





import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
   import torch


A__:    List[Any]  	=	random.Random()



def 				lowerCAmelCase_ (							A_    ,A_=1.0    ,A_=None    ,A_=None):
    if rng is None:
        UpperCamelCase__:	Any					=     global_rng

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

    return values




@require_torch
class        _a    (    unittest.TestCase):

      """simple docstring"""


      def __init__(		self:			str	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			List[Any]=7	,    __lowerCamelCase:			Optional[int]=400	,    __lowerCamelCase:			Dict=2000	,    __lowerCamelCase:			Any=1	,    __lowerCamelCase:			Dict=0.0	,    __lowerCamelCase:			Tuple=1_6000	,    __lowerCamelCase:			Optional[int]=True	,    __lowerCamelCase:			List[Any]=80	,    __lowerCamelCase:			List[Any]=16	,    __lowerCamelCase:			List[str]=64	,    __lowerCamelCase:			Dict="hann_window"	,    __lowerCamelCase:			Tuple=80	,    __lowerCamelCase:			Dict=7600	,    __lowerCamelCase:			List[Any]=1e-10	,    __lowerCamelCase:			Optional[int]=True	,    ):
          '''simple docstring'''


          UpperCamelCase__:	Union[str, Any]					=     parent
          UpperCamelCase__:	Optional[Any]					=     batch_size
          UpperCamelCase__:	Dict					=     min_seq_length
          UpperCamelCase__:	Optional[int]					=     max_seq_length
          UpperCamelCase__:	str					=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          UpperCamelCase__:	Union[str, Any]					=     feature_size
          UpperCamelCase__:	List[str]					=     padding_value
          UpperCamelCase__:	List[str]					=     sampling_rate
          UpperCamelCase__:	List[str]					=     do_normalize
          UpperCamelCase__:	Optional[int]					=     num_mel_bins
          UpperCamelCase__:	Optional[Any]					=     hop_length
          UpperCamelCase__:	str					=     win_length
          UpperCamelCase__:	Dict					=     win_function
          UpperCamelCase__:	Dict					=     fmin
          UpperCamelCase__:	Union[str, Any]					=     fmax
          UpperCamelCase__:	List[str]					=     mel_floor
          UpperCamelCase__:	Tuple					=     return_attention_mask






      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          return {
              "feature_size": self.feature_size,
              "padding_value": self.padding_value,
              "sampling_rate": self.sampling_rate,
              "do_normalize": self.do_normalize,
              "num_mel_bins": self.num_mel_bins,
              "hop_length": self.hop_length,
              "win_length": self.win_length,
              "win_function": self.win_function,
              "fmin": self.fmin,
              "fmax": self.fmax,
              "mel_floor": self.mel_floor,
              "return_attention_mask": self.return_attention_mask,
          }






      def 			UpperCAmelCase_       (		self:			Any	,    __lowerCamelCase:			str=False	,    __lowerCamelCase:			Tuple=False					):
          '''simple docstring'''


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

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

          return speech_inputs






      def 			UpperCAmelCase_       (		self:			Union[str, Any]	,    __lowerCamelCase:			Optional[int]=False	,    __lowerCamelCase:			Dict=False					):
          '''simple docstring'''


          if equal_length:
              UpperCamelCase__:	str					=     [floats_list((self.max_seq_length, self.num_mel_bins)					) for _ in range(self.batch_size					)]
          else:
              # make sure that inputs increase in size
              UpperCamelCase__:	str					=     [
                  floats_list((x, self.num_mel_bins)					)
                  for x in range(self.min_seq_length	,    self.max_seq_length	,    self.seq_length_diff					)
              ]

          if numpify:
              UpperCamelCase__:	List[Any]					=     [np.asarray(__lowerCamelCase					) for x in speech_inputs]

          return speech_inputs




@require_torch
class        _a    (    UpperCamelCase__						,			unittest.TestCase):

      """simple docstring"""


      UpperCamelCase__  		=       SpeechTaFeatureExtractor






      def 			UpperCAmelCase_       (		self:			List[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     SpeechTaFeatureExtractionTester(self					)






      def 			UpperCAmelCase_       (		self:			int	,    __lowerCamelCase:			int					):
          '''simple docstring'''


          self.assertTrue(np.all(np.mean(__lowerCamelCase	,    axis=0					) < 1e-3					)					)
          self.assertTrue(np.all(np.abs(np.var(__lowerCamelCase	,    axis=0					) - 1					) < 1e-3					)					)






      def 			UpperCAmelCase_       (		self:			Optional[int]					):
          '''simple docstring'''


          UpperCamelCase__:	Any					=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
          # create three inputs of length 800, 1000, and 1200
          UpperCamelCase__:	Dict					=     [floats_list((1, x)					)[0] for x in range(800	,    1400	,    200					)]
          UpperCamelCase__:	Union[str, Any]					=     [np.asarray(__lowerCamelCase					) for speech_input in speech_inputs]

