






'''simple docstring'''





import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


__A							:							Optional[Any]  							=		random.Random()

if is_torch_available():
 import torch






def    lowerCamelCase_     (    lowercase__				,     lowercase__=1.0				,     lowercase__=None				,     lowercase__=None):
 if rng is None:
  lowerCamelCase__  =     global_rng

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

 return values
class 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

    def __init__(					self     :     Tuple				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     Tuple=7				,     __lowerCamelCase     :     Union[str, Any]=400				,     __lowerCamelCase     :     int=2000				,     __lowerCamelCase     :     Optional[int]=1				,     __lowerCamelCase     :     Union[str, Any]=0.0				,     __lowerCamelCase     :     Any=16000				,     __lowerCamelCase     :     Dict=True				,     __lowerCamelCase     :     List[Any]=True				,     )							->							List[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     parent
     lowerCamelCase__  =     batch_size
     lowerCamelCase__  =     min_seq_length
     lowerCamelCase__  =     max_seq_length
     lowerCamelCase__  =     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     lowerCamelCase__  =     feature_size
     lowerCamelCase__  =     padding_value
     lowerCamelCase__  =     sampling_rate
     lowerCamelCase__  =     return_attention_mask
     lowerCamelCase__  =     do_normalize

    def    a__				(					self     :     Dict     )							->							str:

     '''simple docstring'''




     return {
         "feature_size": self.feature_size,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }




    def    a__				(					self     :     int				,     __lowerCamelCase     :     Tuple=False				,     __lowerCamelCase     :     Optional[int]=False     )							->							Dict:

     '''simple docstring'''




     def _flatten(__lowerCamelCase     :     Optional[int]     ):
      return list(itertools.chain(*lowerCAmelCase_     )     )

     if equal_length:
      lowerCamelCase__  =     floats_list((self.batch_size, self.max_seq_length)     )
     else:
      # make sure that inputs increase in size
      lowerCamelCase__  =     [
          _flatten(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__  =     [np.asarray(lowerCAmelCase_     ) for x in speech_inputs]

     return speech_inputs



@require_torch
@require_torchaudio
class 				lowercase       (							__lowerCAmelCase ,       unittest.TestCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							ASTFeatureExtractor

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

     '''simple docstring'''




     lowerCamelCase__  =     ASTFeatureExtractionTester(self     )

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

     '''simple docstring'''




     lowerCamelCase__  =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
     # create three inputs of length 800, 1000, and 1200
     lowerCamelCase__  =     [floats_list((1, x)     )[0] for x in range(800				,     1400				,     200     )]
     lowerCamelCase__  =     [np.asarray(lowerCAmelCase_     ) for speech_input in speech_inputs]

     # Test not batched input
     lowerCamelCase__  =     feat_extract(speech_inputs[0]				,     return_tensors="np"     ).input_values
     lowerCamelCase__  =     feat_extract(np_speech_inputs[0]				,     return_tensors="np"     ).input_values
     self.assertTrue(np.allclose(lowerCAmelCase_				,     lowerCAmelCase_				,     atol=1E-3     )     )

     # Test batched
     lowerCamelCase__  =     feat_extract(lowerCAmelCase_				,     padding=lowerCAmelCase_				,     return_tensors="np"     ).input_values
     lowerCamelCase__  =     feat_extract(lowerCAmelCase_				,     padding=lowerCAmelCase_				,     return_tensors="np"     ).input_values
     for enc_seq_a, enc_seq_a in zip(lowerCAmelCase_				,     lowerCAmelCase_     ):
      self.assertTrue(np.allclose(lowerCAmelCase_				,     lowerCAmelCase_				,     atol=1E-3     )     )

     # Test 2-D numpy arrays are batched.
     lowerCamelCase__  =     [floats_list((1, x)     )[0] for x in (800, 800, 800)]
     lowerCamelCase__  =     np.asarray(lowerCAmelCase_     )
     lowerCamelCase__  =     feat_extract(lowerCAmelCase_				,     return_tensors="np"     ).input_values
     lowerCamelCase__  =     feat_extract(lowerCAmelCase_				,     return_tensors="np"     ).input_values
     for enc_seq_a, enc_seq_a in zip(lowerCAmelCase_				,     lowerCAmelCase_     ):
      self.assertTrue(np.allclose(lowerCAmelCase_				,     lowerCAmelCase_				,     atol=1E-3     )     )

    @require_torch
    def    a__				(					self     :     Union[str, Any]     )							->							Optional[Any]:

     '''simple docstring'''




     import torch

     lowerCamelCase__  =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
     lowerCamelCase__  =     np.random.rand(100     ).astype(np.floataa     )
     lowerCamelCase__  =     np_speech_inputs.tolist()

     for inputs in [py_speech_inputs, np_speech_inputs]:
      lowerCamelCase__  =     feature_extractor.pad([{"input_values": inputs}]				,     return_tensors="np"     )
      self.assertTrue(np_processed.input_values.dtype == np.floataa     )
      lowerCamelCase__  =     feature_extractor.pad([{"input_values": inputs}]				,     return_tensors="pt"     )
      self.assertTrue(pt_processed.input_values.dtype == torch.floataa     )

    def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     Dict     )							->							int:

     '''simple docstring'''




     from datasets import load_dataset

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

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




    @require_torch
    def    a__				(					self     :     Union[str, Any]     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     torch.tensor(
         [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
          -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
          -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
          -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]     )
     # fmt: on

     lowerCamelCase__  =     self._load_datasamples(1     )
     lowerCamelCase__  =     ASTFeatureExtractor()
     lowerCamelCase__  =     feature_extractor(lowerCAmelCase_				,     return_tensors="pt"     ).input_values
     self.assertEquals(input_values.shape				,     (1, 1024, 128)     )
     self.assertTrue(torch.allclose(input_values[0, 0, :30]				,     lowerCAmelCase_				,     atol=1E-4     )     )









'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__A							:							Union[str, Any]  							=		{
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Optional[Any]  							=		["""LayoutLMv2TokenizerFast"""]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							List[Any]  							=		["""LayoutLMv2FeatureExtractor"""]
 __A							:							List[Any]  							=		["""LayoutLMv2ImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Any  							=		[
     """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """LayoutLMv2ForQuestionAnswering""",
     """LayoutLMv2ForSequenceClassification""",
     """LayoutLMv2ForTokenClassification""",
     """LayoutLMv2Layer""",
     """LayoutLMv2Model""",
     """LayoutLMv2PreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
 from .processing_layoutlmva import LayoutLMvaProcessor
 from .tokenization_layoutlmva import LayoutLMvaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaLayer,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )
else:
 import sys

 __A							:							Tuple  							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

