






import collections
import os
import re
from pathlib import Path


A__			:    Optional[Any]         =				'src/transformers'


# Matches is_xxx_available()
A__			:    List[str]         =				re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
A__			:    Any         =				re.compile(r'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
A__			:    Tuple         =				re.compile(r'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
A__			:    Optional[int]         =				re.compile(r'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
A__			:    Dict         =				re.compile(r'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
A__			:    Any         =				re.compile(r'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma:     "MyModel",
A__			:    Dict         =				re.compile(r'^\s+"([^"]+)",')
# Catches a line with objects between brackets only:    ["foo", "bar"],
A__			:    Tuple         =				re.compile(r'^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
A__			:    List[str]         =				re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
A__			:    Any         =				re.compile(r'^\s*try:')
# Catches a line with else:
A__			:    Optional[Any]         =				re.compile(r'^\s*else:')






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				if _re_test_backend.search(lowerCamelCase_       ) is None:
								return None
				lowercase__     				=    [b[0] for b in _re_backend.findall(lowerCamelCase_       )]
				backends.sort()
				return "_and_".join(lowerCamelCase_       )






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				with open(lowerCamelCase_	,     '''r'''	,     encoding='''utf-8'''	,     newline='''\n'''       ) as f:
								lowercase__     				=    f.readlines()

				lowercase__     				=    0
				while line_index < len(lowerCamelCase_       ) and not lines[line_index].startswith('''_import_structure = {'''       ):
								line_index += 1

				# If this is a traditional init, just return.
				if line_index >= len(lowerCamelCase_       ):
								return None

				# First grab the objects without a specific backend in _import_structure
				lowercase__     				=    []
				while not lines[line_index].startswith('''if TYPE_CHECKING'''       ) and find_backend(lines[line_index]       ) is None:
								lowercase__     				=    lines[line_index]
								# If we have everything on a single line, let's deal with it.
								if _re_one_line_import_struct.search(lowerCamelCase_       ):
												lowercase__     				=    _re_one_line_import_struct.search(lowerCamelCase_       ).groups()[0]
												lowercase__     				=    re.findall(r'''\[([^\]]+)\]'''	,     lowerCamelCase_       )
												for imp in imports:
																objects.extend([obj[1:-1] for obj in imp.split(''', '''       )]       )
												line_index += 1
												continue
								lowercase__     				=    _re_import_struct_key_value.search(lowerCamelCase_       )
								if single_line_import_search is not None:
												lowercase__     				=    [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', '''       ) if len(lowerCamelCase_       ) > 0]
												objects.extend(lowerCamelCase_       )
								elif line.startswith(''' ''' * 8 + '''"'''       ):
												objects.append(line[9:-3]       )
								line_index += 1

				lowercase__     				=    {'''none''': objects}
				# Let's continue with backend-specific objects in _import_structure
				while not lines[line_index].startswith('''if TYPE_CHECKING'''       ):
								# If the line is an if not is_backend_available, we grab all objects associated.
								lowercase__     				=    find_backend(lines[line_index]       )
								# Check if the backend declaration is inside a try block:
								if _re_try.search(lines[line_index - 1]       ) is None:
												lowercase__     				=    None

								if backend is not None:
												line_index += 1

												# Scroll until we hit the else block of try-except-else
												while _re_else.search(lines[line_index]       ) is None:
																line_index += 1

