



'''simple docstring'''





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


__a          =		random.Random()





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase=1.0     ,   _lowerCAmelCase=None     ,   _lowerCAmelCase=None		)		-> Dict:
      if rng is None:
            snake_case__  :    Optional[int]    						=      global_rng

      snake_case__  :    int    						=      []
      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					:     str ,     snake_case_					:     Dict ,     snake_case_					:     Optional[int]=7 ,     snake_case_					:     Optional[int]=400 ,     snake_case_					:     Optional[Any]=2_000 ,     snake_case_					:     List[Any]=1 ,     snake_case_					:     List[str]=0.0 ,     snake_case_					:     Union[str, Any]=16_000 ,     snake_case_					:     Union[str, Any]=True ,     snake_case_					:     Union[str, Any]=True ,     ):
          snake_case__  :    Optional[Any]    						=      parent
          snake_case__  :    Union[str, Any]    						=      batch_size
          snake_case__  :    List[str]    						=      min_seq_length
          snake_case__  :    Dict    						=      max_seq_length
          snake_case__  :    List[str]    						=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          snake_case__  :    Dict    						=      feature_size
          snake_case__  :    int    						=      padding_value
          snake_case__  :    Any    						=      sampling_rate
          snake_case__  :    str    						=      return_attention_mask
          snake_case__  :    Optional[Any]    						=      do_normalize




    def lowerCamelCase       (    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 lowerCamelCase       (    self					:     int ,     snake_case_					:     str=False ,     snake_case_					:     Any=False						):
          def _flatten(snake_case_					:     Optional[int]						):
                return list(itertools.chain(*snake_case_						)						)

          if equal_length:
                snake_case__  :    Optional[Any]    						=      floats_list((self.batch_size, self.max_seq_length)						)
          else:
                # make sure that inputs increase in size
                snake_case__  :    Optional[Any]    						=      [
                    _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__  :    List[Any]    						=      [np.asarray(snake_case_						) for x in speech_inputs]

          return speech_inputs







class     UpperCAmelCase_       (       _a		,		unittest.TestCase	):
    """simple docstring"""







    lowercase					=		WavaVecaFeatureExtractor




    def lowerCamelCase       (    self					:     Tuple						):
          snake_case__  :    Any    						=      WavaVecaFeatureExtractionTester(self						)




    def lowerCamelCase       (    self					:     int ,     snake_case_					:     Union[str, Any]						):
          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 lowerCamelCase       (    self					:     List[Any]						):
          # Tests that all call wrap to encode_plus and batch_encode_plus
          snake_case__  :    Dict    						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
          # create three inputs of length 800, 1000, and 1200
          snake_case__  :    Union[str, Any]    						=      [floats_list((1, x)						)[0] for x in range(800 ,     1_400 ,     200						)]
          snake_case__  :    int    						=      [np.asarray(snake_case_						) for speech_input in speech_inputs]

          # Test not batched input
          snake_case__  :    str    						=      feat_extract(speech_inputs[0] ,     return_tensors="""np"""						).input_values
          snake_case__  :    str    						=      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__  :    Optional[int]    						=      feat_extract(snake_case_ ,     return_tensors="""np"""						).input_values
          snake_case__  :    Any    						=      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__  :    Optional[Any]    						=      [floats_list((1, x)						)[0] for x in (800, 800, 800)]
          snake_case__  :    Union[str, Any]    						=      np.asarray(snake_case_						)
          snake_case__  :    Union[str, Any]    						=      feat_extract(snake_case_ ,     return_tensors="""np"""						).input_values
          snake_case__  :    Tuple    						=      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 lowerCamelCase       (    self					:     Union[str, Any]						):
          snake_case__  :    str    						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
          snake_case__  :    Optional[Any]    						=      [floats_list((1, x)						)[0] for x in range(800 ,     1_400 ,     200						)]

          snake_case__  :    str    						=      ["""longest""", """max_length""", """do_not_pad"""]
          snake_case__  :    str    						=      [None, 1_600, None]
          for max_length, padding in zip(snake_case_ ,     snake_case_						):
                snake_case__  :    Any    						=      feat_extract(snake_case_ ,     padding=snake_case_ ,     max_length=snake_case_ ,     return_tensors="""np"""						)
                snake_case__  :    List[str]    						=      processed.input_values