          # Test not batched input
          UpperCamelCase__:	int					=     feat_extract(speech_inputs[0]	,    return_tensors="np"					).input_values
          UpperCamelCase__:	List[Any]					=     feat_extract(np_speech_inputs[0]	,    return_tensors="np"					).input_values
          self.assertTrue(np.allclose(__lowerCamelCase	,    __lowerCamelCase	,    atol=1e-3					)					)

          # Test batched
          UpperCamelCase__:	Tuple					=     feat_extract(__lowerCamelCase	,    return_tensors="np"					).input_values
          UpperCamelCase__:	Tuple					=     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					)					)






      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Union[str, Any]					=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
          UpperCamelCase__:	Any					=     [floats_list((1, x)					)[0] for x in range(800	,    1400	,    200					)]

          UpperCamelCase__:	Union[str, Any]					=     ["longest", "max_length", "do_not_pad"]
          UpperCamelCase__:	str					=     [None, 1600, None]
          for max_length, padding in zip(__lowerCamelCase	,    __lowerCamelCase					):
              UpperCamelCase__:	Union[str, Any]					=     feat_extract(__lowerCamelCase	,    padding=__lowerCamelCase	,    max_length=__lowerCamelCase	,    return_tensors="np"					)
              UpperCamelCase__:	int					=     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][:1000]					)
              self.assertTrue(input_values[0][1000:].sum() < 1e-6					)
              self._check_zero_mean_unit_variance(input_values[2][:1200]					)






      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
          UpperCamelCase__:	Optional[Any]					=     range(800	,    1400	,    200					)
          UpperCamelCase__:	Union[str, Any]					=     [floats_list((1, x)					)[0] for x in lengths]

          UpperCamelCase__:	int					=     ["longest", "max_length", "do_not_pad"]
          UpperCamelCase__:	List[Any]					=     [None, 1600, None]

          for max_length, padding in zip(__lowerCamelCase	,    __lowerCamelCase					):
              UpperCamelCase__:	Dict					=     feat_extract(__lowerCamelCase	,    max_length=__lowerCamelCase	,    padding=__lowerCamelCase					)
              UpperCamelCase__:	Dict					=     processed.input_values

              self._check_zero_mean_unit_variance(input_values[0][:800]					)
              self._check_zero_mean_unit_variance(input_values[1][:1000]					)
              self._check_zero_mean_unit_variance(input_values[2][:1200]					)






      def 			UpperCAmelCase_       (		self:			Optional[int]					):
          '''simple docstring'''


          UpperCamelCase__:	Union[str, Any]					=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
          UpperCamelCase__:	Union[str, Any]					=     [floats_list((1, x)					)[0] for x in range(800	,    1400	,    200					)]
          UpperCamelCase__:	str					=     feat_extract(
              __lowerCamelCase	,    truncation=__lowerCamelCase	,    max_length=1000	,    padding="max_length"	,    return_tensors="np"					)
          UpperCamelCase__:	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 			UpperCAmelCase_       (		self:			List[str]					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
          UpperCamelCase__:	Union[str, Any]					=     [floats_list((1, x)					)[0] for x in range(800	,    1400	,    200					)]
          UpperCamelCase__:	int					=     feat_extract(
              __lowerCamelCase	,    truncation=__lowerCamelCase	,    max_length=1000	,    padding="longest"	,    return_tensors="np"					)
          UpperCamelCase__:	List[Any]					=     processed.input_values

          self._check_zero_mean_unit_variance(input_values[0, :800]					)
          self._check_zero_mean_unit_variance(input_values[1, :1000]					)
          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, 1000)					)

          UpperCamelCase__:	List[str]					=     [floats_list((1, x)					)[0] for x in range(800	,    1400	,    200					)]
          UpperCamelCase__:	List[Any]					=     feat_extract(
              __lowerCamelCase	,    truncation=__lowerCamelCase	,    max_length=2000	,    padding="longest"	,    return_tensors="np"					)
          UpperCamelCase__:	str					=     processed.input_values

          self._check_zero_mean_unit_variance(input_values[0, :800]					)
          self._check_zero_mean_unit_variance(input_values[1, :1000]					)
          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, 1200)					)






      def 			UpperCAmelCase_       (		self:			Tuple					):
          '''simple docstring'''


          UpperCamelCase__:	Union[str, Any]					=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
          UpperCamelCase__:	Union[str, Any]					=     np.random.rand(100					).astype(np.floataa					)
          UpperCamelCase__:	List[str]					=     np_speech_inputs.tolist()

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






      def 			UpperCAmelCase_       (		self:			List[str]					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
          # create three inputs of length 800, 1000, and 1200
          UpperCamelCase__:	int					=     [floats_list((1, x)					)[0] for x in range(800	,    1400	,    200					)]
          UpperCamelCase__:	List[str]					=     [np.asarray(__lowerCamelCase					) for speech_input in speech_inputs]

