



"""simple docstring"""

import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np
class     lowerCAmelCase__						:


	'''simple docstring'''







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





					"""simple docstring"""


					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'    )

					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'    )

					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Any							=     UNetaDConditionModel(
					    sample_size=32     ,       layers_per_block=1     ,       block_out_channels=[32, 64]     ,       down_block_types=[
					        'ResnetDownsampleBlock2D',
					        'SimpleCrossAttnDownBlock2D',
					    ]     ,       mid_block_type='UNetMidBlock2DSimpleCrossAttn'     ,       up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']     ,       in_channels=3     ,       out_channels=6     ,       cross_attention_dim=32     ,       encoder_hid_dim=32     ,       attention_head_dim=8     ,       addition_embed_type='text'     ,       addition_embed_type_num_heads=2     ,       cross_attention_norm='group_norm'     ,       resnet_time_scale_shift='scale_shift'     ,       act_fn='gelu'     ,       )
					unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     DDPMScheduler(
					    num_train_timesteps=1_000     ,       beta_schedule='squaredcos_cap_v2'     ,       beta_start=0.0_0_0_1     ,       beta_end=0.0_2     ,       thresholding=_SCREAMING_SNAKE_CASE     ,       dynamic_thresholding_ratio=0.9_5     ,       sample_max_value=1.0     ,       prediction_type='epsilon'     ,       variance_type='learned_range'     ,       )

					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     IFWatermarker()

					return {
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "unet": unet,
					    "scheduler": scheduler,
					    "watermarker": watermarker,
					    "safety_checker": None,
					    "feature_extractor": None,
					}





	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	Tuple:





					"""simple docstring"""


					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Any							=     TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'    )

					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'    )

					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     UNetaDConditionModel(
					    sample_size=32     ,       layers_per_block=[1, 2]     ,       block_out_channels=[32, 64]     ,       down_block_types=[
					        'ResnetDownsampleBlock2D',
					        'SimpleCrossAttnDownBlock2D',
					    ]     ,       mid_block_type='UNetMidBlock2DSimpleCrossAttn'     ,       up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']     ,       in_channels=6     ,       out_channels=6     ,       cross_attention_dim=32     ,       encoder_hid_dim=32     ,       attention_head_dim=8     ,       addition_embed_type='text'     ,       addition_embed_type_num_heads=2     ,       cross_attention_norm='group_norm'     ,       resnet_time_scale_shift='scale_shift'     ,       act_fn='gelu'     ,       class_embed_type='timestep'     ,       mid_block_scale_factor=1.4_1_4     ,       time_embedding_act_fn='gelu'     ,       time_embedding_dim=32     ,       )
					unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     DDPMScheduler(
					    num_train_timesteps=1_000     ,       beta_schedule='squaredcos_cap_v2'     ,       beta_start=0.0_0_0_1     ,       beta_end=0.0_2     ,       thresholding=_SCREAMING_SNAKE_CASE     ,       dynamic_thresholding_ratio=0.9_5     ,       sample_max_value=1.0     ,       prediction_type='epsilon'     ,       variance_type='learned_range'     ,       )

					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Any							=     DDPMScheduler(
					    num_train_timesteps=1_000     ,       beta_schedule='squaredcos_cap_v2'     ,       beta_start=0.0_0_0_1     ,       beta_end=0.0_2     ,       )

					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     IFWatermarker()

					return {
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "unet": unet,
					    "scheduler": scheduler,
					    "image_noising_scheduler": image_noising_scheduler,
					    "watermarker": watermarker,
					    "safety_checker": None,
					    "feature_extractor": None,
					}





	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.get_dummy_components()
					SCREAMING_SNAKE_CASE						:							str							=     self.pipeline_class(**_SCREAMING_SNAKE_CASE    )
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Tuple							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Dict							=     inputs['prompt']
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     inputs['generator']
					SCREAMING_SNAKE_CASE						:							int							=     inputs['num_inference_steps']
					SCREAMING_SNAKE_CASE						:							str							=     inputs['output_type']

					if "image" in inputs:
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     inputs['image']
					else:
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     None

					if "mask_image" in inputs:
									SCREAMING_SNAKE_CASE						:							Dict							=     inputs['mask_image']
					else:
									SCREAMING_SNAKE_CASE						:							Dict							=     None

					if "original_image" in inputs:
									SCREAMING_SNAKE_CASE						:							List[str]							=     inputs['original_image']
					else:
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     None

					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Optional[Any]							=     pipe.encode_prompt(_SCREAMING_SNAKE_CASE    )

