
import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
      from transformers import SpeechaTextFeatureExtractor

_lowerCamelCase     =random.Random()





def        _a   (		lowerCamelCase,      lowerCamelCase=1.0,      lowerCamelCase=None,      lowerCamelCase=None				):




 if rng is None:
  lowerCamelCase						:      Union[str, Any]							     = global_rng

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

 return values





@require_torch
@require_torchaudio
class 				A__						(      unittest.TestCase):
      def __init__( self       ,    __magic_name__       ,    __magic_name__=7       ,    __magic_name__=4_0_0       ,    __magic_name__=2_0_0_0       ,    __magic_name__=2_4       ,    __magic_name__=2_4       ,    __magic_name__=0.0       ,    __magic_name__=1_6_0_0_0       ,    __magic_name__=True       ,    __magic_name__=True       ,    ):
       lowerCamelCase						:      List[Any]							     = parent
       lowerCamelCase						:      str							     = batch_size
       lowerCamelCase						:      int							     = min_seq_length
       lowerCamelCase						:      Any							     = max_seq_length
       lowerCamelCase						:      Union[str, Any]							     = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       lowerCamelCase						:      List[Any]							     = feature_size
       lowerCamelCase						:      Tuple							     = num_mel_bins
       lowerCamelCase						:      Optional[Any]							     = padding_value
       lowerCamelCase						:      Optional[Any]							     = sampling_rate
       lowerCamelCase						:      Optional[Any]							     = return_attention_mask
       lowerCamelCase						:      List[Any]							     = do_normalize
      def 		UpperCamelCase__				( self ):
       return {
           "feature_size": self.feature_size,
           "num_mel_bins": self.num_mel_bins,
           "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       ,    __magic_name__=False       ,    __magic_name__=False ):







       def _flatten(__magic_name__ ):
        return list(itertools.chain(*__magic_name__ ) )

       if equal_length:
        lowerCamelCase						:      Tuple							     = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
       else:
        # make sure that inputs increase in size
        lowerCamelCase						:      Any							     = [
            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						:      str							     = [np.asarray(__magic_name__ ) for x in speech_inputs]
       return speech_inputs





@require_torch
@require_torchaudio
class 				A__						(      a_    ,  unittest.TestCase):
      _UpperCAmelCase							:       str       			=     SpeechaTextFeatureExtractor if is_speech_available() else None
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      Any							     = SpeechaTextFeatureExtractionTester(self )
      def 		UpperCamelCase__				( self       ,    __magic_name__ ):
       self.assertTrue(np.all(np.mean(__magic_name__       ,    axis=0 ) < 1e-3 ) )
       self.assertTrue(np.all(np.abs(np.var(__magic_name__       ,    axis=0 ) - 1 ) < 1e-3 ) )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      Tuple							     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       # create three inputs of length 800, 1000, and 1200
       lowerCamelCase						:      Tuple							     = [floats_list((1, x) )[0] for x in range(8_0_0       ,    1_4_0_0       ,    2_0_0 )]
       lowerCamelCase						:      List[str]							     = [np.asarray(__magic_name__ ) for speech_input in speech_inputs]

       # Test feature size
       lowerCamelCase						:      int							     = feature_extractor(__magic_name__       ,    padding=__magic_name__       ,    return_tensors="""np""" ).input_features
       self.assertTrue(input_features.ndim == 3 )
       self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )

       # Test not batched input
       lowerCamelCase						:      str							     = feature_extractor(speech_inputs[0]       ,    return_tensors="""np""" ).input_features
       lowerCamelCase						:      Dict							     = feature_extractor(np_speech_inputs[0]       ,    return_tensors="""np""" ).input_features
       self.assertTrue(np.allclose(__magic_name__       ,    __magic_name__       ,    atol=1e-3 ) )

       # Test batched
       lowerCamelCase						:      int							     = feature_extractor(__magic_name__       ,    return_tensors="""np""" ).input_features
       lowerCamelCase						:      Optional[Any]							     = feature_extractor(__magic_name__       ,    return_tensors="""np""" ).input_features
       for enc_seq_a, enc_seq_a in zip(__magic_name__       ,    __magic_name__ ):
        self.assertTrue(np.allclose(__magic_name__       ,    __magic_name__       ,    atol=1e-3 ) )

