'''simple docstring'''




import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 (
     TFEfficientFormerForImageClassification,
     TFEfficientFormerForImageClassificationWithTeacher,
     TFEfficientFormerModel,
 )
 from transformers.models.efficientformer.modeling_tf_efficientformer import (
     TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
 )


if is_vision_available():
 from PIL import Image

 from transformers import EfficientFormerImageProcessor


class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a = 13       ,				_a = 64       ,				_a = 2       ,				_a = 3       ,				_a = 3       ,				_a = True       ,				_a = True       ,				_a = 128       ,				_a=[16, 32, 64, 128]       ,				_a = 7       ,				_a = 4       ,				_a = 37       ,				_a = "gelu"       ,				_a = 0.1       ,				_a = 0.1       ,				_a = 10       ,				_a = 0.02       ,				_a = 2       ,				_a = 1       ,				_a = 128       ,				_a = [2, 2, 2, 2]       ,				_a = 2       ,				_a = 2       ,				)    ->     Optional[int]:
        lowercase_					:       List[str]     				=					parent
        lowercase_					:       List[str]     				=					batch_size
        lowercase_					:       Any     				=					image_size
        lowercase_					:       Union[str, Any]     				=					patch_size
        lowercase_					:       Any     				=					num_channels
        lowercase_					:       int     				=					is_training
        lowercase_					:       int     				=					use_labels
        lowercase_					:       Union[str, Any]     				=					hidden_size
        lowercase_					:       str     				=					num_hidden_layers
        lowercase_					:       List[str]     				=					num_attention_heads
        lowercase_					:       List[str]     				=					intermediate_size
        lowercase_					:       List[str]     				=					hidden_act
        lowercase_					:       str     				=					hidden_dropout_prob
        lowercase_					:       List[Any]     				=					attention_probs_dropout_prob
        lowercase_					:       List[Any]     				=					type_sequence_label_size
        lowercase_					:       List[Any]     				=					initializer_range
        lowercase_					:       Optional[int]     				=					encoder_stride
        lowercase_					:       Optional[int]     				=					num_attention_outputs
        lowercase_					:       Union[str, Any]     				=					embed_dim
        lowercase_					:       Union[str, Any]     				=					embed_dim + 1
        lowercase_					:       int     				=					resolution
        lowercase_					:       Tuple     				=					depths
        lowercase_					:       str     				=					hidden_sizes
        lowercase_					:       int     				=					dim
        lowercase_					:       List[Any]     				=					mlp_expansion_ratio
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Optional[int]     				=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

        lowercase_					:       Any     				=					None
        if self.use_labels:
             lowercase_					:       int     				=					ids_tensor([self.batch_size]       ,				self.type_sequence_label_size				)

        lowercase_					:       Dict     				=					self.get_config()

        return config, pixel_values, labels
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        return EfficientFormerConfig(
            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       ,				hidden_act=self.hidden_act       ,				hidden_dropout_prob=self.hidden_dropout_prob       ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,				is_decoder=_a       ,				initializer_range=self.initializer_range       ,				encoder_stride=self.encoder_stride       ,				resolution=self.resolution       ,				depths=self.depths       ,				hidden_sizes=self.hidden_sizes       ,				dim=self.dim       ,				mlp_expansion_ratio=self.mlp_expansion_ratio       ,				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Union[str, Any]:
        lowercase_					:       Optional[Any]     				=					TFEfficientFormerModel(config=_a				)
        lowercase_					:       Tuple     				=					model(_a       ,				training=_a				)
        self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Tuple:
        lowercase_					:       int     				=					self.type_sequence_label_size
        lowercase_					:       str     				=					TFEfficientFormerForImageClassification(_a				)
        lowercase_					:       Optional[Any]     				=					model(_a       ,				labels=_a       ,				training=_a				)
        self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)				)

        # test greyscale images
        lowercase_					:       Optional[int]     				=					1
        lowercase_					:       Dict     				=					TFEfficientFormerForImageClassification(_a				)

        lowercase_					:       Optional[Any]     				=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
        lowercase_					:       Tuple     				=					model(_a       ,				labels=_a				)
        self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)				)





   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       Any     				=					self.prepare_config_and_inputs()
        lowercase_				,lowercase_				,lowercase_					:       Dict     				=					config_and_inputs
        lowercase_					:       Tuple     				=					{'pixel_values': pixel_values}
        return config, inputs_dict



