

'''simple docstring'''

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


lowercase__         =      random.Random()




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_=1.0  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_=None     ):
 if rng is None:
  UpperCAmelCase	:     Union[str, Any]						 =			global_rng

 UpperCAmelCase	:     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




@require_torch
class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def __init__(		self			:					List[str]					,      lowercase_			:					Dict					,      lowercase_			:					List[Any]=7					,      lowercase_			:					Union[str, Any]=400					,      lowercase_			:					List[Any]=2_000					,      lowercase_			:					int=1					,      lowercase_			:					Optional[Any]=0.0					,      lowercase_			:					List[Any]=16_000					,      lowercase_			:					int=True					,      lowercase_			:					List[Any]=80					,      lowercase_			:					Tuple=16					,      lowercase_			:					Optional[Any]=64					,      lowercase_			:					int="hann_window"					,      lowercase_			:					str=80					,      lowercase_			:					List[Any]=7_600					,      lowercase_			:					Optional[Any]=1E-10					,      lowercase_			:					Optional[Any]=True					,      )						->     Tuple:
      UpperCAmelCase	:     Optional[Any]						 =			parent
      UpperCAmelCase	:     Optional[Any]						 =			batch_size
      UpperCAmelCase	:     str						 =			min_seq_length
      UpperCAmelCase	:     Dict						 =			max_seq_length
      UpperCAmelCase	:     Dict						 =			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      UpperCAmelCase	:     Any						 =			feature_size
      UpperCAmelCase	:     List[Any]						 =			padding_value
      UpperCAmelCase	:     str						 =			sampling_rate
      UpperCAmelCase	:     Optional[int]						 =			do_normalize
      UpperCAmelCase	:     Dict						 =			num_mel_bins
      UpperCAmelCase	:     List[str]						 =			hop_length
      UpperCAmelCase	:     List[Any]						 =			win_length
      UpperCAmelCase	:     Union[str, Any]						 =			win_function
      UpperCAmelCase	:     Tuple						 =			fmin
      UpperCAmelCase	:     Union[str, Any]						 =			fmax
      UpperCAmelCase	:     Union[str, Any]						 =			mel_floor
      UpperCAmelCase	:     Tuple						 =			return_attention_mask


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Union[str, Any]:
      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			:					Optional[Any]					,      lowercase_			:					int=False					,      lowercase_			:					Dict=False	)						->     int:
      def _flatten(lowercase_			:					Optional[Any]	):
       return list(itertools.chain(*lowercase_	)	)

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

      return speech_inputs




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Optional[Any]=False					,      lowercase_			:					Optional[Any]=False	)						->     Optional[int]:
      if equal_length:
       UpperCAmelCase	:     List[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	:     List[Any]						 =			[
           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(lowercase_	) for x in speech_inputs]

      return speech_inputs




@require_torch
class 	A_			(    _snake_case	,       unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:     Tuple  		=  SpeechTaFeatureExtractor


     def        UpperCAmelCase_	(		self			:					Tuple	)						->     List[Any]:
      UpperCAmelCase	:     List[str]						 =			SpeechTaFeatureExtractionTester(self	)


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					Union[str, Any]	)						->     Union[str, Any]:
      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        UpperCAmelCase_	(		self			:					str	)						->     Dict:
      # Tests that all call wrap to encode_plus and batch_encode_plus
      UpperCAmelCase	:     Optional[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					,      1_400					,      200	)]
      UpperCAmelCase	:     Optional[int]						 =			[np.asarray(lowercase_	) for speech_input in speech_inputs]

      # Test not batched input
      UpperCAmelCase	:     Optional[int]						 =			feat_extract(speech_inputs[0]					,      return_tensors='np'	).input_values
      UpperCAmelCase	:     Any						 =			feat_extract(np_speech_inputs[0]					,      return_tensors='np'	).input_values
      self.assertTrue(np.allclose(lowercase_					,      lowercase_					,      atol=1E-3	)	)

