






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     _lowerCAmelCase(	unittest.TestCase ):





							"""simple docstring"""







							def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase=7  ,      _lowerCamelCase=3  ,      _lowerCamelCase=1_8  ,      _lowerCamelCase=3_0  ,      _lowerCamelCase=4_0_0  ,      _lowerCamelCase=True  ,      _lowerCamelCase=None  ,      _lowerCamelCase=True  ,      ):
									UpperCamelCase_: Tuple   				=    size if size is not None else {"height": 1_8, "width": 1_8}
									UpperCamelCase_: Optional[Any]   				=    parent
									UpperCamelCase_: Optional[Any]   				=    batch_size
									UpperCamelCase_: str   				=    num_channels
									UpperCamelCase_: Optional[int]   				=    image_size
									UpperCamelCase_: Union[str, Any]   				=    min_resolution
									UpperCamelCase_: int   				=    max_resolution
									UpperCamelCase_: Optional[int]   				=    do_resize
									UpperCamelCase_: Dict   				=    size
									UpperCamelCase_: Optional[int]   				=    do_normalize





							def       _a   ( self							):
									return {
									    # here we create 2 clusters for the sake of simplicity
									    "clusters": np.asarray(
									        [
									            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
									            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
									        ]							),
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									}







@require_torch
@require_vision
class     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





							"""simple docstring"""







							a    :		List[Any]									=ImageGPTImageProcessor if is_vision_available() else None





							def       _a   ( self							):
									UpperCamelCase_: Tuple   				=    ImageGPTImageProcessingTester(self							)





							@property
							def       _a   ( self							):
									return self.image_processor_tester.prepare_image_processor_dict()





							def       _a   ( self							):
									UpperCamelCase_: List[str]   				=    self.image_processing_class(**self.image_processor_dict							)
									self.assertTrue(hasattr(_a  ,      'clusters'							)							)
									self.assertTrue(hasattr(_a  ,      'do_resize'							)							)
									self.assertTrue(hasattr(_a  ,      'size'							)							)
									self.assertTrue(hasattr(_a  ,      'do_normalize'							)							)





							def       _a   ( self							):
									UpperCamelCase_: Optional[int]   				=    self.image_processing_class.from_dict(self.image_processor_dict							)
									self.assertEqual(image_processor.size  ,      {'height': 1_8, 'width': 1_8}							)

									UpperCamelCase_: Any   				=    self.image_processing_class.from_dict(self.image_processor_dict  ,      size=4_2							)
									self.assertEqual(image_processor.size  ,      {'height': 4_2, 'width': 4_2}							)





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





							def       _a   ( self							):
									UpperCamelCase_: str   				=    self.image_processing_class(**self.image_processor_dict							)

									with tempfile.TemporaryDirectory() as tmpdirname:
											UpperCamelCase_: Optional[int]   				=    os.path.join(_a  ,      'image_processor.json'							)
											image_processor_first.to_json_file(_a							)
											UpperCamelCase_: Union[str, Any]   				=    self.image_processing_class.from_json_file(_a							).to_dict()

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





							def       _a   ( self							):
									UpperCamelCase_: int   				=    self.image_processing_class(**self.image_processor_dict							)

									with tempfile.TemporaryDirectory() as tmpdirname:
											image_processor_first.save_pretrained(_a							)
											UpperCamelCase_: Optional[int]   				=    self.image_processing_class.from_pretrained(_a							).to_dict()

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





							@unittest.skip('ImageGPT requires clusters at initialization'							)
							def       _a   ( self							):
									pass







def   snake_case ()	->    Union[str, Any]:
		UpperCamelCase_: str   				=    load_dataset('hf-internal-testing/fixtures_image_utils'     ,   split='test'  )

		UpperCamelCase_: List[Any]   				=    Image.open(dataset[4]['file']  )
		UpperCamelCase_: Tuple   				=    Image.open(dataset[5]['file']  )

		UpperCamelCase_: Dict   				=    [imagea, imagea]

		return images




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





							"""simple docstring"""







