






"""simple docstring"""







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        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def __init__(   self:     List[str]				,		__A:     List[str]				,		__A:     int=7				,		__A:     Optional[int]=3				,		__A:     Optional[int]=18				,		__A:     Dict=30				,		__A:     Tuple=400				,		__A:     Any=True				,		__A:     List[Any]=None				,		__A:     int=True				,		):

												'''simple docstring'''

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





							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												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        _SCREAMING_SNAKE_CASE						(  __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =ImageGPTImageProcessor if is_vision_available() else None




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  ImageGPTImageProcessingTester(self    )




							@property
							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  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  lowercase		(   self:     Any    ):

												'''simple docstring'''

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

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




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.image_processing_class(**self.image_processor_dict    )
												a__          =  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  lowercase		(   self:     Any    ):

												'''simple docstring'''

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

												with tempfile.TemporaryDirectory() as tmpdirname:
																	a__          =  os.path.join(__A				,		'''image_processor.json'''    )
																	image_processor_first.to_json_file(__A    )
																	a__          =  self.image_processing_class.from_json_file(__A    ).to_dict()

												a__          =  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  lowercase		(   self:     Any    ):

												'''simple docstring'''

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

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

												a__          =  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  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												pass





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  load_dataset('''hf-internal-testing/fixtures_image_utils''' ,     split='''test''')

					a__          =  Image.open(dataset[4]['''file'''])
					a__          =  Image.open(dataset[5]['''file'''])

					a__          =  [imagea, imagea]

					return images







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





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''    )

												a__          =  prepare_images()

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

												self.assertIsInstance(encoding.input_ids				,		torch.LongTensor    )
												self.assertEqual(encoding.input_ids.shape				,		(1, 1024)    )

												a__          =  [306, 191, 191]
												self.assertEqual(encoding.input_ids[0, :3].tolist()				,		__A    )

												# test batched
												a__          =  image_processing(__A				,		return_tensors='''pt'''    )

												self.assertIsInstance(encoding.input_ids				,		torch.LongTensor    )
												self.assertEqual(encoding.input_ids.shape				,		(2, 1024)    )

												a__          =  [303, 13, 13]
												self.assertEqual(encoding.input_ids[1, -3:].tolist()				,		__A    )










"""simple docstring"""







from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='EncodecFeatureExtractor'
							_SCREAMING_SNAKE_CASE    =('T5Tokenizer', 'T5TokenizerFast')




							def __init__(   self:     List[Any]				,		__A:     Any				,		__A:     Dict    ):

												'''simple docstring'''

												super().__init__(__A				,		__A    )
												a__          =  self.feature_extractor
												a__          =  False




							def  lowercase		(   self:     Union[str, Any]				,		__A:     List[Any]=None				,		__A:     Optional[Any]=None				,		__A:     List[Any]=True    ):

												'''simple docstring'''

												return self.tokenizer.get_decoder_prompt_ids(task=__A				,		language=__A				,		no_timestamps=__A    )




							def __call__(   self:     Union[str, Any]				,		*__A:     int				,		**__A:     Dict    ):

												'''simple docstring'''

												if self._in_target_context_manager:
																	return self.current_processor(*__A				,		**__A    )

												a__          =  kwargs.pop('''audio'''				,		__A    )
												a__          =  kwargs.pop('''sampling_rate'''				,		__A    )
												a__          =  kwargs.pop('''text'''				,		__A    )
												if len(__A    ) > 0:
																	a__          =  args[0]
																	a__          =  args[1:]

												if audio is None and text is None:
																	raise ValueError('''You need to specify either an `audio` or `text` input to process.'''    )

												if text is not None:
																	a__          =  self.tokenizer(__A				,		**__A    )

												if audio is not None:
																	a__          =  self.feature_extractor(__A				,		*__A				,		sampling_rate=__A				,		**__A    )

												if audio is None:
																	return inputs

												elif text is None:
																	return audio_inputs

												else:
																	a__          =  audio_inputs['''input_values''']
																	if "padding_mask" in audio_inputs:
																						a__          =  audio_inputs['''padding_mask''']
																	return inputs




							def  lowercase		(   self:     Union[str, Any]				,		*__A:     List[str]				,		**__A:     Tuple    ):

												'''simple docstring'''

												a__          =  kwargs.pop('''audio'''				,		__A    )
												a__          =  kwargs.pop('''padding_mask'''				,		__A    )

												if len(__A    ) > 0:
																	a__          =  args[0]
																	a__          =  args[1:]

												if audio_values is not None:
																	return self._decode_audio(__A				,		padding_mask=__A    )
												else:
																	return self.tokenizer.batch_decode(*__A				,		**__A    )




							def  lowercase		(   self:     Union[str, Any]				,		*__A:     Optional[int]				,		**__A:     Optional[Any]    ):

												'''simple docstring'''

												return self.tokenizer.decode(*__A				,		**__A    )





							def  lowercase		(   self:     Union[str, Any]				,		__A:     Dict				,		__A:     Optional = None    ):

												'''simple docstring'''

												a__          =  to_numpy(__A    )
												a__       ,a__       ,a__          =  audio_values.shape

												if padding_mask is None:
																	return list(__A    )

												a__          =  to_numpy(__A    )

												# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
												# token (so that the generated audio values are **not** treated as padded tokens)
												a__          =  seq_len - padding_mask.shape[-1]
												a__          =  1 - self.feature_extractor.padding_value
												a__          =  np.pad(__A				,		((0, 0), (0, difference))				,		'''constant'''				,		constant_values=__A    )

												a__          =  audio_values.tolist()
												for i in range(__A    ):
																	a__          =  np.asarray(audio_values[i]    )[
																	    padding_mask[i][None, :] != self.feature_extractor.padding_value
																	]
																	a__          =  sliced_audio.reshape(__A				,		-1    )

												return audio_values


