




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_      :




							'''simple docstring'''



							def __init__(					self       :	Any       ,     a       :	int       ,     a       :	Union[str, Any]=13       ,     a       :	Union[str, Any]=7       ,     a       :	Union[str, Any]=6       ,     a       :	Tuple=17       ,     a       :	Optional[Any]=23       ,     a       :	Dict=11       ,     a       :	List[str]=True       ,     )			->					Tuple:
										SCREAMING_SNAKE_CASE           =					parent
										SCREAMING_SNAKE_CASE           =					batch_size
										SCREAMING_SNAKE_CASE           =					seq_length
										SCREAMING_SNAKE_CASE           =					act_dim
										SCREAMING_SNAKE_CASE           =					state_dim
										SCREAMING_SNAKE_CASE           =					hidden_size
										SCREAMING_SNAKE_CASE           =					max_length
										SCREAMING_SNAKE_CASE           =					is_training





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					floats_tensor((self.batch_size, self.seq_length, self.state_dim)			)
										SCREAMING_SNAKE_CASE           =					floats_tensor((self.batch_size, self.seq_length, self.act_dim)			)
										SCREAMING_SNAKE_CASE           =					floats_tensor((self.batch_size, self.seq_length, 1)			)
										SCREAMING_SNAKE_CASE           =					floats_tensor((self.batch_size, self.seq_length, 1)			)
										SCREAMING_SNAKE_CASE           =					ids_tensor((self.batch_size, self.seq_length)       ,     vocab_size=1_000			)
										SCREAMING_SNAKE_CASE           =					random_attention_mask((self.batch_size, self.seq_length)			)

										SCREAMING_SNAKE_CASE           =					self.get_config()

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





							def    _UpperCAmelCase     (					self       :	str			)			->					List[str]:
										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    _UpperCAmelCase     (					self       :	int       ,     a       :	Union[str, Any]       ,     a       :	int       ,     a       :	Optional[Any]       ,     a       :	List[Any]       ,     a       :	str       ,     a       :	str       ,     a       :	List[Any]       ,     )			->					Dict:
										SCREAMING_SNAKE_CASE           =					DecisionTransformerModel(config=a			)
										model.to(a			)
										model.eval()
										SCREAMING_SNAKE_CASE           =					model(a       ,     a       ,     a       ,     a       ,     a       ,     a			)

										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    _UpperCAmelCase     (					self       :	List[str]			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					self.prepare_config_and_inputs()
										(
										    (
										    SCREAMING_SNAKE_CASE  
										)     ,  (
										    SCREAMING_SNAKE_CASE  
										)     ,  (
										    SCREAMING_SNAKE_CASE  
										)     ,  (
										    SCREAMING_SNAKE_CASE  
										)     ,  (
										    SCREAMING_SNAKE_CASE  
										)     ,  (
										    SCREAMING_SNAKE_CASE  
										)     ,  (
										    SCREAMING_SNAKE_CASE  
										)     ,    
										)           =					config_and_inputs
										SCREAMING_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_      (   A		,      A		,      A		,      unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     (DecisionTransformerModel,) if is_torch_available() else ()
							a__						  =     ()
							a__						  =     {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

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

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





							def    _UpperCAmelCase     (					self       :	int			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					DecisionTransformerModelTester(self			)
										SCREAMING_SNAKE_CASE           =					ConfigTester(self       ,     config_class=a       ,     hidden_size=37			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					List[str]:
										self.config_tester.run_common_tests()





							def    _UpperCAmelCase     (					self       :	str			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*a			)





							@slow
							def    _UpperCAmelCase     (					self       :	Any			)			->					Dict:
										for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE           =					DecisionTransformerModel.from_pretrained(a			)
													self.assertIsNotNone(a			)







							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Dict:
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs_for_common()

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

													SCREAMING_SNAKE_CASE           =					[
													    """states""",
													    """actions""",
													    """rewards""",
													    """returns_to_go""",
													    """timesteps""",
													    """attention_mask""",
													]

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





@require_torch
class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							@slow
							def    _UpperCAmelCase     (					self       :	int			)			->					str:
										SCREAMING_SNAKE_CASE           =					2  # number of steps of autoregressive prediction we will perform
										SCREAMING_SNAKE_CASE           =					10  # defined by the RL environment, may be normalized
										SCREAMING_SNAKE_CASE           =					DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert"""			)
										SCREAMING_SNAKE_CASE           =					model.to(a			)
										SCREAMING_SNAKE_CASE           =					model.config
										torch.manual_seed(0			)
										SCREAMING_SNAKE_CASE           =					torch.randn(1       ,     1       ,     config.state_dim			).to(device=a       ,     dtype=torch.floataa			)  # env.reset()

										SCREAMING_SNAKE_CASE           =					torch.tensor(
										    [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]       ,     device=a			)

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

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

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

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

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

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









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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					args.log_outputs
			SCREAMING_SNAKE_CASE           =					"""_""".join(args.dataset.split("""/"""   ) + [args.config, args.split]   )

			# load metric
			SCREAMING_SNAKE_CASE           =					load_metric("""wer"""   )
			SCREAMING_SNAKE_CASE           =					load_metric("""cer"""   )

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

			# print & log results
			SCREAMING_SNAKE_CASE           =					f"""WER: {wer_result}\nCER: {cer_result}"""
			print(SCREAMING_SNAKE_CASE   )

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

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

						with open(SCREAMING_SNAKE_CASE					,	"""w"""   ) as p, open(SCREAMING_SNAKE_CASE					,	"""w"""   ) as t:
									# mapping function to write output
									def write_to_file(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
												p.write(f"""{i}""" + """\n"""   )
												p.write(batch["""prediction"""] + """\n"""   )
												t.write(f"""{i}""" + """\n"""   )
												t.write(batch["""target"""] + """\n"""   )

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

			SCREAMING_SNAKE_CASE           =					re.sub(SCREAMING_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!
			SCREAMING_SNAKE_CASE           =					["""\n\n""", """\n""", """   """, """  """]

			for t in token_sequences_to_ignore:
						SCREAMING_SNAKE_CASE           =					""" """.join(text.split(SCREAMING_SNAKE_CASE   )   )

			return text
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			# load dataset
			SCREAMING_SNAKE_CASE           =					load_dataset(args.dataset					,	args.config					,	split=args.split					,	use_auth_token=SCREAMING_SNAKE_CASE   )

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

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

			# resample audio
			SCREAMING_SNAKE_CASE           =					dataset.cast_column("""audio"""					,	Audio(sampling_rate=SCREAMING_SNAKE_CASE   )   )

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

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

						SCREAMING_SNAKE_CASE           =					prediction["""text"""]
						SCREAMING_SNAKE_CASE           =					normalize_text(batch["""sentence"""]   )
						return batch

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

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


if __name__ == "__main__":
		__A						:							List[Any]							=  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						:							Tuple							=  parser.parse_args()

		main(args)