@require_tf
class       UpperCAmelCase__  (       _snake_case					,     _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       List[str]     =    (
       (
           TFEfficientFormerModel,
           TFEfficientFormerForImageClassificationWithTeacher,
           TFEfficientFormerForImageClassification,
       )
       if is_tf_available()
       else ()
   )
   A							:       Optional[int]     =    (
       {
           '''feature-extraction''': TFEfficientFormerModel,
           '''image-classification''': (
               TFEfficientFormerForImageClassification,
               TFEfficientFormerForImageClassificationWithTeacher,
           ),
       }
       if is_tf_available()
       else {}
   )

   A							:       List[str]     =    False

   A							:       Any     =    False
   A							:       List[str]     =    False
   A							:       Optional[Any]     =    False
   A							:       List[Any]     =    False
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       List[Any]     				=					TFEfficientFormerModelTester(self				)
        lowercase_					:       Any     				=					ConfigTester(
            self       ,				config_class=_a       ,				has_text_modality=_a       ,				hidden_size=37				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        self.config_tester.run_common_tests()
   @unittest.skip(reason='EfficientFormer does not use inputs_embeds'				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        pass
   @unittest.skip(reason='EfficientFormer does not support input and output embeddings'				)
   def 		_lowerCamelCase (self				)    ->     str:
        pass
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_				,lowercase_					:       List[Any]     				=					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_					:       Tuple     				=					model_class(_a				)
             lowercase_					:       str     				=					inspect.signature(model.call				)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             lowercase_					:       Any     				=					[*signature.parameters.keys()]

             lowercase_					:       Optional[int]     				=					['pixel_values']
             self.assertListEqual(arg_names[:1]       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        def check_hidden_states_output(_a       ,				_a       ,				_a				):
             lowercase_					:       List[str]     				=					model_class(_a				)

             lowercase_					:       str     				=					model(**self._prepare_for_class(_a       ,				_a				)       ,				training=_a				)
             lowercase_					:       List[Any]     				=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

             lowercase_					:       str     				=					getattr(
                 self.model_tester       ,				'expected_num_hidden_layers'       ,				self.model_tester.num_hidden_layers + 1				)
             self.assertEqual(len(_a				)       ,				_a				)

             if hasattr(self.model_tester       ,				'encoder_seq_length'				):
                  lowercase_					:       Union[str, Any]     				=					self.model_tester.encoder_seq_length
                  if hasattr(self.model_tester       ,				'chunk_length'				) and self.model_tester.chunk_length > 1:
                       lowercase_					:       Any     				=					seq_length * self.model_tester.chunk_length
             else:
                  lowercase_					:       Optional[Any]     				=					self.model_tester.seq_length

             self.assertListEqual(
                 list(hidden_states[-1].shape[-2:]				)       ,				[seq_length, self.model_tester.hidden_size]       ,				)

             if config.is_encoder_decoder:
                  lowercase_					:       List[str]     				=					outputs.decoder_hidden_states

                  self.asseretIsInstance(_a       ,				(list, tuple)				)
                  self.assertEqual(len(_a				)       ,				_a				)
                  lowercase_					:       Tuple     				=					getattr(self.model_tester       ,				'seq_length'       ,				_a				)
                  lowercase_					:       List[str]     				=					getattr(self.model_tester       ,				'decoder_seq_length'       ,				_a				)

                  self.assertListEqual(
                      list(hidden_states[-1].shape[-2:]				)       ,				[decoder_seq_length, self.model_tester.hidden_size]       ,				)

        lowercase_				,lowercase_					:       Any     				=					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_					:       List[str]     				=					True
             check_hidden_states_output(_a       ,				_a       ,				_a				)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowercase_					:       Tuple     				=					True

             check_hidden_states_output(_a       ,				_a       ,				_a				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a=False				)    ->     str:
        lowercase_					:       Optional[Any]     				=					super()._prepare_for_class(_a       ,				_a       ,				return_labels=_a				)

