



"""simple docstring"""
from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor



class       SCREAMING_SNAKE_CASE__  :

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=13      ,     _SCREAMING_SNAKE_CASE=30      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=3      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=32      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=4      ,     _SCREAMING_SNAKE_CASE=37      ,     _SCREAMING_SNAKE_CASE="gelu"      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=10      ,     _SCREAMING_SNAKE_CASE=0.02      ,     _SCREAMING_SNAKE_CASE=3      ,     _SCREAMING_SNAKE_CASE=0.6      ,     _SCREAMING_SNAKE_CASE=None      ,     )  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	parent
    UpperCAmelCase	:					str	=	batch_size
    UpperCAmelCase	:					List[str]	=	image_size
    UpperCAmelCase	:					Tuple	=	patch_size
    UpperCAmelCase	:					Union[str, Any]	=	num_channels
    UpperCAmelCase	:					int	=	is_training
    UpperCAmelCase	:					str	=	use_labels
    UpperCAmelCase	:					List[Any]	=	hidden_size
    UpperCAmelCase	:					Optional[int]	=	num_hidden_layers
    UpperCAmelCase	:					List[str]	=	num_attention_heads
    UpperCAmelCase	:					Dict	=	intermediate_size
    UpperCAmelCase	:					Optional[int]	=	hidden_act
    UpperCAmelCase	:					Union[str, Any]	=	hidden_dropout_prob
    UpperCAmelCase	:					Optional[int]	=	attention_probs_dropout_prob
    UpperCAmelCase	:					str	=	type_sequence_label_size
    UpperCAmelCase	:					Dict	=	initializer_range
    UpperCAmelCase	:					Dict	=	mask_ratio
    UpperCAmelCase	:					Union[str, Any]	=	scope

    # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
    # (we add 1 for the [CLS] token)
    UpperCAmelCase	:					Dict	=	(image_size // patch_size) ** 2
    UpperCAmelCase	:					List[Any]	=	int(math.ceil((1 - mask_ratio) * (num_patches + 1)						)						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

    UpperCAmelCase	:					str	=	None
    if self.use_labels:
     UpperCAmelCase	:					List[Any]	=	ids_tensor([self.batch_size]      ,     self.type_sequence_label_size						)

    UpperCAmelCase	:					Optional[int]	=	self.get_config()

    return config, pixel_values, labels

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    return ViTMAEConfig(
        image_size=self.image_size      ,     patch_size=self.patch_size      ,     num_channels=self.num_channels      ,     hidden_size=self.hidden_size      ,     num_hidden_layers=self.num_hidden_layers      ,     num_attention_heads=self.num_attention_heads      ,     intermediate_size=self.intermediate_size      ,     decoder_hidden_size=self.hidden_size      ,     decoder_num_hidden_layers=self.num_hidden_layers      ,     decoder_num_attention_heads=self.num_attention_heads      ,     decoder_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      ,     is_decoder=_SCREAMING_SNAKE_CASE      ,     initializer_range=self.initializer_range      ,     mask_ratio=self.mask_ratio      ,     )

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	TFViTMAEModel(config=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	model(_SCREAMING_SNAKE_CASE      ,     training=_SCREAMING_SNAKE_CASE						)
    self.parent.assertEqual(result.last_hidden_state.shape      ,     (self.batch_size, self.seq_length, self.hidden_size)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	TFViTMAEForPreTraining(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Any	=	model(_SCREAMING_SNAKE_CASE      ,     training=_SCREAMING_SNAKE_CASE						)
    # expected sequence length = num_patches
    UpperCAmelCase	:					Optional[Any]	=	(self.image_size // self.patch_size) ** 2
    UpperCAmelCase	:					Union[str, Any]	=	self.patch_size**2 * self.num_channels
    self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, num_patches, expected_num_channels)						)

    # test greyscale images
    UpperCAmelCase	:					List[Any]	=	1
    UpperCAmelCase	:					int	=	TFViTMAEForPreTraining(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Any	=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
    UpperCAmelCase	:					Tuple	=	model(_SCREAMING_SNAKE_CASE      ,     training=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Dict	=	self.patch_size**2
    self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, num_patches, expected_num_channels)						)




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	self.prepare_config_and_inputs()
    ((UpperCAmelCase)	,		(UpperCAmelCase)	,		(UpperCAmelCase))	:					Tuple	=	config_and_inputs
    UpperCAmelCase	:					Optional[int]	=	{"""pixel_values""": pixel_values}
    return config, inputs_dict



@require_tf
class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   Dict						=						(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
   __lowerCAmelCase  :   List[Any]						=						{'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

