


'''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

A__						:   Any				=    random.Random()



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any] ,       UpperCAmelCase_       :							Dict=1.0 ,       UpperCAmelCase_       :							str=None ,       UpperCAmelCase_       :							List[Any]=None				)	->						Union[str, Any]:
     if rng is None:
          __lowerCamelCase  :      Any														=						global_rng

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

     return values





class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=7     ,	SCREAMING_SNAKE_CASE_=4_00     ,	SCREAMING_SNAKE_CASE_=20_00     ,	SCREAMING_SNAKE_CASE_=20_48     ,	SCREAMING_SNAKE_CASE_=1_28     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=5_12     ,	SCREAMING_SNAKE_CASE_=30     ,	SCREAMING_SNAKE_CASE_=4_41_00     ,	)							->	str:
         __lowerCamelCase  :      Dict														=						parent
         __lowerCamelCase  :      Union[str, Any]														=						batch_size
         __lowerCamelCase  :      Any														=						min_seq_length
         __lowerCamelCase  :      List[Any]														=						max_seq_length
         __lowerCamelCase  :      List[Any]														=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         __lowerCamelCase  :      Any														=						spectrogram_length
         __lowerCamelCase  :      Any														=						feature_size
         __lowerCamelCase  :      List[str]														=						num_audio_channels
         __lowerCamelCase  :      Dict														=						hop_length
         __lowerCamelCase  :      Optional[Any]														=						chunk_length
         __lowerCamelCase  :      Optional[Any]														=						sampling_rate







    def  lowercase_							(				self      )							->	List[str]:
         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  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False      )							->	Dict:
         def _flatten(SCREAMING_SNAKE_CASE_      ):
              return list(itertools.chain(*SCREAMING_SNAKE_CASE_      )      )

         if equal_length:
              __lowerCamelCase  :      Tuple														=						[floats_list((self.max_seq_length, self.feature_size)      ) for _ in range(self.batch_size      )]
         else:
              # make sure that inputs increase in size
              __lowerCamelCase  :      Dict														=						[
                  floats_list((x, self.feature_size)      )
                  for x in range(self.min_seq_length     ,	self.max_seq_length     ,	self.seq_length_diff      )
              ]
         if numpify:
              __lowerCamelCase  :      Union[str, Any]														=						[np.asarray(SCREAMING_SNAKE_CASE_      ) for x in speech_inputs]
         return speech_inputs







@require_torch
@require_torchaudio
class   UpperCAmelCase_    (_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   Dict					=     TvltFeatureExtractor







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      Any														=						TvltFeatureExtractionTester(self      )







    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase  :      Tuple														=						self.feature_extraction_class(**self.feat_extract_dict      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'spectrogram_length'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'feature_size'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'num_audio_channels'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'hop_length'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'chunk_length'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'sampling_rate'      )      )







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      int														=						self.feature_extraction_class(**self.feat_extract_dict      )

         with tempfile.TemporaryDirectory() as tmpdirname:
              __lowerCamelCase  :      List[str]														=						feat_extract_first.save_pretrained(SCREAMING_SNAKE_CASE_      )[0]
              check_json_file_has_correct_format(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      List[Any]														=						self.feature_extraction_class.from_pretrained(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Tuple														=						feat_extract_first.to_dict()
         __lowerCamelCase  :      Optional[int]														=						feat_extract_second.to_dict()
         __lowerCamelCase  :      Union[str, Any]														=						dict_first.pop('mel_filters'      )
         __lowerCamelCase  :      List[Any]														=						dict_second.pop('mel_filters'      )
         self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )      )
         self.assertEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      int														=						self.feature_extraction_class(**self.feat_extract_dict      )

         with tempfile.TemporaryDirectory() as tmpdirname:
              __lowerCamelCase  :      int														=						os.path.join(SCREAMING_SNAKE_CASE_     ,	'feat_extract.json'      )
              feat_extract_first.to_json_file(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Any														=						self.feature_extraction_class.from_json_file(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Dict														=						feat_extract_first.to_dict()
         __lowerCamelCase  :      Optional[Any]														=						feat_extract_second.to_dict()
         __lowerCamelCase  :      Union[str, Any]														=						dict_first.pop('mel_filters'      )
         __lowerCamelCase  :      str														=						dict_second.pop('mel_filters'      )
         self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )      )
         self.assertEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	List[str]:
         # Initialize feature_extractor
         __lowerCamelCase  :      Dict														=						self.feature_extraction_class(**self.feat_extract_dict      )

         # create three inputs of length 800, 1000, and 1200
         __lowerCamelCase  :      int														=						[floats_list((1, x)      )[0] for x in range(8_00     ,	14_00     ,	2_00      )]
         __lowerCamelCase  :      Union[str, Any]														=						[np.asarray(SCREAMING_SNAKE_CASE_      ) for speech_input in speech_inputs]