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

        return inputs_dict
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       Union[str, Any]     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a				)
   @unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'				)
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       Dict     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        lowercase_					:       int     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_a				)
   @slow
   def 		_lowerCamelCase (self				)    ->     List[str]:
        for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_					:       List[Any]     				=					TFEfficientFormerModel.from_pretrained(_a				)
             self.assertIsNotNone(_a				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_				,lowercase_					:       str     				=					self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_					:       Any     				=					True

        lowercase_					:       Union[str, Any]     				=					getattr(self.model_tester       ,				'seq_length'       ,				_a				)
        lowercase_					:       List[str]     				=					getattr(self.model_tester       ,				'encoder_seq_length'       ,				_a				)
        lowercase_					:       Any     				=					getattr(self.model_tester       ,				'key_length'       ,				_a				)
        lowercase_					:       Any     				=					getattr(self.model_tester       ,				'chunk_length'       ,				_a				)

        if chunk_length is not None and hasattr(self.model_tester       ,				'num_hashes'				):
             lowercase_					:       Tuple     				=					encoder_seq_length * self.model_tester.num_hashes

        for model_class in self.all_model_classes:
             lowercase_					:       Any     				=					True
             lowercase_					:       Optional[Any]     				=					False
             lowercase_					:       str     				=					True
             lowercase_					:       int     				=					model_class(_a				)

             lowercase_					:       Optional[Any]     				=					model(**self._prepare_for_class(_a       ,				_a				)       ,				training=_a				)
             lowercase_					:       Tuple     				=					outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
             self.assertEqual(len(_a				)       ,				self.model_tester.num_attention_outputs				)

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             lowercase_					:       Any     				=					True
             lowercase_					:       str     				=					model_class(_a				)
             lowercase_					:       List[Any]     				=					model(**self._prepare_for_class(_a       ,				_a				)       ,				training=_a				)

             lowercase_					:       Any     				=					outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
             self.assertEqual(len(_a				)       ,				self.model_tester.num_attention_outputs				)

             if chunk_length is not None:
                  self.assertListEqual(
                      list(attentions[0].shape[-4:]				)       ,				[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]       ,				)
             else:
                  self.assertListEqual(
                      list(attentions[0].shape[-3:]				)       ,				[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]       ,				)





   def 		_lowerCamelCase (self				)    ->     List[Any]:
        # We use a simplified version of this test for EfficientFormer because it requires training=False
        # and Keras refuses to let us force that during functional construction
        lowercase_				,lowercase_					:       List[str]     				=					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             # Prepare our model
             lowercase_					:       Dict     				=					model_class(_a				)
             # These are maximally general inputs for the model, with multiple None dimensions
             # Hopefully this will catch any conditionals that fail for flexible shapes
             lowercase_					:       int     				=					{
                 key: tf.keras.Input(shape=val.shape[1:]       ,				dtype=val.dtype       ,				name=_a				)
                 for key, val in model.input_signature.items()
                 if key in model.dummy_inputs
             }
             lowercase_					:       Dict     				=					model(_a				)
             self.assertTrue(outputs_dict is not None				)



def _UpperCamelCase   (       ):
     lowercase_					:       List[str]     				=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
     return image






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






   """simple docstring"""
   @cached_property
   def 		_lowerCamelCase (self				)    ->     Any:
        return (
            EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'				)
            if is_vision_available()
            else None
        )
   @slow
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       int     				=					TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'				)
        lowercase_					:       Dict     				=					self.default_image_processor
        lowercase_					:       List[Any]     				=					prepare_img()
        lowercase_					:       List[str]     				=					image_processor(images=_a       ,				return_tensors='tf'				)
        # forward pass
        lowercase_					:       Optional[Any]     				=					model(**_a       ,				training=_a				)
        # verify the logits
        lowercase_					:       Union[str, Any]     				=					tf.TensorShape((1, 1_000)				)
        self.assertEqual(outputs.logits.shape       ,				_a				)
        lowercase_					:       List[Any]     				=					tf.constant([-0.05_55, 0.48_25, -0.08_52]				)
        self.assertTrue(np.allclose(outputs.logits[0, :3]       ,				_a       ,				atol=1e-4				)				)





   @slow
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Optional[Any]     				=					TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
            'snap-research/efficientformer-l1-300'				)
        lowercase_					:       Dict     				=					self.default_image_processor
        lowercase_					:       Union[str, Any]     				=					prepare_img()
        lowercase_					:       Any     				=					image_processor(images=_a       ,				return_tensors='tf'				)
        # forward pass
        lowercase_					:       int     				=					model(**_a       ,				training=_a				)
        # verify the logits
        lowercase_					:       Any     				=					tf.TensorShape((1, 1_000)				)
        self.assertEqual(outputs.logits.shape       ,				_a				)
        lowercase_					:       Any     				=					tf.constant([-0.13_12, 0.43_53, -1.04_99]				)
        self.assertTrue(np.allclose(outputs.logits[0, :3]       ,				_a       ,				atol=1e-4				)				)





