import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ImageGPTImageProcessor







class __snake_case    (						unittest.TestCase		):




   def __init__(   self      :    str	,     _lowercase      :    Optional[Any]	,     _lowercase      :    Any=7	,     _lowercase      :    List[Any]=3	,     _lowercase      :    Any=18	,     _lowercase      :    Union[str, Any]=30	,     _lowercase      :    Optional[int]=4_00	,     _lowercase      :    Any=True	,     _lowercase      :    List[str]=None	,     _lowercase      :    Optional[Any]=True	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				size if size is not None else {"""height""": 18, """width""": 18}
     SCREAMING_SNAKE_CASE__      							=				parent
     SCREAMING_SNAKE_CASE__      							=				batch_size
     SCREAMING_SNAKE_CASE__      							=				num_channels
     SCREAMING_SNAKE_CASE__      							=				image_size
     SCREAMING_SNAKE_CASE__      							=				min_resolution
     SCREAMING_SNAKE_CASE__      							=				max_resolution
     SCREAMING_SNAKE_CASE__      							=				do_resize
     SCREAMING_SNAKE_CASE__      							=				size
     SCREAMING_SNAKE_CASE__      							=				do_normalize




   def      __a						(   self      :    int				):



     """simple docstring"""






     return {
         # here we create 2 clusters for the sake of simplicity
         "clusters": np.asarray(
             [
                 [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
                 [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
             ]				),
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
     }







@require_torch
@require_vision
class __snake_case    (						lowerCamelCase_       ,		unittest.TestCase		):
   lowerCAmelCase_														=							ImageGPTImageProcessor if is_vision_available() else None




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				ImageGPTImageProcessingTester(self				)




   @property
   def      __a						(   self      :    int				):



     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()




   def      __a						(   self      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class(**self.image_processor_dict				)
     self.assertTrue(hasattr(_lowercase	,     """clusters"""				)				)
     self.assertTrue(hasattr(_lowercase	,     """do_resize"""				)				)
     self.assertTrue(hasattr(_lowercase	,     """size"""				)				)
     self.assertTrue(hasattr(_lowercase	,     """do_normalize"""				)				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class.from_dict(self.image_processor_dict				)
     self.assertEqual(image_processor.size	,     {"""height""": 18, """width""": 18}				)

     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class.from_dict(self.image_processor_dict	,     size=42				)
     self.assertEqual(image_processor.size	,     {"""height""": 42, """width""": 42}				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class(**self.image_processor_dict				)
     SCREAMING_SNAKE_CASE__      							=				json.loads(image_processor.to_json_string()				)
     for key, value in self.image_processor_dict.items():
       if key == "clusters":
         self.assertTrue(np.array_equal(_lowercase	,     obj[key]				)				)
       else:
         self.assertEqual(obj[key]	,     _lowercase				)




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class(**self.image_processor_dict				)

     with tempfile.TemporaryDirectory() as tmpdirname:
       SCREAMING_SNAKE_CASE__      							=				os.path.join(_lowercase	,     """image_processor.json"""				)
       image_processor_first.to_json_file(_lowercase				)
       SCREAMING_SNAKE_CASE__      							=				self.image_processing_class.from_json_file(_lowercase				).to_dict()

     SCREAMING_SNAKE_CASE__      							=				image_processor_first.to_dict()
     for key, value in image_processor_first.items():
       if key == "clusters":
         self.assertTrue(np.array_equal(_lowercase	,     image_processor_second[key]				)				)
       else:
         self.assertEqual(image_processor_first[key]	,     _lowercase				)




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class(**self.image_processor_dict				)

     with tempfile.TemporaryDirectory() as tmpdirname:
       image_processor_first.save_pretrained(_lowercase				)
       SCREAMING_SNAKE_CASE__      							=				self.image_processing_class.from_pretrained(_lowercase				).to_dict()

     SCREAMING_SNAKE_CASE__      							=				image_processor_first.to_dict()
     for key, value in image_processor_first.items():
       if key == "clusters":
         self.assertTrue(np.array_equal(_lowercase	,     image_processor_second[key]				)				)
       else:
         self.assertEqual(image_processor_first[key]	,     _lowercase				)




   @unittest.skip("""ImageGPT requires clusters at initialization"""				)
   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     pass





def      __SCREAMING_SNAKE_CASE  ( )							-> Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				load_dataset("""hf-internal-testing/fixtures_image_utils"""			,					split="""test"""     )

  SCREAMING_SNAKE_CASE__      							=				Image.open(dataset[4]["""file"""]     )
  SCREAMING_SNAKE_CASE__      							=				Image.open(dataset[5]["""file"""]     )

  SCREAMING_SNAKE_CASE__      							=				[imagea, imagea]

  return images







@require_vision
@require_torch
class __snake_case    (						unittest.TestCase		):




