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 :	Any							     =		random.Random()



def   __A		(			_A							,				_A=1.0							,				_A=None							,				_A=None					):



 """simple docstring"""


 if rng is None:
  __a       =  global_rng

 __a       =  []
 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 ):

  def __init__(		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=7     ,							__SCREAMING_SNAKE_CASE :      int=4_00     ,							__SCREAMING_SNAKE_CASE :      str=20_00     ,							__SCREAMING_SNAKE_CASE :      Dict=1     ,							__SCREAMING_SNAKE_CASE :      Tuple=0.0     ,							__SCREAMING_SNAKE_CASE :      Dict=1_60_00     ,							__SCREAMING_SNAKE_CASE :      str=True     ,							__SCREAMING_SNAKE_CASE :      List[Any]=True     ,							):
   __a       =  parent
   __a       =  batch_size
   __a       =  min_seq_length
   __a       =  max_seq_length
   __a       =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   __a       =  feature_size
   __a       =  padding_value
   __a       =  sampling_rate
   __a       =  return_attention_mask
   __a       =  do_normalize

  def 		_UpperCAmelCase   (		self :      Dict       ):
   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 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=False     ,							__SCREAMING_SNAKE_CASE :      List[str]=False       ):
   def _flatten(__SCREAMING_SNAKE_CASE :      Optional[Any]       ):
    return list(itertools.chain(*__SCREAMING_SNAKE_CASE       )       )

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

   return speech_inputs
class    A_ (       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			WavaVecaFeatureExtractor

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  WavaVecaFeatureExtractionTester(self       )

  def 		_UpperCAmelCase   (		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   self.assertTrue(np.all(np.mean(__SCREAMING_SNAKE_CASE     ,							axis=0       ) < 1E-3       )       )
   self.assertTrue(np.all(np.abs(np.var(__SCREAMING_SNAKE_CASE     ,							axis=0       ) - 1       ) < 1E-3       )       )

  def 		_UpperCAmelCase   (		self :      int       ):
   # Tests that all call wrap to encode_plus and batch_encode_plus
   __a       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
   # create three inputs of length 800, 1000, and 1200
   __a       =  [floats_list((1, x)       )[0] for x in range(8_00     ,							14_00     ,							2_00       )]
   __a       =  [np.asarray(__SCREAMING_SNAKE_CASE       ) for speech_input in speech_inputs]

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

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

   # Test 2-D numpy arrays are batched.
   __a       =  [floats_list((1, x)       )[0] for x in (8_00, 8_00, 8_00)]
   __a       =  np.asarray(__SCREAMING_SNAKE_CASE       )
   __a       =  feat_extract(__SCREAMING_SNAKE_CASE     ,							return_tensors="np"       ).input_values
   __a       =  feat_extract(__SCREAMING_SNAKE_CASE     ,							return_tensors="np"       ).input_values
   for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
    self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-3       )       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
   __a       =  [floats_list((1, x)       )[0] for x in range(8_00     ,							14_00     ,							2_00       )]

   __a       =  ["longest", "max_length", "do_not_pad"]
   __a       =  [None, 16_00, None]
   for max_length, padding in zip(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
    __a       =  feat_extract(__SCREAMING_SNAKE_CASE     ,							padding=__SCREAMING_SNAKE_CASE     ,							max_length=__SCREAMING_SNAKE_CASE     ,							return_tensors="np"       )
    __a       =  processed.input_values

    self._check_zero_mean_unit_variance(input_values[0][:8_00]       )
    self.assertTrue(input_values[0][8_00:].sum() < 1E-6       )
    self._check_zero_mean_unit_variance(input_values[1][:10_00]       )
    self.assertTrue(input_values[0][10_00:].sum() < 1E-6       )
    self._check_zero_mean_unit_variance(input_values[2][:12_00]       )

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
   __a       =  range(8_00     ,							14_00     ,							2_00       )
   __a       =  [floats_list((1, x)       )[0] for x in lengths]

   __a       =  ["longest", "max_length", "do_not_pad"]
   __a       =  [None, 16_00, None]

   for max_length, padding in zip(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
    __a       =  feat_extract(__SCREAMING_SNAKE_CASE     ,							max_length=__SCREAMING_SNAKE_CASE     ,							padding=__SCREAMING_SNAKE_CASE       )
    __a       =  processed.input_values

    self._check_zero_mean_unit_variance(input_values[0][:8_00]       )
    self._check_zero_mean_unit_variance(input_values[1][:10_00]       )
    self._check_zero_mean_unit_variance(input_values[2][:12_00]       )

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
   __a       =  [floats_list((1, x)       )[0] for x in range(8_00     ,							14_00     ,							2_00       )]
   __a       =  feat_extract(
       __SCREAMING_SNAKE_CASE     ,							truncation=__SCREAMING_SNAKE_CASE     ,							max_length=10_00     ,							padding="max_length"     ,							return_tensors="np"       )
   __a       =  processed.input_values

   self._check_zero_mean_unit_variance(input_values[0, :8_00]       )
   self._check_zero_mean_unit_variance(input_values[1]       )
   self._check_zero_mean_unit_variance(input_values[2]       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
   __a       =  [floats_list((1, x)       )[0] for x in range(8_00     ,							14_00     ,							2_00       )]
   __a       =  feat_extract(
       __SCREAMING_SNAKE_CASE     ,							truncation=__SCREAMING_SNAKE_CASE     ,							max_length=10_00     ,							padding="longest"     ,							return_tensors="np"       )
   __a       =  processed.input_values

   self._check_zero_mean_unit_variance(input_values[0, :8_00]       )
   self._check_zero_mean_unit_variance(input_values[1, :10_00]       )
   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, 10_00)       )

   __a       =  [floats_list((1, x)       )[0] for x in range(8_00     ,							14_00     ,							2_00       )]
   __a       =  feat_extract(
       __SCREAMING_SNAKE_CASE     ,							truncation=__SCREAMING_SNAKE_CASE     ,							max_length=20_00     ,							padding="longest"     ,							return_tensors="np"       )
   __a       =  processed.input_values

   self._check_zero_mean_unit_variance(input_values[0, :8_00]       )
   self._check_zero_mean_unit_variance(input_values[1, :10_00]       )
   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, 12_00)       )

  @require_torch
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   import torch

   __a       =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
   __a       =  np.random.rand(1_00       ).astype(np.floataa       )
   __a       =  np_speech_inputs.tolist()

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

  @slow
  @require_torch
  def 		_UpperCAmelCase   (		self :      int       ):
   # 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:
    __a       =  WavaVecaConfig.from_pretrained(__SCREAMING_SNAKE_CASE       )
    __a       =  WavaVecaFeatureExtractor.from_pretrained(__SCREAMING_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 ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
  from .pipeline_unclip import UnCLIPPipeline
  from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
  from .text_proj import UnCLIPTextProjModel