'''simple docstring'''




import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   # to overwrite at feature extractactor specific tests
   A							:       List[str]     =    None
   A							:       Tuple     =    None
   @property
   def 		_lowerCamelCase (self				)    ->     Any:
        return self.feat_extract_tester.prepare_feat_extract_dict()
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       Dict     				=					self.feature_extraction_class(**self.feat_extract_dict				)
        self.assertTrue(hasattr(_a       ,				'feature_size'				)				)
        self.assertTrue(hasattr(_a       ,				'sampling_rate'				)				)
        self.assertTrue(hasattr(_a       ,				'padding_value'				)				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       List[str]     				=					self.feat_extract_tester.prepare_inputs_for_common()
        lowercase_					:       Union[str, Any]     				=					self.feature_extraction_class(**self.feat_extract_dict				)
        lowercase_					:       Union[str, Any]     				=					feat_extract.model_input_names[0]

        lowercase_					:       Union[str, Any]     				=					BatchFeature({input_name: speech_inputs}				)

        self.assertTrue(all(len(_a				) == len(_a				) for x, y in zip(_a       ,				processed_features[input_name]				)				)				)

        lowercase_					:       Optional[int]     				=					self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a				)
        lowercase_					:       Tuple     				=					BatchFeature({input_name: speech_inputs}       ,				tensor_type='np'				)

        lowercase_					:       Dict     				=					processed_features[input_name]

        if len(batch_features_input.shape				) < 3:
             lowercase_					:       Optional[Any]     				=					batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)
   @require_torch
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       List[Any]     				=					self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a				)
        lowercase_					:       Any     				=					self.feature_extraction_class(**self.feat_extract_dict				)
        lowercase_					:       int     				=					feat_extract.model_input_names[0]

        lowercase_					:       Union[str, Any]     				=					BatchFeature({input_name: speech_inputs}       ,				tensor_type='pt'				)

        lowercase_					:       Tuple     				=					processed_features[input_name]

        if len(batch_features_input.shape				) < 3:
             lowercase_					:       List[str]     				=					batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)
   @require_tf
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       List[Any]     				=					self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a				)
        lowercase_					:       int     				=					self.feature_extraction_class(**self.feat_extract_dict				)
        lowercase_					:       Dict     				=					feat_extract.model_input_names[0]

        lowercase_					:       List[Any]     				=					BatchFeature({input_name: speech_inputs}       ,				tensor_type='tf'				)

        lowercase_					:       int     				=					processed_features[input_name]

        if len(batch_features_input.shape				) < 3:
             lowercase_					:       Union[str, Any]     				=					batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)
   def 		_lowerCamelCase (self       ,				_a=False				)    ->     Union[str, Any]:
        def _inputs_have_equal_length(_a				):
             lowercase_					:       Optional[Any]     				=					len(input[0]				)
             for input_slice in input[1:]:
                  if len(_a				) != length:
                       return False
             return True

        def _inputs_are_equal(_a       ,				_a				):
             if len(_a				) != len(_a				):
                  return False

             for input_slice_a, input_slice_a in zip(_a       ,				_a				):
                  if not np.allclose(np.asarray(_a				)       ,				np.asarray(_a				)       ,				atol=1e-3				):
                       return False
             return True

        lowercase_					:       int     				=					self.feature_extraction_class(**self.feat_extract_dict				)
        lowercase_					:       str     				=					self.feat_extract_tester.prepare_inputs_for_common(numpify=_a				)
        lowercase_					:       Union[str, Any]     				=					feat_extract.model_input_names[0]

        lowercase_					:       Union[str, Any]     				=					BatchFeature({input_name: speech_inputs}				)

        lowercase_					:       Optional[Any]     				=					self.feat_extract_tester.seq_length_diff
        lowercase_					:       Union[str, Any]     				=					self.feat_extract_tester.max_seq_length + pad_diff
        lowercase_					:       str     				=					self.feat_extract_tester.min_seq_length
        lowercase_					:       Optional[Any]     				=					self.feat_extract_tester.batch_size
        lowercase_					:       Any     				=					self.feat_extract_tester.feature_size