       # Test 2-D numpy arrays are batched.
       lowerCamelCase						:      str							     = [floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
       lowerCamelCase						:      List[Any]							     = np.asarray(__magic_name__ )
       lowerCamelCase						:      Tuple							     = feature_extractor(__magic_name__       ,    return_tensors="""np""" ).input_features
       lowerCamelCase						:      str							     = feature_extractor(__magic_name__       ,    return_tensors="""np""" ).input_features
       for enc_seq_a, enc_seq_a in zip(__magic_name__       ,    __magic_name__ ):
        self.assertTrue(np.allclose(__magic_name__       ,    __magic_name__       ,    atol=1e-3 ) )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      str							     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       lowerCamelCase						:      List[Any]							     = [floats_list((1, x) )[0] for x in range(8_0_0       ,    1_4_0_0       ,    2_0_0 )]

       lowerCamelCase						:      Tuple							     = ["""longest""", """max_length""", """do_not_pad"""]
       lowerCamelCase						:      Dict							     = [None, 1_6, None]
       for max_length, padding in zip(__magic_name__       ,    __magic_name__ ):
        lowerCamelCase						:      int							     = feature_extractor(
            __magic_name__       ,    padding=__magic_name__       ,    max_length=__magic_name__       ,    return_attention_mask=__magic_name__ )
        lowerCamelCase						:      List[Any]							     = inputs.input_features
        lowerCamelCase						:      List[Any]							     = inputs.attention_mask
        lowerCamelCase						:      Optional[int]							     = [np.sum(__magic_name__ ) for x in attention_mask]

        self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
        self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
        self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      str							     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       lowerCamelCase						:      List[str]							     = [floats_list((1, x) )[0] for x in range(8_0_0       ,    1_4_0_0       ,    2_0_0 )]

       lowerCamelCase						:      Dict							     = ["""longest""", """max_length""", """do_not_pad"""]
       lowerCamelCase						:      Optional[int]							     = [None, 1_6, None]
       for max_length, padding in zip(__magic_name__       ,    __magic_name__ ):
        lowerCamelCase						:      Optional[int]							     = feature_extractor(
            __magic_name__       ,    max_length=__magic_name__       ,    padding=__magic_name__       ,    return_tensors="""np"""       ,    return_attention_mask=__magic_name__ )
        lowerCamelCase						:      Dict							     = inputs.input_features
        lowerCamelCase						:      int							     = inputs.attention_mask
        lowerCamelCase						:      List[str]							     = [np.sum(__magic_name__ ) for x in attention_mask]

        self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
        self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6 )
        self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
        self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6 )
        self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      Any							     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       lowerCamelCase						:      int							     = [floats_list((1, x) )[0] for x in range(8_0_0       ,    1_4_0_0       ,    2_0_0 )]
       lowerCamelCase						:      Tuple							     = feature_extractor(
           __magic_name__       ,    padding="""max_length"""       ,    max_length=4       ,    truncation=__magic_name__       ,    return_tensors="""np"""       ,    return_attention_mask=__magic_name__       ,    )
       lowerCamelCase						:      List[str]							     = inputs.input_features
       lowerCamelCase						:      Optional[Any]							     = inputs.attention_mask
       lowerCamelCase						:      int							     = np.sum(attention_mask == 1       ,    axis=1 )

       self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
       self._check_zero_mean_unit_variance(input_features[1] )
       self._check_zero_mean_unit_variance(input_features[2] )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      List[Any]							     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       lowerCamelCase						:      Any							     = [floats_list((1, x) )[0] for x in range(8_0_0       ,    1_4_0_0       ,    2_0_0 )]
       lowerCamelCase						:      Optional[Any]							     = feature_extractor(
           __magic_name__       ,    padding="""longest"""       ,    max_length=4       ,    truncation=__magic_name__       ,    return_tensors="""np"""       ,    return_attention_mask=__magic_name__       ,    )
       lowerCamelCase						:      Any							     = inputs.input_features
       lowerCamelCase						:      int							     = inputs.attention_mask
       lowerCamelCase						:      List[Any]							     = np.sum(attention_mask == 1       ,    axis=1 )

       self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
       self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
       self._check_zero_mean_unit_variance(input_features[2] )

