

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
_UpperCamelCase			      =		"src/diffusers"
_UpperCamelCase			      =		"docs/source/en"
_UpperCamelCase			      =		"."







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

      __lowerCAmelCase :						Optional[Any] = start_index
      while not lines[end_index].startswith(lowercase__						):
            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 |
_UpperCamelCase			      =		"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_UpperCamelCase			      =		re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_UpperCamelCase			      =		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.
_UpperCamelCase			      =		re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


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







def 		_lowercase			(					lowercase__						):
      __lowerCAmelCase :						str = re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''       ,     lowercase__						)
      return [m.group(0						) for m in matches]







def 		_lowercase			(					lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						int = 2 if text == '''✅''' or text == '''❌''' else len(lowercase__						)
      __lowerCAmelCase :						str = (width - text_length) // 2
      __lowerCAmelCase :						Optional[Any] = width - text_length - left_indent
      return " " * left_indent + text + " " * right_indent







def 		_lowercase			(					):
      __lowerCAmelCase :						List[Any] = diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      __lowerCAmelCase :						Tuple = {
          name: config_mapping_names[code]
          for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
          if code in config_mapping_names
      }
      __lowerCAmelCase :						int = {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.
      __lowerCAmelCase :						Dict = collections.defaultdict(lowercase__						)
      __lowerCAmelCase :						List[Any] = collections.defaultdict(lowercase__						)
      __lowerCAmelCase :						List[Any] = collections.defaultdict(lowercase__						)
      __lowerCAmelCase :						Optional[int] = collections.defaultdict(lowercase__						)
      __lowerCAmelCase :						Optional[Any] = collections.defaultdict(lowercase__						)

      # Let's lookup through all diffusers object (once).
      for attr_name in dir(lowercase__						):
            __lowerCAmelCase :						str = None
            if attr_name.endswith('''Tokenizer'''						):
                  __lowerCAmelCase :						List[Any] = slow_tokenizers
                  __lowerCAmelCase :						int = attr_name[:-9]
            elif attr_name.endswith('''TokenizerFast'''						):
                  __lowerCAmelCase :						List[Any] = fast_tokenizers
                  __lowerCAmelCase :						List[Any] = attr_name[:-1_3]
            elif _re_tf_models.match(lowercase__						) is not None:
                  __lowerCAmelCase :						Optional[Any] = tf_models
                  __lowerCAmelCase :						List[Any] = _re_tf_models.match(lowercase__						).groups()[0]
            elif _re_flax_models.match(lowercase__						) is not None:
                  __lowerCAmelCase :						Any = flax_models
                  __lowerCAmelCase :						Optional[Any] = _re_flax_models.match(lowercase__						).groups()[0]
            elif _re_pt_models.match(lowercase__						) is not None:
                  __lowerCAmelCase :						str = pt_models
                  __lowerCAmelCase :						Tuple = _re_pt_models.match(lowercase__						).groups()[0]

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

    # Let's build that table!
      __lowerCAmelCase :						Union[str, Any] = list(model_name_to_config.keys()						)
      model_names.sort(key=str.lower						)
      __lowerCAmelCase :						Optional[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).
      __lowerCAmelCase :						str = [len(lowercase__						) + 2 for c in columns]
      __lowerCAmelCase :						Dict = max([len(lowercase__						) for name in model_names]						) + 2

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

      __lowerCAmelCase :						Optional[Any] = {True: '''✅''', False: '''❌'''}
      for name in model_names:
            __lowerCAmelCase :						Union[str, Any] = model_name_to_prefix[name]
            __lowerCAmelCase :						List[str] = [
                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(lowercase__       ,     lowercase__						) for l, w in zip(lowercase__       ,     lowercase__						)]						) + "|\n"
      return table







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

      if current_table != new_table:
            if overwrite:
                  with open(os.path.join(lowercase__       ,     '''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__":
 _UpperCamelCase			      =		argparse.ArgumentParser()
 parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
 _UpperCamelCase			      =		parser.parse_args()

 check_model_table(args.fix_and_overwrite)





