




import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()






def  snake_case (							A						,       A						,       A      )							->							Optional[int]:




	'''simple docstring'''


	lowerCamelCase_							:		str							       =							json.load(open(A						,       'r'      )      )
	lowerCamelCase_							:		Dict							       =							GPTNeoConfig(
	    hidden_size=config_json['n_embd']						,       num_layers=config_json['n_layer']						,       num_heads=config_json['n_head']						,       attention_types=config_json['attention_types']						,       max_position_embeddings=config_json['n_positions']						,       resid_dropout=config_json['res_dropout']						,       embed_dropout=config_json['embed_dropout']						,       attention_dropout=config_json['attn_dropout']						,       )
	print(f"Building PyTorch model from configuration: {config}"      )
	lowerCamelCase_							:		str							       =							GPTNeoForCausalLM(A      )

	# Load weights from tf checkpoint
	load_tf_weights_in_gpt_neo(A						,       A						,       A      )

	# Save pytorch-model
	print(f"Save PyTorch model to {pytorch_dump_path}"      )
	model.save_pretrained(A      )


if __name__ == "__main__":
							UpperCamelCase__		=						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        'The config json file corresponding to the pre-trained mesh-tf model. \n'
							        'This specifies the model architecture.'
							    ),
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							UpperCamelCase__		=						parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






import sys

from transformers import AutoTokenizer


UpperCamelCase__		=						sys.argv[1]
UpperCamelCase__		=						sys.argv[2]
UpperCamelCase__		=						int(sys.argv[3])

UpperCamelCase__		=						0

UpperCamelCase__		=						AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, 'rt') as f_p:
							for line in f_p:
														UpperCamelCase__		=						line.rstrip()

														if not line:
																					print(line)
																					UpperCamelCase__		=						0
																					continue

														UpperCamelCase__		=						line.split()[0]

														UpperCamelCase__		=						len(tokenizer.tokenize(token))

														# Token contains strange control characters like \x96 or \x95
														# Just filter out the complete line
														if current_subwords_len == 0:
																					continue

														if (subword_len_counter + current_subwords_len) > max_len:
																					print('')
																					print(line)
																					UpperCamelCase__		=						current_subwords_len
																					continue

														subword_len_counter += current_subwords_len

														print(line)