       # make sure that if max_length < longest -> then pad to max_length
       self.assertEqual(input_features.shape       ,    (3, 4, 2_4) )

       lowerCamelCase						:      Optional[int]							     = [floats_list((1, x) )[0] for x in range(8_0_0       ,    1_4_0_0       ,    2_0_0 )]
       lowerCamelCase						:      Optional[Any]							     = feature_extractor(
           __magic_name__       ,    padding="""longest"""       ,    max_length=1_6       ,    truncation=__magic_name__       ,    return_tensors="""np"""       ,    return_attention_mask=__magic_name__       ,    )
       lowerCamelCase						:      Optional[int]							     = inputs.input_features
       lowerCamelCase						:      Any							     = inputs.attention_mask
       lowerCamelCase						:      Optional[int]							     = np.sum(attention_mask == 1       ,    axis=1 )

       self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
       self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
       self._check_zero_mean_unit_variance(input_features[2] )

       # make sure that if max_length < longest -> then pad to max_length
       self.assertEqual(input_features.shape       ,    (3, 6, 2_4) )
      def 		UpperCamelCase__				( self ):
       import torch

       lowerCamelCase						:      Optional[Any]							     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       lowerCamelCase						:      str							     = np.random.rand(1_0_0       ,    3_2 ).astype(np.floataa )
       lowerCamelCase						:      List[Any]							     = np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
        lowerCamelCase						:      Dict							     = feature_extractor.pad([{"""input_features""": inputs}]       ,    return_tensors="""np""" )
        self.assertTrue(np_processed.input_features.dtype == np.floataa )
        lowerCamelCase						:      Dict							     = feature_extractor.pad([{"""input_features""": inputs}]       ,    return_tensors="""pt""" )
        self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
      def 		UpperCamelCase__				( self       ,    __magic_name__ ):
       from datasets import load_dataset

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

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






      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      int							     = np.array([
           -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
           -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
           -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
       ] )
       # fmt: on

       lowerCamelCase						:      List[Any]							     = self._load_datasamples(1 )
       lowerCamelCase						:      str							     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       lowerCamelCase						:      str							     = feature_extractor(__magic_name__       ,    return_tensors="""pt""" ).input_features
       self.assertEquals(input_features.shape       ,    (1, 5_8_4, 2_4) )
       self.assertTrue(np.allclose(input_features[0, 0, :3_0]       ,    __magic_name__       ,    atol=1e-4 ) )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase   :    Tuple     =		{
    "configuration_blenderbot_small": [
        "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotSmallConfig",
        "BlenderbotSmallOnnxConfig",
    ],
    "tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[Any]     =		["BlenderbotSmallTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[str]     =		[
		    "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "BlenderbotSmallForCausalLM",
		    "BlenderbotSmallForConditionalGeneration",
		    "BlenderbotSmallModel",
		    "BlenderbotSmallPreTrainedModel",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    Any     =		[
		    "TFBlenderbotSmallForConditionalGeneration",
		    "TFBlenderbotSmallModel",
		    "TFBlenderbotSmallPreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[Any]     =		[
		    "FlaxBlenderbotSmallForConditionalGeneration",
		    "FlaxBlenderbotSmallModel",
		    "FlaxBlenderbotSmallPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_blenderbot_small import (
		    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlenderbotSmallConfig,
		    BlenderbotSmallOnnxConfig,
		)
		from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blenderbot_small import (
				    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlenderbotSmallForCausalLM,
				    BlenderbotSmallForConditionalGeneration,
				    BlenderbotSmallModel,
				    BlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blenderbot_small import (
				    TFBlenderbotSmallForConditionalGeneration,
				    TFBlenderbotSmallModel,
				    TFBlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_blenderbot_small import (
				    FlaxBlenderbotSmallForConditionalGeneration,
				    FlaxBlenderbotSmallModel,
				    FlaxBlenderbotSmallPreTrainedModel,
				)

else:
		import sys

		UpperCAmelCase   :    List[Any]     =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

