

'''simple docstring'''

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 			__UpperCamelCase							(		lowercase__  :					List[str],   lowercase__  :					List[Any],   lowercase__  :					int	):






     '''simple docstring'''





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

     __lowercase     						=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 			__UpperCamelCase							(		lowercase__  :					Optional[int]	):






     '''simple docstring'''





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

def 			__UpperCamelCase							(		lowercase__  :					Optional[int],   lowercase__  :					Union[str, Any]	):






     '''simple docstring'''





     __lowercase     						=2 if text == '✅' or text == '❌' else len(lowercase__	)
     __lowercase     						=(width - text_length) // 2
     __lowercase     						=width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent

def 			__UpperCamelCase							(		):






     '''simple docstring'''





     __lowercase     						=diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     __lowercase     						={
         name: config_mapping_names[code]
         for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
         if code in config_mapping_names
     }
     __lowercase     						={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.
     __lowercase     						=collections.defaultdict(lowercase__	)
     __lowercase     						=collections.defaultdict(lowercase__	)
     __lowercase     						=collections.defaultdict(lowercase__	)
     __lowercase     						=collections.defaultdict(lowercase__	)
     __lowercase     						=collections.defaultdict(lowercase__	)

     # Let's lookup through all diffusers object (once).
     for attr_name in dir(lowercase__	):
          __lowercase     						=None
          if attr_name.endswith('Tokenizer'	):
               __lowercase     						=slow_tokenizers
               __lowercase     						=attr_name[:-9]
          elif attr_name.endswith('TokenizerFast'	):
               __lowercase     						=fast_tokenizers
               __lowercase     						=attr_name[:-13]
          elif _re_tf_models.match(lowercase__	) is not None:
               __lowercase     						=tf_models
               __lowercase     						=_re_tf_models.match(lowercase__	).groups()[0]
          elif _re_flax_models.match(lowercase__	) is not None:
               __lowercase     						=flax_models
               __lowercase     						=_re_flax_models.match(lowercase__	).groups()[0]
          elif _re_pt_models.match(lowercase__	) is not None:
               __lowercase     						=pt_models
               __lowercase     						=_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():
                         __lowercase     						=True
                         break
                    # Try again after removing the last word in the name
                    __lowercase     						=''.join(camel_case_split(lowercase__	)[:-1]	)

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

     # Build the table per se
     __lowercase     						='|' + '|'.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"

     __lowercase     						={True: '✅', False: '❌'}
     for name in model_names:
          __lowercase     						=model_name_to_prefix[name]
          __lowercase     						=[
              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 			__UpperCamelCase							(		lowercase__  :					Union[str, Any]=False	):






     '''simple docstring'''





     __lowercase						,       __lowercase						,       __lowercase						,       __lowercase     						=_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-->',   )
     __lowercase     						=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)





'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase							=					logging.get_logger(__name__)


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	["pixel_values"]







       def __init__(							self     :	List[Any]  , __lowercase     :	bool = True  , __lowercase     :	Optional[Dict[str, int]] = None  , __lowercase     :	PILImageResampling = PILImageResampling.BILINEAR  , __lowercase     :	bool = True  , __lowercase     :	Union[int, float] = 1 / 255  , __lowercase     :	bool = True  , __lowercase     :	Optional[Union[float, List[float]]] = None  , __lowercase     :	Optional[Union[float, List[float]]] = None  , **__lowercase     :	List[Any]  , ):







            """simple docstring"""







            super().__init__(**__lowercase )
            __lowercase     						=size if size is not None else {'height': 224, 'width': 224}
            __lowercase     						=get_size_dict(__lowercase )
            __lowercase     						=do_resize
            __lowercase     						=do_rescale
            __lowercase     						=do_normalize
            __lowercase     						=size
            __lowercase     						=resample
            __lowercase     						=rescale_factor
            __lowercase     						=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            __lowercase     						=image_std if image_std is not None else IMAGENET_STANDARD_STD







       def      snake_case				(							self     :	Dict  , __lowercase     :	np.ndarray  , __lowercase     :	Dict[str, int]  , __lowercase     :	PILImageResampling = PILImageResampling.BILINEAR  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	Union[str, Any]  , ):







            """simple docstring"""







            __lowercase     						=get_size_dict(__lowercase )
            if "height" not in size or "width" not in size:
                 raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
            return resize(
                __lowercase  , size=(size['height'], size['width'])  , resample=__lowercase  , data_format=__lowercase  , **__lowercase )







       def      snake_case				(							self     :	str  , __lowercase     :	np.ndarray  , __lowercase     :	float  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







            return rescale(__lowercase  , scale=__lowercase  , data_format=__lowercase  , **__lowercase )







       def      snake_case				(							self     :	Any  , __lowercase     :	np.ndarray  , __lowercase     :	Union[float, List[float]]  , __lowercase     :	Union[float, List[float]]  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	str  , ):







            """simple docstring"""







            return normalize(__lowercase  , mean=__lowercase  , std=__lowercase  , data_format=__lowercase  , **__lowercase )







       def      snake_case				(							self     :	str  , __lowercase     :	ImageInput  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Dict[str, int] = None  , __lowercase     :	PILImageResampling = None  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Optional[float] = None  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Optional[Union[float, List[float]]] = None  , __lowercase     :	Optional[Union[float, List[float]]] = None  , __lowercase     :	Optional[Union[str, TensorType]] = None  , __lowercase     :	Union[str, ChannelDimension] = ChannelDimension.FIRST  , **__lowercase     :	Tuple  , ):







            """simple docstring"""







            __lowercase     						=do_resize if do_resize is not None else self.do_resize
            __lowercase     						=do_rescale if do_rescale is not None else self.do_rescale
            __lowercase     						=do_normalize if do_normalize is not None else self.do_normalize
            __lowercase     						=resample if resample is not None else self.resample
            __lowercase     						=rescale_factor if rescale_factor is not None else self.rescale_factor
            __lowercase     						=image_mean if image_mean is not None else self.image_mean
            __lowercase     						=image_std if image_std is not None else self.image_std

            __lowercase     						=size if size is not None else self.size
            __lowercase     						=get_size_dict(__lowercase )

            __lowercase     						=make_list_of_images(__lowercase )

            if not valid_images(__lowercase ):
                 raise ValueError(
                     'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                     'torch.Tensor, tf.Tensor or jax.ndarray.' )

            if do_resize and size is None:
                 raise ValueError('Size must be specified if do_resize is True.' )

            if do_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.' )

            # All transformations expect numpy arrays.
            __lowercase     						=[to_numpy_array(__lowercase ) for image in images]

            if do_resize:
                 __lowercase     						=[self.resize(image=__lowercase  , size=__lowercase  , resample=__lowercase ) for image in images]

            if do_rescale:
                 __lowercase     						=[self.rescale(image=__lowercase  , scale=__lowercase ) for image in images]

            if do_normalize:
                 __lowercase     						=[self.normalize(image=__lowercase  , mean=__lowercase  , std=__lowercase ) for image in images]

            __lowercase     						=[to_channel_dimension_format(__lowercase  , __lowercase ) for image in images]

            __lowercase     						={'pixel_values': images}
            return BatchFeature(data=__lowercase  , tensor_type=__lowercase )


