
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
lowerCamelCase_						: Union[str, Any]         =  """src/transformers"""
lowerCamelCase_						: Union[str, Any]         =  """docs/source/en"""
lowerCamelCase_						: Union[str, Any]         =  """."""







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

				UpperCamelCase_:							Dict	=						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 |
lowerCamelCase_						: Any         =  """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
lowerCamelCase_						: str         =  re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
lowerCamelCase_						: Optional[int]         =  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.
lowerCamelCase_						: Any         =  re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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







def        A__					(						lowerCamelCase						)  ->	Any:
				UpperCamelCase_:							Optional[int]	=						re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""     ,    lowerCamelCase						)
				return [m.group(0						) for m in matches]







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	List[Any]:
				UpperCamelCase_:							List[str]	=						2 if text == """✅""" or text == """❌""" else len(lowerCamelCase						)
				UpperCamelCase_:							int	=						(width - text_length) // 2
				UpperCamelCase_:							List[str]	=						width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent







def        A__					(						)  ->	Tuple:
				UpperCamelCase_:							Dict	=						transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				UpperCamelCase_:							Any	=						{
				    name: config_maping_names[code]
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_maping_names
				}
				UpperCamelCase_:							Optional[int]	=						{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_:							List[Any]	=						collections.defaultdict(lowerCamelCase						)
				UpperCamelCase_:							List[str]	=						collections.defaultdict(lowerCamelCase						)
				UpperCamelCase_:							List[str]	=						collections.defaultdict(lowerCamelCase						)
				UpperCamelCase_:							List[str]	=						collections.defaultdict(lowerCamelCase						)
				UpperCamelCase_:							List[str]	=						collections.defaultdict(lowerCamelCase						)

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

    # Let's build that table!
				UpperCamelCase_:							Optional[int]	=						list(model_name_to_config.keys()						)
				model_names.sort(key=str.lower						)
				UpperCamelCase_:							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).
				UpperCamelCase_:							Tuple	=						[len(lowerCamelCase						) + 2 for c in columns]
				UpperCamelCase_:							List[Any]	=						max([len(lowerCamelCase						) for name in model_names]						) + 2

				# Build the table per se
				UpperCamelCase_:							int	=						"""|""" + """|""".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_:							Optional[Any]	=						{True: """✅""", False: """❌"""}
				for name in model_names:
								UpperCamelCase_:							int	=						model_name_to_prefix[name]
								UpperCamelCase_:							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(lowerCamelCase     ,    lowerCamelCase						) for l, w in zip(lowerCamelCase     ,    lowerCamelCase						)]						) + "|\n"
				return table







def        A__					(						lowerCamelCase=False						)  ->	Dict:
				UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_:							Dict	=						_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_:							Tuple	=						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__":
							lowerCamelCase_						: Optional[int]         =  argparse.ArgumentParser()
							parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
							lowerCamelCase_						: Dict         =  parser.parse_args()

							check_model_table(args.fix_and_overwrite)








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_						: List[str]         =  {
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[Any]         =  [
							    """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MgpstrModel""",
							    """MgpstrPreTrainedModel""",
							    """MgpstrForSceneTextRecognition""",
							]

if TYPE_CHECKING:
							from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
							from .processing_mgp_str import MgpstrProcessor
							from .tokenization_mgp_str import MgpstrTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mgp_str import (
														    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MgpstrForSceneTextRecognition,
														    MgpstrModel,
														    MgpstrPreTrainedModel,
														)
else:
							import sys

							lowerCamelCase_						: int         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






