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

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

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


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
					    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    LayoutLMvaConfig,
					    TFLayoutLMvaForQuestionAnswering,
					    TFLayoutLMvaForSequenceClassification,
					    TFLayoutLMvaForTokenClassification,
					    TFLayoutLMvaModel,
					)

if is_vision_available():
					from PIL import Image

					from transformers import LayoutLMvaImageProcessor



class 					lowerCAmelCase_      :



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase=2						,      lowerCAmelCase=3						,      lowerCAmelCase=4						,      lowerCAmelCase=2						,      lowerCAmelCase=7						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=99						,      lowerCAmelCase=36						,      lowerCAmelCase=2						,      lowerCAmelCase=4						,      lowerCAmelCase=37						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=0.1						,      lowerCAmelCase=0.1						,      lowerCAmelCase=5_12						,      lowerCAmelCase=16						,      lowerCAmelCase=2						,      lowerCAmelCase=0.02						,      lowerCAmelCase=6						,      lowerCAmelCase=6						,      lowerCAmelCase=3						,      lowerCAmelCase=4						,      lowerCAmelCase=None						,      lowerCAmelCase=10_00						,      ):






										"""simple docstring"""

										snake_case        =     parent
										snake_case        =     batch_size
										snake_case        =     num_channels
										snake_case        =     image_size
										snake_case        =     patch_size
										snake_case        =     is_training
										snake_case        =     use_input_mask
										snake_case        =     use_token_type_ids
										snake_case        =     use_labels
										snake_case        =     vocab_size
										snake_case        =     hidden_size
										snake_case        =     num_hidden_layers
										snake_case        =     num_attention_heads
										snake_case        =     intermediate_size
										snake_case        =     hidden_act
										snake_case        =     hidden_dropout_prob
										snake_case        =     attention_probs_dropout_prob
										snake_case        =     max_position_embeddings
										snake_case        =     type_vocab_size
										snake_case        =     type_sequence_label_size
										snake_case        =     initializer_range
										snake_case        =     coordinate_size
										snake_case        =     shape_size
										snake_case        =     num_labels
										snake_case        =     num_choices
										snake_case        =     scope
										snake_case        =     range_bbox

										# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
										snake_case        =     text_seq_length
										snake_case        =     (image_size // patch_size) ** 2 + 1
										snake_case        =     self.text_seq_length + self.image_seq_length

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     ids_tensor([self.batch_size, self.text_seq_length]						,      self.vocab_size      )

										snake_case        =     ids_tensor([self.batch_size, self.text_seq_length, 4]						,      self.range_bbox      )
										snake_case        =     bbox.numpy()
										# Ensure that bbox is legal
										for i in range(bbox.shape[0]      ):
																for j in range(bbox.shape[1]      ):
																						if bbox[i, j, 3] < bbox[i, j, 1]:
																												snake_case        =     bbox[i, j, 3]
																												snake_case        =     bbox[i, j, 1]
																												snake_case        =     tmp_coordinate
																						if bbox[i, j, 2] < bbox[i, j, 0]:
																												snake_case        =     bbox[i, j, 2]
																												snake_case        =     bbox[i, j, 0]
																												snake_case        =     tmp_coordinate
										snake_case        =     tf.constant(lowerCAmelCase      )

										snake_case        =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

										snake_case        =     None
										if self.use_input_mask:
																snake_case        =     random_attention_mask([self.batch_size, self.text_seq_length]      )

										snake_case        =     None
										if self.use_token_type_ids:
																snake_case        =     ids_tensor([self.batch_size, self.text_seq_length]						,      self.type_vocab_size      )

										snake_case        =     None
										snake_case        =     None
										if self.use_labels:
																snake_case        =     ids_tensor([self.batch_size]						,      self.type_sequence_label_size      )
																snake_case        =     ids_tensor([self.batch_size, self.text_seq_length]						,      self.num_labels      )

										snake_case        =     LayoutLMvaConfig(
										    vocab_size=self.vocab_size						,      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						,      max_position_embeddings=self.max_position_embeddings						,      type_vocab_size=self.type_vocab_size						,      initializer_range=self.initializer_range						,      coordinate_size=self.coordinate_size						,      shape_size=self.shape_size						,      input_size=self.image_size						,      patch_size=self.patch_size						,      )

										return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     TFLayoutLMvaModel(config=lowerCAmelCase      )

