






import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     MODEL_MAPPING,
     LevitForImageClassification,
     LevitForImageClassificationWithTeacher,
     LevitModel,
 )
 from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import LevitImageProcessor



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





     """simple docstring"""






     def  _lowercase  (      self  :						Any ):
      snake_case__				:   List[Any]     =						self.config_class(**self.inputs_dict )
      self.parent.assertTrue(hasattr(__A							,	"hidden_sizes" ) )
      self.parent.assertTrue(hasattr(__A							,	"num_attention_heads" ) )



class 							SCREAMING_SNAKE_CASE__		:





     """simple docstring"""






     def __init__(      self  :						Any							,	__A  :						Optional[int]							,	__A  :						Dict=1_3							,	__A  :						Union[str, Any]=6_4							,	__A  :						int=3							,	__A  :						List[str]=3							,	__A  :						Optional[Any]=2							,	__A  :						Dict=1							,	__A  :						Optional[int]=1_6							,	__A  :						Any=[1_2_8, 2_5_6, 3_8_4]							,	__A  :						List[str]=[4, 6, 8]							,	__A  :						Optional[Any]=[2, 3, 4]							,	__A  :						str=[1_6, 1_6, 1_6]							,	__A  :						Dict=0							,	__A  :						Dict=[2, 2, 2]							,	__A  :						Dict=[2, 2, 2]							,	__A  :						Dict=0.0_2							,	__A  :						Dict=True							,	__A  :						Dict=True							,	__A  :						str=2							,	):
      snake_case__				:   Optional[Any]     =						parent
      snake_case__				:   Union[str, Any]     =						batch_size
      snake_case__				:   List[Any]     =						image_size
      snake_case__				:   Tuple     =						num_channels
      snake_case__				:   int     =						kernel_size
      snake_case__				:   Dict     =						stride
      snake_case__				:   Union[str, Any]     =						padding
      snake_case__				:   int     =						hidden_sizes
      snake_case__				:   List[str]     =						num_attention_heads
      snake_case__				:   int     =						depths
      snake_case__				:   Optional[Any]     =						key_dim
      snake_case__				:   Dict     =						drop_path_rate
      snake_case__				:   Union[str, Any]     =						patch_size
      snake_case__				:   str     =						attention_ratio
      snake_case__				:   Union[str, Any]     =						mlp_ratio
      snake_case__				:   Optional[Any]     =						initializer_range
      snake_case__				:   Optional[Any]     =						[
          ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
          ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
      ]
      snake_case__				:   Union[str, Any]     =						is_training
      snake_case__				:   Dict     =						use_labels
      snake_case__				:   List[Any]     =						num_labels
      snake_case__				:   Dict     =						initializer_range



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

      snake_case__				:   Tuple     =						None
      if self.use_labels:
       snake_case__				:   int     =						ids_tensor([self.batch_size]							,	self.num_labels )

      snake_case__				:   Dict     =						self.get_config()
      return config, pixel_values, labels



     def  _lowercase  (      self  :						Dict ):
      return LevitConfig(
          image_size=self.image_size							,	num_channels=self.num_channels							,	kernel_size=self.kernel_size							,	stride=self.stride							,	padding=self.padding							,	patch_size=self.patch_size							,	hidden_sizes=self.hidden_sizes							,	num_attention_heads=self.num_attention_heads							,	depths=self.depths							,	key_dim=self.key_dim							,	drop_path_rate=self.drop_path_rate							,	mlp_ratio=self.mlp_ratio							,	attention_ratio=self.attention_ratio							,	initializer_range=self.initializer_range							,	down_ops=self.down_ops							,	)



     def  _lowercase  (      self  :						Dict							,	__A  :						int							,	__A  :						List[str]							,	__A  :						List[str] ):
      snake_case__				:   Any     =						LevitModel(config=__A )
      model.to(__A )
      model.eval()
      snake_case__				:   Optional[int]     =						model(__A )
      snake_case__				:   Optional[int]     =						(self.image_size, self.image_size)
      snake_case__				:   Any     =						image_size[0], image_size[1]
      for _ in range(4 ):
       snake_case__				:   List[str]     =						floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
       snake_case__				:   str     =						floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
      self.parent.assertEqual(
          result.last_hidden_state.shape							,	(self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1])							,	)



     def  _lowercase  (      self  :						Dict							,	__A  :						Tuple							,	__A  :						Optional[int]							,	__A  :						Optional[Any] ):
      snake_case__				:   Tuple     =						self.num_labels
      snake_case__				:   Optional[Any]     =						LevitForImageClassification(__A )
      model.to(__A )
      model.eval()
      snake_case__				:   Dict     =						model(__A							,	labels=__A )
      self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.num_labels) )






