


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 						lowerCamelCase_							(					lowerCAmelCase__  ,  lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					IFInpaintingPipeline
					__UpperCAmelCase							  =					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
					__UpperCAmelCase							  =					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					__UpperCAmelCase							  =					PipelineTesterMixin.required_optional_params - {"latents"}






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								return self._get_dummy_components()






					def A   (					self			,							snake_case_			,							snake_case_=0    ) ->      Dict:







								'''simple docstring'''

								if str(snake_case_    ).startswith('''mps'''    ):
											__lowercase			       = torch.manual_seed(snake_case_    )
								else:
											__lowercase			       = torch.Generator(device=snake_case_    ).manual_seed(snake_case_    )

								__lowercase			       = floats_tensor((1, 3, 3_2, 3_2)			,							rng=random.Random(snake_case_    )    ).to(snake_case_    )
								__lowercase			       = floats_tensor((1, 3, 3_2, 3_2)			,							rng=random.Random(snake_case_    )    ).to(snake_case_    )

								__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    ) ->      Any:







								'''simple docstring'''

								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )






					def A   (					self    ) ->      Union[str, Any]:







								'''simple docstring'''

								self._test_save_load_optional_components()






					@unittest.skipIf(torch_device != '''cuda'''			,							reason='''float16 requires CUDA'''    )
					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								super().test_save_load_floataa(expected_max_diff=1e-1    )






					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								self._test_save_load_local()






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								self._test_inference_batch_single_identical(
								    expected_max_diff=1e-2			,							)






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
a			:			List[str]												=					logging.get_logger(__name__)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)
			if "model" in sd.keys():
						__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)['''model''']

			# pop unnecessary weights
			__lowercase			       = [
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(_UpperCamelCase	)

			__lowercase			       = {
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									__lowercase			       = sd.pop(_UpperCamelCase	)

			__lowercase			       = list(sd.keys()	)
			for key in keys:
						if ".qkv_proj." in key:
									__lowercase			       = sd[key]
									# We split QKV in separate Q,K,V

									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.q_proj.'''	)
									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.k_proj.'''	)
									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.v_proj.'''	)

									__lowercase			       = value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									__lowercase       ,			__lowercase       ,			__lowercase			       = torch.split(_UpperCamelCase							,      depth // 3							,      dim=0	)

									__lowercase			       = q
									__lowercase			       = k
									__lowercase			       = v
									del sd[key]

			return sd


@torch.no_grad()
def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = load_checkpoint(_UpperCamelCase	)

			if config is not None:
						__lowercase			       = OPTConfig.from_pretrained(_UpperCamelCase	)
			else:
						__lowercase			       = OPTConfig()

			__lowercase			       = OPTModel(_UpperCamelCase	).half().eval()
			model.load_state_dict(_UpperCamelCase	)

			# Check results
			Path(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
			model.save_pretrained(_UpperCamelCase	)


if __name__ == "__main__":
	a			:			List[Any]												=					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--fairseq_path''',
	    type=str,
	    help=(
	        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
	        ''' https://huggingface.co/models?other=opt_metasq'''
	    ),
	)
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
	a			:			Any												=					parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


