
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def 		_UpperCAmelCase   (	UpperCamelCase:     Any  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[
	    "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:     Dict  ):






	"""simple docstring"""


	__lowerCAmelCase   ,						__lowerCAmelCase         =							emb.weight.shape
	__lowerCAmelCase         =							nn.Linear(UpperCamelCase			,							UpperCamelCase			,							bias=UpperCamelCase  )
	__lowerCAmelCase         =							emb.weight.data
	return lin_layer






def 		_UpperCAmelCase   (	UpperCamelCase:     Union[str, Any]  ):






	"""simple docstring"""


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

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

	__lowerCAmelCase         =							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			,							)

	__lowerCAmelCase         =							XGLMForCausalLM(UpperCamelCase  )
	__lowerCAmelCase         =							model.load_state_dict(UpperCamelCase			,							strict=UpperCamelCase  )
	print(UpperCamelCase  )
	__lowerCAmelCase         =							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

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


UpperCamelCase_   	=	False

class       a		(   unittest.TestCase					):
							pass

@slow
@require_torch_gpu
class       a		(   unittest.TestCase					):







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion" )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
								__lowerCAmelCase         =							torch.manual_seed(0 )
								__lowerCAmelCase         =							pipe(
								    image=snake_case__      ,				generator=snake_case__      ,				guidance_scale=7.5      ,				num_inference_steps=50      ,				output_type="numpy"      ,				).images

								__lowerCAmelCase         =							image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								__lowerCAmelCase         =							np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5] )

								assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