										# text + image
										snake_case        =     model(lowerCAmelCase						,      pixel_values=lowerCAmelCase						,      training=lowerCAmelCase      )
										snake_case        =     model(
										    lowerCAmelCase						,      bbox=lowerCAmelCase						,      pixel_values=lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase						,      training=lowerCAmelCase						,      )
										snake_case        =     model(lowerCAmelCase						,      bbox=lowerCAmelCase						,      pixel_values=lowerCAmelCase						,      training=lowerCAmelCase      )

										self.parent.assertEqual(result.last_hidden_state.shape						,      (self.batch_size, self.seq_length, self.hidden_size)      )

										# text only
										snake_case        =     model(lowerCAmelCase						,      training=lowerCAmelCase      )
										self.parent.assertEqual(
										    result.last_hidden_state.shape						,      (self.batch_size, self.text_seq_length, self.hidden_size)      )

										# image only
										snake_case        =     model({'pixel_values': pixel_values}						,      training=lowerCAmelCase      )
										self.parent.assertEqual(
										    result.last_hidden_state.shape						,      (self.batch_size, self.image_seq_length, self.hidden_size)      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.num_labels
										snake_case        =     TFLayoutLMvaForSequenceClassification(config=lowerCAmelCase      )
										snake_case        =     model(
										    lowerCAmelCase						,      bbox=lowerCAmelCase						,      pixel_values=lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase						,      labels=lowerCAmelCase						,      training=lowerCAmelCase						,      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.num_labels)      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.num_labels
										snake_case        =     TFLayoutLMvaForTokenClassification(config=lowerCAmelCase      )
										snake_case        =     model(
										    lowerCAmelCase						,      bbox=lowerCAmelCase						,      pixel_values=lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase						,      labels=lowerCAmelCase						,      training=lowerCAmelCase						,      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.text_seq_length, self.num_labels)      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     2
										snake_case        =     TFLayoutLMvaForQuestionAnswering(config=lowerCAmelCase      )
										snake_case        =     model(
										    lowerCAmelCase						,      bbox=lowerCAmelCase						,      pixel_values=lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase						,      start_positions=lowerCAmelCase						,      end_positions=lowerCAmelCase						,      training=lowerCAmelCase						,      )
										self.parent.assertEqual(result.start_logits.shape						,      (self.batch_size, self.seq_length)      )
										self.parent.assertEqual(result.end_logits.shape						,      (self.batch_size, self.seq_length)      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.prepare_config_and_inputs()
										((snake_case)   ,(snake_case)   ,(snake_case)   ,(snake_case)   ,(snake_case)   ,(snake_case)   ,(snake_case)   ,(snake_case))        =     config_and_inputs
										snake_case        =     {
										    'input_ids': input_ids,
										    'bbox': bbox,
										    'pixel_values': pixel_values,
										    'token_type_ids': token_type_ids,
										    'attention_mask': input_mask,
										}
										return config, inputs_dict





@require_tf
class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase       ,      unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Any     					=  (
				    (
				        TFLayoutLMvaModel,
				        TFLayoutLMvaForQuestionAnswering,
				        TFLayoutLMvaForSequenceClassification,
				        TFLayoutLMvaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				_lowerCAmelCase       :      Optional[int]     					=  (
				    {"""document-question-answering""": TFLayoutLMvaForQuestionAnswering, """feature-extraction""": TFLayoutLMvaModel}
				    if is_tf_available()
				    else {}
				)

				_lowerCAmelCase       :      Any     					=  False
				_lowerCAmelCase       :      Tuple     					=  False
				_lowerCAmelCase       :      Optional[int]     					=  False

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										return True

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase=False      ):






