
"""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     A_	(     unittest.TestCase   ):



							"""simple docstring"""






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





										"""simple docstring"""

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





							def       UpperCAmelCase__							(  self  :List[Any]       ):





										"""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     A_	(     A__      ,					unittest.TestCase   ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_	      =   ImageGPTImageProcessor if is_vision_available() else None





							def       UpperCAmelCase__							(  self  :Any       ):





										"""simple docstring"""

										lowerCamelCase__	:     Optional[int]              =ImageGPTImageProcessingTester(self       )





							@property
							def       UpperCAmelCase__							(  self  :int       ):





										"""simple docstring"""

										return self.image_processor_tester.prepare_image_processor_dict()





							def       UpperCAmelCase__							(  self  :Dict       ):





										"""simple docstring"""

										lowerCamelCase__	:     Union[str, Any]              =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       UpperCAmelCase__							(  self  :Optional[Any]       ):





										"""simple docstring"""

										lowerCamelCase__	:     List[str]              =self.image_processing_class.from_dict(self.image_processor_dict       )
										self.assertEqual(image_processor.size     ,							{'height': 18, 'width': 18}       )

										lowerCamelCase__	:     Optional[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  :List[Any]       ):





										"""simple docstring"""

										lowerCamelCase__	:     Any              =self.image_processing_class(**self.image_processor_dict       )
										lowerCamelCase__	:     Any              =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       UpperCAmelCase__							(  self  :Tuple       ):





										"""simple docstring"""

										lowerCamelCase__	:     Optional[int]              =self.image_processing_class(**self.image_processor_dict       )

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

										lowerCamelCase__	:     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_       )





							def       UpperCAmelCase__							(  self  :Optional[int]       ):





										"""simple docstring"""

										lowerCamelCase__	:     Optional[Any]              =self.image_processing_class(**self.image_processor_dict       )

										with tempfile.TemporaryDirectory() as tmpdirname:
													image_processor_first.save_pretrained(A_       )
													lowerCamelCase__	:     int              =self.image_processing_class.from_pretrained(A_       ).to_dict()

										lowerCamelCase__	:     List[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_       )





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





										"""simple docstring"""

										pass






def    lowerCAmelCase_   (		)   ->List[str]:
			lowerCamelCase__	:     Dict              =load_dataset('hf-internal-testing/fixtures_image_utils'							,    split='test'			)

			lowerCamelCase__	:     List[str]              =Image.open(dataset[4]['file']			)
			lowerCamelCase__	:     str              =Image.open(dataset[5]['file']			)

			lowerCamelCase__	:     List[str]              =[imagea, imagea]

			return images

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



							"""simple docstring"""






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





										"""simple docstring"""

										lowerCamelCase__	:     str              =ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'       )

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

										# test non-batched
										lowerCamelCase__	:     List[Any]              =image_processing(images[0]     ,							return_tensors='pt'       )

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

										lowerCamelCase__	:     Dict              =[306, 191, 191]
										self.assertEqual(encoding.input_ids[0, :3].tolist()     ,							A_       )

										# test batched
										lowerCamelCase__	:     Dict              =image_processing(A_     ,							return_tensors='pt'       )

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

										lowerCamelCase__	:     Dict              =[303, 13, 13]
										self.assertEqual(encoding.input_ids[1, -3:].tolist()     ,							A_       )





import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCamelCase				:		Union[str, Any]     =   "src/diffusers"

# Matches is_xxx_available()
lowerCamelCase				:		Dict     =   re.compile(r"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
lowerCamelCase				:		Union[str, Any]     =   re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")


lowerCamelCase				:		Any     =   "\n{0} = None\n"

lowerCamelCase				:		List[str]     =   "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n"


lowerCamelCase				:		str     =   "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    _re_backend.findall(lowercase     )
      if len(lowercase     ) == 0:
            return None

      return "_and_".join(lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							):


      '''simple docstring'''



      with open(os.path.join(lowercase	,							'__init__.py'     )	,							'r'	,							encoding='utf-8'	,							newline='\n'     ) as f:
            lowerCamelCase_		     =    f.readlines()