					# inputs with prompt converted to embeddings
					SCREAMING_SNAKE_CASE						:							Dict							=     {
					    'prompt_embeds': prompt_embeds,
					    'negative_prompt_embeds': negative_prompt_embeds,
					    'generator': generator,
					    'num_inference_steps': num_inference_steps,
					    'output_type': output_type,
					}

					if image is not None:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     image

					if mask_image is not None:
									SCREAMING_SNAKE_CASE						:							Dict							=     mask_image

					if original_image is not None:
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     original_image

					# set all optional components to None
					for optional_component in pipe._optional_components:
									setattr(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     pipe(**_SCREAMING_SNAKE_CASE    )[0]

					with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Any							=     self.pipeline_class.from_pretrained(_SCREAMING_SNAKE_CASE    )
									pipe_loaded.to(_SCREAMING_SNAKE_CASE    )
									pipe_loaded.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

					for optional_component in pipe._optional_components:
									self.assertTrue(
									    getattr(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) is None     ,       f"""`{optional_component}` did not stay set to None after loading."""     ,       )

					SCREAMING_SNAKE_CASE						:							Tuple							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     inputs['generator']
					SCREAMING_SNAKE_CASE						:							Any							=     inputs['num_inference_steps']
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     inputs['output_type']

					# inputs with prompt converted to embeddings
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     {
					    'prompt_embeds': prompt_embeds,
					    'negative_prompt_embeds': negative_prompt_embeds,
					    'generator': generator,
					    'num_inference_steps': num_inference_steps,
					    'output_type': output_type,
					}

					if image is not None:
									SCREAMING_SNAKE_CASE						:							str							=     image

					if mask_image is not None:
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     mask_image

					if original_image is not None:
									SCREAMING_SNAKE_CASE						:							Any							=     original_image

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     pipe_loaded(**_SCREAMING_SNAKE_CASE    )[0]

					SCREAMING_SNAKE_CASE						:							Dict							=     np.abs(to_np(_SCREAMING_SNAKE_CASE    ) - to_np(_SCREAMING_SNAKE_CASE    )    ).max()
					self.assertLess(_SCREAMING_SNAKE_CASE     ,       1E-4    )







	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.get_dummy_components()
					SCREAMING_SNAKE_CASE						:							int							=     self.pipeline_class(**_SCREAMING_SNAKE_CASE    )
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							List[str]							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     pipe(**_SCREAMING_SNAKE_CASE    )[0]

					with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							int							=     self.pipeline_class.from_pretrained(_SCREAMING_SNAKE_CASE    )
									pipe_loaded.to(_SCREAMING_SNAKE_CASE    )
									pipe_loaded.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

					SCREAMING_SNAKE_CASE						:							Tuple							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     pipe_loaded(**_SCREAMING_SNAKE_CASE    )[0]

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     np.abs(to_np(_SCREAMING_SNAKE_CASE    ) - to_np(_SCREAMING_SNAKE_CASE    )    ).max()
					self.assertLess(_SCREAMING_SNAKE_CASE     ,       1E-4    )










"""simple docstring"""

import argparse
import json

from tqdm import tqdm
def  __snake_case							(	)     ->       Any:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							str							=     argparse.ArgumentParser()

				# Required parameters
				parser.add_argument(
				    '--src_path'							,		type=__A							,		default='biencoder-nq-dev.json'							,		help='Path to raw DPR training data'							,		)
				parser.add_argument(
				    '--evaluation_set'							,		type=__A							,		help='where to store parsed evaluation_set file'							,		)
				parser.add_argument(
				    '--gold_data_path'							,		type=__A							,		help='where to store parsed gold_data_path file'							,		)
				SCREAMING_SNAKE_CASE						:							Tuple							=     parser.parse_args()

				with open(args.src_path							,		'r'     ) as src_file, open(args.evaluation_set							,		'w'     ) as eval_file, open(
				    args.gold_data_path							,		'w'     ) as gold_file:
								SCREAMING_SNAKE_CASE						:							Tuple							=     json.load(__A     )
								for dpr_record in tqdm(__A     ):
												SCREAMING_SNAKE_CASE						:							Tuple							=     dpr_record['question']
												SCREAMING_SNAKE_CASE						:							Optional[int]							=     [context['title'] for context in dpr_record['positive_ctxs']]
												eval_file.write(question + '\n'     )
												gold_file.write('\t'.join(__A     ) + '\n'     )


if __name__ == "__main__":
							main()