										"""simple docstring"""

										snake_case        =     copy.deepcopy(lowerCAmelCase      )

										if model_class in get_values(lowerCAmelCase      ):
																snake_case        =     {
																    k: tf.tile(tf.expand_dims(lowerCAmelCase						,      1      )						,      (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)      )
																    if isinstance(lowerCAmelCase						,      tf.Tensor      ) and v.ndim > 0
																    else v
																    for k, v in inputs_dict.items()
																}

										if return_labels:
																if model_class in get_values(lowerCAmelCase      ):
																						snake_case        =     tf.ones(self.model_tester.batch_size						,      dtype=tf.intaa      )
																elif model_class in get_values(lowerCAmelCase      ):
																						snake_case        =     tf.zeros(self.model_tester.batch_size						,      dtype=tf.intaa      )
																						snake_case        =     tf.zeros(self.model_tester.batch_size						,      dtype=tf.intaa      )
																elif model_class in get_values(lowerCAmelCase      ):
																						snake_case        =     tf.zeros(self.model_tester.batch_size						,      dtype=tf.intaa      )
																elif model_class in get_values(lowerCAmelCase      ):
																						snake_case        =     tf.zeros(
																						    (self.model_tester.batch_size, self.model_tester.text_seq_length)						,      dtype=tf.intaa      )

										return inputs_dict

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     TFLayoutLMvaModelTester(self      )
										snake_case        =     ConfigTester(self						,      config_class=lowerCAmelCase						,      hidden_size=37      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case   ,snake_case        =     self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
																snake_case        =     model_class(lowerCAmelCase      )
																if getattr(lowerCAmelCase						,      'hf_compute_loss'						,      lowerCAmelCase      ):
																						# The number of elements in the loss should be the same as the number of elements in the label
																						snake_case        =     self._prepare_for_class(inputs_dict.copy()						,      lowerCAmelCase						,      return_labels=lowerCAmelCase      )
																						snake_case        =     prepared_for_class[
																						    sorted(prepared_for_class.keys() - inputs_dict.keys()						,      reverse=lowerCAmelCase      )[0]
																						]
																						snake_case        =     added_label.shape.as_list()[:1]

																						# Test that model correctly compute the loss with kwargs
																						snake_case        =     self._prepare_for_class(inputs_dict.copy()						,      lowerCAmelCase						,      return_labels=lowerCAmelCase      )
																						snake_case        =     prepared_for_class.pop('input_ids'      )

																						snake_case        =     model(lowerCAmelCase						,      **lowerCAmelCase      )[0]
																						self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

																						# Test that model correctly compute the loss when we mask some positions
																						snake_case        =     self._prepare_for_class(inputs_dict.copy()						,      lowerCAmelCase						,      return_labels=lowerCAmelCase      )
																						snake_case        =     prepared_for_class.pop('input_ids'      )
																						if "labels" in prepared_for_class:
																												snake_case        =     prepared_for_class['labels'].numpy()
																												if len(labels.shape      ) > 1 and labels.shape[1] != 1:
																																		snake_case        =     -1_00
																																		snake_case        =     tf.convert_to_tensor(lowerCAmelCase      )
																																		snake_case        =     model(lowerCAmelCase						,      **lowerCAmelCase      )[0]
																																		self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )
																																		self.assertTrue(not np.any(np.isnan(loss.numpy()      )      )      )

                # Test that model correctly compute the loss with a dict
																						snake_case        =     self._prepare_for_class(inputs_dict.copy()						,      lowerCAmelCase						,      return_labels=lowerCAmelCase      )
																						snake_case        =     model(lowerCAmelCase      )[0]
																						self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

																						# Test that model correctly compute the loss with a tuple
																						snake_case        =     self._prepare_for_class(inputs_dict.copy()						,      lowerCAmelCase						,      return_labels=lowerCAmelCase      )

																						# Get keys that were added with the _prepare_for_class function
																						snake_case        =     prepared_for_class.keys() - inputs_dict.keys()
																						snake_case        =     inspect.signature(model.call      ).parameters
																						snake_case        =     list(signature.keys()      )

																						# Create a dictionary holding the location of the tensors in the tuple
																						snake_case        =     {0: 'input_ids'}
																						for label_key in label_keys:
																												snake_case        =     signature_names.index(lowerCAmelCase      )
																												snake_case        =     label_key
																						snake_case        =     sorted(tuple_index_mapping.items()      )
																						# Initialize a list with their default values, update the values and convert to a tuple
																						snake_case        =     []

																						for name in signature_names:
																												if name != "kwargs":
																																		list_input.append(signature[name].default      )

																						for index, value in sorted_tuple_index_mapping:
																												snake_case        =     prepared_for_class[value]

																						snake_case        =     tuple(lowerCAmelCase      )