   @slow
   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""				)

     SCREAMING_SNAKE_CASE__      							=				prepare_images()

     # test non-batched
     SCREAMING_SNAKE_CASE__      							=				image_processing(images[0]	,     return_tensors="""pt"""				)

     self.assertIsInstance(encoding.input_ids	,     torch.LongTensor				)
     self.assertEqual(encoding.input_ids.shape	,     (1, 10_24)				)

     SCREAMING_SNAKE_CASE__      							=				[3_06, 1_91, 1_91]
     self.assertEqual(encoding.input_ids[0, :3].tolist()	,     _lowercase				)

     # test batched
     SCREAMING_SNAKE_CASE__      							=				image_processing(_lowercase	,     return_tensors="""pt"""				)

     self.assertIsInstance(encoding.input_ids	,     torch.LongTensor				)
     self.assertEqual(encoding.input_ids.shape	,     (2, 10_24)				)

     SCREAMING_SNAKE_CASE__      							=				[3_03, 13, 13]
     self.assertEqual(encoding.input_ids[1, -3:].tolist()	,     _lowercase				)

from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


__lowerCamelCase      :			Any							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							["input_features", "attention_mask"]




   def __init__(   self      :    int	,     _lowercase      :    Dict=80	,     _lowercase      :    Union[str, Any]=1_60_00	,     _lowercase      :    str=0.0	,     _lowercase      :    int=10	,     _lowercase      :    str=25	,     _lowercase      :    Union[str, Any]="hamming_window"	,     _lowercase      :    Dict=3_27_68.0	,     _lowercase      :    Optional[int]=0.97	,     _lowercase      :    Optional[int]=1.0	,     _lowercase      :    int=True	,     _lowercase      :    Union[str, Any]=True	,     _lowercase      :    Optional[int]=False	,     **_lowercase      :    Union[str, Any]	,     ):



     """simple docstring"""






     super().__init__(feature_size=_lowercase	,     sampling_rate=_lowercase	,     padding_value=_lowercase	,     **_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				feature_size
     SCREAMING_SNAKE_CASE__      							=				sampling_rate
     SCREAMING_SNAKE_CASE__      							=				padding_value
     SCREAMING_SNAKE_CASE__      							=				hop_length
     SCREAMING_SNAKE_CASE__      							=				win_length
     SCREAMING_SNAKE_CASE__      							=				frame_signal_scale
     SCREAMING_SNAKE_CASE__      							=				preemphasis_coeff
     SCREAMING_SNAKE_CASE__      							=				mel_floor
     SCREAMING_SNAKE_CASE__      							=				normalize_means
     SCREAMING_SNAKE_CASE__      							=				normalize_vars
     SCREAMING_SNAKE_CASE__      							=				win_function
     SCREAMING_SNAKE_CASE__      							=				return_attention_mask

     SCREAMING_SNAKE_CASE__      							=				win_length * sampling_rate // 10_00
     SCREAMING_SNAKE_CASE__      							=				hop_length * sampling_rate // 10_00

     SCREAMING_SNAKE_CASE__      							=				optimal_fft_length(self.sample_size				)
     SCREAMING_SNAKE_CASE__      							=				(self.n_fft // 2) + 1




   def      __a						(   self      :    str	,     _lowercase      :    np.array				):



     """simple docstring"""






     if self.win_function == "hamming_window":
       SCREAMING_SNAKE_CASE__      							=				window_function(window_length=self.sample_size	,     name=self.win_function	,     periodic=_lowercase				)
     else:
       SCREAMING_SNAKE_CASE__      							=				window_function(window_length=self.sample_size	,     name=self.win_function				)

     SCREAMING_SNAKE_CASE__      							=				mel_filter_bank(
         num_frequency_bins=self.n_freqs	,     num_mel_filters=self.feature_size	,     min_frequency=0.0	,     max_frequency=self.sampling_rate / 2.0	,     sampling_rate=self.sampling_rate	,     )

     SCREAMING_SNAKE_CASE__      							=				spectrogram(
         one_waveform * self.frame_signal_scale	,     window=_lowercase	,     frame_length=self.sample_size	,     hop_length=self.sample_stride	,     fft_length=self.n_fft	,     center=_lowercase	,     preemphasis=self.preemphasis_coeff	,     mel_filters=_lowercase	,     mel_floor=self.mel_floor	,     log_mel="""log"""	,     )
     return msfc_features.T




   def      __a						(   self      :    Tuple	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Optional[int]	,     _lowercase      :    int				):



