


import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[int]		 =						[
     'decoder.version',
     'decoder.output_projection.weight',
     '_float_tensor',
     'decoder.embed_positions._float_tensor',
 ]
 for k in ignore_keys:
  state_dict.pop(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase ,					__lowerCamelCase		:							Optional[Any]		 =						emb.weight.shape
 __lowerCamelCase		:							int		 =						nn.Linear(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	bias=SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Optional[int]		 =						emb.weight.data
 return lin_layer
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Union[str, Any]		 =						torch.load(SCREAMING_SNAKE_CASE__      ,	map_location='cpu'						)
 __lowerCamelCase		:							Any		 =						Namespace(**checkpoint['cfg']['model']						)
 __lowerCamelCase		:							Dict		 =						checkpoint['model']
 remove_ignore_keys_(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							str		 =						state_dict['decoder.embed_tokens.weight'].shape[0]

 __lowerCamelCase		:							int		 =						{key.replace('decoder'      ,	'model'						): val for key, val in state_dict.items()}

 __lowerCamelCase		:							Any		 =						XGLMConfig(
     vocab_size=SCREAMING_SNAKE_CASE__      ,	max_position_embeddings=args.max_target_positions      ,	num_layers=args.decoder_layers      ,	attention_heads=args.decoder_attention_heads      ,	ffn_dim=args.decoder_ffn_embed_dim      ,	d_model=args.decoder_embed_dim      ,	layerdrop=args.decoder_layerdrop      ,	dropout=args.dropout      ,	attention_dropout=args.attention_dropout      ,	activation_dropout=args.activation_dropout      ,	activation_function='gelu'      ,	scale_embedding=not args.no_scale_embedding      ,	tie_word_embeddings=args.share_decoder_input_output_embed      ,	)

 __lowerCamelCase		:							List[Any]		 =						XGLMForCausalLM(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							int		 =						model.load_state_dict(SCREAMING_SNAKE_CASE__      ,	strict=SCREAMING_SNAKE_CASE__						)
 print(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							List[str]		 =						make_linear_from_emb(model.model.embed_tokens						)

 return model


if __name__ == "__main__":
       lowercase_	   =     argparse.ArgumentParser()
       # Required parameters
       parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
       parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
       lowercase_	   =     parser.parse_args()
       lowercase_	   =     convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
       model.save_pretrained(args.pytorch_dump_folder_path)





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase_	   =     5_0_0_0_0
lowercase_	   =     5_0_0_0

lowercase_   ,lowercase_	   =     os.path.split(__file__)
lowercase_	   =     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 for i in range(SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							Tuple		 =						dataset[i]
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 for i in range(0      ,	len(SCREAMING_SNAKE_CASE__						)      ,	SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							Optional[Any]		 =						dataset[i : i + batch_size]
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__						):
  for i in range(SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase		:							str		 =						dataset[i]
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__						):
  for i in range(0      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase		:							int		 =						dataset[i : i + batch_size]
def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							Union[str, Any]		 =						{'num examples': SPEED_TEST_N_EXAMPLES}
 __lowerCamelCase		:							Optional[Any]		 =						[
     (read, {'length': SMALL_TEST}),
     (read, {'length': SPEED_TEST_N_EXAMPLES}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
     (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
     (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
     (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
     (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
     (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
     (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
 ]

 __lowerCamelCase		:							Any		 =						[
     (read, {'length': SMALL_TEST}),
     (read, {'length': SPEED_TEST_N_EXAMPLES}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
     (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
     (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
     (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
 ]
 with tempfile.TemporaryDirectory() as tmp_dir:
  print('generating dataset'						)
  __lowerCamelCase		:							Optional[int]		 =						datasets.Features(
      {'list': datasets.Sequence(datasets.Value('float32'						)						), 'numbers': datasets.Value('float32'						)}						)
  __lowerCamelCase		:							str		 =						generate_example_dataset(
      os.path.join(SCREAMING_SNAKE_CASE__      ,	'dataset.arrow'						)      ,	SCREAMING_SNAKE_CASE__      ,	num_examples=SCREAMING_SNAKE_CASE__      ,	seq_shapes={'list': (100,)}      ,	)
  print('first set of iterations'						)
  for func, kwargs in functions:
   print(func.__name__      ,	str(SCREAMING_SNAKE_CASE__						)						)
   __lowerCamelCase		:							Optional[int]		 =						func(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						)

  print('shuffling dataset'						)
  __lowerCamelCase		:							str		 =						dataset.shuffle()
  print('Second set of iterations (after shuffling'						)
  for func, kwargs in functions_shuffled:
   print('shuffled '      ,	func.__name__      ,	str(SCREAMING_SNAKE_CASE__						)						)
   __lowerCamelCase		:							int		 =						func(
       SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						)

 with open(SCREAMING_SNAKE_CASE__      ,	'wb'						) as f:
  f.write(json.dumps(SCREAMING_SNAKE_CASE__						).encode('utf-8'						)						)


if __name__ == "__main__":  # useful to run the profiler
       benchmark_iterating()