																						# Send to model
																						snake_case        =     model(tuple_input[:-1]      )[0]

																						self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										(
										    (
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,
										)        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										(
										    (
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,
										)        =     self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																snake_case        =     type
																self.model_tester.create_and_check_model(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										(
										    (
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,
										)        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(
										    lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										(
										    (
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,
										)        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(
										    lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										(
										    (
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,
										)        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(
										    lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case        =     TFLayoutLMvaModel.from_pretrained(lowerCAmelCase      )
																self.assertIsNotNone(lowerCAmelCase      )





def 				lowerCAmelCase__      (     )   ->			str:





						"""simple docstring"""






						snake_case        =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
						return image



@require_tf
class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				@cached_property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase      ) if is_vision_available() else None

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'      )

										snake_case        =     self.default_image_processor
										snake_case        =     prepare_img()
										snake_case        =     image_processor(images=lowerCAmelCase						,      return_tensors='tf'      ).pixel_values

										snake_case        =     tf.constant([[1, 2]]      )
										snake_case        =     tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]      )						,      axis=0      )

										# forward pass
										snake_case        =     model(input_ids=lowerCAmelCase						,      bbox=lowerCAmelCase						,      pixel_values=lowerCAmelCase						,      training=lowerCAmelCase      )

										# verify the logits
										snake_case        =     (1, 1_99, 7_68)
										self.assertEqual(outputs.last_hidden_state.shape						,      lowerCAmelCase      )

										snake_case        =     tf.constant(
										    [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]      )

										self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]						,      lowerCAmelCase						,      atol=1E-4      )      )



"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
					import torch

if is_datasets_available():
					from datasets import load_dataset

SCREAMING_SNAKE_CASE__   =					random.Random()





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[Any]	,     _UpperCamelCase    :			Optional[Any]=1.0	,     _UpperCamelCase    :			Optional[int]=None	,     _UpperCamelCase    :			Optional[int]=None   )   ->			Optional[int]:





						"""simple docstring"""






						if rng is None:
												snake_case        =     global_rng

						snake_case        =     []
						for batch_idx in range(shape[0]   ):
												values.append([]   )
												for _ in range(shape[1]   ):
																		values[-1].append(rng.random() * scale   )

						return values



class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase=7						,      lowerCAmelCase=4_00						,      lowerCAmelCase=20_00						,      lowerCAmelCase=20_48						,      lowerCAmelCase=1_28						,      lowerCAmelCase=1						,      lowerCAmelCase=5_12						,      lowerCAmelCase=30						,      lowerCAmelCase=4_41_00						,      ):






										"""simple docstring"""

										snake_case        =     parent
										snake_case        =     batch_size
										snake_case        =     min_seq_length
										snake_case        =     max_seq_length
										snake_case        =     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										snake_case        =     spectrogram_length
										snake_case        =     feature_size
										snake_case        =     num_audio_channels
										snake_case        =     hop_length
										snake_case        =     chunk_length
										snake_case        =     sampling_rate

				def 						snake_case	(			self      ):






										"""simple docstring"""

										return {
										    "spectrogram_length": self.spectrogram_length,
										    "feature_size": self.feature_size,
										    "num_audio_channels": self.num_audio_channels,
										    "hop_length": self.hop_length,
										    "chunk_length": self.chunk_length,
										    "sampling_rate": self.sampling_rate,
										}

				def 						snake_case	(			self						,      lowerCAmelCase=False						,      lowerCAmelCase=False      ):






										"""simple docstring"""

										def _flatten(lowerCAmelCase      ):
																return list(itertools.chain(*lowerCAmelCase      )      )

										if equal_length:
																snake_case        =     [floats_list((self.max_seq_length, self.feature_size)      ) for _ in range(self.batch_size      )]
										else:
																# make sure that inputs increase in size
																snake_case        =     [
																    floats_list((x, self.feature_size)      )
																    for x in range(self.min_seq_length						,      self.max_seq_length						,      self.seq_length_diff      )
																]
										if numpify:
																snake_case        =     [np.asarray(lowerCAmelCase      ) for x in speech_inputs]
										return speech_inputs





