





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
UpperCAmelCase__  :     Union[str, Any]   			='''src/transformers'''
UpperCAmelCase__  :     Optional[Any]   			='''docs/source/en'''
UpperCAmelCase__  :     Any   			='''.'''





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

				lowerCamelCase			=start_index
				while not lines[end_index].startswith(_UpperCAmelCase			):
								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__  :     str   			='''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
UpperCAmelCase__  :     Dict   			=re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
UpperCAmelCase__  :     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.
UpperCAmelCase__  :     Any   			=re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


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





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





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					Optional[Any]:
				lowerCamelCase			=2 if text == """✅""" or text == """❌""" else len(_UpperCAmelCase			)
				lowerCamelCase			=(width - text_length) // 2
				lowerCamelCase			=width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent





def        _lowercase   (  )  ->					Optional[int]:
				lowerCamelCase			=transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowerCamelCase			={
				    name: config_maping_names[code]
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_maping_names
				}
				lowerCamelCase			={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.
				lowerCamelCase			=collections.defaultdict(_UpperCAmelCase			)
				lowerCamelCase			=collections.defaultdict(_UpperCAmelCase			)
				lowerCamelCase			=collections.defaultdict(_UpperCAmelCase			)
				lowerCamelCase			=collections.defaultdict(_UpperCAmelCase			)
				lowerCamelCase			=collections.defaultdict(_UpperCAmelCase			)

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

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

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

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

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





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

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

							check_model_table(args.fix_and_overwrite)












import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class  __A       (  a      ,   unittest.TestCase      ):
				__A								=			BioGptTokenizer
				__A								=			False


				def _snake_case    (						self							):
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowerCamelCase			=[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """w</w>""",
								    """r</w>""",
								    """t</w>""",
								    """lo""",
								    """low""",
								    """er</w>""",
								    """low</w>""",
								    """lowest</w>""",
								    """newer</w>""",
								    """wider</w>""",
								    """<unk>""",
								]
								lowerCamelCase			=dict(zip(UpperCAmelCase_				,	range(len(UpperCAmelCase_							)							)							)							)
								lowerCamelCase			=["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

								lowerCamelCase			=os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES["""vocab_file"""]							)
								lowerCamelCase			=os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES["""merges_file"""]							)
								with open(self.vocab_file				,	"""w"""							) as fp:
												fp.write(json.dumps(UpperCAmelCase_							)							)
								with open(self.merges_file				,	"""w"""							) as fp:
												fp.write("""\n""".join(UpperCAmelCase_							)							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			="""lower newer"""
								lowerCamelCase			="""lower newer"""
								return input_text, output_text


				def _snake_case    (						self							):
								lowerCamelCase			=BioGptTokenizer(self.vocab_file				,	self.merges_file							)

								lowerCamelCase			="""lower"""
								lowerCamelCase			=["""low""", """er</w>"""]
								lowerCamelCase			=tokenizer.tokenize(UpperCAmelCase_							)
								self.assertListEqual(UpperCAmelCase_				,	UpperCAmelCase_							)

								lowerCamelCase			=tokens + ["""<unk>"""]
								lowerCamelCase			=[14, 15, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)				,	UpperCAmelCase_							)


				@slow
				def _snake_case    (						self							):
								lowerCamelCase			=BioGptTokenizer.from_pretrained("""microsoft/biogpt"""							)

								lowerCamelCase			=tokenizer.encode("""sequence builders"""				,	add_special_tokens=UpperCAmelCase_							)
								lowerCamelCase			=tokenizer.encode("""multi-sequence build"""				,	add_special_tokens=UpperCAmelCase_							)

								lowerCamelCase			=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_							)
								lowerCamelCase			=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_				,	UpperCAmelCase_							)

								self.assertTrue(encoded_sentence == [2] + text							)
								self.assertTrue(encoded_pair == [2] + text + [2] + text_a							)