      # Test batched
      UpperCAmelCase	:     List[str]						 =			feat_extract(lowercase_					,      return_tensors='np'	).input_values
      UpperCAmelCase	:     Optional[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        UpperCAmelCase_	(		self			:					str	)						->     List[Any]:
      UpperCAmelCase	:     Union[str, Any]						 =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
      UpperCAmelCase	:     List[str]						 =			[floats_list((1, x)	)[0] for x in range(800					,      1_400					,      200	)]

      UpperCAmelCase	:     Any						 =			['longest', 'max_length', 'do_not_pad']
      UpperCAmelCase	:     Dict						 =			[None, 1_600, None]
      for max_length, padding in zip(lowercase_					,      lowercase_	):
       UpperCAmelCase	:     Union[str, Any]						 =			feat_extract(lowercase_					,      padding=lowercase_					,      max_length=lowercase_					,      return_tensors='np'	)
       UpperCAmelCase	:     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        UpperCAmelCase_	(		self			:					str	)						->     Tuple:
      UpperCAmelCase	:     Dict						 =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
      UpperCAmelCase	:     Dict						 =			range(800					,      1_400					,      200	)
      UpperCAmelCase	:     str						 =			[floats_list((1, x)	)[0] for x in lengths]

      UpperCAmelCase	:     int						 =			['longest', 'max_length', 'do_not_pad']
      UpperCAmelCase	:     Any						 =			[None, 1_600, None]

      for max_length, padding in zip(lowercase_					,      lowercase_	):
       UpperCAmelCase	:     Tuple						 =			feat_extract(lowercase_					,      max_length=lowercase_					,      padding=lowercase_	)
       UpperCAmelCase	:     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][:1_200]	)


     def        UpperCAmelCase_	(		self			:					Tuple	)						->     str:
      UpperCAmelCase	:     Any						 =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
      UpperCAmelCase	:     int						 =			[floats_list((1, x)	)[0] for x in range(800					,      1_400					,      200	)]
      UpperCAmelCase	:     Any						 =			feat_extract(
          lowercase_					,      truncation=lowercase_					,      max_length=1_000					,      padding='max_length'					,      return_tensors='np'	)
      UpperCAmelCase	:     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]	)
      self._check_zero_mean_unit_variance(input_values[2]	)


     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Any:
      UpperCAmelCase	:     str						 =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
      UpperCAmelCase	:     Tuple						 =			[floats_list((1, x)	)[0] for x in range(800					,      1_400					,      200	)]
      UpperCAmelCase	:     Tuple						 =			feat_extract(
          lowercase_					,      truncation=lowercase_					,      max_length=1_000					,      padding='longest'					,      return_tensors='np'	)
      UpperCAmelCase	:     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 max_length
      self.assertTrue(input_values.shape == (3, 1_000)	)

      UpperCAmelCase	:     Any						 =			[floats_list((1, x)	)[0] for x in range(800					,      1_400					,      200	)]
      UpperCAmelCase	:     Union[str, Any]						 =			feat_extract(
          lowercase_					,      truncation=lowercase_					,      max_length=2_000					,      padding='longest'					,      return_tensors='np'	)
      UpperCAmelCase	:     Optional[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)	)


     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Optional[Any]:
      UpperCAmelCase	:     List[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	:     Dict						 =			feature_extractor.pad([{'input_values': inputs}]					,      return_tensors='np'	)
       self.assertTrue(np_processed.input_values.dtype == np.floataa	)
       UpperCAmelCase	:     List[str]						 =			feature_extractor.pad([{'input_values': inputs}]					,      return_tensors='pt'	)
       self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)


     def        UpperCAmelCase_	(		self			:					List[str]	)						->     List[str]:
      # Tests that all call wrap to encode_plus and batch_encode_plus
      UpperCAmelCase	:     Tuple						 =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
      # create three inputs of length 800, 1000, and 1200
      UpperCAmelCase	:     Optional[Any]						 =			[floats_list((1, x)	)[0] for x in range(800					,      1_400					,      200	)]
      UpperCAmelCase	:     List[str]						 =			[np.asarray(lowercase_	) for speech_input in speech_inputs]

