
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

__lowerCAmelCase			:     Tuple           =   random.Random()

def a__    (       A_,     A_=1.0,     A_=None,     A_=None					):






  '''simple docstring'''





  if rng is None:
    __magic_name__				     =       global_rng

  __magic_name__				     =       []
  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			: Optional[int]			,							UpperCamelCase__			: List[str]			,							UpperCamelCase__			: Tuple=7			,							UpperCamelCase__			: int=400			,							UpperCamelCase__			: List[str]=2000			,							UpperCamelCase__			: Tuple=2048			,							UpperCamelCase__			: Union[str, Any]=128			,							UpperCamelCase__			: List[Any]=1			,							UpperCamelCase__			: Tuple=512			,							UpperCamelCase__			: int=30			,							UpperCamelCase__			: Optional[Any]=4_4100			,							)			->	int:







    """simple docstring"""






    __magic_name__				     =       parent
    __magic_name__				     =       batch_size
    __magic_name__				     =       min_seq_length
    __magic_name__				     =       max_seq_length
    __magic_name__				     =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
    __magic_name__				     =       spectrogram_length
    __magic_name__				     =       feature_size
    __magic_name__				     =       num_audio_channels
    __magic_name__				     =       hop_length
    __magic_name__				     =       chunk_length
    __magic_name__				     =       sampling_rate
  def 							_lowercase							( self			: Optional[int]    )			->	int:







    """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 							_lowercase							( self			: Any			,							UpperCamelCase__			: Tuple=False			,							UpperCamelCase__			: List[Any]=False    )			->	List[Any]:







    """simple docstring"""






    def _flatten(UpperCamelCase__			: Tuple    ):
      return list(itertools.chain(*UpperCamelCase__    )    )

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







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


  '''simple docstring'''







  a__           =		TvltFeatureExtractor
  def 							_lowercase							( self			: List[Any]    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       TvltFeatureExtractionTester(self    )
  def 							_lowercase							( self			: Optional[Any]    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       self.feature_extraction_class(**self.feat_extract_dict    )
    self.assertTrue(hasattr(UpperCamelCase__			,							"""spectrogram_length"""    )    )
    self.assertTrue(hasattr(UpperCamelCase__			,							"""feature_size"""    )    )
    self.assertTrue(hasattr(UpperCamelCase__			,							"""num_audio_channels"""    )    )
    self.assertTrue(hasattr(UpperCamelCase__			,							"""hop_length"""    )    )
    self.assertTrue(hasattr(UpperCamelCase__			,							"""chunk_length"""    )    )
    self.assertTrue(hasattr(UpperCamelCase__			,							"""sampling_rate"""    )    )
  def 							_lowercase							( self			: Tuple    )			->	int:







    """simple docstring"""






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

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

    __magic_name__				     =       feat_extract_first.to_dict()
    __magic_name__				     =       feat_extract_second.to_dict()
    __magic_name__				     =       dict_first.pop("""mel_filters"""    )
    __magic_name__				     =       dict_second.pop("""mel_filters"""    )
    self.assertTrue(np.allclose(UpperCamelCase__			,							UpperCamelCase__    )    )
    self.assertEqual(UpperCamelCase__			,							UpperCamelCase__    )
  def 							_lowercase							( self			: Dict    )			->	str:







    """simple docstring"""






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

    with tempfile.TemporaryDirectory() as tmpdirname:
      __magic_name__				     =       os.path.join(UpperCamelCase__			,							"""feat_extract.json"""    )
      feat_extract_first.to_json_file(UpperCamelCase__    )
      __magic_name__				     =       self.feature_extraction_class.from_json_file(UpperCamelCase__    )

    __magic_name__				     =       feat_extract_first.to_dict()
    __magic_name__				     =       feat_extract_second.to_dict()
    __magic_name__				     =       dict_first.pop("""mel_filters"""    )
    __magic_name__				     =       dict_second.pop("""mel_filters"""    )
    self.assertTrue(np.allclose(UpperCamelCase__			,							UpperCamelCase__    )    )
    self.assertEqual(UpperCamelCase__			,							UpperCamelCase__    )
  def 							_lowercase							( self			: List[Any]    )			->	Optional[int]:







    """simple docstring"""






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

    # create three inputs of length 800, 1000, and 1200
    __magic_name__				     =       [floats_list((1, x)    )[0] for x in range(800			,							1400			,							200    )]
    __magic_name__				     =       [np.asarray(UpperCamelCase__    ) for speech_input in speech_inputs]

    # Test not batched input
    __magic_name__				     =       feature_extractor(np_speech_inputs[0]			,							return_tensors="""np"""			,							sampling_rate=4_4100    ).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
    __magic_name__				     =       feature_extractor(UpperCamelCase__			,							return_tensors="""np"""			,							sampling_rate=4_4100    ).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
    __magic_name__				     =       feature_extractor(
        UpperCamelCase__			,							return_tensors="""np"""			,							sampling_rate=4_4100			,							mask_audio=UpperCamelCase__    ).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.
    __magic_name__				     =       [floats_list((1, x)    )[0] for x in (800, 800, 800)]
    __magic_name__				     =       np.asarray(UpperCamelCase__    )
    __magic_name__				     =       feature_extractor(UpperCamelCase__			,							return_tensors="""np"""			,							sampling_rate=4_4100    ).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			: Tuple			,							UpperCamelCase__			: Tuple    )			->	List[Any]:







    """simple docstring"""






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

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





  def 							_lowercase							( self			: Union[str, Any]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       self._load_datasamples(1    )
    __magic_name__				     =       TvltFeatureExtractor()
    __magic_name__				     =       feature_extractor(UpperCamelCase__			,							return_tensors="""pt"""    ).audio_values

    self.assertEquals(audio_values.shape			,							(1, 1, 192, 128)    )