        # test padding for List[int] + numpy
        lowercase_					:       List[str]     				=					feat_extract.pad(_a       ,				padding=_a				)
        lowercase_					:       Any     				=					input_a[input_name]

        lowercase_					:       str     				=					feat_extract.pad(_a       ,				padding='longest'				)
        lowercase_					:       List[str]     				=					input_a[input_name]

        lowercase_					:       Dict     				=					feat_extract.pad(_a       ,				padding='max_length'       ,				max_length=len(speech_inputs[-1]				)				)
        lowercase_					:       Any     				=					input_a[input_name]

        lowercase_					:       Any     				=					feat_extract.pad(_a       ,				padding='longest'       ,				return_tensors='np'				)
        lowercase_					:       str     				=					input_a[input_name]

        # max_length parameter has to be provided when setting `padding="max_length"`
        with self.assertRaises(_a				):
             feat_extract.pad(_a       ,				padding='max_length'				)[input_name]

        lowercase_					:       Union[str, Any]     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=_a       ,				return_tensors='np'				)
        lowercase_					:       Any     				=					input_a[input_name]

        self.assertFalse(_inputs_have_equal_length(_a				)				)
        self.assertTrue(_inputs_have_equal_length(_a				)				)
        self.assertTrue(_inputs_have_equal_length(_a				)				)
        self.assertTrue(_inputs_are_equal(_a       ,				_a				)				)
        self.assertTrue(len(input_a[0]				) == pad_min_length				)
        self.assertTrue(len(input_a[1]				) == pad_min_length + pad_diff				)
        self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]				))				)
        self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)				)

        if feature_size > 1:
             self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size				)

        # test padding for `pad_to_multiple_of` for List[int] + numpy
        lowercase_					:       Tuple     				=					feat_extract.pad(_a       ,				pad_to_multiple_of=10				)
        lowercase_					:       Dict     				=					input_a[input_name]

        lowercase_					:       int     				=					feat_extract.pad(_a       ,				padding='longest'       ,				pad_to_multiple_of=10				)
        lowercase_					:       Union[str, Any]     				=					input_a[input_name]

        lowercase_					:       Optional[int]     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				pad_to_multiple_of=10       ,				max_length=_a				)
        lowercase_					:       Tuple     				=					input_a[input_name]

        lowercase_					:       Optional[int]     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				pad_to_multiple_of=10       ,				max_length=_a       ,				return_tensors='np'       ,				)
        lowercase_					:       Union[str, Any]     				=					input_a[input_name]

        self.assertTrue(all(len(_a				) % 10 == 0 for x in input_a				)				)
        self.assertTrue(_inputs_are_equal(_a       ,				_a				)				)

        lowercase_					:       str     				=					pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
        self.assertTrue(all(len(_a				) == expected_mult_pad_length for x in input_a				)				)
        self.assertEqual(input_a.shape[:2]       ,				(batch_size, expected_mult_pad_length)				)

        if feature_size > 1:
             self.assertTrue(input_a.shape[2] == feature_size				)

        # Check padding value is correct
        lowercase_					:       Optional[int]     				=					(np.ones(self.feat_extract_tester.feature_size				) * feat_extract.padding_value).sum()
        self.assertTrue(
            abs(np.asarray(input_a[0]				)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)				)
            < 1e-3				)
        self.assertTrue(
            abs(
                np.asarray(input_a[1]				)[pad_min_length + pad_diff :].sum()
                - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)				)
            < 1e-3				)
        self.assertTrue(
            abs(
                np.asarray(input_a[2]				)[pad_min_length + 2 * pad_diff :].sum()
                - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)				)
            < 1e-3				)
        self.assertTrue(
            abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)				) < 1e-3				)
        self.assertTrue(
            abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)				)
            < 1e-3				)
   def 		_lowerCamelCase (self       ,				_a=False				)    ->     Union[str, Any]:
        def _inputs_have_equal_length(_a				):
             lowercase_					:       str     				=					len(input[0]				)
             for input_slice in input[1:]:
                  if len(_a				) != length:
                       return False
             return True

        def _inputs_are_equal(_a       ,				_a				):
             if len(_a				) != len(_a				):
                  return False

             for input_slice_a, input_slice_a in zip(_a       ,				_a				):
                  if not np.allclose(np.asarray(_a				)       ,				np.asarray(_a				)       ,				atol=1e-3				):
                       return False
             return True

        lowercase_					:       Any     				=					self.feature_extraction_class(**self.feat_extract_dict				)
        lowercase_					:       List[Any]     				=					self.feat_extract_tester.prepare_inputs_for_common(numpify=_a				)
        lowercase_					:       List[str]     				=					feat_extract.model_input_names[0]

        lowercase_					:       str     				=					BatchFeature({input_name: speech_inputs}				)