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



@require_torch
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     UpperCamelCase_				,     unittest.TestCase		):





     """simple docstring"""






     a_			=	(
         (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
         if is_torch_available()
         else ()
     )
     a_			=	(
         {
             "feature-extraction": LevitModel,
             "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
         }
         if is_torch_available()
         else {}
     )

     a_			=	False
     a_			=	False
     a_			=	False
     a_			=	False
     a_			=	False



     def  _lowercase  (      self  :						Any ):
      snake_case__				:   str     =						LevitModelTester(self )
      snake_case__				:   Optional[Any]     =						ConfigTester(self							,	config_class=__A							,	has_text_modality=__A							,	hidden_size=3_7 )



     def  _lowercase  (      self  :						List[str] ):
      self.create_and_test_config_common_properties()
      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] ):
      return



     @unittest.skip(reason="Levit does not use inputs_embeds" )
     def  _lowercase  (      self  :						Dict ):
      pass



     @unittest.skip(reason="Levit does not support input and output embeddings" )
     def  _lowercase  (      self  :						str ):
      pass



     @unittest.skip(reason="Levit does not output attentions" )
     def  _lowercase  (      self  :						Optional[int] ):
      pass



     def  _lowercase  (      self  :						int ):
      snake_case__				:   str     =						self.model_tester.prepare_config_and_inputs_for_common()

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

       snake_case__				:   Tuple     =						["pixel_values"]
       self.assertListEqual(arg_names[:1]							,	__A )



     def  _lowercase  (      self  :						Tuple ):
      def check_hidden_states_output(__A  :						Any							,	__A  :						Dict							,	__A  :						Any ):
       snake_case__				:   int     =						model_class(__A )
       model.to(__A )
       model.eval()

       with torch.no_grad():
        snake_case__				:   List[Any]     =						model(**self._prepare_for_class(__A							,	__A ) )

       snake_case__				:   Optional[Any]     =						outputs.hidden_states

       snake_case__				:   int     =						len(self.model_tester.depths ) + 1
       self.assertEqual(len(__A )							,	__A )

       snake_case__				:   Dict     =						(self.model_tester.image_size, self.model_tester.image_size)
       snake_case__				:   str     =						image_size[0], image_size[1]
       for _ in range(4 ):
        snake_case__				:   Dict     =						floor(
            (
                (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                / self.model_tester.stride
            )
            + 1 )
        snake_case__				:   Union[str, Any]     =						floor(
            (
                (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                / self.model_tester.stride
            )
            + 1 )
       # verify the first hidden states (first block)
       self.assertListEqual(
           list(hidden_states[0].shape[-2:] )							,	[
               height * width,
               self.model_tester.hidden_sizes[0],
           ]							,	)

      snake_case__				:   Union[str, Any]     =						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       snake_case__				:   Any     =						True
       check_hidden_states_output(__A							,	__A							,	__A )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       snake_case__				:   List[Any]     =						True

       check_hidden_states_output(__A							,	__A							,	__A )



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



     def  _lowercase  (      self  :						List[str]							,	__A  :						str							,	__A  :						Optional[int]							,	__A  :						Optional[int]=False ):
      snake_case__				:   Optional[Any]     =						super()._prepare_for_class(__A							,	__A							,	return_labels=__A )

      if return_labels:
       if model_class.__name__ == "LevitForImageClassificationWithTeacher":
        del inputs_dict["labels"]

      return inputs_dict



     def  _lowercase  (      self  :						List[str] ):
      snake_case__				:   List[Any]     =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__A )



     def  _lowercase  (      self  :						Tuple ):
      snake_case__				:   Dict     =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__A )



     def  _lowercase  (      self  :						List[Any] ):
      if not self.model_tester.is_training:
       return

      snake_case__				:   List[Any]     =						self.model_tester.prepare_config_and_inputs_for_common()
      snake_case__				:   Union[str, Any]     =						True

