






import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
 import torch

 from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin



class 							SCREAMING_SNAKE_CASE__		:





     """simple docstring"""






     def __init__(      self  :						Dict							,	__A  :						Tuple							,	__A  :						Tuple=None							,	__A  :						str=None							,	__A  :						List[str]=None							,	__A  :						Dict="resnet50"							,	__A  :						Tuple=3							,	__A  :						Optional[int]=3_2							,	__A  :						List[str]=3							,	__A  :						Dict=True							,	__A  :						List[str]=True							,	):
      snake_case__				:   List[Any]     =						parent
      snake_case__				:   Any     =						out_indices if out_indices is not None else [4]
      snake_case__				:   Union[str, Any]     =						stage_names
      snake_case__				:   Dict     =						out_features
      snake_case__				:   Optional[Any]     =						backbone
      snake_case__				:   Optional[Any]     =						batch_size
      snake_case__				:   Tuple     =						image_size
      snake_case__				:   int     =						num_channels
      snake_case__				:   Tuple     =						use_pretrained_backbone
      snake_case__				:   Dict     =						is_training



     def  _lowercase  (      self  :						Optional[Any] ):
      snake_case__				:   Optional[Any]     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
      snake_case__				:   Tuple     =						self.get_config()

      return config, pixel_values



     def  _lowercase  (      self  :						int ):
      return TimmBackboneConfig(
          image_size=self.image_size							,	num_channels=self.num_channels							,	out_features=self.out_features							,	out_indices=self.out_indices							,	stage_names=self.stage_names							,	use_pretrained_backbone=self.use_pretrained_backbone							,	backbone=self.backbone							,	)



     def  _lowercase  (      self  :						str							,	__A  :						Dict							,	__A  :						Tuple ):
      snake_case__				:   Optional[Any]     =						TimmBackbone(config=__lowercase )
      model.to(__lowercase )
      model.eval()
      with torch.no_grad():
       snake_case__				:   Optional[Any]     =						model(__lowercase )
      self.parent.assertEqual(
          result.feature_map[-1].shape							,	(self.batch_size, model.channels[-1], 1_4, 1_4)							,	)






     def  _lowercase  (      self  :						Any ):
      snake_case__				:   List[Any]     =						self.prepare_config_and_inputs()
      snake_case__,		snake_case__				:   str     =						config_and_inputs
      snake_case__				:   List[Any]     =						{"pixel_values": pixel_values}
      return config, inputs_dict



@require_torch
@require_timm
class 							SCREAMING_SNAKE_CASE__		(       lowercase__				,     lowercase__				,     lowercase__				,     unittest.TestCase		):





     """simple docstring"""






     a_			=	(TimmBackbone,) if is_torch_available() else ()
     a_			=	{"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
     a_			=	False
     a_			=	False
     a_			=	False
     a_			=	False



     def  _lowercase  (      self  :						List[Any] ):
      snake_case__				:   Dict     =						TimmBackboneModelTester(self )
      snake_case__				:   List[str]     =						ConfigTester(self							,	config_class=__lowercase							,	has_text_modality=__lowercase )



     def  _lowercase  (      self  :						Union[str, Any] ):
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()



     def  _lowercase  (      self  :						Optional[int] ):
      snake_case__				:   Union[str, Any]     =						"resnet18"
      snake_case__				:   Any     =						"microsoft/resnet-18"

      snake_case__				:   List[str]     =						AutoBackbone.from_pretrained(__lowercase							,	use_timm_backbone=__lowercase )
      snake_case__				:   List[str]     =						AutoBackbone.from_pretrained(__lowercase )

      self.assertEqual(len(timm_model.out_features )							,	len(transformers_model.out_features ) )
      self.assertEqual(len(timm_model.stage_names )							,	len(transformers_model.stage_names ) )
      self.assertEqual(timm_model.channels							,	transformers_model.channels )
      # Out indices are set to the last layer by default. For timm models, we don't know
      # the number of layers in advance, so we set it to (-1,), whereas for transformers
      # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
      self.assertEqual(timm_model.out_indices							,	(-1,) )
      self.assertEqual(transformers_model.out_indices							,	[len(timm_model.stage_names ) - 1] )