        # truncate to smallest
        lowercase_					:       Union[str, Any]     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=len(speech_inputs[0]				)       ,				truncation=_a				)
        lowercase_					:       List[str]     				=					input_a[input_name]

        lowercase_					:       Optional[int]     				=					feat_extract.pad(_a       ,				padding='max_length'       ,				max_length=len(speech_inputs[0]				)				)
        lowercase_					:       Union[str, Any]     				=					input_a[input_name]

        self.assertTrue(_inputs_have_equal_length(_a				)				)
        self.assertFalse(_inputs_have_equal_length(_a				)				)

        # truncate to smallest with np
        lowercase_					:       List[str]     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=len(speech_inputs[0]				)       ,				return_tensors='np'       ,				truncation=_a       ,				)
        lowercase_					:       List[str]     				=					input_a[input_name]

        lowercase_					:       List[Any]     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=len(speech_inputs[0]				)       ,				return_tensors='np'				)
        lowercase_					:       List[Any]     				=					input_a[input_name]

        self.assertTrue(_inputs_have_equal_length(_a				)				)
        self.assertTrue(input_a.shape[1] == len(speech_inputs[0]				)				)

        # since truncation forces padding to be smaller than longest input
        # function can't return `np.ndarray`, but has to return list
        self.assertFalse(_inputs_have_equal_length(_a				)				)

        # truncate to middle
        lowercase_					:       int     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=len(speech_inputs[1]				)       ,				truncation=_a       ,				return_tensors='np'       ,				)
        lowercase_					:       Union[str, Any]     				=					input_a[input_name]

        lowercase_					:       str     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=len(speech_inputs[1]				)       ,				truncation=_a				)
        lowercase_					:       int     				=					input_a[input_name]

        lowercase_					:       Dict     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=len(speech_inputs[1]				)       ,				return_tensors='np'				)
        lowercase_					:       Optional[int]     				=					input_a[input_name]

        self.assertTrue(input_a.shape[1] == len(speech_inputs[1]				)				)
        self.assertTrue(_inputs_have_equal_length(_a				)				)
        self.assertTrue(_inputs_have_equal_length(_a				)				)
        self.assertTrue(_inputs_are_equal(_a       ,				_a				)				)

        # since truncation forces padding to be smaller than longest input
        # function can't return `np.ndarray`, but has to return list
        self.assertFalse(_inputs_have_equal_length(_a				)				)
        self.assertTrue(len(input_a[-1]				) == len(speech_inputs[-1]				)				)

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(_a				):
             feat_extract.pad(_a       ,				truncation=_a				)[input_name]

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(_a				):
             feat_extract.pad(_a       ,				padding='longest'       ,				truncation=_a				)[input_name]

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(_a				):
             feat_extract.pad(_a       ,				padding='longest'       ,				truncation=_a				)[input_name]

        # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
        with self.assertRaises(_a				):
             feat_extract.pad(_a       ,				padding='max_length'       ,				truncation=_a				)[input_name]

        # test truncation for `pad_to_multiple_of` for List[int] + numpy
        lowercase_					:       str     				=					12
        lowercase_					:       List[Any]     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=len(speech_inputs[0]				)       ,				pad_to_multiple_of=_a       ,				truncation=_a       ,				)
        lowercase_					:       Any     				=					input_a[input_name]

        lowercase_					:       List[Any]     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=len(speech_inputs[0]				)       ,				pad_to_multiple_of=_a       ,				)
        lowercase_					:       str     				=					input_a[input_name]