          # Test feature size
          UpperCamelCase__:	Optional[int]					=     feature_extractor(audio_target=__lowerCamelCase	,    padding=__lowerCamelCase	,    return_tensors="np"					).input_values
          self.assertTrue(input_values.ndim == 3					)
          self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins					)

          # Test not batched input
          UpperCamelCase__:	Optional[int]					=     feature_extractor(speech_inputs[0]	,    return_tensors="np"					).input_values
          UpperCamelCase__:	Optional[Any]					=     feature_extractor(np_speech_inputs[0]	,    return_tensors="np"					).input_values
          self.assertTrue(np.allclose(__lowerCamelCase	,    __lowerCamelCase	,    atol=1e-3					)					)

          # Test batched
          UpperCamelCase__:	Dict					=     feature_extractor(__lowerCamelCase	,    return_tensors="np"					).input_values
          UpperCamelCase__:	Tuple					=     feature_extractor(__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.
          UpperCamelCase__:	int					=     [floats_list((1, x)					)[0] for x in (800, 800, 800)]
          UpperCamelCase__:	Dict					=     np.asarray(__lowerCamelCase					)
          UpperCamelCase__:	Union[str, Any]					=     feature_extractor(__lowerCamelCase	,    return_tensors="np"					).input_values
          UpperCamelCase__:	Union[str, Any]					=     feature_extractor(__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					)					)






      def 			UpperCAmelCase_       (		self:			Optional[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Any					=     self.feat_extract_tester.prepare_inputs_for_target()
          UpperCamelCase__:	Union[str, Any]					=     self.feature_extraction_class(**self.feat_extract_dict					)
          UpperCamelCase__:	Any					=     feat_extract.model_input_names[0]

          UpperCamelCase__:	List[str]					=     BatchFeature({input_name: speech_inputs}					)

          self.assertTrue(all(len(__lowerCamelCase					) == len(__lowerCamelCase					) for x, y in zip(__lowerCamelCase	,    processed_features[input_name]					)					)					)

          UpperCamelCase__:	Optional[int]					=     self.feat_extract_tester.prepare_inputs_for_target(equal_length=__lowerCamelCase					)
          UpperCamelCase__:	Dict					=     BatchFeature({input_name: speech_inputs}	,    tensor_type="np"					)

          UpperCamelCase__:	Any					=     processed_features[input_name]

          if len(batch_features_input.shape					) < 3:
              UpperCamelCase__:	Optional[int]					=     batch_features_input[:, :, None]

          self.assertTrue(
              batch_features_input.shape
              == (self.feat_extract_tester.batch_size, len(speech_inputs[0]					), self.feat_extract_tester.num_mel_bins)					)






      @require_torch
      def 			UpperCAmelCase_       (		self:			Optional[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Union[str, Any]					=     self.feat_extract_tester.prepare_inputs_for_target(equal_length=__lowerCamelCase					)
          UpperCamelCase__:	str					=     self.feature_extraction_class(**self.feat_extract_dict					)
          UpperCamelCase__:	Union[str, Any]					=     feat_extract.model_input_names[0]

          UpperCamelCase__:	List[str]					=     BatchFeature({input_name: speech_inputs}	,    tensor_type="pt"					)

          UpperCamelCase__:	Dict					=     processed_features[input_name]

          if len(batch_features_input.shape					) < 3:
              UpperCamelCase__:	Optional[Any]					=     batch_features_input[:, :, None]

          self.assertTrue(
              batch_features_input.shape
              == (self.feat_extract_tester.batch_size, len(speech_inputs[0]					), self.feat_extract_tester.num_mel_bins)					)






      @require_torch
      def 			UpperCAmelCase_       (		self:			List[str]					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     self.feature_extraction_class(**self.feat_extract_dict					)
          UpperCamelCase__:	List[Any]					=     self.feat_extract_tester.prepare_inputs_for_target()
          UpperCamelCase__:	Union[str, Any]					=     feat_extract.model_input_names[0]

          UpperCamelCase__:	Union[str, Any]					=     BatchFeature({input_name: speech_inputs}					)

          UpperCamelCase__:	List[str]					=     feat_extract.num_mel_bins  # hack!

