




import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class     snake_case__							(pl.LightningModule   ):
				"""simple docstring"""



				def __init__(      self				:     Any					,      __lowerCamelCase				:     Optional[int]							)					->     List[str]:
							super().__init__()
							a    =			model
							a    =			2
							a    =			nn.Linear(self.model.config.hidden_size					,      self.num_labels							)





				def 							__UpperCAmelCase							(      self				:     str							)					->     int:
							pass




def 			__magic_name__				( A		:					str,  A		:					str,  A		:					str   ):






			'''simple docstring'''







			a    =			LongformerModel.from_pretrained(A   )
			a    =			LightningModel(A   )

			a    =			torch.load(A,  map_location=torch.device("cpu"   )   )
			lightning_model.load_state_dict(ckpt["state_dict"]   )

			# init longformer question answering model
			a    =			LongformerForQuestionAnswering.from_pretrained(A   )

			# transfer weights
			longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()   )
			longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()   )
			longformer_for_qa.eval()

			# save model
			longformer_for_qa.save_pretrained(A   )

			print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}"""   )


if __name__ == "__main__":
		__lowerCAmelCase		:				Optional[int]       			=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--longformer_model',
		    default=None,
		    type=str,
		    required=True,
		    help='model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.',
		)
		parser.add_argument(
		    '--longformer_question_answering_ckpt_path',
		    default=None,
		    type=str,
		    required=True,
		    help='Path the official PyTorch Lightning Checkpoint.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__lowerCAmelCase		:				List[str]       			=		parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)










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	:     Optional[int]   ="""src/transformers"""
_UpperCAmelCase	:     str   ="""docs/source/en"""
_UpperCAmelCase	:     Optional[int]   ="""."""



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_       )->       List[str]:
				with open(lowerCAmelCase_       ,      '''r'''       ,      encoding='''utf-8'''       ,      newline='''\n'''       ) as f:
								lowerCAmelCase_       :					int			 = f.readlines()
				# Find the start prompt.
				lowerCAmelCase_       :					List[Any]			 = 0
				while not lines[start_index].startswith(lowerCAmelCase_       ):
								start_index += 1
				start_index += 1

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


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



def 	lowerCAmelCase       (						lowerCAmelCase_       )->       Optional[int]:
				lowerCAmelCase_       :					str			 = re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''       ,      lowerCAmelCase_       )
				return [m.group(0       ) for m in matches]



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       )->       Optional[int]:
				lowerCAmelCase_       :					Tuple			 = 2 if text == '''✅''' or text == '''❌''' else len(lowerCAmelCase_       )
				lowerCAmelCase_       :					int			 = (width - text_length) // 2
				lowerCAmelCase_       :					Union[str, Any]			 = width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent



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

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

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

				# Build the table per se
				lowerCAmelCase_       :					Dict			 = '''|''' + '''|'''.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"

				lowerCAmelCase_       :					List[str]			 = {True: '''✅''', False: '''❌'''}
				for name in model_names:
								lowerCAmelCase_       :					List[Any]			 = model_name_to_prefix[name]
								lowerCAmelCase_       :					Union[str, 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 	lowerCAmelCase       (						lowerCAmelCase_=False       )->       Tuple:
				lowerCAmelCase_ ,     lowerCAmelCase_ ,     lowerCAmelCase_ ,     lowerCAmelCase_       :					str			 = _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-->'''       ,      )
				lowerCAmelCase_       :					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__":
						_UpperCAmelCase	:     List[Any]   =argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						_UpperCAmelCase	:     Tuple   =parser.parse_args()

						check_model_table(args.fix_and_overwrite)