


import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM
def        UpperCamelCase_(				_A   :Any  )->		Optional[int]:

			'''simple docstring'''





			UpperCamelCase__					=       [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(_A				,     _A  )
def        UpperCamelCase_(				_A   :Dict  )->		Optional[int]:

			'''simple docstring'''





			UpperCamelCase__,      UpperCamelCase__					=       emb.weight.shape
			UpperCamelCase__					=       nn.Linear(_A				,     _A				,     bias=_A  )
			UpperCamelCase__					=       emb.weight.data
			return lin_layer
def        UpperCamelCase_(				_A   :List[Any]  )->		Union[str, Any]:

			'''simple docstring'''





			UpperCamelCase__					=       torch.load(_A				,     map_location="cpu"  )
			UpperCamelCase__					=       Namespace(**checkpoint["cfg"]["model"]  )
			UpperCamelCase__					=       checkpoint["model"]
			remove_ignore_keys_(_A  )
			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=_A				,     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(_A  )
			UpperCamelCase__					=       model.load_state_dict(_A				,     strict=_A  )
			print(_A  )
			UpperCamelCase__					=       make_linear_from_emb(model.model.embed_tokens  )

			return model


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






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
						import jax







@require_flax
class        lowerCamelCase__	(							UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					List[str]    =		FlaxAutoencoderKL




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       4
						UpperCamelCase__					=       3
						UpperCamelCase__					=       (32, 32)

						UpperCamelCase__					=       jax.random.PRNGKey(0     )
						UpperCamelCase__					=       jax.random.uniform(snake_case      ,						((batch_size, num_channels) + sizes)     )

						return {"sample": image, "prng_key": prng_key}







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       {
						    "block_out_channels": [32, 64],
						    "in_channels": 3,
						    "out_channels": 3,
						    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
						    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "latent_channels": 4,
						}
						UpperCamelCase__					=       self.dummy_input
						return init_dict, inputs_dict


