


'''simple docstring'''







import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
					    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    LayoutLMvaConfig,
					    LayoutLMvaForQuestionAnswering,
					    LayoutLMvaForSequenceClassification,
					    LayoutLMvaForTokenClassification,
					    LayoutLMvaModel,
					)
					from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
					from PIL import Image

					from transformers import LayoutLMvaImageProcessor


class 		__UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=2					,     lowerCAmelCase_=3					,     lowerCAmelCase_=4					,     lowerCAmelCase_=2					,     lowerCAmelCase_=7					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=99					,     lowerCAmelCase_=36					,     lowerCAmelCase_=3					,     lowerCAmelCase_=4					,     lowerCAmelCase_=37					,     lowerCAmelCase_="gelu"					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=5_12					,     lowerCAmelCase_=16					,     lowerCAmelCase_=2					,     lowerCAmelCase_=0.02					,     lowerCAmelCase_=6					,     lowerCAmelCase_=6					,     lowerCAmelCase_=3					,     lowerCAmelCase_=4					,     lowerCAmelCase_=None					,     lowerCAmelCase_=10_00					,     ):





								"""simple docstring"""






								_snake_case     		=	parent
								_snake_case     		=	batch_size
								_snake_case     		=	num_channels
								_snake_case     		=	image_size
								_snake_case     		=	patch_size
								_snake_case     		=	text_seq_length
								_snake_case     		=	is_training
								_snake_case     		=	use_input_mask
								_snake_case     		=	use_token_type_ids
								_snake_case     		=	use_labels
								_snake_case     		=	vocab_size
								_snake_case     		=	hidden_size
								_snake_case     		=	num_hidden_layers
								_snake_case     		=	num_attention_heads
								_snake_case     		=	intermediate_size
								_snake_case     		=	hidden_act
								_snake_case     		=	hidden_dropout_prob
								_snake_case     		=	attention_probs_dropout_prob
								_snake_case     		=	max_position_embeddings
								_snake_case     		=	type_vocab_size
								_snake_case     		=	type_sequence_label_size
								_snake_case     		=	initializer_range
								_snake_case     		=	coordinate_size
								_snake_case     		=	shape_size
								_snake_case     		=	num_labels
								_snake_case     		=	num_choices
								_snake_case     		=	scope
								_snake_case     		=	range_bbox

								# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
								_snake_case     		=	text_seq_length
								_snake_case     		=	(image_size // patch_size) ** 2 + 1
								_snake_case     		=	self.text_seq_length + self.image_seq_length


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	ids_tensor([self.batch_size, self.text_seq_length]					,     self.vocab_size						)

								_snake_case     		=	ids_tensor([self.batch_size, self.text_seq_length, 4]					,     self.range_bbox						)
								# Ensure that bbox is legal
								for i in range(bbox.shape[0]						):
									for j in range(bbox.shape[1]						):
										if bbox[i, j, 3] < bbox[i, j, 1]:
											_snake_case     		=	bbox[i, j, 3]
											_snake_case     		=	bbox[i, j, 1]
											_snake_case     		=	t
										if bbox[i, j, 2] < bbox[i, j, 0]:
											_snake_case     		=	bbox[i, j, 2]
											_snake_case     		=	bbox[i, j, 0]
											_snake_case     		=	t

								_snake_case     		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

								_snake_case     		=	None
								if self.use_input_mask:
									_snake_case     		=	random_attention_mask([self.batch_size, self.text_seq_length]						)

								_snake_case     		=	None
								if self.use_token_type_ids:
									_snake_case     		=	ids_tensor([self.batch_size, self.text_seq_length]					,     self.type_vocab_size						)

								_snake_case     		=	None
								_snake_case     		=	None
								if self.use_labels:
									_snake_case     		=	ids_tensor([self.batch_size]					,     self.type_sequence_label_size						)
									_snake_case     		=	ids_tensor([self.batch_size, self.text_seq_length]					,     self.num_labels						)

