
import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
lowerCamelCase__	=							logging.getLogger()

lowerCamelCase__	=							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class        lowerCAmelCase__      (   __lowercase	):

						def  A_				(			self       ,							a				)					->      Optional[Any]:


								'''simple docstring'''

								os.makedirs(a       ,							exist_ok=a				)
								_UpperCamelCase						    =	{"""source""": """What is love ?""", """target""": """life"""}
								_UpperCamelCase						    =	{"""train""": 12, """val""": 2, """test""": 2}
								for split in ["train", "test", "val"]:
										for field in ["source", "target"]:
												_UpperCamelCase						    =	"""\n""".join([contents[field]] * n_lines[split]				)
												with open(os.path.join(a       ,							F'{split}.{field}'				)       ,							"""w"""				) as f:
														f.write(a				)

						def  A_				(			self       ,							a       ,							a = "pytorch"				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_auto_remove_tmp_dir()
								_UpperCamelCase						    =	os.path.join(a       ,							"""output"""				)
								_UpperCamelCase						    =	os.path.join(a       ,							"""data"""				)
								self._create_dummy_data(data_dir=a				)

								_UpperCamelCase						    =	F'\n                --data_dir {data_dir} \\n                --output_dir {output_dir} \\n                --model_name_or_path facebook/rag-sequence-base \\n                --model_type rag_sequence \\n                --do_train \\n                --do_predict \\n                --n_val -1 \\n                --val_check_interval 1.0 \\n                --train_batch_size 2 \\n                --eval_batch_size 1 \\n                --max_source_length 25 \\n                --max_target_length 25 \\n                --val_max_target_length 25 \\n                --test_max_target_length 25 \\n                --label_smoothing 0.1 \\n                --dropout 0.1 \\n                --attention_dropout 0.1 \\n                --weight_decay 0.001 \\n                --adam_epsilon 1e-08 \\n                --max_grad_norm 0.1 \\n                --lr_scheduler polynomial \\n                --learning_rate 3e-04 \\n                --num_train_epochs 1 \\n                --warmup_steps 4 \\n                --gradient_accumulation_steps 1 \\n                --distributed-port 8787 \\n                --use_dummy_dataset 1 \\n                --distributed_retriever {distributed_retriever} \\n            '.split()

								if gpus > 0:
										testargs.append(F'--gpus={gpus}'				)
										if is_apex_available():
												testargs.append("""--fp16"""				)
								else:
										testargs.append("""--gpus=0"""				)
										testargs.append("""--distributed_backend=ddp_cpu"""				)
										testargs.append("""--num_processes=2"""				)

								_UpperCamelCase						    =	[sys.executable, str(Path(finetune_rag.__file__				).resolve()				)] + testargs
								execute_subprocess_async(a       ,							env=self.get_env()				)

								_UpperCamelCase						    =	os.path.join(a       ,							"""metrics.json"""				)
								with open(a				) as f:
										_UpperCamelCase						    =	json.load(a				)
								return result

						@require_torch_gpu
						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	self._run_finetune(gpus=1				)
								self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,							0.2				)

						@require_torch_multi_gpu
						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	self._run_finetune(gpus=2				)
								self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,							0.2				)

						@require_torch_gpu
						@require_ray
						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self._run_finetune(gpus=1       ,							distributed_retriever="""ray"""				)
								self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,							0.2				)







						@require_torch_multi_gpu
						@require_ray
						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	self._run_finetune(gpus=1       ,							distributed_retriever="""ray"""				)
								self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,							0.2				)





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 							__A(lowerCAmelCase )		->				Any:

		"""simple docstring"""






		_UpperCamelCase						    =	[
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase						,  lowerCAmelCase )


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase    ,	_UpperCamelCase						    =	emb.weight.shape
		_UpperCamelCase						    =	nn.Linear(lowerCAmelCase						,  lowerCAmelCase						,  bias=lowerCAmelCase )
		_UpperCamelCase						    =	emb.weight.data
		return lin_layer


def 							__A(lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )
		_UpperCamelCase						    =	Namespace(**checkpoint["""cfg"""]["""model"""] )
		_UpperCamelCase						    =	checkpoint["""model"""]
		remove_ignore_keys_(lowerCAmelCase )
		_UpperCamelCase						    =	state_dict["""decoder.embed_tokens.weight"""].shape[0]

		_UpperCamelCase						    =	{key.replace("""decoder"""						,  """model""" ): val for key, val in state_dict.items()}

		_UpperCamelCase						    =	XGLMConfig(
		    vocab_size=lowerCAmelCase						,  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						,  )

		_UpperCamelCase						    =	XGLMForCausalLM(lowerCAmelCase )
		_UpperCamelCase						    =	model.load_state_dict(lowerCAmelCase						,  strict=lowerCAmelCase )
		print(lowerCAmelCase )
		_UpperCamelCase						    =	make_linear_from_emb(model.model.embed_tokens )

		return model


if __name__ == "__main__":
		lowerCamelCase__	=							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.")
		lowerCamelCase__	=							parser.parse_args()
		lowerCamelCase__	=							convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)



