




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 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






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





           '''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 			SCREAMING_SNAKE_CASE				(     self :      str)		->Union[str, Any]:





           '''simple docstring'''

           return {
               # here we create 2 clusters for the sake of simplicity
               "clusters": np.asarray(
                   [
                       [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
                       [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
                   ]),
               "do_resize": self.do_resize,
               "size": self.size,
               "do_normalize": self.do_normalize,
           }


@require_torch
@require_vision
class 							UpperCamelCase_       (					UpperCAmelCase__						,   unittest.TestCase	):

    '''simple docstring'''






    UpperCAmelCase__				    = ImageGPTImageProcessor if is_vision_available() else None


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Union[str, Any]:





           '''simple docstring'''

           A__ 					=       ImageGPTImageProcessingTester(self)


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->Union[str, Any]:





           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()


    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->Any:





           '''simple docstring'''

           A__ 					=       self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(UpperCAmelCase__   ,					'''clusters'''))
           self.assertTrue(hasattr(UpperCAmelCase__   ,					'''do_resize'''))
           self.assertTrue(hasattr(UpperCAmelCase__   ,					'''size'''))
           self.assertTrue(hasattr(UpperCAmelCase__   ,					'''do_normalize'''))


    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->List[str]:





           '''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 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Tuple:





           '''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(UpperCAmelCase__   ,					obj[key]))
                  else:
                         self.assertEqual(obj[key]   ,					UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->List[str]:





           '''simple docstring'''

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

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

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->int:





           '''simple docstring'''

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

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

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





    @unittest.skip('''ImageGPT requires clusters at initialization''')
    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->int:





           '''simple docstring'''

           pass







def SCREAMING_SNAKE_CASE							(			)  ->			int:



       """simple docstring"""

       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 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






    @slow
    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->int:





           '''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, 1_024))

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

           # test batched
           A__ 					=       image_processing(UpperCAmelCase__   ,					return_tensors='''pt''')

           self.assertIsInstance(encoding.input_ids   ,					torch.LongTensor)
           self.assertEqual(encoding.input_ids.shape   ,					(2, 1_024))

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








'''simple docstring'''

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   Optional[int]			,							lowercase_       :   NestedDataStructureLike[PathLike]			,							lowercase_       :   Optional[NamedSplit] = None			,							lowercase_       :   Optional[Features] = None			,							lowercase_       :   str = None			,							lowercase_       :   bool = False			,							lowercase_       :   bool = False			,							lowercase_       :   Optional[str] = None			,							lowercase_       :   Optional[int] = None			,							**lowercase_       :   Optional[Any]			,							):
									super().__init__(
									    lowercase_			,							split=lowercase_			,							features=lowercase_			,							cache_dir=lowercase_			,							keep_in_memory=lowercase_			,							streaming=lowercase_			,							num_proc=lowercase_			,							**lowercase_			,							)
									snake_case_		     =							field
									snake_case_		     =							path_or_paths if isinstance(lowercase_			,							lowercase_   ) else {self.split: path_or_paths}
									snake_case_		     =							Json(
									    cache_dir=lowercase_			,							data_files=lowercase_			,							features=lowercase_			,							field=lowercase_			,							**lowercase_			,							)


				def     A_					(						self       :   Optional[Any]   ):
									# Build iterable dataset
									if self.streaming:
														snake_case_		     =							self.builder.as_streaming_dataset(split=self.split   )
									# Build regular (map-style) dataset
									else:
														snake_case_		     =							None
														snake_case_		     =							None
														snake_case_		     =							None
														snake_case_		     =							None

														self.builder.download_and_prepare(
														    download_config=lowercase_			,							download_mode=lowercase_			,							verification_mode=lowercase_			,							base_path=lowercase_			,							num_proc=self.num_proc			,							)
														snake_case_		     =							self.builder.as_dataset(
														    split=self.split			,							verification_mode=lowercase_			,							in_memory=self.keep_in_memory   )
									return dataset







class 		a							:


