase , resid_pdrop=_lowerCamelCase , embd_pdrop=_lowerCamelCase , attn_pdrop=_lowerCamelCase , layer_norm_epsilon=_lowerCamelCase , initializer_range=_lowerCamelCase , scale_attn_weights=_lowerCamelCase , use_cache=_lowerCamelCase , scale_attn_by_inverse_layer_idx=_lowerCamelCase , reorder_and_upcast_attn=_lowerCamelCase , )
UpperCAmelCase__ : int = GPTaLMHeadModel(_lowerCamelCase)
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = None , ):
UpperCAmelCase__ : Dict = self.transformer.transformer.wte(_lowerCamelCase)
UpperCAmelCase__ : Any = self.encode_prefix(_lowerCamelCase)
UpperCAmelCase__ : Tuple = self.decode_prefix(_lowerCamelCase)
UpperCAmelCase__ : List[Any] = torch.cat((prefix_embeds, embedding_text) , dim=1)
if labels is not None:
UpperCAmelCase__ : str = self.get_dummy_token(input_ids.shape[0] , input_ids.device)
UpperCAmelCase__ : Tuple = torch.cat((dummy_token, input_ids) , dim=1)
UpperCAmelCase__ : Union[str, Any] = self.transformer(inputs_embeds=_lowerCamelCase , labels=_lowerCamelCase , attention_mask=_lowerCamelCase)
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase):
return torch.zeros(_lowerCamelCase , self.prefix_length , dtype=torch.intaa , device=_lowerCamelCase)
def snake_case__ ( self , _lowerCamelCase):
return self.encode_prefix(_lowerCamelCase)
@torch.no_grad()
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase):
UpperCAmelCase__ : Dict = torch.split(_lowerCamelCase , 1 , dim=0)
UpperCAmelCase__ : Optional[Any] = []
UpperCAmelCase__ : Any = []
for feature in features:
UpperCAmelCase__ : int = self.decode_prefix(feature.to(_lowerCamelCase)) # back to the clip feature
# Only support beam search for now
UpperCAmelCase__ , UpperCAmelCase__ : str = self.generate_beam(
input_embeds=_lowerCamelCase , device=_lowerCamelCase , eos_token_id=_lowerCamelCase)
generated_tokens.append(output_tokens[0])
generated_seq_lengths.append(seq_lengths[0])
UpperCAmelCase__ : List[Any] = torch.stack(_lowerCamelCase)
UpperCAmelCase__ : Optional[int] = torch.stack(_lowerCamelCase)
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def snake_case__ ( self , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase = 5 , _lowerCamelCase = 67 , _lowerCamelCase = 1.0 , _lowerCamelCase = None , ):
UpperCAmelCase__ : Dict = eos_token_id
UpperCAmelCase__ : Any = None
UpperCAmelCase__ : Union[str, Any] = None
UpperCAmelCase__ : Any = torch.ones(_lowerCamelCase , device=_lowerCamelCase , dtype=torch.int)
UpperCAmelCase__ : Any = torch.zeros(_lowerCamelCase , device=_lowerCamelCase , dtype=torch.bool)
if input_embeds is not None:
UpperCAmelCase__ : Optional[int] = input_embeds
else:
UpperCAmelCase__ : Any = self.transformer.transformer.wte(_lowerCamelCase)
for i in range(_lowerCamelCase):
UpperCAmelCase__ : Union[str, Any] = self.transformer(inputs_embeds=_lowerCamelCase)
UpperCAmelCase__ : List[str] = outputs.logits
UpperCAmelCase__ : List[str] = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
UpperCAmelCase__ : int = logits.softmax(-1).log()
if scores is None:
UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = logits.topk(_lowerCamelCase , -1)
UpperCAmelCase__ : int = generated.expand(_lowerCamelCase , *generated.shape[1:])
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = next_tokens.permute(1 , 0), scores.squeeze(0)
if tokens is None:
UpperCAmelCase__ : Any = next_tokens
else:
UpperCAmelCase__ : Tuple = tokens.expand(_lowerCamelCase , *tokens.shape[1:])
UpperCAmelCase__ : List[Any] = torch.cat((tokens, next_tokens) , dim=1)
else:
UpperCAmelCase__ : Any = -float(np.inf)
UpperCAmelCase__ : Union[str, Any] = 0
UpperCAmelCase__ : Any = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
UpperCAmelCase__ : Optional[Any] = scores_sum / seq_lengths[:, None]
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = scores_sum_average.view(-1).topk(_lowerCamelCase , -1)
UpperCAmelCase__ : str = next_tokens // scores_sum.shape[1]
UpperCAmelCase__ : Optional[int] = seq_lengths[next_tokens_source]
UpperCAmelCase__ : List[str] = next_tokens % scores_sum.shape[1]
UpperCAmelCase__ : List[Any] = next_tokens.unsqueeze(1)
UpperCAmelCase__ : Dict = tokens[next_tokens_source]
UpperCAmelCase__ : Optional[int] = torch.cat((tokens, next_tokens) , dim=1)
UpperCAmelCase__ : Optional[Any] = generated[next_tokens_source]
UpperCAmelCase__ : List[Any] = scores_sum_average * seq_lengths
UpperCAmelCase__ : Union[str, Any] = is_stopped[next_tokens_source]
UpperCAmelCase__ : Union[str, Any] = self.transformer.transformer.wte(next_tokens.squeeze()).view(generated.shape[0] , 1 , -1)
