





'''simple docstring'''




from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {}
__UpperCAmelCase            =       {}
__UpperCAmelCase            =       {}



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_ = None			,			):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Any		=			aliases if aliases is not None else []
		if format_type in _FORMAT_TYPES:
				logger.warning(
				    f'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''	)
		SCREAMING_SNAKE_CASE :				int		=			formatter_cls
		for alias in set(aliases + [format_type]	):
				if alias in _FORMAT_TYPES_ALIASES:
						logger.warning(
						    f'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''	)
				SCREAMING_SNAKE_CASE :				Optional[int]		=			format_type



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_ = None	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Dict		=			aliases if aliases is not None else []
		for alias in set(aliases + [format_type]	):
				SCREAMING_SNAKE_CASE :				Dict		=			unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["""python"""])
_register_formatter(ArrowFormatter, """arrow""", aliases=["""pa""", """pyarrow"""])
_register_formatter(NumpyFormatter, """numpy""", aliases=["""np"""])
_register_formatter(PandasFormatter, """pandas""", aliases=["""pd"""])
_register_formatter(CustomFormatter, """custom""")

if config.TORCH_AVAILABLE:
							from .torch_formatter import TorchFormatter

							_register_formatter(TorchFormatter, """torch""", aliases=["""pt""", """pytorch"""])
else:
							__UpperCAmelCase            =       ValueError("""PyTorch needs to be installed to be able to return PyTorch tensors.""")
							_register_unavailable_formatter(_torch_error, """torch""", aliases=["""pt""", """pytorch"""])

if config.TF_AVAILABLE:
							from .tf_formatter import TFFormatter

							_register_formatter(TFFormatter, """tensorflow""", aliases=["""tf"""])
else:
							__UpperCAmelCase            =       ValueError("""Tensorflow needs to be installed to be able to return Tensorflow tensors.""")
							_register_unavailable_formatter(_tf_error, """tensorflow""", aliases=["""tf"""])

if config.JAX_AVAILABLE:
							from .jax_formatter import JaxFormatter

							_register_formatter(JaxFormatter, """jax""", aliases=[])
else:
							__UpperCAmelCase            =       ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
							_register_unavailable_formatter(_jax_error, """jax""", aliases=[])



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		if format_type in _FORMAT_TYPES_ALIASES:
				return _FORMAT_TYPES_ALIASES[format_type]
		else:
				return format_type



def 		__A		(  lowerCamelCase_			,			**lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				List[Any]		=			get_format_type_from_alias(lowercase__	)
		if format_type in _FORMAT_TYPES:
				return _FORMAT_TYPES[format_type](**lowercase__	)
		if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
				raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
		else:
				raise ValueError(
				    f'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None	)}, but got \'{format_type}\''''	)





'''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 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def __init__(					self		:  Optional[int]			,				snake_case__		:  int			,				snake_case__		:  Optional[Any]=7			,				snake_case__		:  Tuple=3			,				snake_case__		:  Optional[Any]=18			,				snake_case__		:  Dict=30			,				snake_case__		:  Optional[int]=4_00			,				snake_case__		:  str=True			,				snake_case__		:  str=None			,				snake_case__		:  Optional[int]=True			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							size if size is not None else {"height": 18, "width": 18}
   UpperCAmelCase__      :					List[str]				       =							parent
   UpperCAmelCase__      :					Union[str, Any]				       =							batch_size
   UpperCAmelCase__      :					List[str]				       =							num_channels
   UpperCAmelCase__      :					List[Any]				       =							image_size
   UpperCAmelCase__      :					Tuple				       =							min_resolution
   UpperCAmelCase__      :					Optional[int]				       =							max_resolution
   UpperCAmelCase__      :					Any				       =							do_resize
   UpperCAmelCase__      :					Any				       =							size
   UpperCAmelCase__      :					int				       =							do_normalize







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''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_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Any				=							ImageGPTImageProcessor if is_vision_available() else None







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							ImageGPTImageProcessingTester(self )







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return self.image_processor_tester.prepare_image_processor_dict()







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							self.image_processing_class(**self.image_processor_dict )
   self.assertTrue(hasattr(snake_case__			,				"clusters" ) )
   self.assertTrue(hasattr(snake_case__			,				"do_resize" ) )
   self.assertTrue(hasattr(snake_case__			,				"size" ) )
   self.assertTrue(hasattr(snake_case__			,				"do_normalize" ) )







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




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

   UpperCAmelCase__      :					Union[str, Any]				       =							self.image_processing_class.from_dict(self.image_processor_dict			,				size=42 )
   self.assertEqual(image_processor.size			,				{"height": 42, "width": 42} )







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.image_processing_class(**self.image_processor_dict )
   UpperCAmelCase__      :					List[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(snake_case__			,				obj[key] ) )
     else:
       self.assertEqual(obj[key]			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							self.image_processing_class(**self.image_processor_dict )

   with tempfile.TemporaryDirectory() as tmpdirname:
     UpperCAmelCase__      :					Union[str, Any]				       =							os.path.join(snake_case__			,				"image_processor.json" )
     image_processor_first.to_json_file(snake_case__ )
     UpperCAmelCase__      :					Any				       =							self.image_processing_class.from_json_file(snake_case__ ).to_dict()

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







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.image_processing_class(**self.image_processor_dict )

   with tempfile.TemporaryDirectory() as tmpdirname:
     image_processor_first.save_pretrained(snake_case__ )
     UpperCAmelCase__      :					List[str]				       =							self.image_processing_class.from_pretrained(snake_case__ ).to_dict()

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







 @unittest.skip("ImageGPT requires clusters at initialization" )
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   pass







def snake_case_     (					):
  UpperCAmelCase__      :					List[str]				       =							load_dataset("hf-internal-testing/fixtures_image_utils"   ,       split="test"		)

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

  UpperCAmelCase__      :					Optional[Any]				       =							[imagea, imagea]

  return images






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

 '''simple docstring'''







 @slow
 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small" )

   UpperCAmelCase__      :					Union[str, Any]				       =							prepare_images()

   # test non-batched
   UpperCAmelCase__      :					Optional[Any]				       =							image_processing(images[0]			,				return_tensors="pt" )

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

   UpperCAmelCase__      :					Union[str, Any]				       =							[3_06, 1_91, 1_91]
   self.assertEqual(encoding.input_ids[0, :3].tolist()			,				snake_case__ )

   # test batched
   UpperCAmelCase__      :					List[Any]				       =							image_processing(snake_case__			,				return_tensors="pt" )

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

   UpperCAmelCase__      :					List[str]				       =							[3_03, 13, 13]
   self.assertEqual(encoding.input_ids[1, -3:].tolist()			,				snake_case__ )



