
import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# 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
lowercase						:      Any									= 'src/transformers'
lowercase						:      Optional[Any]									= 'docs/source/en'
lowercase						:      List[str]									= '.'




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    List[Any]		,						_lowerCamelCase				:    List[Any])      ->					Tuple:






							'''simple docstring'''





							with open(_lowerCamelCase		,						"r"		,						encoding="utf-8"		,						newline="\n") as f:
														__UpperCamelCase			:     Union[str, Any]         =      f.readlines()
							# Find the start prompt.
							__UpperCamelCase			:     Union[str, Any]         =      0
							while not lines[start_index].startswith(_lowerCamelCase):
														start_index += 1
							start_index += 1

							__UpperCamelCase			:     Optional[Any]         =      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 |
lowercase						:      str									= 'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
lowercase						:      int									= re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowercase						:      List[Any]									= 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.
lowercase						:      Optional[Any]									= re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
lowercase						:      int									= direct_transformers_import(TRANSFORMERS_PATH)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict)      ->					int:






							'''simple docstring'''





							__UpperCamelCase			:     Dict         =      re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"		,						_lowerCamelCase)
							return [m.group(0) for m in matches]




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    Dict)      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      2 if text == "✅" or text == "❌" else len(_lowerCamelCase)
							__UpperCamelCase			:     Union[str, Any]         =      (width - text_length) // 2
							__UpperCamelCase			:     Any         =      width - text_length - left_indent
							return " " * left_indent + text + " " * right_indent




def 			_SCREAMING_SNAKE_CASE      (	)      ->					int:






							'''simple docstring'''





							__UpperCamelCase			:     int         =      transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
							__UpperCamelCase			:     Optional[Any]         =      {
							    name: config_maping_names[code]
							    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
							    if code in config_maping_names
							}
							__UpperCamelCase			:     Optional[Any]         =      {name: config.replace("Config"		,						"") 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.
							__UpperCamelCase			:     Optional[int]         =      collections.defaultdict(_lowerCamelCase)
							__UpperCamelCase			:     Optional[Any]         =      collections.defaultdict(_lowerCamelCase)
							__UpperCamelCase			:     Dict         =      collections.defaultdict(_lowerCamelCase)
							__UpperCamelCase			:     List[str]         =      collections.defaultdict(_lowerCamelCase)
							__UpperCamelCase			:     Tuple         =      collections.defaultdict(_lowerCamelCase)

							# Let's lookup through all transformers object (once).
							for attr_name in dir(_lowerCamelCase):
														__UpperCamelCase			:     Any         =      None
														if attr_name.endswith("Tokenizer"):
																					__UpperCamelCase			:     List[str]         =      slow_tokenizers
																					__UpperCamelCase			:     Union[str, Any]         =      attr_name[:-9]
														elif attr_name.endswith("TokenizerFast"):
																					__UpperCamelCase			:     Any         =      fast_tokenizers
																					__UpperCamelCase			:     Optional[Any]         =      attr_name[:-13]
														elif _re_tf_models.match(_lowerCamelCase) is not None:
																					__UpperCamelCase			:     List[Any]         =      tf_models
																					__UpperCamelCase			:     Tuple         =      _re_tf_models.match(_lowerCamelCase).groups()[0]
														elif _re_flax_models.match(_lowerCamelCase) is not None:
																					__UpperCamelCase			:     int         =      flax_models
																					__UpperCamelCase			:     List[str]         =      _re_flax_models.match(_lowerCamelCase).groups()[0]
														elif _re_pt_models.match(_lowerCamelCase) is not None:
																					__UpperCamelCase			:     List[Any]         =      pt_models
																					__UpperCamelCase			:     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():
																																			__UpperCamelCase			:     str         =      True
																																			break
																												# Try again after removing the last word in the name
																												__UpperCamelCase			:     Dict         =      "".join(camel_case_split(_lowerCamelCase)[:-1])

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

							# Build the table per se
							__UpperCamelCase			:     Union[str, Any]         =      "|" + "|".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"

							__UpperCamelCase			:     List[Any]         =      {True: "✅", False: "❌"}
							for name in model_names:
														__UpperCamelCase			:     int         =      model_name_to_prefix[name]
														__UpperCamelCase			:     Any         =      [
														    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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int=False)      ->					Optional[int]:






							'''simple docstring'''





							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Optional[Any]         =      _find_text_in_file(
							    filename=os.path.join(_lowerCamelCase		,						"index.md")		,						start_prompt="<!--This table is updated automatically from the auto modules"		,						end_prompt="<!-- End table-->"		,						)
							__UpperCamelCase			:     Optional[int]         =      get_model_table_from_auto_modules()

							if current_table != new_table:
														if overwrite:
																					with open(os.path.join(_lowerCamelCase		,						"index.md")		,						"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.md` has not been updated. Run `make fix-copies` to fix this.")


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

		check_model_table(args.fix_and_overwrite)

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase						:      List[str]									= argparse.ArgumentParser()

		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

		parser.add_argument(
		    '--txt2img_unclip',
		    default='kakaobrain/karlo-v1-alpha',
		    type=str,
		    required=False,
		    help='The pretrained txt2img unclip.',
		)

		lowercase						:      int									= parser.parse_args()

		lowercase						:      str									= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase						:      int									= CLIPImageProcessor()
		lowercase						:      Optional[int]									= CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase						:      int									= UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)