UpperCAmelCase__ : Union[str, Any] = torch.cat((generated, next_token_embed) , dim=1)
UpperCAmelCase__ : Union[str, Any] = is_stopped + next_tokens.eq(_lowerCamelCase).squeeze()
if is_stopped.all():
break
UpperCAmelCase__ : Tuple = scores / seq_lengths
UpperCAmelCase__ : Union[str, Any] = scores.argsort(descending=_lowerCamelCase)
# tokens tensors are already padded to max_seq_length
UpperCAmelCase__ : Optional[Any] = [tokens[i] for i in order]
UpperCAmelCase__ : Optional[Any] = torch.stack(_lowerCamelCase , dim=0)
UpperCAmelCase__ : Dict = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype)
return output_texts, seq_lengths | 163 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class lowerCAmelCase__ ( _a , _a , _a , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ =StableUnCLIPImgaImgPipeline
SCREAMING_SNAKE_CASE_ =TEXT_GUIDED_IMAGE_VARIATION_PARAMS
SCREAMING_SNAKE_CASE_ =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
SCREAMING_SNAKE_CASE_ =frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
SCREAMING_SNAKE_CASE_ =frozenset([] )
def __a ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = 3_2
UpperCAmelCase__ : int = embedder_hidden_size
# image encoding components
UpperCAmelCase__ : Tuple = CLIPImageProcessor(crop_size=3_2 , size=3_2 )
torch.manual_seed(0 )
UpperCAmelCase__ : List[Any] = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=snake_case_ , projection_dim=snake_case_ , num_hidden_layers=5 , num_attention_heads=4 , image_size=3_2 , intermediate_size=3_7 , patch_size=1 , ) )
# regular denoising components
torch.manual_seed(0 )
UpperCAmelCase__ : Any = StableUnCLIPImageNormalizer(embedding_dim=snake_case_ )
UpperCAmelCase__ : Optional[Any] = DDPMScheduler(beta_schedule="squaredcos_cap_v2" )
torch.manual_seed(0 )
UpperCAmelCase__ : Optional[Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
UpperCAmelCase__ : List[str] = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=snake_case_ , projection_dim=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , ) )
torch.manual_seed(0 )
UpperCAmelCase__ : Dict = UNetaDConditionModel(
sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(3_2, 6_4) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=snake_case_ , layers_per_block=1 , upcast_attention=snake_case_ , use_linear_projection=snake_case_ , )
torch.manual_seed(0 )
UpperCAmelCase__ : Optional[int] = DDIMScheduler(
beta_schedule="scaled_linear" , beta_start=0.0_0085 , beta_end=0.012 , prediction_type="v_prediction" , set_alpha_to_one=snake_case_ , steps_offset=1 , )
torch.manual_seed(0 )
UpperCAmelCase__ : List[str] = AutoencoderKL()
UpperCAmelCase__ : List[Any] = {
# image encoding components
"""feature_extractor""": feature_extractor,
"""image_encoder""": image_encoder.eval(),
# image noising components
"""image_normalizer""": image_normalizer.eval(),
"""image_noising_scheduler""": image_noising_scheduler,
# regular denoising components
"""tokenizer""": tokenizer,
"""text_encoder""": text_encoder.eval(),
"""unet""": unet.eval(),
"""scheduler""": scheduler,
"""vae""": vae.eval(),
}
return components
def __a ( self : str , snake_case__ : List[Any] , snake_case__ : Optional[Any]=0 , snake_case__ : str=True ):
'''simple docstring'''
if str(snake_case_ ).startswith("mps" ):
UpperCAmelCase__ : int = torch.manual_seed(snake_case_ )
else:
UpperCAmelCase__ : Tuple = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
UpperCAmelCase__ : Dict = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(snake_case_ ) ).to(snake_case_ )
if pil_image:
UpperCAmelCase__ : Tuple = input_image * 0.5 + 0.5
UpperCAmelCase__ : Tuple = input_image.clamp(0 , 1 )
UpperCAmelCase__ : Tuple = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
UpperCAmelCase__ : Any = DiffusionPipeline.numpy_to_pil(snake_case_ )[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def __a ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase__ : Any = self.get_dummy_components()
UpperCAmelCase__ : int = StableUnCLIPImgaImgPipeline(**snake_case_ )
UpperCAmelCase__ : Any = sd_pipe.to(snake_case_ )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
UpperCAmelCase__ : Union[str, Any] = self.get_dummy_inputs(snake_case_ )
inputs.update({"image_embeds": None} )
UpperCAmelCase__ : Dict = sd_pipe(**snake_case_ ).images
UpperCAmelCase__ : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
UpperCAmelCase__ : Union[str, Any] = np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __a ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = torch_device in ["""cpu""", """mps"""]