        # retrieve expected_length as multiple of pad_to_multiple_of
        lowercase_					:       Dict     				=					len(speech_inputs[0]				)
        if expected_length % pad_to_multiple_of != 0:
             lowercase_					:       List[Any]     				=					((len(speech_inputs[0]				) // pad_to_multiple_of) + 1) * pad_to_multiple_of

        self.assertTrue(len(input_a[0]				) == expected_length				)
        self.assertTrue(_inputs_have_equal_length(_a				)				)
        self.assertFalse(_inputs_have_equal_length(_a				)				)
   def 		_lowerCamelCase (self				)    ->     str:
        self._check_padding(numpify=_a				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        self._check_padding(numpify=_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        self._check_truncation(numpify=_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        self._check_truncation(numpify=_a				)
   @require_torch
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Tuple     				=					self.feature_extraction_class(**self.feat_extract_dict				)
        lowercase_					:       List[Any]     				=					self.feat_extract_tester.prepare_inputs_for_common()
        lowercase_					:       Optional[Any]     				=					feat_extract.model_input_names[0]

        lowercase_					:       Dict     				=					BatchFeature({input_name: speech_inputs}				)

        lowercase_					:       Tuple     				=					feat_extract.pad(_a       ,				padding='longest'       ,				return_tensors='np'				)[input_name]
        lowercase_					:       List[Any]     				=					feat_extract.pad(_a       ,				padding='longest'       ,				return_tensors='pt'				)[input_name]

        self.assertTrue(abs(input_np.astype(np.floataa				).sum() - input_pt.numpy().astype(np.floataa				).sum()				) < 1e-2				)
   @require_tf
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Any     				=					self.feature_extraction_class(**self.feat_extract_dict				)
        lowercase_					:       Optional[int]     				=					self.feat_extract_tester.prepare_inputs_for_common()
        lowercase_					:       List[Any]     				=					feat_extract.model_input_names[0]

        lowercase_					:       Tuple     				=					BatchFeature({input_name: speech_inputs}				)

        lowercase_					:       List[Any]     				=					feat_extract.pad(_a       ,				padding='longest'       ,				return_tensors='np'				)[input_name]
        lowercase_					:       Optional[int]     				=					feat_extract.pad(_a       ,				padding='longest'       ,				return_tensors='tf'				)[input_name]

        self.assertTrue(abs(input_np.astype(np.floataa				).sum() - input_tf.numpy().astype(np.floataa				).sum()				) < 1e-2				)
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       Any     				=					self.feat_extract_dict
        lowercase_					:       List[Any]     				=					True
        lowercase_					:       Tuple     				=					self.feature_extraction_class(**_a				)
        lowercase_					:       Optional[Any]     				=					self.feat_extract_tester.prepare_inputs_for_common()
        lowercase_					:       str     				=					[len(_a				) for x in speech_inputs]
        lowercase_					:       Union[str, Any]     				=					feat_extract.model_input_names[0]

        lowercase_					:       List[Any]     				=					BatchFeature({input_name: speech_inputs}				)

        lowercase_					:       str     				=					feat_extract.pad(_a       ,				padding='longest'       ,				return_tensors='np'				)
        self.assertIn('attention_mask'       ,				_a				)
        self.assertListEqual(list(processed.attention_mask.shape				)       ,				list(processed[input_name].shape[:2]				)				)
        self.assertListEqual(processed.attention_mask.sum(-1				).tolist()       ,				_a				)





   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Union[str, Any]     				=					self.feat_extract_dict
        lowercase_					:       List[Any]     				=					True
        lowercase_					:       List[Any]     				=					self.feature_extraction_class(**_a				)
        lowercase_					:       List[Any]     				=					self.feat_extract_tester.prepare_inputs_for_common()
        lowercase_					:       Optional[int]     				=					[len(_a				) for x in speech_inputs]
        lowercase_					:       int     				=					feat_extract.model_input_names[0]

        lowercase_					:       Tuple     				=					BatchFeature({input_name: speech_inputs}				)
        lowercase_					:       Optional[Any]     				=					min(_a				)

        lowercase_					:       int     				=					feat_extract.pad(
            _a       ,				padding='max_length'       ,				max_length=_a       ,				truncation=_a       ,				return_tensors='np'				)
        self.assertIn('attention_mask'       ,				_a				)
        self.assertListEqual(
            list(processed_pad.attention_mask.shape				)       ,				[processed_pad[input_name].shape[0], max_length]				)
        self.assertListEqual(
            processed_pad.attention_mask[:, :max_length].sum(-1				).tolist()       ,				[max_length for x in speech_inputs]				)