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 BlipImageProcessor







class   __lowercase				(unittest.TestCase			):
 def __init__(							self				,							A_				,							A_=7				,							A_=3				,							A_=18				,							A_=30				,							A_=400				,							A_=True				,							A_=None				,							A_=True				,							A_=False				,							A_=[0.48_145_466, 0.4_578_275, 0.40_821_073]				,							A_=[0.26_862_954, 0.26_130_258, 0.27_577_711]				,							A_=True				,							) ->Dict:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = size if size is not None else {'''height''': 20, '''width''': 20}
       __lowerCAmelCase :						List[str] = parent
       __lowerCAmelCase :						List[str] = batch_size
       __lowerCAmelCase :						Optional[Any] = num_channels
       __lowerCAmelCase :						List[Any] = image_size
       __lowerCAmelCase :						Optional[int] = min_resolution
       __lowerCAmelCase :						Optional[int] = max_resolution
       __lowerCAmelCase :						List[Any] = do_resize
       __lowerCAmelCase :						Dict = size
       __lowerCAmelCase :						Dict = do_normalize
       __lowerCAmelCase :						Any = image_mean
       __lowerCAmelCase :						Optional[int] = image_std
       __lowerCAmelCase :						Any = do_pad
       __lowerCAmelCase :						Any = do_convert_rgb
 def      UpperCamelCase__  (							self ) ->List[str]:



       '''simple docstring'''





       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_convert_rgb": self.do_convert_rgb,
           "do_pad": self.do_pad,
       }





 def      UpperCamelCase__  (							self				,							A_=False				,							A_=False				,							A_=False ) ->Dict:



       '''simple docstring'''





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

       if equal_resolution:
             __lowerCAmelCase :						int = []
             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 :						List[Any] = []
             for i in range(self.batch_size ):
                   __lowerCAmelCase,							__lowerCAmelCase :						Any = 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 :						List[str] = [Image.fromarray(np.moveaxis(A_				,							0				,							-1 ) ) for x in image_inputs]

       if torchify:
             __lowerCAmelCase :						int = [torch.from_numpy(A_ ) for x in image_inputs]

       return image_inputs







@require_torch
@require_vision
class   __lowercase				(_UpperCAmelCase		,       unittest.TestCase			):
 _UpperCamelCase        =  BlipImageProcessor if is_vision_available() else None
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = BlipImageProcessingTester(self )
 @property
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       __lowerCAmelCase :						str = self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(A_				,							'''do_resize''' ) )
       self.assertTrue(hasattr(A_				,							'''size''' ) )
       self.assertTrue(hasattr(A_				,							'''do_normalize''' ) )
       self.assertTrue(hasattr(A_				,							'''image_mean''' ) )
       self.assertTrue(hasattr(A_				,							'''image_std''' ) )
       self.assertTrue(hasattr(A_				,							'''do_convert_rgb''' ) )
 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       pass
 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Dict = self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       __lowerCAmelCase :						List[str] = self.image_processor_tester.prepare_inputs(equal_resolution=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							Image.Image )

       # Test not batched input
       __lowerCAmelCase :						Optional[Any] = image_processor(image_inputs[0]				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						str = image_processor(A_				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)
 def      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[Any] = self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       __lowerCAmelCase :						Union[str, Any] = self.image_processor_tester.prepare_inputs(equal_resolution=A_				,							numpify=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							np.ndarray )

       # Test not batched input
       __lowerCAmelCase :						Dict = image_processor(image_inputs[0]				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						List[Any] = image_processor(A_				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)





 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						List[Any] = self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       __lowerCAmelCase :						str = self.image_processor_tester.prepare_inputs(equal_resolution=A_				,							torchify=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							torch.Tensor )

       # Test not batched input
       __lowerCAmelCase :						List[Any] = image_processor(image_inputs[0]				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						Tuple = image_processor(A_				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)







@require_torch
@require_vision
class   __lowercase				(_UpperCAmelCase		,       unittest.TestCase			):
 _UpperCamelCase        =  BlipImageProcessor if is_vision_available() else None
 def      UpperCamelCase__  (							self ) ->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Any = BlipImageProcessingTester(self				,							num_channels=4 )
       __lowerCAmelCase :						str = 3
 @property
 def      UpperCamelCase__  (							self ) ->List[str]:



       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase :						int = self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(A_				,							'''do_resize''' ) )
       self.assertTrue(hasattr(A_				,							'''size''' ) )
       self.assertTrue(hasattr(A_				,							'''do_normalize''' ) )
       self.assertTrue(hasattr(A_				,							'''image_mean''' ) )
       self.assertTrue(hasattr(A_				,							'''image_std''' ) )
       self.assertTrue(hasattr(A_				,							'''do_convert_rgb''' ) )
 def      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       pass





 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       __lowerCAmelCase :						Optional[int] = self.image_processor_tester.prepare_inputs(equal_resolution=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							Image.Image )

       # Test not batched input
       __lowerCAmelCase :						Tuple = image_processor(image_inputs[0]				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               1,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						List[str] = image_processor(A_				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               self.image_processor_tester.batch_size,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)


