




"""simple docstring"""





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Tuple:


			"""simple docstring"""
			__UpperCAmelCase						:			int												=       [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(UpperCamelCase					,							UpperCamelCase				)

def        _UpperCamelCase     (      UpperCamelCase				)      ->      List[Any]:


			"""simple docstring"""
			__UpperCAmelCase						,  __UpperCAmelCase						:			Any												=       emb.weight.shape
			__UpperCAmelCase						:			Dict												=       nn.Linear(UpperCamelCase					,							UpperCamelCase					,							bias=UpperCamelCase				)
			__UpperCAmelCase						:			Optional[Any]												=       emb.weight.data
			return lin_layer

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Dict:


			"""simple docstring"""
			__UpperCAmelCase						:			Tuple												=       torch.load(UpperCamelCase					,							map_location="cpu"				)
			__UpperCAmelCase						:			Tuple												=       Namespace(**checkpoint["cfg"]["model"]				)
			__UpperCAmelCase						:			str												=       checkpoint["model"]
			remove_ignore_keys_(UpperCamelCase				)
			__UpperCAmelCase						:			int												=       state_dict["decoder.embed_tokens.weight"].shape[0]

			__UpperCAmelCase						:			Optional[int]												=       {key.replace("decoder"					,							"model"				): val for key, val in state_dict.items()}

			__UpperCAmelCase						:			Any												=       XGLMConfig(
			    vocab_size=UpperCamelCase					,							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						:			Union[str, Any]												=       XGLMForCausalLM(UpperCamelCase				)
			__UpperCAmelCase						:			Union[str, Any]												=       model.load_state_dict(UpperCamelCase					,							strict=UpperCamelCase				)
			print(UpperCamelCase				)
			__UpperCAmelCase						:			int												=       make_linear_from_emb(model.model.embed_tokens				)

			return model


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








'''simple docstring'''


import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
				import optax
				from flax.training.common_utils import onehot

				from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
				from transformers.models.ta.modeling_flax_ta import shift_tokens_right

@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class      A       (						unittest.TestCase       ):







							@slow
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''   )
														_a      	=							AutoTokenizer.from_pretrained('''google/mt5-small'''   )

														_a      	=							tokenizer('''Hello there'''				,     return_tensors='''np'''   ).input_ids
														_a      	=							tokenizer('''Hi I am'''				,     return_tensors='''np'''   ).input_ids

														_a      	=							shift_tokens_right(lowerCAmelCase_				,     model.config.pad_token_id				,     model.config.decoder_start_token_id   )

														_a      	=							model(lowerCAmelCase_				,     decoder_input_ids=lowerCAmelCase_   ).logits
														_a      	=							optax.softmax_cross_entropy(lowerCAmelCase_				,     onehot(lowerCAmelCase_				,     logits.shape[-1]   )   ).mean()

														_a      	=							-(labels.shape[-1] * loss.item())

														_a      	=							-8_4.9_1_2_7
														self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1e-4   )