      for model_class in self.all_model_classes:
       # LevitForImageClassificationWithTeacher supports inference-only
       if (
           model_class in get_values(__A )
           or model_class.__name__ == "LevitForImageClassificationWithTeacher"
       ):
        continue
       snake_case__				:   List[str]     =						model_class(__A )
       model.to(__A )
       model.train()
       snake_case__				:   Dict     =						self._prepare_for_class(__A							,	__A							,	return_labels=__A )
       snake_case__				:   Tuple     =						model(**__A ).loss
       loss.backward()



     def  _lowercase  (      self  :						Tuple ):
      snake_case__				:   List[str]     =						self.model_tester.prepare_config_and_inputs_for_common()
      if not self.model_tester.is_training:
       return

      snake_case__				:   str     =						False
      snake_case__				:   List[str]     =						True

      for model_class in self.all_model_classes:
       if model_class in get_values(__A ) or not model_class.supports_gradient_checkpointing:
        continue
       # LevitForImageClassificationWithTeacher supports inference-only
       if model_class.__name__ == "LevitForImageClassificationWithTeacher":
        continue
       snake_case__				:   Union[str, Any]     =						model_class(__A )
       model.gradient_checkpointing_enable()
       model.to(__A )
       model.train()
       snake_case__				:   int     =						self._prepare_for_class(__A							,	__A							,	return_labels=__A )
       snake_case__				:   Tuple     =						model(**__A ).loss
       loss.backward()



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

      snake_case__				:   Tuple     =						[
          {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
          {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
          {"title": "regression", "num_labels": 1, "dtype": torch.float},
      ]

      for model_class in self.all_model_classes:
       if (
           model_class
           not in [
               *get_values(__A ),
           ]
           or model_class.__name__ == "LevitForImageClassificationWithTeacher"
       ):
        continue

       for problem_type in problem_types:
        with self.subTest(msg=f'''Testing {model_class} with {problem_type['title']}''' ):
         snake_case__				:   Optional[Any]     =						problem_type["title"]
         snake_case__				:   str     =						problem_type["num_labels"]

         snake_case__				:   int     =						model_class(__A )
         model.to(__A )
         model.train()

         snake_case__				:   Optional[int]     =						self._prepare_for_class(__A							,	__A							,	return_labels=__A )

         if problem_type["num_labels"] > 1:
          snake_case__				:   Optional[int]     =						inputs["labels"].unsqueeze(1 ).repeat(1							,	problem_type["num_labels"] )

         snake_case__				:   List[Any]     =						inputs["labels"].to(problem_type["dtype"] )

         # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
         # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
         # they have the same size." which is a symptom something in wrong for the regression problem.
         # See https://github.com/huggingface/transformers/issues/11780
         with warnings.catch_warnings(record=__A ) as warning_list:
          snake_case__				:   List[str]     =						model(**__A ).loss
         for w in warning_list:
          if "Using a target size that is different to the input size" in str(w.message ):
           raise ValueError(
               f'''Something is going wrong in the regression problem: intercepted {w.message}''' )

         loss.backward()






     @slow
     def  _lowercase  (      self  :						List[str] ):
      for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       snake_case__				:   int     =						LevitModel.from_pretrained(__A )
       self.assertIsNotNone(__A )







def   SCREAMING_SNAKE_CASE       (				):
 snake_case__				:   Any     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
 return image





@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





     """simple docstring"""






     @cached_property
     def  _lowercase  (      self  :						Dict ):
      return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )






     @slow
     def  _lowercase  (      self  :						Optional[Any] ):
      snake_case__				:   Dict     =						LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
          __A )

      snake_case__				:   Any     =						self.default_image_processor
      snake_case__				:   List[Any]     =						prepare_img()
      snake_case__				:   Optional[int]     =						image_processor(images=__A							,	return_tensors="pt" ).to(__A )

      # forward pass
      with torch.no_grad():
       snake_case__				:   Optional[int]     =						model(**__A )

      # verify the logits
      snake_case__				:   Optional[int]     =						torch.Size((1, 1_0_0_0) )
      self.assertEqual(outputs.logits.shape							,	__A )

      snake_case__				:   Union[str, Any]     =						torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7] ).to(__A )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]							,	__A							,	atol=1e-4 ) )











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)