self._test_attention_slicing_forward_pass(test_max_difference=snake_case_ )
def __a ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = torch_device in ["""cpu""", """mps"""]
self._test_inference_batch_single_identical(test_max_difference=snake_case_ )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def __a ( self : List[Any] ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=snake_case_ )
@slow
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
def __a ( self : List[Any] ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
UpperCAmelCase__ : Optional[int] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy" )
UpperCAmelCase__ : Dict = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-l-img2img" , torch_dtype=torch.floataa )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
UpperCAmelCase__ : Any = torch.Generator(device="cpu" ).manual_seed(0 )
UpperCAmelCase__ : str = pipe(snake_case_ , "anime turle" , generator=snake_case_ , output_type="np" )
UpperCAmelCase__ : Union[str, Any] = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(snake_case_ , snake_case_ )
def __a ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
UpperCAmelCase__ : str = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy" )
UpperCAmelCase__ : List[Any] = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
UpperCAmelCase__ : Dict = torch.Generator(device="cpu" ).manual_seed(0 )
UpperCAmelCase__ : str = pipe(snake_case_ , "anime turle" , generator=snake_case_ , output_type="np" )
UpperCAmelCase__ : int = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(snake_case_ , snake_case_ )
def __a ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
UpperCAmelCase__ : List[Any] = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
UpperCAmelCase__ : List[Any] = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
UpperCAmelCase__ : Any = pipe(
snake_case_ , "anime turtle" , num_inference_steps=2 , output_type="np" , )
UpperCAmelCase__ : Optional[int] = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 1_0**9
| 371 |
"""simple docstring"""
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Dict = logging.get_logger(__name__)
_lowerCAmelCase : Union[str, Any] = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
),
}
class lowerCAmelCase__ ( __magic_name__ ):
SCREAMING_SNAKE_CASE_ ='''efficientformer'''
def __init__( self : List[Any] , snake_case__ : List[int] = [3, 2, 6, 4] , snake_case__ : List[int] = [4_8, 9_6, 2_2_4, 4_4_8] , snake_case__ : List[bool] = [True, True, True, True] , snake_case__ : int = 4_4_8 , snake_case__ : int = 3_2 , snake_case__ : int = 4 , snake_case__ : int = 7 , snake_case__ : int = 5 , snake_case__ : int = 8 , snake_case__ : int = 4 , snake_case__ : float = 0.0 , snake_case__ : int = 1_6 , snake_case__ : int = 3 , snake_case__ : int = 3 , snake_case__ : int = 3 , snake_case__ : int = 2 , snake_case__ : int = 1 , snake_case__ : float = 0.0 , snake_case__ : int = 1 , snake_case__ : bool = True , snake_case__ : bool = True , snake_case__ : float = 1e-5 , snake_case__ : str = "gelu" , snake_case__ : float = 0.02 , snake_case__ : float = 1e-12 , snake_case__ : int = 2_2_4 , snake_case__ : float = 1e-05 , **snake_case__ : str , ):
'''simple docstring'''
super().__init__(**snake_case__ )
UpperCAmelCase__ : int = hidden_act
UpperCAmelCase__ : Optional[int] = hidden_dropout_prob
UpperCAmelCase__ : List[str] = hidden_sizes
UpperCAmelCase__ : Union[str, Any] = num_hidden_layers
UpperCAmelCase__ : int = num_attention_heads
UpperCAmelCase__ : List[Any] = initializer_range
UpperCAmelCase__ : List[Any] = layer_norm_eps
UpperCAmelCase__ : Optional[int] = patch_size
UpperCAmelCase__ : Tuple = num_channels
UpperCAmelCase__ : Optional[int] = depths
UpperCAmelCase__ : Union[str, Any] = mlp_expansion_ratio
UpperCAmelCase__ : Dict = downsamples
UpperCAmelCase__ : Any = dim
UpperCAmelCase__ : str = key_dim
UpperCAmelCase__ : List[Any] = attention_ratio
UpperCAmelCase__ : Optional[Any] = resolution
UpperCAmelCase__ : Optional[Any] = pool_size
UpperCAmelCase__ : Any = downsample_patch_size
UpperCAmelCase__ : int = downsample_stride
UpperCAmelCase__ : Dict = downsample_pad
UpperCAmelCase__ : List[Any] = drop_path_rate
UpperCAmelCase__ : Optional[Any] = num_metaad_blocks
UpperCAmelCase__ : List[str] = distillation
UpperCAmelCase__ : Dict = use_layer_scale
UpperCAmelCase__ : List[Any] = layer_scale_init_value
UpperCAmelCase__ : Optional[Any] = image_size
UpperCAmelCase__ : Optional[int] = batch_norm_eps
| 298 | 0 |
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