






import argparse
import os
import re

import packaging.version


A__			:    Dict         =				'examples/'
A__			:    int         =				{
    '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'),
}
A__			:    List[Any]         =				{
    'init': 'src/diffusers/__init__.py',
    'setup': 'setup.py',
}
A__			:    Optional[int]         =				'README.md'






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				with open(lowerCamelCase_	,     '''r'''	,     encoding='''utf-8'''	,     newline='''\n'''       ) as f:
								lowercase__     				=    f.read()
				lowercase__				,		lowercase__     				=    REPLACE_PATTERNS[pattern]
				lowercase__     				=    replace.replace('''VERSION'''	,     lowerCamelCase_       )
				lowercase__     				=    re_pattern.sub(lowerCamelCase_	,     lowerCamelCase_       )
				with open(lowerCamelCase_	,     '''w'''	,     encoding='''utf-8'''	,     newline='''\n'''       ) as f:
								f.write(lowerCamelCase_       )






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				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 						a    (		lowerCamelCase_	,     lowerCamelCase_=False       ):






				'''simple docstring'''
				for pattern, fname in REPLACE_FILES.items():
								update_version_in_file(lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       )
				if not patch:
								update_version_in_examples(lowerCamelCase_       )






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    '''🤗 Transformers currently provides the following architectures'''
				lowercase__     				=    '''1. Want to contribute a new model?'''
				with open(lowerCamelCase_	,     '''r'''	,     encoding='''utf-8'''	,     newline='''\n'''       ) as f:
								lowercase__     				=    f.readlines()

				# Find the start of the list.
				lowercase__     				=    0
				while not lines[start_index].startswith(_start_prompt       ):
								start_index += 1
				start_index += 1

				lowercase__     				=    start_index
				# Update the lines in the model list.
				while not lines[index].startswith(_end_prompt       ):
								if lines[index].startswith('''1.'''       ):
												lowercase__     				=    lines[index].replace(
												    '''https://huggingface.co/docs/diffusers/main/model_doc'''	,     '''https://huggingface.co/docs/diffusers/model_doc'''	,     )
								index += 1

				with open(lowerCamelCase_	,     '''w'''	,     encoding='''utf-8'''	,     newline='''\n'''       ) as f:
								f.writelines(lowerCamelCase_       )






def 						a    (		):






				'''simple docstring'''
				with open(REPLACE_FILES['''init''']	,     '''r'''       ) as f:
								lowercase__     				=    f.read()
				lowercase__     				=    REPLACE_PATTERNS['''init'''][0].search(lowerCamelCase_       ).groups()[0]
				return packaging.version.parse(lowerCamelCase_       )






def 						a    (		lowerCamelCase_=False       ):






				'''simple docstring'''
				lowercase__     				=    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:
								lowercase__     				=    default_version.base_version
				elif patch:
								lowercase__     				=    F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
				else:
								lowercase__     				=    F"""{default_version.major}.{default_version.minor + 1}.0"""

				# Now let's ask nicely if that's the right one.
				lowercase__     				=    input(F"""Which version are you releasing? [{default_version}]"""       )
				if len(lowerCamelCase_       ) == 0:
								lowercase__     				=    default_version

				print(F"""Updating version to {version}."""       )
				global_version_update(lowerCamelCase_	,     patch=lowerCamelCase_       )






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    get_version()
				lowercase__     				=    F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
				lowercase__     				=    current_version.base_version

				# Check with the user we got that right.
				lowercase__     				=    input(F"""Which version are we developing now? [{dev_version}]"""       )
				if len(lowerCamelCase_       ) == 0:
								lowercase__     				=    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__":
							A__			:    List[str]         =				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.')
							A__			:    str         =				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()












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)