          UpperCamelCase__:	Dict					=     feat_extract.pad(__lowerCamelCase	,    padding="longest"	,    return_tensors="np"					)[input_name]
          UpperCamelCase__:	Optional[int]					=     feat_extract.pad(__lowerCamelCase	,    padding="longest"	,    return_tensors="pt"					)[input_name]

          self.assertTrue(abs(input_np.astype(np.floataa					).sum() - input_pt.numpy().astype(np.floataa					).sum()					) < 1e-2					)






      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	str					=     self.feat_extract_dict
          UpperCamelCase__:	List[str]					=     True
          UpperCamelCase__:	Optional[Any]					=     self.feature_extraction_class(**__lowerCamelCase					)
          UpperCamelCase__:	int					=     self.feat_extract_tester.prepare_inputs_for_target()
          UpperCamelCase__:	Union[str, Any]					=     [len(__lowerCamelCase					) for x in speech_inputs]
          UpperCamelCase__:	Any					=     feat_extract.model_input_names[0]

          UpperCamelCase__:	Tuple					=     BatchFeature({input_name: speech_inputs}					)

          UpperCamelCase__:	str					=     feat_extract.num_mel_bins  # hack!

          UpperCamelCase__:	Optional[Any]					=     feat_extract.pad(__lowerCamelCase	,    padding="longest"	,    return_tensors="np"					)
          self.assertIn("attention_mask"	,    __lowerCamelCase					)
          self.assertListEqual(list(processed.attention_mask.shape					)	,    list(processed[input_name].shape[:2]					)					)
          self.assertListEqual(processed.attention_mask.sum(-1					).tolist()	,    __lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     self.feat_extract_dict
          UpperCamelCase__:	Optional[int]					=     True
          UpperCamelCase__:	Tuple					=     self.feature_extraction_class(**__lowerCamelCase					)
          UpperCamelCase__:	Dict					=     self.feat_extract_tester.prepare_inputs_for_target()
          UpperCamelCase__:	Union[str, Any]					=     [len(__lowerCamelCase					) for x in speech_inputs]
          UpperCamelCase__:	Any					=     feat_extract.model_input_names[0]

          UpperCamelCase__:	int					=     BatchFeature({input_name: speech_inputs}					)
          UpperCamelCase__:	List[str]					=     min(__lowerCamelCase					)

          UpperCamelCase__:	List[str]					=     feat_extract.num_mel_bins  # hack!

          UpperCamelCase__:	Union[str, Any]					=     feat_extract.pad(
              __lowerCamelCase	,    padding="max_length"	,    max_length=__lowerCamelCase	,    truncation=__lowerCamelCase	,    return_tensors="np"					)
          self.assertIn("attention_mask"	,    __lowerCamelCase					)
          self.assertListEqual(
              list(processed_pad.attention_mask.shape					)	,    [processed_pad[input_name].shape[0], max_length]					)
          self.assertListEqual(
              processed_pad.attention_mask[:, :max_length].sum(-1					).tolist()	,    [max_length for x in speech_inputs]					)






      def 			UpperCAmelCase_       (		self:			Dict	,    __lowerCamelCase:			List[Any]					):
          '''simple docstring'''


          from datasets import load_dataset

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

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






      def 			UpperCAmelCase_       (		self:			str					):
          '''simple docstring'''


          UpperCamelCase__:	List[Any]					=     torch.tensor(
              [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
               3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
               2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
               4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
               7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
               4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03]					)
          # fmt: on

          UpperCamelCase__:	List[str]					=     self._load_datasamples(1					)
          UpperCamelCase__:	Dict					=     SpeechTaFeatureExtractor()
          UpperCamelCase__:	Tuple					=     feature_extractor(__lowerCamelCase	,    return_tensors="pt"					).input_values
          self.assertEquals(input_values.shape	,    (1, 9_3680)					)
          self.assertTrue(torch.allclose(input_values[0, :30]	,    __lowerCamelCase	,    atol=1e-6					)					)






      def 			UpperCAmelCase_       (		self:			Optional[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	int					=     torch.tensor(
              [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,
               -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,
               -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,
               -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998]					)
          # fmt: on

          UpperCamelCase__:	Dict					=     self._load_datasamples(1					)
          UpperCamelCase__:	List[Any]					=     SpeechTaFeatureExtractor()
          UpperCamelCase__:	Tuple					=     feature_extractor(audio_target=__lowerCamelCase	,    return_tensors="pt"					).input_values
          self.assertEquals(input_values.shape	,    (1, 366, 80)					)
          self.assertTrue(torch.allclose(input_values[0, 0, :30]	,    __lowerCamelCase	,    atol=1e-4					)					)








from typing import TYPE_CHECKING

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


A__:    Any  	=	{
    '''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
    '''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Union[str, Any]  	=	[
       '''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''GPTNeoXJapaneseForCausalLM''',
       '''GPTNeoXJapaneseLayer''',
       '''GPTNeoXJapaneseModel''',
       '''GPTNeoXJapanesePreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
   from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_gpt_neox_japanese import (
          GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
          GPTNeoXJapaneseForCausalLM,
          GPTNeoXJapaneseLayer,
          GPTNeoXJapaneseModel,
          GPTNeoXJapanesePreTrainedModel,
      )


else:
   import sys

   A__:    str  	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