   __lowerCAmelCase  :   str						=						False
   __lowerCAmelCase  :   Optional[int]						=						False
   __lowerCAmelCase  :   List[Any]						=						False
   __lowerCAmelCase  :   Tuple						=						False

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	TFViTMAEModelTester(self						)
    UpperCAmelCase	:					List[Any]	=	ConfigTester(self      ,     config_class=_SCREAMING_SNAKE_CASE      ,     has_text_modality=_SCREAMING_SNAKE_CASE      ,     hidden_size=37						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    self.config_tester.run_common_tests()

   @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""						)
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




    pass

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     UpperCAmelCase	:					Any	=	model_class(_SCREAMING_SNAKE_CASE						)
     self.assertIsInstance(model.get_input_embeddings()      ,     (tf.keras.layers.Layer)						)
     UpperCAmelCase	:					Optional[int]	=	model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE      ,     tf.keras.layers.Layer						)						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					Optional[Any]	=	self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     UpperCAmelCase	:					Any	=	model_class(_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					List[Any]	=	inspect.signature(model.call						)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     UpperCAmelCase	:					Optional[int]	=	[*signature.parameters.keys()]

     UpperCAmelCase	:					Dict	=	["""pixel_values"""]
     self.assertListEqual(arg_names[:1]      ,     _SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_pretraining(*_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    np.random.seed(2						)

    UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	self.model_tester.prepare_config_and_inputs_for_common()

    UpperCAmelCase	:					Optional[int]	=	int((config.image_size // config.patch_size) ** 2						)
    UpperCAmelCase	:					Any	=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)

    for model_class in self.all_model_classes:
     UpperCAmelCase	:					Optional[int]	=	model_class(_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Any	=	self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

     UpperCAmelCase	:					Optional[int]	=	model(_SCREAMING_SNAKE_CASE      ,     noise=_SCREAMING_SNAKE_CASE						)

     UpperCAmelCase	:					str	=	copy.deepcopy(self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)						)
     UpperCAmelCase	:					str	=	model(**_SCREAMING_SNAKE_CASE      ,     noise=_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Union[str, Any]	=	outputs_dict[0].numpy()
     UpperCAmelCase	:					Dict	=	outputs_keywords[0].numpy()

     self.assertLess(np.sum(np.abs(output_dict - output_keywords						)						)      ,     1E-6						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    np.random.seed(2						)

    UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	self.model_tester.prepare_config_and_inputs_for_common()

    UpperCAmelCase	:					int	=	int((config.image_size // config.patch_size) ** 2						)
    UpperCAmelCase	:					Optional[int]	=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)

    def prepare_numpy_arrays(_SCREAMING_SNAKE_CASE						):
     UpperCAmelCase	:					Optional[Any]	=	{}
     for k, v in inputs_dict.items():
      if tf.is_tensor(_SCREAMING_SNAKE_CASE						):
       UpperCAmelCase	:					int	=	v.numpy()
      else:
       UpperCAmelCase	:					str	=	np.array(_SCREAMING_SNAKE_CASE						)

     return inputs_np_dict

    for model_class in self.all_model_classes:
     UpperCAmelCase	:					Optional[int]	=	model_class(_SCREAMING_SNAKE_CASE						)

     UpperCAmelCase	:					str	=	self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Union[str, Any]	=	prepare_numpy_arrays(_SCREAMING_SNAKE_CASE						)

     UpperCAmelCase	:					Optional[Any]	=	model(_SCREAMING_SNAKE_CASE      ,     noise=_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Tuple	=	model(**_SCREAMING_SNAKE_CASE      ,     noise=_SCREAMING_SNAKE_CASE						)
     self.assert_outputs_same(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Tuple:





    '''simple docstring'''




    np.random.seed(2						)

    UpperCAmelCase	:					List[Any]	=	int((tf_model.config.image_size // tf_model.config.patch_size) ** 2						)
    UpperCAmelCase	:					str	=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)
    UpperCAmelCase	:					List[str]	=	tf.constant(_SCREAMING_SNAKE_CASE						)

    # Add `noise` argument.
    # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
    UpperCAmelCase	:					Any	=	tf_noise

    super().check_pt_tf_models(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    np.random.seed(2						)

    UpperCAmelCase	,		UpperCAmelCase	:					Tuple	=	self.model_tester.prepare_config_and_inputs_for_common()