								_snake_case     		=	LayoutLMvaConfig(
								    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					,     coordinate_size=self.coordinate_size					,     shape_size=self.shape_size					,     input_size=self.image_size					,     patch_size=self.patch_size					,     )

								return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	LayoutLMvaModel(config=lowercase_						)
								model.to(lowercase_						)
								model.eval()

								# text + image
								_snake_case     		=	model(lowercase_					,     pixel_values=lowercase_						)
								_snake_case     		=	model(
								    lowercase_					,     bbox=lowercase_					,     pixel_values=lowercase_					,     attention_mask=lowercase_					,     token_type_ids=lowercase_						)
								_snake_case     		=	model(lowercase_					,     bbox=lowercase_					,     pixel_values=lowercase_					,     token_type_ids=lowercase_						)
								_snake_case     		=	model(lowercase_					,     bbox=lowercase_					,     pixel_values=lowercase_						)

								self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.hidden_size)						)

								# text only
								_snake_case     		=	model(lowercase_						)
								self.parent.assertEqual(
								    result.last_hidden_state.shape					,     (self.batch_size, self.text_seq_length, self.hidden_size)						)

								# image only
								_snake_case     		=	model(pixel_values=lowercase_						)
								self.parent.assertEqual(
								    result.last_hidden_state.shape					,     (self.batch_size, self.image_seq_length, self.hidden_size)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	self.num_labels
								_snake_case     		=	LayoutLMvaForSequenceClassification(lowercase_						)
								model.to(lowercase_						)
								model.eval()
								_snake_case     		=	model(
								    lowercase_					,     bbox=lowercase_					,     pixel_values=lowercase_					,     attention_mask=lowercase_					,     token_type_ids=lowercase_					,     labels=lowercase_					,     )
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_labels)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	self.num_labels
								_snake_case     		=	LayoutLMvaForTokenClassification(config=lowercase_						)
								model.to(lowercase_						)
								model.eval()
								_snake_case     		=	model(
								    lowercase_					,     bbox=lowercase_					,     pixel_values=lowercase_					,     attention_mask=lowercase_					,     token_type_ids=lowercase_					,     labels=lowercase_					,     )
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.text_seq_length, self.num_labels)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	LayoutLMvaForQuestionAnswering(config=lowercase_						)
								model.to(lowercase_						)
								model.eval()
								_snake_case     		=	model(
								    lowercase_					,     bbox=lowercase_					,     pixel_values=lowercase_					,     attention_mask=lowercase_					,     token_type_ids=lowercase_					,     start_positions=lowercase_					,     end_positions=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)						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.prepare_config_and_inputs()
								(
								    _snake_case 
								)     		=	config_and_inputs
								_snake_case     		=	{
								    "input_ids": input_ids,
								    "bbox": bbox,
								    "pixel_values": pixel_values,
								    "token_type_ids": token_type_ids,
								    "attention_mask": input_mask,
								}
								return config, inputs_dict


@require_torch
class 		__UpperCAmelCase   (      snake_case__		,     snake_case__		,     unittest.TestCase		):
							__lowercase   	= False
							__lowercase   	= False
							__lowercase   	= False

							__lowercase   	= (
							    (
							        LayoutLMvaModel,
							        LayoutLMvaForSequenceClassification,
							        LayoutLMvaForTokenClassification,
							        LayoutLMvaForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							__lowercase   	= (
							    {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
							    if is_torch_available()
							    else {}
							)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								return True


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	LayoutLMvaModelTester(self						)
								_snake_case     		=	ConfigTester(self					,     config_class=lowercase_					,     hidden_size=37						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_=False						):





								"""simple docstring"""