      # Test feature size
      UpperCAmelCase	:     Optional[int]						 =			feature_extractor(audio_target=lowercase_					,      padding=lowercase_					,      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	:     Any						 =			feature_extractor(speech_inputs[0]					,      return_tensors='np'	).input_values
      UpperCAmelCase	:     List[Any]						 =			feature_extractor(np_speech_inputs[0]					,      return_tensors='np'	).input_values
      self.assertTrue(np.allclose(lowercase_					,      lowercase_					,      atol=1E-3	)	)

      # Test batched
      UpperCAmelCase	:     Tuple						 =			feature_extractor(lowercase_					,      return_tensors='np'	).input_values
      UpperCAmelCase	:     str						 =			feature_extractor(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.
      UpperCAmelCase	:     Any						 =			[floats_list((1, x)	)[0] for x in (800, 800, 800)]
      UpperCAmelCase	:     Tuple						 =			np.asarray(lowercase_	)
      UpperCAmelCase	:     List[Any]						 =			feature_extractor(lowercase_					,      return_tensors='np'	).input_values
      UpperCAmelCase	:     str						 =			feature_extractor(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        UpperCAmelCase_	(		self			:					Tuple	)						->     Tuple:
      UpperCAmelCase	:     Optional[Any]						 =			self.feat_extract_tester.prepare_inputs_for_target()
      UpperCAmelCase	:     Any						 =			self.feature_extraction_class(**self.feat_extract_dict	)
      UpperCAmelCase	:     Optional[int]						 =			feat_extract.model_input_names[0]

      UpperCAmelCase	:     int						 =			BatchFeature({input_name: speech_inputs}	)

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

      UpperCAmelCase	:     List[str]						 =			self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase_	)
      UpperCAmelCase	:     Tuple						 =			BatchFeature({input_name: speech_inputs}					,      tensor_type='np'	)

      UpperCAmelCase	:     Optional[Any]						 =			processed_features[input_name]

      if len(batch_features_input.shape	) < 3:
       UpperCAmelCase	:     Tuple						 =			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]	)						->     Union[str, Any]:
      UpperCAmelCase	:     Optional[int]						 =			self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase_	)
      UpperCAmelCase	:     Dict						 =			self.feature_extraction_class(**self.feat_extract_dict	)
      UpperCAmelCase	:     str						 =			feat_extract.model_input_names[0]

      UpperCAmelCase	:     Optional[int]						 =			BatchFeature({input_name: speech_inputs}					,      tensor_type='pt'	)

      UpperCAmelCase	:     Optional[Any]						 =			processed_features[input_name]

      if len(batch_features_input.shape	) < 3:
       UpperCAmelCase	:     List[str]						 =			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			:					str	)						->     List[str]:
      UpperCAmelCase	:     Tuple						 =			self.feature_extraction_class(**self.feat_extract_dict	)
      UpperCAmelCase	:     Dict						 =			self.feat_extract_tester.prepare_inputs_for_target()
      UpperCAmelCase	:     Any						 =			feat_extract.model_input_names[0]

      UpperCAmelCase	:     str						 =			BatchFeature({input_name: speech_inputs}	)

      UpperCAmelCase	:     Tuple						 =			feat_extract.num_mel_bins  # hack!

      UpperCAmelCase	:     Tuple						 =			feat_extract.pad(lowercase_					,      padding='longest'					,      return_tensors='np'	)[input_name]
      UpperCAmelCase	:     List[Any]						 =			feat_extract.pad(lowercase_					,      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			:					Tuple	)						->     Dict:
      UpperCAmelCase	:     str						 =			self.feat_extract_dict
      UpperCAmelCase	:     int						 =			True
      UpperCAmelCase	:     str						 =			self.feature_extraction_class(**lowercase_	)
      UpperCAmelCase	:     List[Any]						 =			self.feat_extract_tester.prepare_inputs_for_target()
      UpperCAmelCase	:     Any						 =			[len(lowercase_	) for x in speech_inputs]
      UpperCAmelCase	:     Optional[Any]						 =			feat_extract.model_input_names[0]

      UpperCAmelCase	:     Dict						 =			BatchFeature({input_name: speech_inputs}	)

      UpperCAmelCase	:     int						 =			feat_extract.num_mel_bins  # hack!

