'''simple docstring'''





import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
    from transformers import WhisperFeatureExtractor

if is_torch_available():
    import torch

SCREAMING_SNAKE_CASE_:					Tuple  =random.Random()





def      lowerCAmelCase_    ( snake_case_	:			str ,   snake_case_	:			int=1.0 ,   snake_case_	:			List[Any]=None ,   snake_case_	:			List[Any]=None      )   ->				Tuple:


     '''simple docstring'''




     if rng is None:
          UpperCAmelCase_	   =       global_rng

     UpperCAmelCase_	   =       []
     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   :   Optional[Any]       ,      __a   :   List[Any]       ,      __a   :   Any=7       ,      __a   :   Optional[Any]=400       ,      __a   :   Union[str, Any]=2000       ,      __a   :   Any=10       ,      __a   :   List[Any]=160       ,      __a   :   int=8       ,      __a   :   Any=0.0       ,      __a   :   Tuple=4000       ,      __a   :   Tuple=False       ,      __a   :   Tuple=True       ,      ):
         UpperCAmelCase_	   =       parent
         UpperCAmelCase_	   =       batch_size
         UpperCAmelCase_	   =       min_seq_length
         UpperCAmelCase_	   =       max_seq_length
         UpperCAmelCase_	   =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         UpperCAmelCase_	   =       padding_value
         UpperCAmelCase_	   =       sampling_rate
         UpperCAmelCase_	   =       return_attention_mask
         UpperCAmelCase_	   =       do_normalize
         UpperCAmelCase_	   =       feature_size
         UpperCAmelCase_	   =       chunk_length
         UpperCAmelCase_	   =       hop_length



    def  _lowercase						(self   :   Any			):
         return {
             "feature_size": self.feature_size,
             "hop_length": self.hop_length,
             "chunk_length": self.chunk_length,
             "padding_value": self.padding_value,
             "sampling_rate": self.sampling_rate,
             "return_attention_mask": self.return_attention_mask,
             "do_normalize": self.do_normalize,
         }






    def  _lowercase						(self   :   List[str]       ,      __a   :   int=False       ,      __a   :   str=False			):
         def _flatten(__a   :   Dict			):
              return list(itertools.chain(*__a			)			)

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




@require_torch
@require_torchaudio
class 	__A				(			UpperCamelCase__		,     unittest.TestCase     ):
    a__	:						Dict	       =  WhisperFeatureExtractor if is_speech_available() else None



    def  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_	   =       WhisperFeatureExtractionTester(self			)



    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_	   =       self.feature_extraction_class(**self.feat_extract_dict			)

         with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCAmelCase_	   =       feat_extract_first.save_pretrained(__a			)[0]
              check_json_file_has_correct_format(__a			)
              UpperCAmelCase_	   =       self.feature_extraction_class.from_pretrained(__a			)

         UpperCAmelCase_	   =       feat_extract_first.to_dict()
         UpperCAmelCase_	   =       feat_extract_second.to_dict()
         UpperCAmelCase_	   =       feat_extract_first.mel_filters
         UpperCAmelCase_	   =       feat_extract_second.mel_filters
         self.assertTrue(np.allclose(__a       ,      __a			)			)
         self.assertEqual(__a       ,      __a			)



    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       self.feature_extraction_class(**self.feat_extract_dict			)

         with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCAmelCase_	   =       os.path.join(__a       ,      "feat_extract.json"			)
              feat_extract_first.to_json_file(__a			)
              UpperCAmelCase_	   =       self.feature_extraction_class.from_json_file(__a			)

         UpperCAmelCase_	   =       feat_extract_first.to_dict()
         UpperCAmelCase_	   =       feat_extract_second.to_dict()
         UpperCAmelCase_	   =       feat_extract_first.mel_filters
         UpperCAmelCase_	   =       feat_extract_second.mel_filters
         self.assertTrue(np.allclose(__a       ,      __a			)			)
         self.assertEqual(__a       ,      __a			)



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

         # Test feature size
         UpperCAmelCase_	   =       feature_extractor(__a       ,      padding="max_length"       ,      return_tensors="np"			).input_features
         self.assertTrue(input_features.ndim == 3			)
         self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames			)
         self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size			)

