

import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
						import onnxruntime as ort






class 	__magic_name__    (			lowerCAmelCase_  ,    unittest.TestCase      ):
			# FIXME: add fast tests
			pass






@nightly
@require_onnxruntime
@require_torch_gpu
class 	__magic_name__    (			unittest.TestCase      ):





			@property
			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)





			@property
			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =ort.SessionOptions()
						__a        =False
						return options





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo.png'					)
						__a        =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'					)
						__a        =OnnxStableDiffusionInpaintPipeline.from_pretrained(
						    'runwayml/stable-diffusion-inpainting'					,  revision='onnx'					,  safety_checker=__snake_case					,  feature_extractor=__snake_case					,  provider=self.gpu_provider					,  sess_options=self.gpu_options					,  )
						pipe.set_progress_bar_config(disable=__snake_case					)

						__a        ='A red cat sitting on a park bench'

						__a        =np.random.RandomState(0					)
						__a        =pipe(
						    prompt=__snake_case					,  image=__snake_case					,  mask_image=__snake_case					,  guidance_scale=7.5					,  num_inference_steps=10					,  generator=__snake_case					,  output_type='np'					,  )
						__a        =output.images
						__a        =images[0, 255:258, 255:258, -1]

						assert images.shape == (1, 512, 512, 3)
						__a        =np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]					)

						assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo.png'					)
						__a        =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'					)
						__a        =LMSDiscreteScheduler.from_pretrained(
						    'runwayml/stable-diffusion-inpainting'					,  subfolder='scheduler'					,  revision='onnx'					)
						__a        =OnnxStableDiffusionInpaintPipeline.from_pretrained(
						    'runwayml/stable-diffusion-inpainting'					,  revision='onnx'					,  scheduler=__snake_case					,  safety_checker=__snake_case					,  feature_extractor=__snake_case					,  provider=self.gpu_provider					,  sess_options=self.gpu_options					,  )
						pipe.set_progress_bar_config(disable=__snake_case					)

						__a        ='A red cat sitting on a park bench'

						__a        =np.random.RandomState(0					)
						__a        =pipe(
						    prompt=__snake_case					,  image=__snake_case					,  mask_image=__snake_case					,  guidance_scale=7.5					,  num_inference_steps=20					,  generator=__snake_case					,  output_type='np'					,  )
						__a        =output.images
						__a        =images[0, 255:258, 255:258, -1]

						assert images.shape == (1, 512, 512, 3)
						__a        =np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]					)

						assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3









import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def        UpperCamelCase_(							_snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			__a        =[
			    'decoder.version',
			    'decoder.output_projection.weight',
			    '_float_tensor',
			    'decoder.embed_positions._float_tensor',
			]
			for k in ignore_keys:
						state_dict.pop(_snake_case       ,     _snake_case	)






def        UpperCamelCase_(							_snake_case   :  Any	):




			"""simple docstring"""






			__a      ,    __a        =emb.weight.shape
			__a        =nn.Linear(_snake_case       ,     _snake_case       ,     bias=_snake_case	)
			__a        =emb.weight.data
			return lin_layer






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     map_location='cpu'	)
			__a        =Namespace(**checkpoint['cfg']['model']	)
			__a        =checkpoint['model']
			remove_ignore_keys_(_snake_case	)
			__a        =state_dict['decoder.embed_tokens.weight'].shape[0]

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

			__a        =XGLMConfig(
			    vocab_size=_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       ,     )

			__a        =XGLMForCausalLM(_snake_case	)
			__a        =model.load_state_dict(_snake_case       ,     strict=_snake_case	)
			print(_snake_case	)
			__a        =make_linear_from_emb(model.model.embed_tokens	)

			return model


if __name__ == "__main__":
						_lowerCAmelCase					:			str  				=			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					:			str  				=			parser.parse_args()
						_lowerCAmelCase					:			Union[str, Any]  				=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)