							@slow
							def       _a   ( self							):
									UpperCamelCase_: Optional[Any]   				=    ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'							)

									UpperCamelCase_: Optional[Any]   				=    prepare_images()

									# test non-batched
									UpperCamelCase_: str   				=    image_processing(images[0]  ,      return_tensors='pt'							)

									self.assertIsInstance(encoding.input_ids  ,      torch.LongTensor							)
									self.assertEqual(encoding.input_ids.shape  ,      (1, 1_0_2_4)							)

									UpperCamelCase_: List[str]   				=    [3_0_6, 1_9_1, 1_9_1]
									self.assertEqual(encoding.input_ids[0, :3].tolist()  ,      _a							)

									# test batched
									UpperCamelCase_: int   				=    image_processing(_a  ,      return_tensors='pt'							)

									self.assertIsInstance(encoding.input_ids  ,      torch.LongTensor							)
									self.assertEqual(encoding.input_ids.shape  ,      (2, 1_0_2_4)							)

									UpperCamelCase_: Dict   				=    [3_0_3, 1_3, 1_3]
									self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,      _a							)







import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    np.array:
  UpperCamelCase_: Dict   				=    F'''{sampling_rate}'''
  UpperCamelCase_: Any   				=    '1'
  UpperCamelCase_: Any   				=    'f32le'
  UpperCamelCase_: Union[str, Any]   				=    [
      'ffmpeg',
      '-i',
      'pipe:0',
      '-ac',
      ac,
      '-ar',
      ar,
      '-f',
      format_for_conversion,
      '-hide_banner',
      '-loglevel',
      'quiet',
      'pipe:1',
  ]

  try:
    with subprocess.Popen(UpperCAmelCase__     ,   stdin=subprocess.PIPE     ,   stdout=subprocess.PIPE  ) as ffmpeg_process:
      UpperCamelCase_: Optional[Any]   				=    ffmpeg_process.communicate(UpperCAmelCase__  )
  except FileNotFoundError as error:
    raise ValueError('ffmpeg was not found but is required to load audio files from filename'  ) from error
  UpperCamelCase_: Union[str, Any]   				=    output_stream[0]
  UpperCamelCase_: List[str]   				=    np.frombuffer(UpperCAmelCase__     ,   np.floataa  )
  if audio.shape[0] == 0:
    raise ValueError('Malformed soundfile'  )
  return audio

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = "f32le"     ,   )	->    Tuple:
  UpperCamelCase_: Any   				=    F'''{sampling_rate}'''
  UpperCamelCase_: Union[str, Any]   				=    '1'
  if format_for_conversion == "s16le":
    UpperCamelCase_: Optional[Any]   				=    2
  elif format_for_conversion == "f32le":
    UpperCamelCase_: Any   				=    4
  else:
    raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''  )

  UpperCamelCase_: int   				=    platform.system()
  if system == "Linux":
    UpperCamelCase_: Tuple   				=    'alsa'
    UpperCamelCase_: List[str]   				=    'default'
  elif system == "Darwin":
    UpperCamelCase_: int   				=    'avfoundation'
    UpperCamelCase_: Union[str, Any]   				=    ':0'
  elif system == "Windows":
    UpperCamelCase_: Tuple   				=    'dshow'
    UpperCamelCase_: Dict   				=    'default'

  UpperCamelCase_: Any   				=    [
      'ffmpeg',
      '-f',
      format_,
      '-i',
      input_,
      '-ac',
      ac,
      '-ar',
      ar,
      '-f',
      format_for_conversion,
      '-fflags',
      'nobuffer',
      '-hide_banner',
      '-loglevel',
      'quiet',
      'pipe:1',
  ]
  UpperCamelCase_: Tuple   				=    int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
  UpperCamelCase_: Optional[int]   				=    _ffmpeg_stream(UpperCAmelCase__     ,   UpperCAmelCase__  )
  for item in iterator:
    yield item

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = "f32le"     ,   )	->    Any:
  if stream_chunk_s is not None:
    UpperCamelCase_: List[Any]   				=    stream_chunk_s
  else:
    UpperCamelCase_: Dict   				=    chunk_length_s