								_snake_case     		=	copy.deepcopy(lowercase_						)
								if model_class in get_values(lowercase_						):
									_snake_case     		=	{
									    k: v.unsqueeze(1						).expand(-1					,     self.model_tester.num_choices					,     -1						).contiguous()
									    if isinstance(lowercase_					,     torch.Tensor						) and v.ndim > 1
									    else v
									    for k, v in inputs_dict.items()
									}
								if return_labels:
									if model_class in get_values(lowercase_						):
										_snake_case     		=	torch.ones(self.model_tester.batch_size					,     dtype=torch.long					,     device=lowercase_						)
									elif model_class in get_values(lowercase_						):
										_snake_case     		=	torch.zeros(
										    self.model_tester.batch_size					,     dtype=torch.long					,     device=lowercase_						)
										_snake_case     		=	torch.zeros(
										    self.model_tester.batch_size					,     dtype=torch.long					,     device=lowercase_						)
									elif model_class in [
									    *get_values(lowercase_						),
									]:
										_snake_case     		=	torch.zeros(
										    self.model_tester.batch_size					,     dtype=torch.long					,     device=lowercase_						)
									elif model_class in [
									    *get_values(lowercase_						),
									]:
										_snake_case     		=	torch.zeros(
										    (self.model_tester.batch_size, self.model_tester.text_seq_length)					,     dtype=torch.long					,     device=lowercase_					,     )

								return inputs_dict


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								self.config_tester.run_common_tests()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowercase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
									_snake_case     		=	type
									self.model_tester.create_and_check_model(*lowercase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowercase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowercase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowercase_						)




							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_snake_case     		=	LayoutLMvaModel.from_pretrained(lowercase_						)
									self.assertIsNotNone(lowercase_						)







def        SCREAMING_SNAKE_CASE__			(     ) ->			Any:
	_snake_case     		=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
	return image





@require_torch
class 		__UpperCAmelCase   (      unittest.TestCase		):


							@cached_property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return LayoutLMvaImageProcessor(apply_ocr=lowercase_						) if is_vision_available() else None




							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'						).to(lowercase_						)

								_snake_case     		=	self.default_image_processor
								_snake_case     		=	prepare_img()
								_snake_case     		=	image_processor(images=lowercase_					,     return_tensors='pt'						).pixel_values.to(lowercase_						)

								_snake_case     		=	torch.tensor([[1, 2]]						)
								_snake_case     		=	torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]						).unsqueeze(0						)

								# forward pass
								_snake_case     		=	model(
								    input_ids=input_ids.to(lowercase_						)					,     bbox=bbox.to(lowercase_						)					,     pixel_values=pixel_values.to(lowercase_						)					,     )

								# verify the logits
								_snake_case     		=	torch.Size((1, 1_99, 7_68)						)
								self.assertEqual(outputs.last_hidden_state.shape					,     lowercase_						)

								_snake_case     		=	torch.tensor(
								    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]						).to(lowercase_						)

								self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]					,     lowercase_					,     atol=1E-4						)						)



'''simple docstring'''







import argparse
import re
from typing import Dict

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

from transformers import AutoFeatureExtractor, pipeline







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			int:
	_snake_case     		=	args.log_outputs
	_snake_case     		=	'_'.join(args.dataset.split('/'			) + [args.config, args.split]			)

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

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

	# print & log results
	_snake_case     		=	F'WER: {wer_result}\nCER: {cer_result}'
	print(__A			)

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

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

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

			result.map(__A    ,       with_indices=__A			)







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

	_snake_case     		=	re.sub(__A    ,       ''    ,       text.lower()			)

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

	for t in token_sequences_to_ignore:
		_snake_case     		=	' '.join(text.split(__A			)			)

	return text







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			Optional[int]:
	# load dataset
	_snake_case     		=	load_dataset(args.dataset    ,       args.config    ,       split=args.split    ,       use_auth_token=__A			)

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

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

	# resample audio
	_snake_case     		=	dataset.cast_column('audio'    ,       Audio(sampling_rate=__A			)			)

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

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

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

	# run inference on all examples
	_snake_case     		=	dataset.map(__A    ,       remove_columns=dataset.column_names			)

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


if __name__ == "__main__":
					lowercase					:							str   =      argparse.ArgumentParser()

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

					main(args)