@require_torch
@require_torchaudio
class 					lowerCAmelCase_      (     lowerCAmelCase       ,      unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Union[str, Any]     					=  TvltFeatureExtractor

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     TvltFeatureExtractionTester(self      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.feature_extraction_class(**self.feat_extract_dict      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'spectrogram_length'      )      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'feature_size'      )      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'num_audio_channels'      )      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'hop_length'      )      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'chunk_length'      )      )
										self.assertTrue(hasattr(lowerCAmelCase						,      'sampling_rate'      )      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.feature_extraction_class(**self.feat_extract_dict      )

										with tempfile.TemporaryDirectory() as tmpdirname:
																snake_case        =     feat_extract_first.save_pretrained(lowerCAmelCase      )[0]
																check_json_file_has_correct_format(lowerCAmelCase      )
																snake_case        =     self.feature_extraction_class.from_pretrained(lowerCAmelCase      )

										snake_case        =     feat_extract_first.to_dict()
										snake_case        =     feat_extract_second.to_dict()
										snake_case        =     dict_first.pop('mel_filters'      )
										snake_case        =     dict_second.pop('mel_filters'      )
										self.assertTrue(np.allclose(lowerCAmelCase						,      lowerCAmelCase      )      )
										self.assertEqual(lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.feature_extraction_class(**self.feat_extract_dict      )

										with tempfile.TemporaryDirectory() as tmpdirname:
																snake_case        =     os.path.join(lowerCAmelCase						,      'feat_extract.json'      )
																feat_extract_first.to_json_file(lowerCAmelCase      )
																snake_case        =     self.feature_extraction_class.from_json_file(lowerCAmelCase      )

										snake_case        =     feat_extract_first.to_dict()
										snake_case        =     feat_extract_second.to_dict()
										snake_case        =     dict_first.pop('mel_filters'      )
										snake_case        =     dict_second.pop('mel_filters'      )
										self.assertTrue(np.allclose(lowerCAmelCase						,      lowerCAmelCase      )      )
										self.assertEqual(lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.feature_extraction_class(**self.feat_extract_dict      )

										# create three inputs of length 800, 1000, and 1200
										snake_case        =     [floats_list((1, x)      )[0] for x in range(8_00						,      14_00						,      2_00      )]
										snake_case        =     [np.asarray(lowerCAmelCase      ) for speech_input in speech_inputs]

										# Test not batched input
										snake_case        =     feature_extractor(np_speech_inputs[0]						,      return_tensors='np'						,      sampling_rate=4_41_00      ).audio_values

										self.assertTrue(encoded_audios.ndim == 4      )
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

										# Test batched
										snake_case        =     feature_extractor(lowerCAmelCase						,      return_tensors='np'						,      sampling_rate=4_41_00      ).audio_values

										self.assertTrue(encoded_audios.ndim == 4      )
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

										# Test audio masking
										snake_case        =     feature_extractor(
										    lowerCAmelCase						,      return_tensors='np'						,      sampling_rate=4_41_00						,      mask_audio=lowerCAmelCase      ).audio_values

										self.assertTrue(encoded_audios.ndim == 4      )
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

										# Test 2-D numpy arrays are batched.
										snake_case        =     [floats_list((1, x)      )[0] for x in (8_00, 8_00, 8_00)]
										snake_case        =     np.asarray(lowerCAmelCase      )
										snake_case        =     feature_extractor(lowerCAmelCase						,      return_tensors='np'						,      sampling_rate=4_41_00      ).audio_values
										self.assertTrue(encoded_audios.ndim == 4      )
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     load_dataset('hf-internal-testing/librispeech_asr_dummy'						,      'clean'						,      split='validation'      )
										# automatic decoding with librispeech
										snake_case        =     ds.sort('id'      ).select(range(lowerCAmelCase      )      )[:num_samples]['audio']

										return [x["array"] for x in speech_samples]

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self._load_datasamples(1      )
										snake_case        =     TvltFeatureExtractor()
										snake_case        =     feature_extractor(lowerCAmelCase						,      return_tensors='pt'      ).audio_values

										self.assertEquals(audio_values.shape						,      (1, 1, 1_92, 1_28)      )

										snake_case        =     torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]      )
										self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]						,      lowerCAmelCase						,      atol=1E-4      )      )


