

'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						IFInpaintingSuperResolutionPipeline
				lowerCamelCase_							=						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
				lowerCamelCase_							=						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}			)
				lowerCamelCase_							=						PipelineTesterMixin.required_optional_params - {'latents'}







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										return self._get_superresolution_dummy_components()







				def    lowerCamelCase_  (					self    :		List[str]		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		Tuple=0      ):
										'''simple docstring'''
										if str(UpperCAmelCase__      ).startswith('''mps'''      ):
																lowercase   :       Tuple								=torch.manual_seed(UpperCAmelCase__      )
										else:
																lowercase   :       str								=torch.Generator(device=UpperCAmelCase__      ).manual_seed(UpperCAmelCase__      )

										lowercase   :       str								=floats_tensor((1, 3, 16, 16)		,				rng=random.Random(UpperCAmelCase__      )      ).to(UpperCAmelCase__      )
										lowercase   :       List[Any]								=floats_tensor((1, 3, 32, 32)		,				rng=random.Random(UpperCAmelCase__      )      ).to(UpperCAmelCase__      )
										lowercase   :       Optional[Any]								=floats_tensor((1, 3, 32, 32)		,				rng=random.Random(UpperCAmelCase__      )      ).to(UpperCAmelCase__      )

										lowercase   :       str								={
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''image''': image,
										    '''original_image''': original_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    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										self._test_save_load_optional_components()







				@unittest.skipIf(torch_device != '''cuda'''		,				reason='''float16 requires CUDA'''      )
				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										# 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    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										self._test_attention_slicing_forward_pass(expected_max_diff=1E-2      )







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										self._test_save_load_local()







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										self._test_inference_batch_single_identical(
										    expected_max_diff=1E-2		,				)








'''simple docstring'''





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							UpperCamelCase_							=					argparse.ArgumentParser(
							    description=(
							        """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
							        """ Distillation"""
							    )
							)
							parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
							parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
							parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
							parser.add_argument("""--vocab_transform""", action="""store_true""")
							UpperCamelCase_							=					parser.parse_args()

							if args.model_type == "roberta":
														UpperCamelCase_							=					RobertaForMaskedLM.from_pretrained(args.model_name)
														UpperCamelCase_							=					"""roberta"""
							elif args.model_type == "gpt2":
														UpperCamelCase_							=					GPTaLMHeadModel.from_pretrained(args.model_name)
														UpperCamelCase_							=					"""transformer"""

							UpperCamelCase_							=					model.state_dict()
							UpperCamelCase_							=					{}

							# Embeddings #
							if args.model_type == "gpt2":
														for param_name in ["wte.weight", "wpe.weight"]:
																					UpperCamelCase_							=					state_dict[f'''{prefix}.{param_name}''']
							else:
														for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																					UpperCamelCase_							=					f'''{prefix}.embeddings.{w}.weight'''
																					UpperCamelCase_							=					state_dict[param_name]
														for w in ["weight", "bias"]:
																					UpperCamelCase_							=					f'''{prefix}.embeddings.LayerNorm.{w}'''
																					UpperCamelCase_							=					state_dict[param_name]

    # Transformer Blocks #
							UpperCamelCase_							=					0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														if args.model_type == "gpt2":
																					for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																												for w in ["weight", "bias"]:
																																			UpperCamelCase_							=					state_dict[
																																			    f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
																																			]
																					UpperCamelCase_							=					state_dict[f'''{prefix}.h.{teacher_idx}.attn.bias''']
														else:
																					for layer in [
																					    "attention.self.query",
																					    "attention.self.key",
																					    "attention.self.value",
																					    "attention.output.dense",
																					    "attention.output.LayerNorm",
																					    "intermediate.dense",
																					    "output.dense",
																					    "output.LayerNorm",
																					]:
																												for w in ["weight", "bias"]:
																																			UpperCamelCase_							=					state_dict[
																																			    f'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
																																			]
														std_idx += 1

							# Language Modeling Head ###s
							if args.model_type == "roberta":
														for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																					UpperCamelCase_							=					state_dict[f'''{layer}''']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												UpperCamelCase_							=					state_dict[f'''lm_head.dense.{w}''']
																												UpperCamelCase_							=					state_dict[f'''lm_head.layer_norm.{w}''']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					UpperCamelCase_							=					state_dict[f'''{prefix}.ln_f.{w}''']
														UpperCamelCase_							=					state_dict["""lm_head.weight"""]

							print(f'''N layers selected for distillation: {std_idx}''')
							print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

							print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
							torch.save(compressed_sd, args.dump_checkpoint)