    UpperCAmelCase	:					Union[str, Any]	=	{
        module_member
        for model_class in self.all_model_classes
        for module in (import_module(model_class.__module__						),)
        for module_member_name in dir(_SCREAMING_SNAKE_CASE						)
        if module_member_name.endswith("""MainLayer"""						)
        # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
        and module_member_name[: -len("""MainLayer"""						)] == model_class.__name__[: -len("""Model"""						)]
        for module_member in (getattr(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						),)
        if isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
        and tf.keras.layers.Layer in module_member.__bases__
        and getattr(_SCREAMING_SNAKE_CASE      ,     """_keras_serializable"""      ,     _SCREAMING_SNAKE_CASE						)
    }

    UpperCAmelCase	:					List[Any]	=	int((config.image_size // config.patch_size) ** 2						)
    UpperCAmelCase	:					str	=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)
    UpperCAmelCase	:					str	=	tf.convert_to_tensor(_SCREAMING_SNAKE_CASE						)
    inputs_dict.update({"""noise""": noise}						)

    for main_layer_class in tf_main_layer_classes:
     UpperCAmelCase	:					List[Any]	=	main_layer_class(_SCREAMING_SNAKE_CASE						)

     UpperCAmelCase	:					str	=	{
         name: tf.keras.Input(tensor.shape[1:]      ,     dtype=tensor.dtype						) for name, tensor in inputs_dict.items()
     }

     UpperCAmelCase	:					str	=	tf.keras.Model(_SCREAMING_SNAKE_CASE      ,     outputs=main_layer(_SCREAMING_SNAKE_CASE						)						)
     UpperCAmelCase	:					Dict	=	model(_SCREAMING_SNAKE_CASE						)

     with tempfile.TemporaryDirectory() as tmpdirname:
      UpperCAmelCase	:					Any	=	os.path.join(_SCREAMING_SNAKE_CASE      ,     """keras_model.h5"""						)
      model.save(_SCREAMING_SNAKE_CASE						)
      UpperCAmelCase	:					Dict	=	tf.keras.models.load_model(
          _SCREAMING_SNAKE_CASE      ,     custom_objects={main_layer_class.__name__: main_layer_class}						)
      assert isinstance(_SCREAMING_SNAKE_CASE      ,     tf.keras.Model						)
      UpperCAmelCase	:					int	=	model(_SCREAMING_SNAKE_CASE						)
      self.assert_outputs_same(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    np.random.seed(2						)

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

    UpperCAmelCase	:					Dict	=	int((config.image_size // config.patch_size) ** 2						)
    UpperCAmelCase	:					List[Any]	=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)

    for model_class in self.all_model_classes:
     UpperCAmelCase	:					Dict	=	model_class(_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					str	=	self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					str	=	model(_SCREAMING_SNAKE_CASE      ,     noise=_SCREAMING_SNAKE_CASE						)

     if model_class.__name__ == "TFViTMAEModel":
      UpperCAmelCase	:					int	=	outputs.last_hidden_state.numpy()
      UpperCAmelCase	:					int	=	0
     else:
      UpperCAmelCase	:					Optional[Any]	=	outputs.logits.numpy()
      UpperCAmelCase	:					int	=	0

     with tempfile.TemporaryDirectory() as tmpdirname:
      model.save_pretrained(_SCREAMING_SNAKE_CASE      ,     saved_model=_SCREAMING_SNAKE_CASE						)
      UpperCAmelCase	:					Optional[Any]	=	model_class.from_pretrained(_SCREAMING_SNAKE_CASE						)
      UpperCAmelCase	:					Optional[int]	=	model(_SCREAMING_SNAKE_CASE      ,     noise=_SCREAMING_SNAKE_CASE						)

      if model_class.__name__ == "TFViTMAEModel":
       UpperCAmelCase	:					Tuple	=	after_outputs["""last_hidden_state"""].numpy()
       UpperCAmelCase	:					Optional[int]	=	0
      else:
       UpperCAmelCase	:					str	=	after_outputs["""logits"""].numpy()
       UpperCAmelCase	:					Dict	=	0

      UpperCAmelCase	:					List[Any]	=	np.amax(np.abs(out_a - out_a						)						)
      self.assertLessEqual(_SCREAMING_SNAKE_CASE      ,     1E-5						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




    np.random.seed(2						)

    UpperCAmelCase	,		UpperCAmelCase	:					List[str]	=	self.model_tester.prepare_config_and_inputs_for_common()

    UpperCAmelCase	:					List[str]	=	int((config.image_size // config.patch_size) ** 2						)
    UpperCAmelCase	:					Union[str, Any]	=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)

    for model_class in self.all_model_classes:
     UpperCAmelCase	:					Dict	=	model_class(_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Union[str, Any]	=	self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