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

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

         # Test 2-D numpy arrays are batched.
         UpperCAmelCase_	   =       [floats_list((1, x)			)[0] for x in (800, 800, 800)]
         UpperCAmelCase_	   =       np.asarray(__a			)
         UpperCAmelCase_	   =       feature_extractor(__a       ,      return_tensors="np"			).input_features
         UpperCAmelCase_	   =       feature_extractor(__a       ,      return_tensors="np"			).input_features
         for enc_seq_a, enc_seq_a in zip(__a       ,      __a			):
              self.assertTrue(np.allclose(__a       ,      __a       ,      atol=1E-3			)			)

         # Test truncation required
         UpperCAmelCase_	   =       [floats_list((1, x)			)[0] for x in range(200       ,      (feature_extractor.n_samples + 500)       ,      200			)]
         UpperCAmelCase_	   =       [np.asarray(__a			) for speech_input in speech_inputs]

         UpperCAmelCase_	   =       [x[: feature_extractor.n_samples] for x in speech_inputs]
         UpperCAmelCase_	   =       [np.asarray(__a			) for speech_input in speech_inputs_truncated]

         UpperCAmelCase_	   =       feature_extractor(__a       ,      return_tensors="np"			).input_features
         UpperCAmelCase_	   =       feature_extractor(__a       ,      return_tensors="np"			).input_features
         for enc_seq_a, enc_seq_a in zip(__a       ,      __a			):
              self.assertTrue(np.allclose(__a       ,      __a       ,      atol=1E-3			)			)



    def  _lowercase						(self   :   str			):
         import torch

         UpperCAmelCase_	   =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
         UpperCAmelCase_	   =       np.random.rand(100       ,      32			).astype(np.floataa			)
         UpperCAmelCase_	   =       np_speech_inputs.tolist()

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



    def  _lowercase						(self   :   Tuple       ,      __a   :   Optional[int]			):
         UpperCAmelCase_	   =       load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,      "clean"       ,      split="validation"			)
         # automatic decoding with librispeech
         UpperCAmelCase_	   =       ds.sort("id"			).select(range(__a			)			)[:num_samples]["audio"]

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



    def  _lowercase						(self   :   int			):
         # fmt: off
         UpperCAmelCase_	   =       torch.tensor(
             [
                 0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
                 0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
                 0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
                 -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
             ]			)
         # fmt: on

         UpperCAmelCase_	   =       self._load_datasamples(1			)
         UpperCAmelCase_	   =       WhisperFeatureExtractor()
         UpperCAmelCase_	   =       feature_extractor(__a       ,      return_tensors="pt"			).input_features
         self.assertEqual(input_features.shape       ,      (1, 80, 3000)			)
         self.assertTrue(torch.allclose(input_features[0, 0, :30]       ,      __a       ,      atol=1E-4			)			)






    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
         UpperCAmelCase_	   =       self._load_datasamples(1			)[0]
         UpperCAmelCase_	   =       ((audio - audio.min()) / (audio.max() - audio.min())) * 65535  # Rescale to [0, 65535] to show issue
         UpperCAmelCase_	   =       feat_extract.zero_mean_unit_var_norm([audio]       ,      attention_mask=__a			)[0]

         self.assertTrue(np.all(np.mean(__a			) < 1E-3			)			)
         self.assertTrue(np.all(np.abs(np.var(__a			) - 1			) < 1E-3			)			)





'''simple docstring'''





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_:					int  ={
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					List[str]  =['MaskFormerFeatureExtractor']
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =['MaskFormerImageProcessor']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					Dict  =[
        'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MaskFormerForInstanceSegmentation',
        'MaskFormerModel',
        'MaskFormerPreTrainedModel',
    ]
    SCREAMING_SNAKE_CASE_:					List[str]  =[
        'MaskFormerSwinBackbone',
        'MaskFormerSwinModel',
        'MaskFormerSwinPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .configuration_maskformer_swin import MaskFormerSwinConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_maskformer import MaskFormerFeatureExtractor
        from .image_processing_maskformer import MaskFormerImageProcessor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .modeling_maskformer_swin import (
            MaskFormerSwinBackbone,
            MaskFormerSwinModel,
            MaskFormerSwinPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_:					Dict  =_LazyModule(__name__, globals()['__file__'], _import_structure)




