"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 SwinaSRImageProcessor
     from transformers.image_transforms import get_image_size






class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=7 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=18 ,				_SCREAMING_SNAKE_CASE=30 ,				_SCREAMING_SNAKE_CASE=400 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=1 / 255 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=8 ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					batch_size
      __lowerCamelCase    		=					num_channels
      __lowerCamelCase    		=					image_size
      __lowerCamelCase    		=					min_resolution
      __lowerCamelCase    		=					max_resolution
      __lowerCamelCase    		=					do_rescale
      __lowerCamelCase    		=					rescale_factor
      __lowerCamelCase    		=					do_pad
      __lowerCamelCase    		=					pad_size







   def    _A				(	self	):


      '''simple docstring'''

      return {
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_pad": self.do_pad,
          "pad_size": self.pad_size,
      }







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE=False ,				_SCREAMING_SNAKE_CASE=False ,				_SCREAMING_SNAKE_CASE=False	):


      '''simple docstring'''

      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      if equal_resolution:
         __lowerCamelCase    		=					[]
         for i in range(self.batch_size	):
            image_inputs.append(
                np.random.randint(
                    255 ,				size=(self.num_channels, self.max_resolution, self.max_resolution) ,				dtype=np.uinta	)	)
      else:
         __lowerCamelCase    		=					[]
         for i in range(self.batch_size	):
            __lowerCamelCase		,__lowerCamelCase    		=					np.random.choice(np.arange(self.min_resolution ,				self.max_resolution	) ,				2	)
            image_inputs.append(np.random.randint(255 ,				size=(self.num_channels, width, height) ,				dtype=np.uinta	)	)

      if not numpify and not torchify:
         # PIL expects the channel dimension as last dimension
         __lowerCamelCase    		=					[Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE ,				0 ,				-1	)	) for x in image_inputs]

      if torchify:
         __lowerCamelCase    		=					[torch.from_numpy(_SCREAMING_SNAKE_CASE	) for x in image_inputs]

      return image_inputs






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



   '''simple docstring'''



   __UpperCAmelCase	:							List[str]    =    SwinaSRImageProcessor if is_vision_available() else None







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					SwinaSRImageProcessingTester(self	)







   @property
   def    _A				(	self	):


      '''simple docstring'''

      return self.image_processor_tester.prepare_image_processor_dict()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.image_processing_class(**self.image_processor_dict	)
      self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'do_rescale'	)	)
      self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'rescale_factor'	)	)
      self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'do_pad'	)	)
      self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'pad_size'	)	)







   def    _A				(	self	):


      '''simple docstring'''

      pass







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					get_image_size(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					self.image_processor_tester.pad_size

      __lowerCamelCase    		=					(old_height // size + 1) * size - old_height
      __lowerCamelCase    		=					(old_width // size + 1) * size - old_width
      return old_height + pad_height, old_width + pad_width







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      __lowerCamelCase    		=					self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE	)
      for image in image_inputs:
         self.assertIsInstance(_SCREAMING_SNAKE_CASE ,				Image.Image	)

      # Test not batched input
      __lowerCamelCase    		=					image_processor(image_inputs[0] ,				return_tensors='pt'	).pixel_values
      __lowerCamelCase		,__lowerCamelCase    		=					self.calculate_expected_size(np.array(image_inputs[0]	)	)
      self.assertEqual(
          encoded_images.shape ,				(
              1,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          ) ,				)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      __lowerCamelCase    		=					self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE ,				numpify=_SCREAMING_SNAKE_CASE	)
      for image in image_inputs:
         self.assertIsInstance(_SCREAMING_SNAKE_CASE ,				np.ndarray	)

      # Test not batched input
      __lowerCamelCase    		=					image_processor(image_inputs[0] ,				return_tensors='pt'	).pixel_values
      __lowerCamelCase		,__lowerCamelCase    		=					self.calculate_expected_size(image_inputs[0]	)
      self.assertEqual(
          encoded_images.shape ,				(
              1,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          ) ,				)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      __lowerCamelCase    		=					self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE ,				torchify=_SCREAMING_SNAKE_CASE	)
      for image in image_inputs:
         self.assertIsInstance(_SCREAMING_SNAKE_CASE ,				torch.Tensor	)

      # Test not batched input
      __lowerCamelCase    		=					image_processor(image_inputs[0] ,				return_tensors='pt'	).pixel_values
      __lowerCamelCase		,__lowerCamelCase    		=					self.calculate_expected_size(image_inputs[0]	)
      self.assertEqual(
          encoded_images.shape ,				(
              1,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          ) ,				)














import argparse
import collections
import importlib.util
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_table.py
A										=      'src/diffusers'
A										=      'docs/source/en'
A										=      '.'



def        _lowerCamelCase(      lowerCAmelCase__					:		List[Any]     ,     lowerCAmelCase__					:		int     ,     lowerCAmelCase__					:		Tuple   ):





				'''simple docstring'''







				with open(lowerCAmelCase__     ,     'r'     ,     encoding='utf-8'     ,     newline='\n'   ) as f:
								SCREAMING_SNAKE_CASE_ :						Dict    				= f.readlines()
				# Find the start prompt.
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= 0
				while not lines[start_index].startswith(lowerCAmelCase__   ):
								start_index += 1
				start_index += 1