     UpperCAmelCase	:					Union[str, Any]	=	model(_SCREAMING_SNAKE_CASE      ,     noise=_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					int	=	model.get_config()
     # make sure that returned config is jsonifiable, which is required by keras
     json.dumps(_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Dict	=	model_class.from_config(model.get_config()						)
     # make sure it also accepts a normal config
     UpperCAmelCase	:					List[Any]	=	model_class.from_config(model.config						)
     UpperCAmelCase	:					List[Any]	=	new_model(_SCREAMING_SNAKE_CASE						)  # Build model
     new_model.set_weights(model.get_weights()						)
     UpperCAmelCase	:					Union[str, Any]	=	new_model(_SCREAMING_SNAKE_CASE      ,     noise=_SCREAMING_SNAKE_CASE						)

     self.assert_outputs_same(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

   @unittest.skip(
       reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""						)
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    pass

   @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""						)
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    pass




   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""						)
    self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)

def       _snake_case						(						):
 UpperCAmelCase	:					Union[str, Any]	=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
 return image



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

   @cached_property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""						) if is_vision_available() else None




   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    np.random.seed(2						)

    UpperCAmelCase	:					Optional[int]	=	TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""						)

    UpperCAmelCase	:					List[str]	=	self.default_image_processor
    UpperCAmelCase	:					Optional[int]	=	prepare_img()
    UpperCAmelCase	:					int	=	image_processor(images=_SCREAMING_SNAKE_CASE      ,     return_tensors="""tf"""						)

    # prepare a noise vector that will be also used for testing the TF model
    # (this way we can ensure that the PT and TF models operate on the same inputs)
    UpperCAmelCase	:					int	=	ViTMAEConfig()
    UpperCAmelCase	:					List[Any]	=	int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2						)
    UpperCAmelCase	:					Any	=	np.random.uniform(size=(1, num_patches)						)

    # forward pass
    UpperCAmelCase	:					str	=	model(**_SCREAMING_SNAKE_CASE      ,     noise=_SCREAMING_SNAKE_CASE						)

    # verify the logits
    UpperCAmelCase	:					List[str]	=	tf.convert_to_tensor([1, 196, 768]						)
    self.assertEqual(outputs.logits.shape      ,     _SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Any	=	tf.convert_to_tensor(
        [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]						)

    tf.debugging.assert_near(outputs.logits[0, :3, :3]      ,     _SCREAMING_SNAKE_CASE      ,     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       _snake_case						(						UpperCamelCase						:		Dataset			,		UpperCamelCase						:		Dict[str, str]						):
 UpperCAmelCase	:					Any	=	args.log_outputs
 UpperCAmelCase	:					Any	=	"""_""".join(args.dataset.split("""/"""						) + [args.config, args.split]						)

 # load metric
 UpperCAmelCase	:					List[Any]	=	load_metric("""wer"""						)
 UpperCAmelCase	:					Any	=	load_metric("""cer"""						)

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

 # print & log results
 UpperCAmelCase	:					Tuple	=	F"WER: {wer_result}\nCER: {cer_result}"
 print(UpperCamelCase						)

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

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

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

   result.map(UpperCamelCase			,		with_indices=UpperCamelCase						)

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

 UpperCAmelCase	:					Dict	=	re.sub(UpperCamelCase			,		""""""			,		text.lower()						)

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

 for t in token_sequences_to_ignore:
  UpperCAmelCase	:					Optional[Any]	=	""" """.join(text.split(UpperCamelCase						)						)

 return text

def       _snake_case						(						UpperCamelCase						:		Tuple						):
 # load dataset
 UpperCAmelCase	:					Union[str, Any]	=	load_dataset(args.dataset			,		args.config			,		split=args.split			,		use_auth_token=UpperCamelCase						)

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

 # load processor
 UpperCAmelCase	:					Optional[int]	=	AutoFeatureExtractor.from_pretrained(args.model_id						)
 UpperCAmelCase	:					Any	=	feature_extractor.sampling_rate

 # resample audio
 UpperCAmelCase	:					List[str]	=	dataset.cast_column("""audio"""			,		Audio(sampling_rate=UpperCamelCase						)						)

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

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

  UpperCAmelCase	:					Tuple	=	prediction["""text"""]
  UpperCAmelCase	:					List[str]	=	normalize_text(batch["""sentence"""]						)
  return batch

 # run inference on all examples
 UpperCAmelCase	:					int	=	dataset.map(UpperCamelCase			,		remove_columns=dataset.column_names						)

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


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:   Union[str, Any]								=		parser.parse_args()

      main(args)