                self._check_zero_mean_unit_variance(input_values[0][:800]						)
                self.assertTrue(input_values[0][800:].sum() < 1E-6						)
                self._check_zero_mean_unit_variance(input_values[1][:1_000]						)
                self.assertTrue(input_values[0][1_000:].sum() < 1E-6						)
                self._check_zero_mean_unit_variance(input_values[2][:1_200]						)




    def lowerCamelCase       (    self					:     Any						):
          snake_case__  :    List[str]    						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
          snake_case__  :    str    						=      range(800 ,     1_400 ,     200						)
          snake_case__  :    Optional[int]    						=      [floats_list((1, x)						)[0] for x in lengths]

          snake_case__  :    Optional[Any]    						=      ["""longest""", """max_length""", """do_not_pad"""]
          snake_case__  :    Optional[Any]    						=      [None, 1_600, None]

          for max_length, padding in zip(snake_case_ ,     snake_case_						):
                snake_case__  :    int    						=      feat_extract(snake_case_ ,     max_length=snake_case_ ,     padding=snake_case_						)
                snake_case__  :    List[str]    						=      processed.input_values

                self._check_zero_mean_unit_variance(input_values[0][:800]						)
                self._check_zero_mean_unit_variance(input_values[1][:1_000]						)
                self._check_zero_mean_unit_variance(input_values[2][:1_200]						)




    def lowerCamelCase       (    self					:     Tuple						):
          snake_case__  :    Optional[int]    						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
          snake_case__  :    Tuple    						=      [floats_list((1, x)						)[0] for x in range(800 ,     1_400 ,     200						)]
          snake_case__  :    Any    						=      feat_extract(
              snake_case_ ,     truncation=snake_case_ ,     max_length=1_000 ,     padding="""max_length""" ,     return_tensors="""np"""						)
          snake_case__  :    Optional[int]    						=      processed.input_values

          self._check_zero_mean_unit_variance(input_values[0, :800]						)
          self._check_zero_mean_unit_variance(input_values[1]						)
          self._check_zero_mean_unit_variance(input_values[2]						)




    def lowerCamelCase       (    self					:     Any						):
          snake_case__  :    List[Any]    						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
          snake_case__  :    Optional[int]    						=      [floats_list((1, x)						)[0] for x in range(800 ,     1_400 ,     200						)]
          snake_case__  :    List[str]    						=      feat_extract(
              snake_case_ ,     truncation=snake_case_ ,     max_length=1_000 ,     padding="""longest""" ,     return_tensors="""np"""						)
          snake_case__  :    Tuple    						=      processed.input_values

          self._check_zero_mean_unit_variance(input_values[0, :800]						)
          self._check_zero_mean_unit_variance(input_values[1, :1_000]						)
          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_000)						)

          snake_case__  :    Optional[int]    						=      [floats_list((1, x)						)[0] for x in range(800 ,     1_400 ,     200						)]
          snake_case__  :    Dict    						=      feat_extract(
              snake_case_ ,     truncation=snake_case_ ,     max_length=2_000 ,     padding="""longest""" ,     return_tensors="""np"""						)
          snake_case__  :    Union[str, Any]    						=      processed.input_values

          self._check_zero_mean_unit_variance(input_values[0, :800]						)
          self._check_zero_mean_unit_variance(input_values[1, :1_000]						)
          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_200)						)




    @require_torch
    def lowerCamelCase       (    self					:     List[Any]						):
          import torch

          snake_case__  :    int    						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
          snake_case__  :    List[str]    						=      np.random.rand(100						).astype(np.floataa						)
          snake_case__  :    Dict    						=      np_speech_inputs.tolist()

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




    @slow
    @require_torch
    def lowerCamelCase       (    self					:     Any						):
          # 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__  :    List[str]    						=      WavaVecaConfig.from_pretrained(snake_case_						)
                snake_case__  :    List[str]    						=      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"""						)





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a          =		{
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		["BloomTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		[
           "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BloomForCausalLM",
           "BloomModel",
           "BloomPreTrainedModel",
           "BloomForSequenceClassification",
           "BloomForTokenClassification",
           "BloomForQuestionAnswering",
       ]

if TYPE_CHECKING:
       from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bloom_fast import BloomTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bloom import (
                  BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BloomForCausalLM,
                  BloomForQuestionAnswering,
                  BloomForSequenceClassification,
                  BloomForTokenClassification,
                  BloomModel,
                  BloomPreTrainedModel,
              )

else:
       import sys

       __a          =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