												line_index += 1

												lowercase__     				=    []
												# Until we unindent, add backend objects to the list
												while len(lines[line_index]       ) <= 1 or lines[line_index].startswith(''' ''' * 4       ):
																lowercase__     				=    lines[line_index]
																if _re_import_struct_add_one.search(lowerCamelCase_       ) is not None:
																				objects.append(_re_import_struct_add_one.search(lowerCamelCase_       ).groups()[0]       )
																elif _re_import_struct_add_many.search(lowerCamelCase_       ) is not None:
																				lowercase__     				=    _re_import_struct_add_many.search(lowerCamelCase_       ).groups()[0].split(''', '''       )
																				lowercase__     				=    [obj[1:-1] for obj in imports if len(lowerCamelCase_       ) > 0]
																				objects.extend(lowerCamelCase_       )
																elif _re_between_brackets.search(lowerCamelCase_       ) is not None:
																				lowercase__     				=    _re_between_brackets.search(lowerCamelCase_       ).groups()[0].split(''', '''       )
																				lowercase__     				=    [obj[1:-1] for obj in imports if len(lowerCamelCase_       ) > 0]
																				objects.extend(lowerCamelCase_       )
																elif _re_quote_object.search(lowerCamelCase_       ) is not None:
																				objects.append(_re_quote_object.search(lowerCamelCase_       ).groups()[0]       )
																elif line.startswith(''' ''' * 8 + '''"'''       ):
																				objects.append(line[9:-3]       )
																elif line.startswith(''' ''' * 12 + '''"'''       ):
																				objects.append(line[13:-3]       )
																line_index += 1

												lowercase__     				=    objects
								else:
												line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
				lowercase__     				=    []
				while (
				    line_index < len(lowerCamelCase_       )
				    and find_backend(lines[line_index]       ) is None
				    and not lines[line_index].startswith('''else'''       )
				):
								lowercase__     				=    lines[line_index]
								lowercase__     				=    _re_import.search(lowerCamelCase_       )
								if single_line_import_search is not None:
												objects.extend(single_line_import_search.groups()[0].split(''', '''       )       )
								elif line.startswith(''' ''' * 8       ):
												objects.append(line[8:-2]       )
								line_index += 1

				lowercase__     				=    {'''none''': objects}
				# Let's continue with backend-specific objects
				while line_index < len(lowerCamelCase_       ):
								# If the line is an if is_backend_available, we grab all objects associated.
								lowercase__     				=    find_backend(lines[line_index]       )
								# Check if the backend declaration is inside a try block:
								if _re_try.search(lines[line_index - 1]       ) is None:
												lowercase__     				=    None

								if backend is not None:
												line_index += 1

												# Scroll until we hit the else block of try-except-else
												while _re_else.search(lines[line_index]       ) is None:
																line_index += 1

												line_index += 1

												lowercase__     				=    []
												# Until we unindent, add backend objects to the list
												while len(lines[line_index]       ) <= 1 or lines[line_index].startswith(''' ''' * 8       ):
																lowercase__     				=    lines[line_index]
																lowercase__     				=    _re_import.search(lowerCamelCase_       )
																if single_line_import_search is not None:
																				objects.extend(single_line_import_search.groups()[0].split(''', '''       )       )
																elif line.startswith(''' ''' * 12       ):
																				objects.append(line[12:-2]       )
																line_index += 1

												lowercase__     				=    objects
								else:
												line_index += 1

				return import_dict_objects, type_hint_objects






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''

				def find_duplicates(lowerCamelCase_       ):
								return [k for k, v in collections.Counter(lowerCamelCase_       ).items() if v > 1]

				if list(import_dict_objects.keys()       ) != list(type_hint_objects.keys()       ):
								return ["Both sides of the init do not have the same backends!"]

				lowercase__     				=    []
				for key in import_dict_objects.keys():
								lowercase__     				=    find_duplicates(import_dict_objects[key]       )
								if duplicate_imports:
												errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}"""       )
								lowercase__     				=    find_duplicates(type_hint_objects[key]       )
								if duplicate_type_hints:
												errors.append(F"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"""       )

								if sorted(set(import_dict_objects[key]       )       ) != sorted(set(type_hint_objects[key]       )       ):
												lowercase__     				=    '''base imports''' if key == '''none''' else F"""{key} backend"""
												errors.append(F"""Differences for {name}:"""       )
												for a in type_hint_objects[key]:
																if a not in import_dict_objects[key]:
																				errors.append(F"""  {a} in TYPE_HINT but not in _import_structure."""       )
												for a in import_dict_objects[key]:
																if a not in type_hint_objects[key]:
																				errors.append(F"""  {a} in _import_structure but not in TYPE_HINT."""       )
				return errors






