




'''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
a__         =     '''src/diffusers'''
a__         =     '''docs/source/en'''
a__         =     '''.'''




def    snake_case__     (  a       ,      a       ,      a	)			->       List[Any]:





			'''simple docstring'''






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

			snake_case__          =						start_index
			while not lines[end_index].startswith(a	):
						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    snake_case__     (  a	)			->       Any:





			'''simple docstring'''






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




def    snake_case__     (  a       ,      a	)			->       Tuple:





			'''simple docstring'''






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




def    snake_case__     (  )			->       List[str]:





			'''simple docstring'''






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

			# Let's lookup through all diffusers object (once).
			for attr_name in dir(a	):
						snake_case__          =						None
						if attr_name.endswith("""Tokenizer"""	):
									snake_case__          =						slow_tokenizers
									snake_case__          =						attr_name[:-9]
						elif attr_name.endswith("""TokenizerFast"""	):
									snake_case__          =						fast_tokenizers
									snake_case__          =						attr_name[:-13]
						elif _re_tf_models.match(a	) is not None:
									snake_case__          =						tf_models
									snake_case__          =						_re_tf_models.match(a	).groups()[0]
						elif _re_flax_models.match(a	) is not None:
									snake_case__          =						flax_models
									snake_case__          =						_re_flax_models.match(a	).groups()[0]
						elif _re_pt_models.match(a	) is not None:
									snake_case__          =						pt_models
									snake_case__          =						_re_pt_models.match(a	).groups()[0]

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

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

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

			snake_case__          =						{True: """✅""", False: """❌"""}
			for name in model_names:
						snake_case__          =						model_name_to_prefix[name]
						snake_case__          =						[
						    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(a       ,      a	) for l, w in zip(a       ,      a	)]	) + "|\n"
			return table




def    snake_case__     (  a=False	)			->       str:





			'''simple docstring'''






			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__          =						_find_text_in_file(
			    filename=os.path.join(a       ,      """index.mdx"""	)       ,      start_prompt="""<!--This table is updated automatically from the auto modules"""       ,      end_prompt="""<!-- End table-->"""       ,      )
			snake_case__          =						get_model_table_from_auto_modules()

			if current_table != new_table:
						if overwrite:
									with open(os.path.join(a       ,      """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)





'''simple docstring'''

import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

a__         =     [(1,)]




def    snake_case__     (  )			->       Tuple:





			'''simple docstring'''






			snake_case__          =						bool(os.getenv("""RUN_SLOW"""       ,      0	)	)
			if run_slow:
						assert True
			else:
						assert False, "should have been skipped"




def    snake_case__     (  )			->       Dict:





			'''simple docstring'''






			snake_case__          =						bool(os.getenv("""RUN_SLOW"""       ,      0	)	)
			if run_slow and torch_device == "cuda":
						assert True
			else:
						assert False, "should have been skipped"





@require_torch
class __magic_name__(						unittest.TestCase      ):






						@slow
						@require_torch_gpu
						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									check_slow_torch_cuda()






						@require_torch_gpu
						@slow
						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									check_slow_torch_cuda()







						@parameterized.expand(__UpperCamelCase       )
						@slow
						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						int=None       ):



									'''simple docstring'''






									check_slow()







@slow
@require_torch_gpu
def    snake_case__     (  )			->       Tuple:





			'''simple docstring'''






			check_slow_torch_cuda()




@require_torch_gpu
@slow
def    snake_case__     (  )			->       List[Any]:





			'''simple docstring'''






			check_slow_torch_cuda()




@slow
@pytest.mark.parametrize("""param"""       ,      [1]	)
def    snake_case__     (  a	)			->       Union[str, Any]:





			'''simple docstring'''






			check_slow()




@pytest.mark.parametrize("""param"""       ,      [1]	)
@slow
def    snake_case__     (  a	)			->       Dict:





			'''simple docstring'''






			check_slow()