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


     def        UpperCAmelCase_	(		self			:					Dict	)						->     Optional[Any]:
      UpperCAmelCase	:     Union[str, Any]						 =			self.feat_extract_dict
      UpperCAmelCase	:     Dict						 =			True
      UpperCAmelCase	:     Union[str, Any]						 =			self.feature_extraction_class(**lowercase_	)
      UpperCAmelCase	:     Tuple						 =			self.feat_extract_tester.prepare_inputs_for_target()
      UpperCAmelCase	:     Union[str, Any]						 =			[len(lowercase_	) for x in speech_inputs]
      UpperCAmelCase	:     List[Any]						 =			feat_extract.model_input_names[0]

      UpperCAmelCase	:     Tuple						 =			BatchFeature({input_name: speech_inputs}	)
      UpperCAmelCase	:     int						 =			min(lowercase_	)

      UpperCAmelCase	:     Optional[Any]						 =			feat_extract.num_mel_bins  # hack!

      UpperCAmelCase	:     List[Any]						 =			feat_extract.pad(
          lowercase_					,      padding='max_length'					,      max_length=lowercase_					,      truncation=lowercase_					,      return_tensors='np'	)
      self.assertIn('attention_mask'					,      lowercase_	)
      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			:					Any					,      lowercase_			:					List[str]	)						->     int:
      from datasets import load_dataset

      UpperCAmelCase	:     Union[str, Any]						 =			load_dataset('hf-internal-testing/librispeech_asr_dummy'					,      'clean'					,      split='validation'	)
      # automatic decoding with librispeech
      UpperCAmelCase	:     Tuple						 =			ds.sort('id'	).select(range(lowercase_	)	)[:num_samples]['audio']

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


     def        UpperCAmelCase_	(		self			:					int	)						->     Union[str, Any]:
      # fmt: off
      UpperCAmelCase	:     Optional[Any]						 =			torch.tensor(
          [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
           3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
           2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
           4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
           7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
           4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]	)
      # fmt: on

      UpperCAmelCase	:     Tuple						 =			self._load_datasamples(1	)
      UpperCAmelCase	:     int						 =			SpeechTaFeatureExtractor()
      UpperCAmelCase	:     List[str]						 =			feature_extractor(lowercase_					,      return_tensors='pt'	).input_values
      self.assertEquals(input_values.shape					,      (1, 93_680)	)
      self.assertTrue(torch.allclose(input_values[0, :30]					,      lowercase_					,      atol=1E-6	)	)




     def        UpperCAmelCase_	(		self			:					int	)						->     Any:
      # fmt: off
      UpperCAmelCase	:     List[str]						 =			torch.tensor(
          [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
           -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
           -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
           -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]	)
      # fmt: on

      UpperCAmelCase	:     List[Any]						 =			self._load_datasamples(1	)
      UpperCAmelCase	:     int						 =			SpeechTaFeatureExtractor()
      UpperCAmelCase	:     List[Any]						 =			feature_extractor(audio_target=lowercase_					,      return_tensors='pt'	).input_values
      self.assertEquals(input_values.shape					,      (1, 366, 80)	)
      self.assertTrue(torch.allclose(input_values[0, 0, :30]					,      lowercase_					,      atol=1E-4	)	)







'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowercase__         =      logging.get_logger(__name__)




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[Any]						 =			b.T
 UpperCAmelCase	:     Optional[int]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=1     )
 UpperCAmelCase	:     List[Any]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=0     )
 UpperCAmelCase	:     List[str]						 =			np.matmul(UpperCAmelCase_  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     Union[str, Any]						 =			aa[:, None] - 2 * ab + ba[None, :]
 return d




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			x.reshape(-1  ,	3     )
 UpperCAmelCase	:     Optional[int]						 =			squared_euclidean_distance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return np.argmin(UpperCAmelCase_  ,	axis=1     )