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    []
				for root, _, files in os.walk(lowerCamelCase_       ):
								if "__init__.py" in files:
												lowercase__     				=    os.path.join(lowerCamelCase_	,     '''__init__.py'''       )
												lowercase__     				=    parse_init(lowerCamelCase_       )
												if objects is not None:
																lowercase__     				=    analyze_results(*lowerCamelCase_       )
																if len(lowerCamelCase_       ) > 0:
																				lowercase__     				=    F"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
																				failures.append('''\n'''.join(lowerCamelCase_       )       )
				if len(lowerCamelCase_       ) > 0:
								raise ValueError('''\n\n'''.join(lowerCamelCase_       )       )






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    []
				for path, directories, files in os.walk(lowerCamelCase_       ):
								for folder in directories:
												# Ignore private modules
												if folder.startswith('''_'''       ):
																directories.remove(lowerCamelCase_       )
																continue
												# Ignore leftovers from branches (empty folders apart from pycache)
												if len(list((Path(lowerCamelCase_       ) / folder).glob('''*.py'''       )       )       ) == 0:
																continue
												lowercase__     				=    str((Path(lowerCamelCase_       ) / folder).relative_to(lowerCamelCase_       )       )
												lowercase__     				=    short_path.replace(os.path.sep	,     '''.'''       )
												submodules.append(lowerCamelCase_       )
								for fname in files:
												if fname == "__init__.py":
																continue
												lowercase__     				=    str((Path(lowerCamelCase_       ) / fname).relative_to(lowerCamelCase_       )       )
												lowercase__     				=    short_path.replace('''.py'''	,     ''''''       ).replace(os.path.sep	,     '''.'''       )
												if len(submodule.split('''.'''       )       ) == 1:
																submodules.append(lowerCamelCase_       )
				return submodules


A__			:    List[str]         =				[
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
    'models.esm.openfold_utils',
]






def 						a    (		):






				'''simple docstring'''
				# This is to make sure the transformers module imported is the one in the repo.
				from transformers.utils import direct_transformers_import

				lowercase__     				=    direct_transformers_import(lowerCamelCase_       )

				lowercase__     				=    set(transformers._import_structure.keys()       )
				# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
				# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
				# (potentiall re-) add them.
				with open(os.path.join(lowerCamelCase_	,     '''__init__.py'''       )	,     '''r'''       ) as f:
								lowercase__     				=    f.read()
				import_structure_keys.update(set(re.findall(r'''import_structure\[\"([^\"]*)\"\]'''	,     lowerCamelCase_       )       )       )

				lowercase__     				=    [
				    module
				    for module in get_transformers_submodules()
				    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
				]

				if len(lowerCamelCase_       ) > 0:
								lowercase__     				=    '''\n'''.join(F"""- {module}""" for module in module_not_registered       )
								raise ValueError(
								    '''The following submodules are not properly registed in the main init of Transformers:\n'''
								    F"""{list_of_modules}\n"""
								    '''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'''       )


if __name__ == "__main__":
							check_all_inits()
							check_submodules()












import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    args.log_outputs
				lowercase__     				=    '''_'''.join(args.dataset.split('''/'''       ) + [args.config, args.split]       )

				# load metric
				lowercase__     				=    load_metric('''wer'''       )
				lowercase__     				=    load_metric('''cer'''       )

				# compute metrics
				lowercase__     				=    wer.compute(references=result['''target''']	,     predictions=result['''prediction''']       )
				lowercase__     				=    cer.compute(references=result['''target''']	,     predictions=result['''prediction''']       )

				# print & log results
				lowercase__     				=    F"""WER: {wer_result}\nCER: {cer_result}"""
				print(lowerCamelCase_       )

				with open(F"""{dataset_id}_eval_results.txt"""	,     '''w'''       ) as f:
								f.write(lowerCamelCase_       )