    __magic_name__				     =       torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]    )
    self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			,							UpperCamelCase__			,							atol=1E-4    )    )








import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        LiltForQuestionAnswering,
        LiltForSequenceClassification,
        LiltForTokenClassification,
        LiltModel,
    )
    from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST

class 		SCREAMING_SNAKE_CASE__				:


      def __init__(self       :      Any				,			a__       :      Union[str, Any]				,			a__       :      int=13				,			a__       :      int=7				,			a__       :      Optional[Any]=True				,			a__       :      Optional[int]=True				,			a__       :      Any=True				,			a__       :      str=True				,			a__       :      List[Any]=99				,			a__       :      Any=24				,			a__       :      List[str]=2				,			a__       :      Optional[int]=6				,			a__       :      int=37				,			a__       :      List[str]="gelu"				,			a__       :      List[Any]=0.1				,			a__       :      Optional[int]=0.1				,			a__       :      Union[str, Any]=512				,			a__       :      List[str]=16				,			a__       :      Optional[int]=2				,			a__       :      Union[str, Any]=0.0_2				,			a__       :      str=3				,			a__       :      Optional[Any]=None				,			a__       :      Any=1000				,			):





         """simple docstring"""



         __snake_case							       =   parent
         __snake_case							       =   batch_size
         __snake_case							       =   seq_length
         __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							       =   num_labels
         __snake_case							       =   scope
         __snake_case							       =   range_bbox


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size      )

         __snake_case							       =   ids_tensor([self.batch_size, self.seq_length, 4]				,			self.range_bbox      )
         # 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							       =   t
               if bbox[i, j, 2] < bbox[i, j, 0]:
                  __snake_case							       =   bbox[i, j, 2]
                  __snake_case							       =   bbox[i, j, 0]
                  __snake_case							       =   t

         __snake_case							       =   None
         if self.use_input_mask:
            __snake_case							       =   ids_tensor([self.batch_size, self.seq_length]				,			vocab_size=2      )

         __snake_case							       =   None
         if self.use_token_type_ids:
            __snake_case							       =   ids_tensor([self.batch_size, self.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.seq_length]				,			self.num_labels      )

         __snake_case							       =   self.get_config()

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


      def    a (self       :      List[str]      ):





         """simple docstring"""



         return LiltConfig(
             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				,			)


      def    a (self       :      List[Any]				,			a__       :      List[Any]				,			a__       :      Optional[Any]				,			a__       :      List[str]				,			a__       :      int				,			a__       :      Optional[int]				,			a__       :      str				,			a__       :      Optional[int]				,			):





         """simple docstring"""



         __snake_case							       =   LiltModel(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(a__				,			bbox=a__				,			attention_mask=a__				,			token_type_ids=a__      )
         __snake_case							       =   model(a__				,			bbox=a__				,			token_type_ids=a__      )
         __snake_case							       =   model(a__				,			bbox=a__      )

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


      def    a (self       :      Any				,			a__       :      Tuple				,			a__       :      Dict				,			a__       :      Optional[int]				,			a__       :      Dict				,			a__       :      Union[str, Any]				,			a__       :      str				,			a__       :      Tuple				,			):





         """simple docstring"""



         __snake_case							       =   self.num_labels
         __snake_case							       =   LiltForTokenClassification(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(
             a__				,			bbox=a__				,			attention_mask=a__				,			token_type_ids=a__				,			labels=a__      )
         self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.num_labels)      )


      def    a (self       :      int				,			a__       :      Optional[Any]				,			a__       :      int				,			a__       :      int				,			a__       :      Optional[Any]				,			a__       :      Tuple				,			a__       :      Union[str, Any]				,			a__       :      str				,			):





         """simple docstring"""



         __snake_case							       =   LiltForQuestionAnswering(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(
             a__				,			bbox=a__				,			attention_mask=a__				,			token_type_ids=a__				,			start_positions=a__				,			end_positions=a__				,			)
         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    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   self.prepare_config_and_inputs()
         (
             (
             __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,
             '''token_type_ids''': token_type_ids,
             '''attention_mask''': input_mask,
         }
         return config, inputs_dict






@require_torch
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[Any]							=    (
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      A_						:				Any							=    (
          {
              'feature-extraction': LiltModel,
              'question-answering': LiltForQuestionAnswering,
              'text-classification': LiltForSequenceClassification,
              'token-classification': LiltForTokenClassification,
              'zero-shot': LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      A_						:				Optional[int]							=    False
      A_						:				List[Any]							=    False


      def    a (self       :      Dict				,			a__       :      Tuple				,			a__       :      Tuple				,			a__       :      Tuple				,			a__       :      Union[str, Any]				,			a__       :      Any      ):





         """simple docstring"""



         return True


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   LiltModelTester(self      )
         __snake_case							       =   ConfigTester(self				,			config_class=a__				,			hidden_size=37      )


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         self.config_tester.run_common_tests()


      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__      )


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __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(*a__      )


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*a__      )


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*a__      )


      @slow
      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __snake_case							       =   LiltModel.from_pretrained(a__      )
            self.assertIsNotNone(a__      )






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


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''      ).to(a__      )

         __snake_case							       =   torch.tensor([[1, 2]]				,			device=a__      )
         __snake_case							       =   torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]				,			device=a__      )

         # forward pass
         with torch.no_grad():
            __snake_case							       =   model(input_ids=a__				,			bbox=a__      )

         __snake_case							       =   torch.Size([1, 2, 768]      )
         __snake_case							       =   torch.tensor(
             [[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]]				,			device=a__				,			)

         self.assertTrue(outputs.last_hidden_state.shape				,			a__      )
         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]				,			a__				,			atol=1E-3      )      )