     """simple docstring"""






     if self.normalize_means:
       SCREAMING_SNAKE_CASE__      							=				x[:input_length].mean(axis=0				)
       SCREAMING_SNAKE_CASE__      							=				np.subtract(_lowercase	,     _lowercase				)
     if self.normalize_vars:
       SCREAMING_SNAKE_CASE__      							=				x[:input_length].std(axis=0				)
       SCREAMING_SNAKE_CASE__      							=				np.divide(_lowercase	,     _lowercase				)

     if input_length < x.shape[0]:
       SCREAMING_SNAKE_CASE__      							=				padding_value

     # make sure array is in float32
     SCREAMING_SNAKE_CASE__      							=				x.astype(np.floataa				)

     return x




   def      __a						(   self      :    int	,     _lowercase      :    List[np.ndarray]	,     _lowercase      :    Optional[np.ndarray] = None				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				attention_mask.sum(-1				) if attention_mask is not None else [x.shape[0] for x in input_features]
     return [self._normalize_one(_lowercase	,     _lowercase	,     self.padding_value				) for x, n in zip(_lowercase	,     _lowercase				)]




   def __call__(   self      :    Dict	,     _lowercase      :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,     _lowercase      :    Union[bool, str, PaddingStrategy] = False	,     _lowercase      :    Optional[int] = None	,     _lowercase      :    bool = False	,     _lowercase      :    Optional[int] = None	,     _lowercase      :    Optional[bool] = None	,     _lowercase      :    Optional[Union[str, TensorType]] = None	,     _lowercase      :    Optional[int] = None	,     **_lowercase      :    List[str]	,     ):



     """simple docstring"""






     if sampling_rate is not None:
       if sampling_rate != self.sampling_rate:
         raise ValueError(
             f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
             f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
             f""" {self.sampling_rate} and not {sampling_rate}."""				)
     else:
       logger.warning(
           """It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
           """Failing to do so can result in silent errors that might be hard to debug."""				)

     SCREAMING_SNAKE_CASE__      							=				isinstance(_lowercase	,     np.ndarray				) and len(raw_speech.shape				) > 1
     if is_batched_numpy and len(raw_speech.shape				) > 2:
       raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""				)
     SCREAMING_SNAKE_CASE__      							=				is_batched_numpy or (
         isinstance(_lowercase	,     (list, tuple)				) and (isinstance(raw_speech[0]	,     (np.ndarray, tuple, list)				))
     )

     if is_batched:
       SCREAMING_SNAKE_CASE__      							=				[np.asarray(_lowercase	,     dtype=np.floataa				) for speech in raw_speech]
     elif not is_batched and not isinstance(_lowercase	,     np.ndarray				):
       SCREAMING_SNAKE_CASE__      							=				np.asarray(_lowercase	,     dtype=np.floataa				)
     elif isinstance(_lowercase	,     np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
       SCREAMING_SNAKE_CASE__      							=				raw_speech.astype(np.floataa				)

     # always return batch
     if not is_batched:
       SCREAMING_SNAKE_CASE__      							=				[raw_speech]

     # extract fbank features
     SCREAMING_SNAKE_CASE__      							=				[self._extract_mfsc_features(_lowercase				) for one_waveform in raw_speech]

     # convert into correct format for padding
     SCREAMING_SNAKE_CASE__      							=				BatchFeature({"""input_features""": features}				)

     SCREAMING_SNAKE_CASE__      							=				self.pad(
         _lowercase	,     padding=_lowercase	,     max_length=_lowercase	,     truncation=_lowercase	,     pad_to_multiple_of=_lowercase	,     return_attention_mask=_lowercase	,     **_lowercase	,     )
     # make sure list is in array format
     SCREAMING_SNAKE_CASE__      							=				padded_inputs.get("""input_features"""				)
     if isinstance(input_features[0]	,     _lowercase				):
       SCREAMING_SNAKE_CASE__      							=				[np.asarray(_lowercase	,     dtype=np.floataa				) for feature in input_features]

     SCREAMING_SNAKE_CASE__      							=				padded_inputs.get("""attention_mask"""				)
     if attention_mask is not None:
       SCREAMING_SNAKE_CASE__      							=				[np.asarray(_lowercase	,     dtype=np.intaa				) for array in attention_mask]

     if self.normalize_means or self.normalize_vars:
       SCREAMING_SNAKE_CASE__      							=				(
           np.array(_lowercase	,     dtype=np.intaa				)
           if self._get_padding_strategies(_lowercase	,     max_length=_lowercase				) is not PaddingStrategy.DO_NOT_PAD
           and padding
           else None
       )
       SCREAMING_SNAKE_CASE__      							=				self.normalize(
           padded_inputs["""input_features"""]	,     attention_mask=_lowercase				)

     if return_tensors is not None:
       SCREAMING_SNAKE_CASE__      							=				padded_inputs.convert_to_tensors(_lowercase				)

     return padded_inputs