				SCREAMING_SNAKE_CASE_ :						List[str]    				= start_index
				while not lines[end_index].startswith(lowerCAmelCase__   ):
								end_index += 1
				end_index -= 1

				while len(lines[start_index]   ) <= 1:
								start_index += 1
				while len(lines[end_index]   ) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index]   ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
A										=      'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
A										=      re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
A										=      re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
A										=      re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
A										=      importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
A										=      spec.loader.load_module()



def        _lowerCamelCase(      lowerCAmelCase__					:		Any   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'     ,     lowerCAmelCase__   )
				return [m.group(0   ) for m in matches]



def        _lowerCamelCase(      lowerCAmelCase__					:		Optional[Any]     ,     lowerCAmelCase__					:		Tuple   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Any    				= 2 if text == '✅' or text == '❌' else len(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						int    				= (width - text_length) // 2
				SCREAMING_SNAKE_CASE_ :						str    				= width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent



def        _lowerCamelCase(      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= {
				    name: config_mapping_names[code]
				    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_mapping_names
				}
				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= {name: config.replace('ConfigMixin'     ,     ''   ) for name, config in model_name_to_config.items()}

				# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
				SCREAMING_SNAKE_CASE_ :						int    				= collections.defaultdict(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= collections.defaultdict(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= collections.defaultdict(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						str    				= collections.defaultdict(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						Dict    				= collections.defaultdict(lowerCAmelCase__   )

				# Let's lookup through all diffusers object (once).
				for attr_name in dir(lowerCAmelCase__   ):
								SCREAMING_SNAKE_CASE_ :						List[str]    				= None
								if attr_name.endswith('Tokenizer'   ):
												SCREAMING_SNAKE_CASE_ :						List[Any]    				= slow_tokenizers
												SCREAMING_SNAKE_CASE_ :						Dict    				= attr_name[:-9]
								elif attr_name.endswith('TokenizerFast'   ):
												SCREAMING_SNAKE_CASE_ :						Any    				= fast_tokenizers
												SCREAMING_SNAKE_CASE_ :						int    				= attr_name[:-13]
								elif _re_tf_models.match(lowerCAmelCase__   ) is not None:
												SCREAMING_SNAKE_CASE_ :						List[Any]    				= tf_models
												SCREAMING_SNAKE_CASE_ :						Optional[int]    				= _re_tf_models.match(lowerCAmelCase__   ).groups()[0]
								elif _re_flax_models.match(lowerCAmelCase__   ) is not None:
												SCREAMING_SNAKE_CASE_ :						List[str]    				= flax_models
												SCREAMING_SNAKE_CASE_ :						int    				= _re_flax_models.match(lowerCAmelCase__   ).groups()[0]
								elif _re_pt_models.match(lowerCAmelCase__   ) is not None:
												SCREAMING_SNAKE_CASE_ :						Optional[int]    				= pt_models
												SCREAMING_SNAKE_CASE_ :						Any    				= _re_pt_models.match(lowerCAmelCase__   ).groups()[0]

								if lookup_dict is not None:
												while len(lowerCAmelCase__   ) > 0:
																if attr_name in model_name_to_prefix.values():
																				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= True
																				break
																# Try again after removing the last word in the name
																SCREAMING_SNAKE_CASE_ :						str    				= ''.join(camel_case_split(lowerCAmelCase__   )[:-1]   )

    # Let's build that table!
				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= list(model_name_to_config.keys()   )
				model_names.sort(key=str.lower   )
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= ['Model', 'Tokenizer slow', 'Tokenizer fast', 'PyTorch support', 'TensorFlow support', 'Flax Support']
				# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
				SCREAMING_SNAKE_CASE_ :						str    				= [len(lowerCAmelCase__   ) + 2 for c in columns]
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= max([len(lowerCAmelCase__   ) for name in model_names]   ) + 2

				# Build the table per se
				SCREAMING_SNAKE_CASE_ :						Dict    				= '|' + '|'.join([_center_text(lowerCAmelCase__     ,     lowerCAmelCase__   ) for c, w in zip(lowerCAmelCase__     ,     lowerCAmelCase__   )]   ) + '|\n'
				# Use ":-----:" format to center-aligned table cell texts
				table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths]   ) + "|\n"

				SCREAMING_SNAKE_CASE_ :						int    				= {True: '✅', False: '❌'}
				for name in model_names:
								SCREAMING_SNAKE_CASE_ :						int    				= model_name_to_prefix[name]
								SCREAMING_SNAKE_CASE_ :						int    				= [
								    name,
								    check[slow_tokenizers[prefix]],
								    check[fast_tokenizers[prefix]],
								    check[pt_models[prefix]],
								    check[tf_models[prefix]],
								    check[flax_models[prefix]],
								]
								table += "|" + "|".join([_center_text(lowerCAmelCase__     ,     lowerCAmelCase__   ) for l, w in zip(lowerCAmelCase__     ,     lowerCAmelCase__   )]   ) + "|\n"
				return table



def        _lowerCamelCase(      lowerCAmelCase__					:		Optional[int]=False   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						str    				= _find_text_in_file(
				    filename=os.path.join(lowerCAmelCase__     ,     'index.mdx'   )     ,     start_prompt='<!--This table is updated automatically from the auto modules'     ,     end_prompt='<!-- End table-->'     ,     )
				SCREAMING_SNAKE_CASE_ :						Tuple    				= get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(lowerCAmelCase__     ,     'index.mdx'   )     ,     'w'     ,     encoding='utf-8'     ,     newline='\n'   ) as f:
																f.writelines(lines[:start_index] + [new_table] + lines[end_index:]   )
								else:
												raise ValueError(
												    'The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this.'   )


if __name__ == "__main__":
				A										=      argparse.ArgumentParser()
				parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
				A										=      parser.parse_args()

				check_model_table(args.fix_and_overwrite)