				# log all results in text file. Possibly interesting for analysis
				if log_outputs is not None:
								lowercase__     				=    F"""log_{dataset_id}_predictions.txt"""
								lowercase__     				=    F"""log_{dataset_id}_targets.txt"""

								with open(lowerCamelCase_	,     '''w'''       ) as p, open(lowerCamelCase_	,     '''w'''       ) as t:
												# mapping function to write output
												def write_to_file(lowerCamelCase_	,     lowerCamelCase_       ):
																p.write(F"""{i}""" + '''\n'''       )
																p.write(batch['''prediction'''] + '''\n'''       )
																t.write(F"""{i}""" + '''\n'''       )
																t.write(batch['''target'''] + '''\n'''       )

												result.map(lowerCamelCase_	,     with_indices=lowerCamelCase_       )






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    '''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

				lowercase__     				=    re.sub(lowerCamelCase_	,     ''''''	,     text.lower()       )

				# In addition, we can normalize the target text, e.g. removing new lines characters etc...
				# note that order is important here!
				lowercase__     				=    ['''\n\n''', '''\n''', '''   ''', '''  ''']

				for t in token_sequences_to_ignore:
								lowercase__     				=    ''' '''.join(text.split(lowerCamelCase_       )       )

				return text






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				# load dataset
				lowercase__     				=    load_dataset(args.dataset	,     args.config	,     split=args.split	,     use_auth_token=lowerCamelCase_       )

				# for testing: only process the first two examples as a test
				# dataset = dataset.select(range(10))

				# load processor
				lowercase__     				=    AutoFeatureExtractor.from_pretrained(args.model_id       )
				lowercase__     				=    feature_extractor.sampling_rate

				# resample audio
				lowercase__     				=    dataset.cast_column('''audio'''	,     Audio(sampling_rate=lowerCamelCase_       )       )

				# load eval pipeline
				if args.device is None:
								lowercase__     				=    0 if torch.cuda.is_available() else -1
				lowercase__     				=    pipeline('''automatic-speech-recognition'''	,     model=args.model_id	,     device=args.device       )

				# map function to decode audio
				def map_to_pred(lowerCamelCase_       ):
								lowercase__     				=    asr(
								    batch['''audio''']['''array''']	,     chunk_length_s=args.chunk_length_s	,     stride_length_s=args.stride_length_s       )

								lowercase__     				=    prediction['''text''']
								lowercase__     				=    normalize_text(batch['''sentence''']       )
								return batch

				# run inference on all examples
				lowercase__     				=    dataset.map(lowerCamelCase_	,     remove_columns=dataset.column_names       )

				# compute and log_results
				# do not change function below
				log_results(lowerCamelCase_	,     lowerCamelCase_       )


if __name__ == "__main__":
							A__			:    int         =				argparse.ArgumentParser()

							parser.add_argument(
							    '--model_id', type=str, required=True, help='Model identifier. Should be loadable with 🤗 Transformers'
							)
							parser.add_argument(
							    '--dataset',
							    type=str,
							    required=True,
							    help='Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets',
							)
							parser.add_argument(
							    '--config', type=str, required=True, help='Config of the dataset. *E.g.* `\'en\'`  for Common Voice'
							)
							parser.add_argument('--split', type=str, required=True, help='Split of the dataset. *E.g.* `\'test\'`')
							parser.add_argument(
							    '--chunk_length_s', type=float, default=None, help='Chunk length in seconds. Defaults to 5 seconds.'
							)
							parser.add_argument(
							    '--stride_length_s', type=float, default=None, help='Stride of the audio chunks. Defaults to 1 second.'
							)
							parser.add_argument(
							    '--log_outputs', action='store_true', help='If defined, write outputs to log file for analysis.'
							)
							parser.add_argument(
							    '--device',
							    type=int,
							    default=None,
							    help='The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.',
							)
							A__			:    Union[str, Any]         =				parser.parse_args()

							main(args)