				def __init__(						self       :   Dict			,							lowercase_       :   Dataset			,							lowercase_       :   Union[PathLike, BinaryIO]			,							lowercase_       :   Optional[int] = None			,							lowercase_       :   Optional[int] = None			,							**lowercase_       :   List[str]			,							):
									if num_proc is not None and num_proc <= 0:
														raise ValueError(F"num_proc {num_proc} must be an integer > 0."   )

									snake_case_		     =							dataset
									snake_case_		     =							path_or_buf
									snake_case_		     =							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									snake_case_		     =							num_proc
									snake_case_		     =							'''utf-8'''
									snake_case_		     =							to_json_kwargs


				def     A_					(						self       :   str   ):
									snake_case_		     =							self.to_json_kwargs.pop('''path_or_buf'''			,							lowercase_   )
									snake_case_		     =							self.to_json_kwargs.pop('''orient'''			,							'''records'''   )
									snake_case_		     =							self.to_json_kwargs.pop('''lines'''			,							True if orient == '''records''' else False   )
									snake_case_		     =							self.to_json_kwargs.pop('''index'''			,							False if orient in ['''split''', '''table'''] else True   )
									snake_case_		     =							self.to_json_kwargs.pop('''compression'''			,							lowercase_   )

									if compression not in [None, "infer", "gzip", "bz2", "xz"]:
														raise NotImplementedError(F"`datasets` currently does not support {compression} compression"   )

									if isinstance(self.path_or_buf			,							(str, bytes, os.PathLike)   ):
														with fsspec.open(self.path_or_buf			,							'''wb'''			,							compression=lowercase_   ) as buffer:
																			snake_case_		     =							self._write(file_obj=lowercase_			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**self.to_json_kwargs   )
									else:
														if compression:
																			raise NotImplementedError(
																			    F"The compression parameter is not supported when writing to a buffer, but compression={compression}"
																			    ''' was passed. Please provide a local path instead.'''   )
														snake_case_		     =							self._write(
														    file_obj=self.path_or_buf			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**self.to_json_kwargs   )
									return written


				def     A_					(						self       :   List[str]			,							lowercase_       :   Dict   ):
									snake_case_     ,snake_case_     ,snake_case_     ,snake_case_     ,snake_case_		     =							args

									snake_case_		     =							query_table(
									    table=self.dataset.data			,							key=slice(lowercase_			,							offset + self.batch_size   )			,							indices=self.dataset._indices			,							)
									snake_case_		     =							batch.to_pandas().to_json(
									    path_or_buf=lowercase_			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**lowercase_   )
									if not json_str.endswith('''\n'''   ):
														json_str += "\n"
									return json_str.encode(self.encoding   )


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   BinaryIO			,							lowercase_       :   List[Any]			,							lowercase_       :   str			,							lowercase_       :   int			,							**lowercase_       :   Optional[int]			,							):
									snake_case_		     =							0

									if self.num_proc is None or self.num_proc == 1:
														for offset in logging.tqdm(
														    range(0			,							len(self.dataset   )			,							self.batch_size   )			,							unit='''ba'''			,							disable=not logging.is_progress_bar_enabled()			,							desc='''Creating json from Arrow format'''			,							):
																			snake_case_		     =							self._batch_json((offset, orient, lines, index, to_json_kwargs)   )
																			written += file_obj.write(lowercase_   )
									else:
														snake_case_     ,snake_case_		     =							len(self.dataset   ), self.batch_size
														with multiprocessing.Pool(self.num_proc   ) as pool:
																			for json_str in logging.tqdm(
																			    pool.imap(
																			        self._batch_json			,							[(offset, orient, lines, index, to_json_kwargs) for offset in range(0			,							lowercase_			,							lowercase_   )]			,							)			,							total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,							unit='''ba'''			,							disable=not logging.is_progress_bar_enabled()			,							desc='''Creating json from Arrow format'''			,							):
																								written += file_obj.write(lowercase_   )

									return written