      snake_case__				:   str     =						AutoBackbone.from_pretrained(__lowercase							,	use_timm_backbone=__lowercase							,	out_indices=[1, 2, 3] )
      snake_case__				:   Union[str, Any]     =						AutoBackbone.from_pretrained(__lowercase							,	out_indices=[1, 2, 3] )

      self.assertEqual(timm_model.out_indices							,	transformers_model.out_indices )
      self.assertEqual(len(timm_model.out_features )							,	len(transformers_model.out_features ) )
      self.assertEqual(timm_model.channels							,	transformers_model.channels )



     @unittest.skip("TimmBackbone doesn't support feed forward chunking" )
     def  _lowercase  (      self  :						Optional[Any] ):
      pass



     @unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute" )
     def  _lowercase  (      self  :						Optional[int] ):
      pass



     @unittest.skip("TimmBackbone initialization is managed on the timm side" )
     def  _lowercase  (      self  :						Optional[int] ):
      pass



     @unittest.skip("TimmBackbone models doesn't have inputs_embeds" )
     def  _lowercase  (      self  :						Optional[Any] ):
      pass



     @unittest.skip("TimmBackbone models doesn't have inputs_embeds" )
     def  _lowercase  (      self  :						Dict ):
      pass



     @unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint" )
     def  _lowercase  (      self  :						Optional[int] ):
      pass



     @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
     def  _lowercase  (      self  :						Dict ):
      pass



     @unittest.skip("model weights aren't tied in TimmBackbone." )
     def  _lowercase  (      self  :						Union[str, Any] ):
      pass



     @unittest.skip("model weights aren't tied in TimmBackbone." )
     def  _lowercase  (      self  :						List[Any] ):
      pass



     @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
     def  _lowercase  (      self  :						str ):
      pass



     @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
     def  _lowercase  (      self  :						List[str] ):
      pass



     @unittest.skip("TimmBackbone doesn't have hidden size info in its configuration." )
     def  _lowercase  (      self  :						Any ):
      pass



     @unittest.skip("TimmBackbone doesn't support output_attentions." )
     def  _lowercase  (      self  :						Dict ):
      pass



     @unittest.skip("Safetensors is not supported by timm." )
     def  _lowercase  (      self  :						Optional[int] ):
      pass



     @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
     def  _lowercase  (      self  :						Dict ):
      pass



     def  _lowercase  (      self  :						Tuple ):
      snake_case__,		snake_case__				:   Tuple     =						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       snake_case__				:   Union[str, Any]     =						model_class(__lowercase )
       snake_case__				:   str     =						inspect.signature(model.forward )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       snake_case__				:   Tuple     =						[*signature.parameters.keys()]

       snake_case__				:   Union[str, Any]     =						["pixel_values"]
       self.assertListEqual(arg_names[:1]							,	__lowercase )



     def  _lowercase  (      self  :						Optional[Any] ):
      snake_case__,		snake_case__				:   Tuple     =						self.model_tester.prepare_config_and_inputs_for_common()
      snake_case__				:   Optional[Any]     =						True
      snake_case__				:   str     =						self.has_attentions

      # no need to test all models as different heads yield the same functionality
      snake_case__				:   Any     =						self.all_model_classes[0]
      snake_case__				:   Optional[Any]     =						model_class(__lowercase )
      model.to(__lowercase )

      snake_case__				:   Optional[Any]     =						self._prepare_for_class(__lowercase							,	__lowercase )
      snake_case__				:   Optional[Any]     =						model(**__lowercase )
      snake_case__				:   Union[str, Any]     =						outputs[0][-1]

