


import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_SCREAMING_SNAKE_CASE      :			List[str]      				=   random.Random()
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_=1.0							,UpperCamelCase_=None							,UpperCamelCase_=None    ):



     """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   A__ (   unittest.TestCase     ):







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case=7      , __snake_case=4_0_0      , __snake_case=2_0_0_0      , __snake_case=1      , __snake_case=0.0      , __snake_case=1_6_0_0_0      , __snake_case=True      , __snake_case=True      , ):
         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					=    feature_size
         snake_case					=    padding_value
         snake_case					=    sampling_rate
         snake_case					=    return_attention_mask
         snake_case					=    do_normalize
    def 		a_      (	self   ):
         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      , __snake_case=False      , __snake_case=False   ):
         def _flatten(__snake_case   ):
              return list(itertools.chain(*__snake_case   )   )

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

         return speech_inputs






class   A__ (   snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		WavaVecaFeatureExtractor
    def 		a_      (	self   ):
         snake_case					=    WavaVecaFeatureExtractionTester(self   )
    def 		a_      (	self      , __snake_case   ):
         self.assertTrue(np.all(np.mean(__snake_case      , axis=0   ) < 1E-3   )   )
         self.assertTrue(np.all(np.abs(np.var(__snake_case      , axis=0   ) - 1   ) < 1E-3   )   )
    def 		a_      (	self   ):
         # Tests that all call wrap to encode_plus and batch_encode_plus
         snake_case					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
         # create three inputs of length 800, 1000, and 1200
         snake_case					=    [floats_list((1, x)   )[0] for x in range(8_0_0      , 1_4_0_0      , 2_0_0   )]
         snake_case					=    [np.asarray(__snake_case   ) for speech_input in speech_inputs]

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

         # Test batched
         snake_case					=    feat_extract(__snake_case      , return_tensors='''np'''   ).input_values
         snake_case					=    feat_extract(__snake_case      , return_tensors='''np'''   ).input_values
         for enc_seq_a, enc_seq_a in zip(__snake_case      , __snake_case   ):
              self.assertTrue(np.allclose(__snake_case      , __snake_case      , atol=1E-3   )   )

         # Test 2-D numpy arrays are batched.
         snake_case					=    [floats_list((1, x)   )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
         snake_case					=    np.asarray(__snake_case   )
         snake_case					=    feat_extract(__snake_case      , return_tensors='''np'''   ).input_values
         snake_case					=    feat_extract(__snake_case      , return_tensors='''np'''   ).input_values
         for enc_seq_a, enc_seq_a in zip(__snake_case      , __snake_case   ):
              self.assertTrue(np.allclose(__snake_case      , __snake_case      , atol=1E-3   )   )
    def 		a_      (	self   ):
         snake_case					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
         snake_case					=    [floats_list((1, x)   )[0] for x in range(8_0_0      , 1_4_0_0      , 2_0_0   )]

         snake_case					=    ['''longest''', '''max_length''', '''do_not_pad''']
         snake_case					=    [None, 1_6_0_0, None]
         for max_length, padding in zip(__snake_case      , __snake_case   ):
              snake_case					=    feat_extract(__snake_case      , padding=__snake_case      , max_length=__snake_case      , return_tensors='''np'''   )
              snake_case					=    processed.input_values

              self._check_zero_mean_unit_variance(input_values[0][:8_0_0]   )
              self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6   )
              self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]   )
              self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6   )
              self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]   )
    def 		a_      (	self   ):
         snake_case					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
         snake_case					=    range(8_0_0      , 1_4_0_0      , 2_0_0   )
         snake_case					=    [floats_list((1, x)   )[0] for x in lengths]

         snake_case					=    ['''longest''', '''max_length''', '''do_not_pad''']
         snake_case					=    [None, 1_6_0_0, None]

         for max_length, padding in zip(__snake_case      , __snake_case   ):
              snake_case					=    feat_extract(__snake_case      , max_length=__snake_case      , padding=__snake_case   )
              snake_case					=    processed.input_values

              self._check_zero_mean_unit_variance(input_values[0][:8_0_0]   )
              self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]   )
              self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]   )
    def 		a_      (	self   ):
         snake_case					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
         snake_case					=    [floats_list((1, x)   )[0] for x in range(8_0_0      , 1_4_0_0      , 2_0_0   )]
         snake_case					=    feat_extract(
             __snake_case      , truncation=__snake_case      , max_length=1_0_0_0      , padding='''max_length'''      , return_tensors='''np'''   )
         snake_case					=    processed.input_values

         self._check_zero_mean_unit_variance(input_values[0, :8_0_0]   )
         self._check_zero_mean_unit_variance(input_values[1]   )
         self._check_zero_mean_unit_variance(input_values[2]   )
    def 		a_      (	self   ):
         snake_case					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
         snake_case					=    [floats_list((1, x)   )[0] for x in range(8_0_0      , 1_4_0_0      , 2_0_0   )]
         snake_case					=    feat_extract(
             __snake_case      , truncation=__snake_case      , max_length=1_0_0_0      , padding='''longest'''      , return_tensors='''np'''   )
         snake_case					=    processed.input_values

         self._check_zero_mean_unit_variance(input_values[0, :8_0_0]   )
         self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]   )
         self._check_zero_mean_unit_variance(input_values[2]   )

         # make sure that if max_length < longest -> then pad to max_length
         self.assertTrue(input_values.shape == (3, 1_0_0_0)   )

         snake_case					=    [floats_list((1, x)   )[0] for x in range(8_0_0      , 1_4_0_0      , 2_0_0   )]
         snake_case					=    feat_extract(
             __snake_case      , truncation=__snake_case      , max_length=2_0_0_0      , padding='''longest'''      , return_tensors='''np'''   )
         snake_case					=    processed.input_values

         self._check_zero_mean_unit_variance(input_values[0, :8_0_0]   )
         self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]   )
         self._check_zero_mean_unit_variance(input_values[2]   )

         # make sure that if max_length > longest -> then pad to longest
         self.assertTrue(input_values.shape == (3, 1_2_0_0)   )
    @require_torch
    def 		a_      (	self   ):
         import torch

         snake_case					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
         snake_case					=    np.random.rand(1_0_0   ).astype(np.floataa   )
         snake_case					=    np_speech_inputs.tolist()

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




    @slow
    @require_torch
    def 		a_      (	self   ):
         # this test makes sure that models that are using
         # group norm don't have their feature extractor return the
         # attention_mask
         for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
              snake_case					=    WavaVecaConfig.from_pretrained(__snake_case   )
              snake_case					=    WavaVecaFeatureExtractor.from_pretrained(__snake_case   )

              # only "layer" feature extraction norm should make use of
              # attention_mask
              self.assertEqual(feat_extract.return_attention_mask      , config.feat_extract_norm == '''layer'''   )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE      :			int      				=   {
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE      :			Dict      				=   [
     "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TimesformerModel",
     "TimesformerForVideoClassification",
     "TimesformerPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_timesformer import (
      TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TimesformerForVideoClassification,
      TimesformerModel,
      TimesformerPreTrainedModel,
  )

else:
 import sys

 _SCREAMING_SNAKE_CASE      :			int      				=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




