




import argparse
import re
from typing import Dict

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

from transformers import AutoFeatureExtractor, pipeline





def 				A__ (snake_case   :		Dataset		,	snake_case   :		Dict[str, str]				)     ->						int:
					__UpperCamelCase					:	Any             =      args.log_outputs
					__UpperCamelCase					:	int             =      """_""".join(args.dataset.split("""/"""				) + [args.config, args.split]				)

					# load metric
					__UpperCamelCase					:	Tuple             =      load_metric("""wer"""				)
					__UpperCamelCase					:	Optional[Any]             =      load_metric("""cer"""				)

					# compute metrics
					__UpperCamelCase					:	Union[str, Any]             =      wer.compute(references=result["""target"""]		,	predictions=result["""prediction"""]				)
					__UpperCamelCase					:	List[str]             =      cer.compute(references=result["""target"""]		,	predictions=result["""prediction"""]				)

					# print & log results
					__UpperCamelCase					:	Optional[Any]             =      F'''WER: {wer_result}\nCER: {cer_result}'''
					print(snake_case				)

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

					# log all results in text file. Possibly interesting for analysis
					if log_outputs is not None:
										__UpperCamelCase					:	str             =      F'''log_{dataset_id}_predictions.txt'''
										__UpperCamelCase					:	Union[str, Any]             =      F'''log_{dataset_id}_targets.txt'''

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

															result.map(snake_case		,	with_indices=snake_case				)





def 				A__ (snake_case   :		str				)     ->						str:
					__UpperCamelCase					:	Dict             =      """[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

					__UpperCamelCase					:	Any             =      re.sub(snake_case		,	""""""		,	text.lower()				)

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

					for t in token_sequences_to_ignore:
										__UpperCamelCase					:	List[Any]             =      """ """.join(text.split(snake_case				)				)

					return text





def 				A__ (snake_case   :		List[Any]				)     ->						List[str]:
					# load dataset
					__UpperCamelCase					:	Optional[int]             =      load_dataset(args.dataset		,	args.config		,	split=args.split		,	use_auth_token=snake_case				)

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

					# load processor
					__UpperCamelCase					:	Any             =      AutoFeatureExtractor.from_pretrained(args.model_id				)
					__UpperCamelCase					:	int             =      feature_extractor.sampling_rate

					# resample audio
					__UpperCamelCase					:	Union[str, Any]             =      dataset.cast_column("""audio"""		,	Audio(sampling_rate=snake_case				)				)

					# load eval pipeline
					if args.device is None:
										__UpperCamelCase					:	Optional[Any]             =      0 if torch.cuda.is_available() else -1
					__UpperCamelCase					:	List[Any]             =      pipeline("""automatic-speech-recognition"""		,	model=args.model_id		,	device=args.device				)

					# map function to decode audio
					def map_to_pred(snake_case   :		str				):
										__UpperCamelCase					:	int             =      asr(
										    batch["""audio"""]["""array"""]		,	chunk_length_s=args.chunk_length_s		,	stride_length_s=args.stride_length_s				)

										__UpperCamelCase					:	int             =      prediction["""text"""]
										__UpperCamelCase					:	Tuple             =      normalize_text(batch["""sentence"""]				)
										return batch

					# run inference on all examples
					__UpperCamelCase					:	List[Any]             =      dataset.map(snake_case		,	remove_columns=dataset.column_names				)

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


if __name__ == "__main__":
	a__		     =     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__		     =     parser.parse_args()

	main(args)












import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


a__		     =     {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def __init__(					self		:						Tuple					,     lowerCAmelCase		:						List[Any]	)	->       Union[str, Any]:







						"""simple docstring"""







						super().__init__()
						__UpperCamelCase					:	Tuple             =      torchvision.models.resnetaaa(pretrained=lowerCAmelCase	)
						__UpperCamelCase					:	Union[str, Any]             =      list(model.children()	)[:-2]
						__UpperCamelCase					:	List[str]             =      nn.Sequential(*lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						Optional[Any]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      self.pool(self.model(lowerCAmelCase	)	)
						__UpperCamelCase					:	Dict             =      torch.flatten(lowerCAmelCase					,     start_dim=2	)
						__UpperCamelCase					:	Optional[int]             =      out.transpose(1					,     2	).contiguous()
						return out  # BxNx2048






class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def __init__(					self		:						Optional[Any]					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						Optional[int]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      [json.loads(lowerCAmelCase	) for l in open(lowerCAmelCase	)]
						__UpperCamelCase					:	Optional[Any]             =      os.path.dirname(lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      tokenizer
						__UpperCamelCase					:	Dict             =      labels
						__UpperCamelCase					:	int             =      len(lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      max_seq_length

						__UpperCamelCase					:	int             =      transforms






	def __len__(					self		:						Union[str, Any]	)	->       List[Any]:







						"""simple docstring"""







						return len(self.data	)






	def __getitem__(					self		:						Optional[Any]					,     lowerCAmelCase		:						List[Any]	)	->       int:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]					,     add_special_tokens=lowerCAmelCase	)	)
						__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	Tuple             =      sentence[0], sentence[1:-1], sentence[-1]
						__UpperCamelCase					:	Optional[int]             =      sentence[: self.max_seq_length]

						__UpperCamelCase					:	List[str]             =      torch.zeros(self.n_classes	)
						__UpperCamelCase					:	List[str]             =      1

						__UpperCamelCase					:	List[Any]             =      Image.open(os.path.join(self.data_dir					,     self.data[index]["""img"""]	)	).convert("""RGB"""	)
						__UpperCamelCase					:	Dict             =      self.transforms(lowerCAmelCase	)

						return {
						    "image_start_token": start_token,
						    "image_end_token": end_token,
						    "sentence": sentence,
						    "image": image,
						    "label": label,
						}






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Optional[int]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      Counter()
						for row in self.data:
											label_freqs.update(row["""label"""]	)
						return label_freqs






def 				A__ (snake_case   :		int				)     ->						Optional[int]:
					__UpperCamelCase					:	str             =      [len(row["""sentence"""]				) for row in batch]
					__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      len(snake_case				), max(snake_case				)

					__UpperCamelCase					:	List[str]             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)
					__UpperCamelCase					:	Tuple             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)

					for i_batch, (input_row, length) in enumerate(zip(snake_case		,	snake_case				)				):
										__UpperCamelCase					:	Optional[Any]             =      input_row["""sentence"""]
										__UpperCamelCase					:	Any             =      1

					__UpperCamelCase					:	List[Any]             =      torch.stack([row["""image"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""label"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""image_start_token"""] for row in batch]				)
					__UpperCamelCase					:	List[str]             =      torch.stack([row["""image_end_token"""] for row in batch]				)

					return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor





def 				A__ ()     ->						Optional[Any]:
					return [
					    "Crime",
					    "Drama",
					    "Thriller",
					    "Action",
					    "Comedy",
					    "Romance",
					    "Documentary",
					    "Short",
					    "Mystery",
					    "History",
					    "Family",
					    "Adventure",
					    "Fantasy",
					    "Sci-Fi",
					    "Western",
					    "Horror",
					    "Sport",
					    "War",
					    "Music",
					    "Musical",
					    "Animation",
					    "Biography",
					    "Film-Noir",
					]







def 				A__ ()     ->						Union[str, Any]:
					return transforms.Compose(
					    [
					        transforms.Resize(2_56				),
					        transforms.CenterCrop(2_24				),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46777044, 0.44531429, 0.40661017]		,	std=[0.12221994, 0.12145835, 0.14380469]		,	),
					    ]				)