      # Encoder-/Decoder-only models
      snake_case__				:   List[str]     =						outputs.hidden_states[0]
      hidden_states.retain_grad()

      if self.has_attentions:
       snake_case__				:   List[Any]     =						outputs.attentions[0]
       attentions.retain_grad()

      output.flatten()[0].backward(retain_graph=__lowercase )

      self.assertIsNotNone(hidden_states.grad )

      if self.has_attentions:
       self.assertIsNotNone(attentions.grad )






     def  _lowercase  (      self  :						List[str] ):
      snake_case__,		snake_case__				:   Any     =						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       snake_case__				:   int     =						model_class(__lowercase )
       model.to(__lowercase )
       model.eval()
       snake_case__				:   Tuple     =						model(**__lowercase )

       self.assertEqual(len(result.feature_maps )							,	len(config.out_indices ) )
       self.assertEqual(len(model.channels )							,	len(config.out_indices ) )

       # Check output of last stage is taken if out_features=None, out_indices=None
       snake_case__				:   List[str]     =						copy.deepcopy(__lowercase )
       snake_case__				:   Union[str, Any]     =						None
       snake_case__				:   Union[str, Any]     =						model_class(__lowercase )
       model.to(__lowercase )
       model.eval()
       snake_case__				:   List[Any]     =						model(**__lowercase )

       self.assertEqual(len(result.feature_maps )							,	1 )
       self.assertEqual(len(model.channels )							,	1 )

       # Check backbone can be initialized with fresh weights
       snake_case__				:   Union[str, Any]     =						copy.deepcopy(__lowercase )
       snake_case__				:   Any     =						False
       snake_case__				:   Union[str, Any]     =						model_class(__lowercase )
       model.to(__lowercase )
       model.eval()
       snake_case__				:   int     =						model(**__lowercase )











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       (				snake_case_      :							Dataset  ,						snake_case_      :							Dict[str, str]     ):
	snake_case__				:   Tuple     =						args.log_outputs
	snake_case__				:   Union[str, Any]     =						"_".join(args.dataset.split("/"     ) + [args.config, args.split]     )

	# load metric
	snake_case__				:   List[str]     =						load_metric("wer"     )
	snake_case__				:   List[str]     =						load_metric("cer"     )

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

	# print & log results
	snake_case__				:   Dict     =						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:
		snake_case__				:   Union[str, Any]     =						F'''log_{dataset_id}_predictions.txt'''
		snake_case__				:   int     =						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_      :							Union[str, Any]  ,						snake_case_      :							Any     ):
				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   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	snake_case__				:   List[Any]     =						"[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

	snake_case__				:   Optional[int]     =						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!
	snake_case__				:   Optional[Any]     =						["\n\n", "\n", "   ", "  "]

	for t in token_sequences_to_ignore:
		snake_case__				:   Optional[int]     =						" ".join(text.split(snake_case_     )     )

	return text







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	# load dataset
	snake_case__				:   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
	snake_case__				:   List[str]     =						AutoFeatureExtractor.from_pretrained(args.model_id     )
	snake_case__				:   List[Any]     =						feature_extractor.sampling_rate

	# resample audio
	snake_case__				:   Dict     =						dataset.cast_column("audio"  ,						Audio(sampling_rate=snake_case_     )     )

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

	# map function to decode audio
	def map_to_pred(snake_case_      :							Any     ):
		snake_case__				:   Union[str, Any]     =						asr(
		    batch["audio"]["array"]  ,						chunk_length_s=args.chunk_length_s  ,						stride_length_s=args.stride_length_s     )

		snake_case__				:   Optional[int]     =						prediction["text"]
		snake_case__				:   Optional[Any]     =						normalize_text(batch["sentence"]     )
		return batch

	# run inference on all examples
	snake_case__				:   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__":
	__lowerCamelCase      :    Dict   		=   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.""",
	)
	__lowerCamelCase      :    str   		=   parser.parse_args()

	main(args)