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[Any]  		=  ["""pixel_values"""]


     def __init__(		self			:					List[Any]					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					bool = True					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					bool = True					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     None:
      super().__init__(**lowercase_	)
      UpperCAmelCase	:     Any						 =			size if size is not None else {'height': 256, 'width': 256}
      UpperCAmelCase	:     List[Any]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     str						 =			np.array(lowercase_	) if clusters is not None else None
      UpperCAmelCase	:     Any						 =			do_resize
      UpperCAmelCase	:     List[Any]						 =			size
      UpperCAmelCase	:     Any						 =			resample
      UpperCAmelCase	:     Dict						 =			do_normalize
      UpperCAmelCase	:     List[Any]						 =			do_color_quantize


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Any					,      )						->     np.ndarray:
      UpperCAmelCase	:     Dict						 =			get_size_dict(lowercase_	)
      if "height" not in size or "width" not in size:
       raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""	)
      return resize(
          lowercase_					,      size=(size['height'], size['width'])					,      resample=lowercase_					,      data_format=lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					np.ndarray					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      )						->     np.ndarray:
      UpperCAmelCase	:     int						 =			rescale(image=lowercase_					,      scale=1 / 127.5					,      data_format=lowercase_	)
      UpperCAmelCase	:     Dict						 =			image - 1
      return image




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					ImageInput					,      lowercase_			:					bool = None					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = None					,      lowercase_			:					bool = None					,      lowercase_			:					Optional[bool] = None					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					Optional[Union[str, TensorType]] = None					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					,      **lowercase_			:					List[str]					,      )						->     PIL.Image.Image:
      UpperCAmelCase	:     Optional[int]						 =			do_resize if do_resize is not None else self.do_resize
      UpperCAmelCase	:     Optional[Any]						 =			size if size is not None else self.size
      UpperCAmelCase	:     Optional[int]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     Any						 =			resample if resample is not None else self.resample
      UpperCAmelCase	:     Optional[Any]						 =			do_normalize if do_normalize is not None else self.do_normalize
      UpperCAmelCase	:     str						 =			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      UpperCAmelCase	:     Optional[int]						 =			clusters if clusters is not None else self.clusters
      UpperCAmelCase	:     List[str]						 =			np.array(lowercase_	)

      UpperCAmelCase	:     int						 =			make_list_of_images(lowercase_	)

      if not valid_images(lowercase_	):
       raise ValueError(
           'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
           'torch.Tensor, tf.Tensor or jax.ndarray.'	)

      if do_resize and size is None or resample is None:
       raise ValueError('Size and resample must be specified if do_resize is True.'	)

      if do_color_quantize and clusters is None:
       raise ValueError('Clusters must be specified if do_color_quantize is True.'	)

      # All transformations expect numpy arrays.
      UpperCAmelCase	:     Dict						 =			[to_numpy_array(lowercase_	) for image in images]

      if do_resize:
       UpperCAmelCase	:     List[Any]						 =			[self.resize(image=lowercase_					,      size=lowercase_					,      resample=lowercase_	) for image in images]

      if do_normalize:
       UpperCAmelCase	:     Tuple						 =			[self.normalize(image=lowercase_	) for image in images]

      if do_color_quantize:
       UpperCAmelCase	:     List[str]						 =			[to_channel_dimension_format(lowercase_					,      ChannelDimension.LAST	) for image in images]
       # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
       UpperCAmelCase	:     int						 =			np.array(lowercase_	)
       UpperCAmelCase	:     str						 =			color_quantize(lowercase_					,      lowercase_	).reshape(images.shape[:-1]	)

       # flatten to (batch_size, height*width)
       UpperCAmelCase	:     Optional[int]						 =			images.shape[0]
       UpperCAmelCase	:     Union[str, Any]						 =			images.reshape(lowercase_					,      -1	)

       # We need to convert back to a list of images to keep consistent behaviour across processors.
       UpperCAmelCase	:     int						 =			list(lowercase_	)
      else:
       UpperCAmelCase	:     Dict						 =			[to_channel_dimension_format(lowercase_					,      lowercase_	) for image in images]

      UpperCAmelCase	:     Any						 =			{'input_ids': images}
      return BatchFeature(data=lowercase_					,      tensor_type=lowercase_	)