  UpperCamelCase_: List[str]   				=    ffmpeg_microphone(UpperCAmelCase__     ,   UpperCAmelCase__     ,   format_for_conversion=UpperCAmelCase__  )
  if format_for_conversion == "s16le":
    UpperCamelCase_: Union[str, Any]   				=    np.intaa
    UpperCamelCase_: List[Any]   				=    2
  elif format_for_conversion == "f32le":
    UpperCamelCase_: str   				=    np.floataa
    UpperCamelCase_: Tuple   				=    4
  else:
    raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''  )

  if stride_length_s is None:
    UpperCamelCase_: int   				=    chunk_length_s / 6
  UpperCamelCase_: Tuple   				=    int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
  if isinstance(UpperCAmelCase__     ,   (int, float)  ):
    UpperCamelCase_: Union[str, Any]   				=    [stride_length_s, stride_length_s]

  UpperCamelCase_: Any   				=    int(round(sampling_rate * stride_length_s[0]  )  ) * size_of_sample
  UpperCamelCase_: Dict   				=    int(round(sampling_rate * stride_length_s[1]  )  ) * size_of_sample
  UpperCamelCase_: Optional[int]   				=    datetime.datetime.now()
  UpperCamelCase_: Optional[int]   				=    datetime.timedelta(seconds=UpperCAmelCase__  )
  for item in chunk_bytes_iter(UpperCAmelCase__     ,   UpperCAmelCase__     ,   stride=(stride_left, stride_right)     ,   stream=UpperCAmelCase__  ):
    # Put everything back in numpy scale
    UpperCamelCase_: Tuple   				=    np.frombuffer(item['raw']     ,   dtype=UpperCAmelCase__  )
    UpperCamelCase_: Optional[int]   				=    (
        item['stride'][0] // size_of_sample,
        item['stride'][1] // size_of_sample,
    )
    UpperCamelCase_: int   				=    sampling_rate
    audio_time += delta
    if datetime.datetime.now() > audio_time + 1_0 * delta:
      # We're late !! SKIP
      continue
    yield item

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = False  )	->    int:
  UpperCamelCase_: str   				=    b''
  UpperCamelCase_				,UpperCamelCase_: Union[str, Any]   				=    stride
  if stride_left + stride_right >= chunk_len:
    raise ValueError(
        F'''Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'''  )
  UpperCamelCase_: List[str]   				=    0
  for raw in iterator:
    acc += raw
    if stream and len(UpperCAmelCase__  ) < chunk_len:
      UpperCamelCase_: Optional[Any]   				=    (_stride_left, 0)
      yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
    else:
      while len(UpperCAmelCase__  ) >= chunk_len:
        # We are flushing the accumulator
        UpperCamelCase_: int   				=    (_stride_left, stride_right)
        UpperCamelCase_: Optional[Any]   				=    {'raw': acc[:chunk_len], 'stride': stride}
        if stream:
          UpperCamelCase_: Any   				=    False
        yield item
        UpperCamelCase_: Optional[int]   				=    stride_left
        UpperCamelCase_: Optional[Any]   				=    acc[chunk_len - stride_left - stride_right :]
    # Last chunk
  if len(UpperCAmelCase__  ) > stride_left:
    UpperCamelCase_: int   				=    {'raw': acc, 'stride': (_stride_left, 0)}
    if stream:
      UpperCamelCase_: Optional[Any]   				=    False
    yield item

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  UpperCamelCase_: Any   				=    2**2_4  # 16Mo
  try:
    with subprocess.Popen(UpperCAmelCase__     ,   stdout=subprocess.PIPE     ,   bufsize=UpperCAmelCase__  ) as ffmpeg_process:
      while True:
        UpperCamelCase_: Any   				=    ffmpeg_process.stdout.read(UpperCAmelCase__  )
        if raw == b"":
          break
        yield raw
  except FileNotFoundError as error:
    raise ValueError('ffmpeg was not found but is required to stream audio files from filename'  ) from error