


'''simple docstring'''







import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 DecisionTransformerModel
					from transformers.models.decision_transformer.modeling_decision_transformer import (
					    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
					)


class 		__UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=13					,     lowerCAmelCase_=7					,     lowerCAmelCase_=6					,     lowerCAmelCase_=17					,     lowerCAmelCase_=23					,     lowerCAmelCase_=11					,     lowerCAmelCase_=True					,     ):





								"""simple docstring"""






								_snake_case     		=	parent
								_snake_case     		=	batch_size
								_snake_case     		=	seq_length
								_snake_case     		=	act_dim
								_snake_case     		=	state_dim
								_snake_case     		=	hidden_size
								_snake_case     		=	max_length
								_snake_case     		=	is_training


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	floats_tensor((self.batch_size, self.seq_length, self.state_dim)						)
								_snake_case     		=	floats_tensor((self.batch_size, self.seq_length, self.act_dim)						)
								_snake_case     		=	floats_tensor((self.batch_size, self.seq_length, 1)						)
								_snake_case     		=	floats_tensor((self.batch_size, self.seq_length, 1)						)
								_snake_case     		=	ids_tensor((self.batch_size, self.seq_length)					,     vocab_size=10_00						)
								_snake_case     		=	random_attention_mask((self.batch_size, self.seq_length)						)

								_snake_case     		=	self.get_config()

								return (
								    config,
								    states,
								    actions,
								    rewards,
								    returns_to_go,
								    timesteps,
								    attention_mask,
								)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return DecisionTransformerConfig(
								    batch_size=self.batch_size					,     seq_length=self.seq_length					,     act_dim=self.act_dim					,     state_dim=self.state_dim					,     hidden_size=self.hidden_size					,     max_length=self.max_length					,     )


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





								"""simple docstring"""






								_snake_case     		=	DecisionTransformerModel(config=lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()
								_snake_case     		=	model(lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						)

								self.parent.assertEqual(result.state_preds.shape					,     states.shape						)
								self.parent.assertEqual(result.action_preds.shape					,     actions.shape						)
								self.parent.assertEqual(result.return_preds.shape					,     returns_to_go.shape						)
								self.parent.assertEqual(
								    result.last_hidden_state.shape					,     (self.batch_size, self.seq_length * 3, self.hidden_size)						)  # seq length *3 as there are 3 modelities: states, returns and actions




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.prepare_config_and_inputs()
								(
								    (
								    _snake_case				
								)		,				(
								    _snake_case				
								)		,				(
								    _snake_case				
								)		,				(
								    _snake_case				
								)		,				(
								    _snake_case				
								)		,				(
								    _snake_case				
								)		,				(
								    _snake_case				
								)		,								
								)     		=	config_and_inputs
								_snake_case     		=	{
								    'states': states,
								    'actions': actions,
								    'rewards': rewards,
								    'returns_to_go': returns_to_go,
								    'timesteps': timesteps,
								    'attention_mask': attention_mask,
								}
								return config, inputs_dict


@require_torch
class 		__UpperCAmelCase   (      _lowerCamelCase		,     _lowerCamelCase		,     _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= (DecisionTransformerModel,) if is_torch_available() else ()
							__lowercase   	= ()
							__lowercase   	= {"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}

							# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
							__lowercase   	= False

							# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
							__lowercase   	= False
							__lowercase   	= False
							__lowercase   	= False
							__lowercase   	= False
							__lowercase   	= False
							__lowercase   	= False
							__lowercase   	= False
							__lowercase   	= False
							__lowercase   	= False


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	DecisionTransformerModelTester(self						)
								_snake_case     		=	ConfigTester(self					,     config_class=lowerCAmelCase_					,     hidden_size=37						)


							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(*lowerCAmelCase_						)


							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_snake_case     		=	DecisionTransformerModel.from_pretrained(lowerCAmelCase_						)
									self.assertIsNotNone(lowerCAmelCase_						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case		,				_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									_snake_case     		=	model_class(lowerCAmelCase_						)
									_snake_case     		=	inspect.signature(model.forward						)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									_snake_case     		=	[*signature.parameters.keys()]

									_snake_case     		=	[
									    'states',
									    'actions',
									    'rewards',
									    'returns_to_go',
									    'timesteps',
									    'attention_mask',
									]

									self.assertListEqual(arg_names[: len(lowerCAmelCase_						)]					,     lowerCAmelCase_						)


@require_torch
class 		__UpperCAmelCase   (      unittest.TestCase		):


							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	2  # number of steps of autoregressive prediction we will perform
								_snake_case     		=	10  # defined by the RL environment, may be normalized
								_snake_case     		=	DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-expert'						)
								_snake_case     		=	model.to(lowerCAmelCase_						)
								_snake_case     		=	model.config
								torch.manual_seed(0						)
								_snake_case     		=	torch.randn(1					,     1					,     config.state_dim						).to(device=lowerCAmelCase_					,     dtype=torch.floataa						)  # env.reset()

								_snake_case     		=	torch.tensor(
								    [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]					,     device=lowerCAmelCase_						)

								_snake_case     		=	torch.tensor(lowerCAmelCase_					,     device=lowerCAmelCase_					,     dtype=torch.floataa						).reshape(1					,     1					,     1						)
								_snake_case     		=	state
								_snake_case     		=	torch.zeros(1					,     0					,     config.act_dim					,     device=lowerCAmelCase_					,     dtype=torch.floataa						)
								_snake_case     		=	torch.zeros(1					,     0					,     device=lowerCAmelCase_					,     dtype=torch.floataa						)
								_snake_case     		=	torch.tensor(0					,     device=lowerCAmelCase_					,     dtype=torch.long						).reshape(1					,     1						)

								for step in range(lowerCAmelCase_						):
									_snake_case     		=	torch.cat([actions, torch.zeros(1					,     1					,     config.act_dim					,     device=lowerCAmelCase_						)]					,     dim=1						)
									_snake_case     		=	torch.cat([rewards, torch.zeros(1					,     1					,     device=lowerCAmelCase_						)]					,     dim=1						)

									_snake_case     		=	torch.ones(1					,     states.shape[1]						).to(dtype=torch.long					,     device=states.device						)

									with torch.no_grad():
										_snake_case		,				_snake_case		,				_snake_case     		=	model(
										    states=lowerCAmelCase_					,     actions=lowerCAmelCase_					,     rewards=lowerCAmelCase_					,     returns_to_go=lowerCAmelCase_					,     timesteps=lowerCAmelCase_					,     attention_mask=lowerCAmelCase_					,     return_dict=lowerCAmelCase_					,     )

									self.assertEqual(action_pred.shape					,     actions.shape						)
									self.assertTrue(torch.allclose(action_pred[0, -1]					,     expected_outputs[step]					,     atol=1E-4						)						)
									_snake_case		,				_snake_case		,				_snake_case		,				_snake_case     		=	(  # env.step(action)
									    torch.randn(1					,     1					,     config.state_dim						).to(device=lowerCAmelCase_					,     dtype=torch.floataa						),
									    1.0,
									    False,
									    {},
									)

									_snake_case     		=	action_pred[0, -1]
									_snake_case     		=	torch.cat([states, state]					,     dim=1						)
									_snake_case     		=	returns_to_go[0, -1] - reward
									_snake_case     		=	torch.cat([returns_to_go, pred_return.reshape(1					,     1					,     1						)]					,     dim=1						)
									_snake_case     		=	torch.cat(
									    [timesteps, torch.ones((1, 1)					,     device=lowerCAmelCase_					,     dtype=torch.long						) * (step + 1)]					,     dim=1						)



'''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)

