'''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


_lowercase		:				Optional[int]	       =		random.Random()




def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      int=1.0   ,   UpperCAmelCase__ :      List[str]=None   ,   UpperCAmelCase__ :      List[Any]=None				)    ->   List[str]:
       if rng is None:
              lowercase_      :    str       		= global_rng

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

       return values





class 					__magic_name__		(					unittest.TestCase):







   def __init__(			self       :						int       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						int=7       ,    lowercase_       :						List[Any]=400       ,    lowercase_       :						List[Any]=2000       ,    lowercase_       :						Union[str, Any]=1       ,    lowercase_       :						Any=0.0       ,    lowercase_       :						Any=16000       ,    lowercase_       :						Optional[Any]=True       ,    lowercase_       :						Tuple=True       ,    ):
          lowercase_      :    List[str]       		= parent
          lowercase_      :    int       		= batch_size
          lowercase_      :    str       		= min_seq_length
          lowercase_      :    int       		= max_seq_length
          lowercase_      :    Dict       		= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          lowercase_      :    str       		= feature_size
          lowercase_      :    Optional[Any]       		= padding_value
          lowercase_      :    Dict       		= sampling_rate
          lowercase_      :    List[str]       		= return_attention_mask
          lowercase_      :    str       		= do_normalize







   def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
          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 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						List[Any]=False       ,    lowercase_       :						str=False					):
          def _flatten(lowercase_       :						str					):
                 return list(itertools.chain(*lowercase_					)					)

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

          return speech_inputs





class 					__magic_name__		(					_UpperCAmelCase,  unittest.TestCase):
   UpperCamelCase__       = WavaVecaFeatureExtractor







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
          lowercase_      :    Tuple       		= WavaVecaFeatureExtractionTester(self					)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						List[str]					):
          self.assertTrue(np.all(np.mean(lowercase_       ,    axis=0					) < 1E-3					)					)
          self.assertTrue(np.all(np.abs(np.var(lowercase_       ,    axis=0					) - 1					) < 1E-3					)					)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
          # Tests that all call wrap to encode_plus and batch_encode_plus
          lowercase_      :    int       		= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
          # create three inputs of length 800, 1000, and 1200
          lowercase_      :    List[Any]       		= [floats_list((1, x)					)[0] for x in range(800       ,    1400       ,    200					)]
          lowercase_      :    Dict       		= [np.asarray(lowercase_					) for speech_input in speech_inputs]

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

          # Test batched
          lowercase_      :    List[str]       		= feat_extract(lowercase_       ,    return_tensors="""np"""					).input_values
          lowercase_      :    Union[str, Any]       		= feat_extract(lowercase_       ,    return_tensors="""np"""					).input_values
          for enc_seq_a, enc_seq_a in zip(lowercase_       ,    lowercase_					):
                 self.assertTrue(np.allclose(lowercase_       ,    lowercase_       ,    atol=1E-3					)					)

          # Test 2-D numpy arrays are batched.
          lowercase_      :    Union[str, Any]       		= [floats_list((1, x)					)[0] for x in (800, 800, 800)]
          lowercase_      :    str       		= np.asarray(lowercase_					)
          lowercase_      :    Any       		= feat_extract(lowercase_       ,    return_tensors="""np"""					).input_values
          lowercase_      :    Any       		= feat_extract(lowercase_       ,    return_tensors="""np"""					).input_values
          for enc_seq_a, enc_seq_a in zip(lowercase_       ,    lowercase_					):
                 self.assertTrue(np.allclose(lowercase_       ,    lowercase_       ,    atol=1E-3					)					)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
          lowercase_      :    List[Any]       		= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
          lowercase_      :    Any       		= [floats_list((1, x)					)[0] for x in range(800       ,    1400       ,    200					)]

          lowercase_      :    List[str]       		= ["""longest""", """max_length""", """do_not_pad"""]
          lowercase_      :    Optional[int]       		= [None, 1600, None]
          for max_length, padding in zip(lowercase_       ,    lowercase_					):
                 lowercase_      :    Union[str, Any]       		= feat_extract(lowercase_       ,    padding=lowercase_       ,    max_length=lowercase_       ,    return_tensors="""np"""					)
                 lowercase_      :    Optional[Any]       		= 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 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
          lowercase_      :    Optional[Any]       		= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
          lowercase_      :    List[str]       		= range(800       ,    1400       ,    200					)
          lowercase_      :    Any       		= [floats_list((1, x)					)[0] for x in lengths]

          lowercase_      :    List[str]       		= ["""longest""", """max_length""", """do_not_pad"""]
          lowercase_      :    Union[str, Any]       		= [None, 1600, None]

          for max_length, padding in zip(lowercase_       ,    lowercase_					):
                 lowercase_      :    Optional[Any]       		= feat_extract(lowercase_       ,    max_length=lowercase_       ,    padding=lowercase_					)
                 lowercase_      :    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][:1000]					)
                 self._check_zero_mean_unit_variance(input_values[2][:1200]					)







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

          lowercase_      :    Union[str, Any]       		= [floats_list((1, x)					)[0] for x in range(800       ,    1400       ,    200					)]
          lowercase_      :    Optional[int]       		= feat_extract(
              lowercase_       ,    truncation=lowercase_       ,    max_length=2000       ,    padding="""longest"""       ,    return_tensors="""np"""					)
          lowercase_      :    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]					)

          # make sure that if max_length > longest -> then pad to longest
          self.assertTrue(input_values.shape == (3, 1200)					)







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

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

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







   @slow
   @require_torch
   def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, 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:
                 lowercase_      :    Any       		= WavaVecaConfig.from_pretrained(lowercase_					)
                 lowercase_      :    Dict       		= WavaVecaFeatureExtractor.from_pretrained(lowercase_					)

                 # 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_torch_available,
)


_lowercase		:				Optional[Any]	       =		{
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Any	       =		[
						    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SwiftFormerForImageClassification",
						    "SwiftFormerModel",
						    "SwiftFormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

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