         # Test not batched input
         __lowerCamelCase  :      Union[str, Any]														=						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
         __lowerCamelCase  :      Any														=						feature_extractor(SCREAMING_SNAKE_CASE_     ,	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
         __lowerCamelCase  :      Dict														=						feature_extractor(
             SCREAMING_SNAKE_CASE_     ,	return_tensors='np'     ,	sampling_rate=4_41_00     ,	mask_audio=SCREAMING_SNAKE_CASE_      ).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.
         __lowerCamelCase  :      Optional[Any]														=						[floats_list((1, x)      )[0] for x in (8_00, 8_00, 8_00)]
         __lowerCamelCase  :      int														=						np.asarray(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Dict														=						feature_extractor(SCREAMING_SNAKE_CASE_     ,	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  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         __lowerCamelCase  :      Union[str, Any]														=						load_dataset('hf-internal-testing/librispeech_asr_dummy'     ,	'clean'     ,	split='validation'      )
         # automatic decoding with librispeech
         __lowerCamelCase  :      Union[str, Any]														=						ds.sort('id'      ).select(range(SCREAMING_SNAKE_CASE_      )      )[:num_samples]['audio']

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







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      Tuple														=						self._load_datasamples(1      )
         __lowerCamelCase  :      List[Any]														=						TvltFeatureExtractor()
         __lowerCamelCase  :      List[str]														=						feature_extractor(SCREAMING_SNAKE_CASE_     ,	return_tensors='pt'      ).audio_values

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

         __lowerCamelCase  :      str														=						torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]      )
         self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]     ,	SCREAMING_SNAKE_CASE_     ,	atol=1E-4      )      )










from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowerCAmelCase__    :Dict       =				logging.get_logger(__name__)

lowerCAmelCase__    :Optional[int]       =				{
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}

class 						__a (							UpperCAmelCase     ):
		_a      :    Tuple           =      'perceiver'
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=256     ,       _SCREAMING_SNAKE_CASE=1280     ,       _SCREAMING_SNAKE_CASE=768     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=26     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE="kv"     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=1e-1_2     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=262     ,       _SCREAMING_SNAKE_CASE=2048     ,       _SCREAMING_SNAKE_CASE=56     ,       _SCREAMING_SNAKE_CASE=[368, 496]     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=1920     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=[1, 16, 224, 224]     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Any:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     num_latents
									_UpperCAmelCase											=     d_latents
									_UpperCAmelCase											=     d_model
									_UpperCAmelCase											=     num_blocks
									_UpperCAmelCase											=     num_self_attends_per_block
									_UpperCAmelCase											=     num_self_attention_heads
									_UpperCAmelCase											=     num_cross_attention_heads
									_UpperCAmelCase											=     qk_channels
									_UpperCAmelCase											=     v_channels
									_UpperCAmelCase											=     cross_attention_shape_for_attention
									_UpperCAmelCase											=     self_attention_widening_factor
									_UpperCAmelCase											=     cross_attention_widening_factor
									_UpperCAmelCase											=     hidden_act
									_UpperCAmelCase											=     attention_probs_dropout_prob
									_UpperCAmelCase											=     initializer_range
									_UpperCAmelCase											=     layer_norm_eps
									_UpperCAmelCase											=     use_query_residual
									# masked language modeling attributes
									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     max_position_embeddings
									# image classification attributes
									_UpperCAmelCase											=     image_size
									# flow attributes
									_UpperCAmelCase											=     train_size
									# multimodal autoencoding attributes
									_UpperCAmelCase											=     num_frames
									_UpperCAmelCase											=     audio_samples_per_frame
									_UpperCAmelCase											=     samples_per_patch
									_UpperCAmelCase											=     output_shape



class 						__a (							UpperCAmelCase     ):
		@property
		def       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									if self.task == "multiple-choice":
																_UpperCAmelCase											=     {0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
																_UpperCAmelCase											=     {0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('inputs', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]		)
		@property
		def       UpperCAmelCase__							(     self		)					->			float:




									"""simple docstring"""





									return 1e-4






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = 3     ,       _SCREAMING_SNAKE_CASE = 40     ,       _SCREAMING_SNAKE_CASE = 40     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																_UpperCAmelCase											=     compute_effective_axis_dimension(
																    _SCREAMING_SNAKE_CASE     ,       fixed_dimension=OnnxConfig.default_fixed_batch     ,       num_token_to_add=0		)
																# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
																_UpperCAmelCase											=     preprocessor.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     compute_effective_axis_dimension(
																    _SCREAMING_SNAKE_CASE     ,       fixed_dimension=OnnxConfig.default_fixed_sequence     ,       num_token_to_add=_SCREAMING_SNAKE_CASE		)
																# Generate dummy inputs according to compute batch and sequence
																_UpperCAmelCase											=     [' '.join(['a']		) * seq_length] * batch_size
																_UpperCAmelCase											=     dict(preprocessor(_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE		)		)
																_UpperCAmelCase											=     inputs.pop('input_ids'		)
																return inputs
									elif isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) and preprocessor.model_input_names[0] == "pixel_values":
																# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																_UpperCAmelCase											=     compute_effective_axis_dimension(_SCREAMING_SNAKE_CASE     ,       fixed_dimension=OnnxConfig.default_fixed_batch		)
																_UpperCAmelCase											=     self._generate_dummy_images(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     dict(preprocessor(images=_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE		)		)
																_UpperCAmelCase											=     inputs.pop('pixel_values'		)
																return inputs
									else:
																raise ValueError(
																    'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'		)