      # Get to the point we do the actual imports for type checking
      lowerCamelCase_		     =    0
      lowerCamelCase_		     =    {}
      # Go through the end of the file
      while line_index < len(lowercase     ):
            # If the line contains is_backend_available, we grab all objects associated with the `else` block
            lowerCamelCase_		     =    find_backend(lines[line_index]     )
            if backend is not None:
                  while not lines[line_index].startswith('else:'     ):
                        line_index += 1
                  line_index += 1
                  lowerCamelCase_		     =    []
                  # Until we unindent, add backend objects to the list
                  while line_index < len(lowercase     ) and len(lines[line_index]     ) > 1:
                        lowerCamelCase_		     =    lines[line_index]
                        lowerCamelCase_		     =    _re_single_line_import.search(lowercase     )
                        if single_line_import_search is not None:
                              objects.extend(single_line_import_search.groups()[0].split(', '     )     )
                        elif line.startswith(' ' * 8     ):
                              objects.append(line[8:-2]     )
                        line_index += 1

                  if len(lowercase     ) > 0:
                        lowerCamelCase_		     =    objects
            else:
                  line_index += 1

      return backend_specific_objects

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[str]	,							lowercase	:       str     ):


      '''simple docstring'''



      if name.isupper():
            return DUMMY_CONSTANT.format(lowercase     )
      elif name.islower():
            return DUMMY_FUNCTION.format(lowercase	,							lowercase     )
      else:
            return DUMMY_CLASS.format(lowercase	,							lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]=None     ):


      '''simple docstring'''



      if backend_specific_objects is None:
            lowerCamelCase_		     =    read_init()
      # For special correspondence backend to module name as used in the function requires_modulename
      lowerCamelCase_		     =    {}

      for backend, objects in backend_specific_objects.items():
            lowerCamelCase_		     =    '[' + ', '.join(f"""\"{b}\"""" for b in backend.split('_and_'     )     ) + ']'
            lowerCamelCase_		     =    '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
            dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
            dummy_file += "\n".join([create_dummy_object(lowercase	,							lowercase     ) for o in objects]     )
            lowerCamelCase_		     =    dummy_file

      return dummy_files

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]=False     ):


      '''simple docstring'''



      lowerCamelCase_		     =    create_dummy_files()
      # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
      lowerCamelCase_		     =    {'torch': 'pt'}

      # Locate actual dummy modules and read their content.
      lowerCamelCase_		     =    os.path.join(lowercase	,							'utils'     )
      lowerCamelCase_		     =    {
          backend: os.path.join(lowercase	,							f"""dummy_{short_names.get(lowercase	,							lowercase     )}_objects.py"""     )
          for backend in dummy_files.keys()
      }

      lowerCamelCase_		     =    {}
      for backend, file_path in dummy_file_paths.items():
            if os.path.isfile(lowercase     ):
                  with open(lowercase	,							'r'	,							encoding='utf-8'	,							newline='\n'     ) as f:
                        lowerCamelCase_		     =    f.read()
            else:
                  lowerCamelCase_		     =    ''

      for backend in dummy_files.keys():
            if dummy_files[backend] != actual_dummies[backend]:
                  if overwrite:
                        print(
                            f"""Updating diffusers.utils.dummy_{short_names.get(lowercase	,							lowercase     )}_objects.py as the main """
                            '__init__ has new objects.'     )
                        with open(dummy_file_paths[backend]	,							'w'	,							encoding='utf-8'	,							newline='\n'     ) as f:
                              f.write(dummy_files[backend]     )
                  else:
                        raise ValueError(
                            'The main __init__ has objects that are not present in '
                            f"""diffusers.utils.dummy_{short_names.get(lowercase	,							lowercase     )}_objects.py. Run `make fix-copies` """
                            'to fix this.'     )


if __name__ == "__main__":
 lowerCamelCase				:		List[Any]     =   argparse.ArgumentParser()
 parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
 lowerCamelCase				:		Tuple     =   parser.parse_args()

 check_dummies(args.fix_and_overwrite)



