



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 	a__       (       _snake_case  ,			_snake_case  ,			unittest.TestCase       ):


					"""simple docstring"""



					A__  :				Optional[Any]					 =				StableDiffusionParadigmsPipeline
					A__  :				int					 =				TEXT_TO_IMAGE_PARAMS
					A__  :				Dict					 =				TEXT_TO_IMAGE_BATCH_PARAMS
					A__  :				Optional[int]					 =				TEXT_TO_IMAGE_IMAGE_PARAMS
					A__  :				Any					 =				TEXT_TO_IMAGE_IMAGE_PARAMS

					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										torch.manual_seed(0    )
										lowercase						   =       UNetaDConditionModel(
										    block_out_channels=(32, 64)      ,  layers_per_block=2      ,  sample_size=32      ,  in_channels=4      ,  out_channels=4      ,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,  up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,  cross_attention_dim=32      ,  attention_head_dim=(2, 4)      ,  use_linear_projection=lowercase__      ,  )
										lowercase						   =       DDIMParallelScheduler(
										    beta_start=0.0_0085      ,  beta_end=0.012      ,  beta_schedule='scaled_linear'      ,  clip_sample=lowercase__      ,  set_alpha_to_one=lowercase__      ,  )
										torch.manual_seed(0    )
										lowercase						   =       AutoencoderKL(
										    block_out_channels=[32, 64]      ,  in_channels=3      ,  out_channels=3      ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,  latent_channels=4      ,  sample_size=128      ,  )
										torch.manual_seed(0    )
										lowercase						   =       CLIPTextConfig(
										    bos_token_id=0      ,  eos_token_id=2      ,  hidden_size=32      ,  intermediate_size=37      ,  layer_norm_eps=1E-05      ,  num_attention_heads=4      ,  num_hidden_layers=5      ,  pad_token_id=1      ,  vocab_size=1000      ,  hidden_act='gelu'      ,  projection_dim=512      ,  )
										lowercase						   =       CLIPTextModel(lowercase__    )
										lowercase						   =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

										lowercase						   =       {
										    'unet': unet,
										    'scheduler': scheduler,
										    'vae': vae,
										    'text_encoder': text_encoder,
										    'tokenizer': tokenizer,
										    'safety_checker': None,
										    'feature_extractor': None,
										}
										return components

					def 				__UpperCAmelCase		(		self     :Tuple      ,  lowercase__     :Tuple      ,  lowercase__     :str=0    ):
										if str(lowercase__    ).startswith('mps'    ):
															lowercase						   =       torch.manual_seed(lowercase__    )
										else:
															lowercase						   =       torch.Generator(device=lowercase__    ).manual_seed(lowercase__    )
										lowercase						   =       {
										    'prompt': 'a photograph of an astronaut riding a horse',
										    'generator': generator,
										    'num_inference_steps': 10,
										    'guidance_scale': 6.0,
										    'output_type': 'numpy',
										    'parallel': 3,
										    'debug': True,
										}
										return inputs

					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										lowercase						   =       'cpu'  # ensure determinism for the device-dependent torch.Generator
										lowercase						   =       self.get_dummy_components()
										lowercase						   =       StableDiffusionParadigmsPipeline(**lowercase__    )
										lowercase						   =       sd_pipe.to(lowercase__    )
										sd_pipe.set_progress_bar_config(disable=lowercase__    )

										lowercase						   =       self.get_dummy_inputs(lowercase__    )
										lowercase						   =       sd_pipe(**lowercase__    ).images
										lowercase						   =       image[0, -3:, -3:, -1]
										assert image.shape == (1, 64, 64, 3)

										lowercase						   =       np.array([0.4773, 0.5417, 0.4723, 0.4925, 0.5631, 0.4752, 0.5240, 0.4935, 0.5023]    )

