


"""simple docstring"""




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def        lowercase							(		lowerCAmelCase__ :     str			) ->      int:
		__a           =			[
		    '''decoder.version''',
		    '''decoder.output_projection.weight''',
		    '''_float_tensor''',
		    '''decoder.embed_positions._float_tensor''',
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase__    ,					lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     Optional[int]			) ->      str:
		__a      ,     __a           =			emb.weight.shape
		__a           =			nn.Linear(lowerCAmelCase__    ,					lowerCAmelCase__    ,					bias=lowerCAmelCase__			)
		__a           =			emb.weight.data
		return lin_layer

def        lowercase							(		lowerCAmelCase__ :     Optional[int]			) ->      Tuple:
		__a           =			torch.load(lowerCAmelCase__    ,					map_location='''cpu'''			)
		__a           =			Namespace(**checkpoint['''cfg''']['''model''']			)
		__a           =			checkpoint['''model''']
		remove_ignore_keys_(lowerCAmelCase__			)
		__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=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    ,					)

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

		return model


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








import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       List[Any]       					=IFInpaintingPipeline
							lowercase_       :       Optional[int]       					=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
							lowercase_       :       Any       					=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowercase_       :       str       					=PipelineTesterMixin.required_optional_params - {'''latents'''}




							def  A__      (				self):
												return self._get_dummy_components()




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)

												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)
												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)

												lowercase							=						{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''image''': image,
												    '''mask_image''': mask_image,
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}

												return inputs




							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()   ,reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,)
							def  A__      (				self):
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)




							def  A__      (				self):
												self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != '''cuda'''   ,reason='''float16 requires CUDA''')
							def  A__      (				self):
												# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
												super().test_save_load_floataa(expected_max_diff=1E-1)




							def  A__      (				self):
												self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)




							def  A__      (				self):
												self._test_save_load_local()





							def  A__      (				self):
												self._test_inference_batch_single_identical(
												    expected_max_diff=1E-2   ,)