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

					def 				__UpperCAmelCase		(		self     :List[str]    ):
										lowercase						   =       'cpu'  # ensure determinism for the device-dependent torch.Generator
										lowercase						   =       self.get_dummy_components()
										torch.manual_seed(0    )
										lowercase						   =       DDPMParallelScheduler()
										torch.manual_seed(0    )
										lowercase						   =       StableDiffusionParadigmsPipeline(**lowercase__    )
										lowercase						   =       sd_pipe.to(lowercase__    )
										sd_pipe.set_progress_bar_config(disable=lowercase__    )

										lowercase						   =       self.get_dummy_inputs(lowercase__    )
										lowercase						   =       sd_pipe(**lowercase__    ).images
										lowercase						   =       image[0, -3:, -3:, -1]
										assert image.shape == (1, 64, 64, 3)

										lowercase						   =       np.array([0.3573, 0.4420, 0.4960, 0.4799, 0.3796, 0.3879, 0.4819, 0.4365, 0.4468]    )

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

					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										super().test_inference_batch_consistent(batch_sizes=[1, 2]    )

					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										super().test_inference_batch_single_identical(batch_size=2      ,  expected_max_diff=3E-3    )



					def 				__UpperCAmelCase		(		self     :Optional[int]    ):
										lowercase						   =       'cpu'  # ensure determinism for the device-dependent torch.Generator
										lowercase						   =       self.get_dummy_components()
										lowercase						   =       StableDiffusionParadigmsPipeline(**lowercase__    )
										lowercase						   =       sd_pipe.to(lowercase__    )
										sd_pipe.set_progress_bar_config(disable=lowercase__    )

										lowercase						   =       self.get_dummy_inputs(lowercase__    )
										lowercase						   =       'french fries'
										lowercase						   =       sd_pipe(**lowercase__      ,  negative_prompt=lowercase__    )
										lowercase						   =       output.images
										lowercase						   =       image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										lowercase						   =       np.array([0.4771, 0.5420, 0.4683, 0.4918, 0.5636, 0.4725, 0.5230, 0.4923, 0.5015]    )

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




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


					"""simple docstring"""



					def 				__UpperCAmelCase		(		self     :List[Any]    ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

					def 				__UpperCAmelCase		(		self     :Dict      ,  lowercase__     :str=0    ):
										lowercase						   =       torch.Generator(device=lowercase__    ).manual_seed(lowercase__    )
										lowercase						   =       {
										    'prompt': 'a photograph of an astronaut riding a horse',
										    'generator': generator,
										    'num_inference_steps': 10,
										    'guidance_scale': 7.5,
										    'output_type': 'numpy',
										    'parallel': 3,
										    'debug': True,
										}
										return inputs



					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										lowercase						   =       'stabilityai/stable-diffusion-2-base'
										lowercase						   =       DDIMParallelScheduler.from_pretrained(lowercase__      ,  subfolder='scheduler'    )
										lowercase						   =       StableDiffusionParadigmsPipeline.from_pretrained(lowercase__      ,  scheduler=lowercase__      ,  safety_checker=lowercase__    )
										pipe.to(lowercase__    )
										pipe.set_progress_bar_config(disable=lowercase__    )
										pipe.enable_attention_slicing()

										lowercase						   =       self.get_inputs()
										lowercase						   =       pipe(**lowercase__    ).images
										lowercase						   =       image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)

										lowercase						   =       np.array([0.9622, 0.9602, 0.9748, 0.9591, 0.9630, 0.9691, 0.9661, 0.9631, 0.9741]    )

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











import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       RoFormerConfig.from_json_file(_UpperCAmelCase				)
					print(f"""Building PyTorch model from configuration: {config}"""				)
					lowercase						   =       RoFormerForMaskedLM(_UpperCAmelCase				)

					# Load weights from tf checkpoint
					load_tf_weights_in_roformer(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				)

					# Save pytorch-model
					print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
					torch.save(model.state_dict()       ,			_UpperCAmelCase       ,			_use_new_zipfile_serialization=_UpperCAmelCase				)


if __name__ == "__main__":
		__magic_name__       =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--bert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained BERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__magic_name__       =     parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






