



import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


UpperCAmelCase							=       collections.namedtuple(
    """CheckpointState""", ["""model_dict""", """optimizer_dict""", """scheduler_dict""", """offset""", """epoch""", """encoder_params"""]
)

def       A_   (						__a  :					str		):

				"""simple docstring"""
				print(F'''Reading saved model from {model_file}'''		)
				a__					   =		torch.load(__a			,		map_location=lambda __a			,		__a		: default_restore_location(__a			,		"""cpu"""		)		)
				return CheckpointState(**__a		)

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_		):
											a__					   =		src_file


							def _a   (				self		):
											raise NotImplementedError







							@staticmethod
							def _a   (				a_	,	*a_	,	**a_		):
											if comp_type.startswith("""c"""		):
															return DPRContextEncoderState(*a_	,	**a_		)
											if comp_type.startswith("""q"""		):
															return DPRQuestionEncoderState(*a_	,	**a_		)
											if comp_type.startswith("""r"""		):
															return DPRReaderState(*a_	,	**a_		)
											else:
															raise ValueError("""Component type must be either 'ctx_encoder', 'question_encoder' or 'reader'."""		)




class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def _a   (				self		):
											a__					   =		DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("""bert-base-uncased"""		)[0]		)		)
											print(F'''Loading DPR biencoder from {self.src_file}'''		)
											a__					   =		load_states_from_checkpoint(self.src_file		)
											a__   ,      a__					   =		model.ctx_encoder, """ctx_model."""
											# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
											a__					   =		{"""bert_model.embeddings.position_ids""": model.ctx_encoder.bert_model.embeddings.position_ids}
											for key, value in saved_state.model_dict.items():
															if key.startswith(a_		):
																			a__					   =		key[len(a_		) :]
																			if not key.startswith("""encode_proj."""		):
																							a__					   =		"""bert_model.""" + key
																			a__					   =		value
											encoder.load_state_dict(a_		)
											return model




class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def _a   (				self		):
											a__					   =		DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("""bert-base-uncased"""		)[0]		)		)
											print(F'''Loading DPR biencoder from {self.src_file}'''		)
											a__					   =		load_states_from_checkpoint(self.src_file		)
											a__   ,      a__					   =		model.question_encoder, """question_model."""
											# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
											a__					   =		{"""bert_model.embeddings.position_ids""": model.question_encoder.bert_model.embeddings.position_ids}
											for key, value in saved_state.model_dict.items():
															if key.startswith(a_		):
																			a__					   =		key[len(a_		) :]
																			if not key.startswith("""encode_proj."""		):
																							a__					   =		"""bert_model.""" + key
																			a__					   =		value
											encoder.load_state_dict(a_		)
											return model




class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def _a   (				self		):
											a__					   =		DPRReader(DPRConfig(**BertConfig.get_config_dict("""bert-base-uncased"""		)[0]		)		)
											print(F'''Loading DPR reader from {self.src_file}'''		)
											a__					   =		load_states_from_checkpoint(self.src_file		)
											# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
											a__					   =		{
											    """encoder.bert_model.embeddings.position_ids""": model.span_predictor.encoder.bert_model.embeddings.position_ids
											}
											for key, value in saved_state.model_dict.items():
															if key.startswith("""encoder."""		) and not key.startswith("""encoder.encode_proj"""		):
																			a__					   =		"""encoder.bert_model.""" + key[len("""encoder."""		) :]
															a__					   =		value
											model.span_predictor.load_state_dict(a_		)
											return model




def       A_   (						__a  :					str			,		__a  :					Path			,		__a  :					Path		):

				"""simple docstring"""
				a__					   =		Path(__a		)
				dest_dir.mkdir(exist_ok=__a		)

				a__					   =		DPRState.from_type(__a			,		src_file=__a		)
				a__					   =		dpr_state.load_dpr_model()
				model.save_pretrained(__a		)
				model.from_pretrained(__a		)  # sanity check


if __name__ == "__main__":
	UpperCAmelCase							=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--type""", type=str, help="""Type of the component to convert: 'ctx_encoder', 'question_encoder' or 'reader'."""
	)
	parser.add_argument(
	    """--src""",
	    type=str,
	    help=(
	        """Path to the dpr checkpoint file. They can be downloaded from the official DPR repo"""
	        """ https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the"""
	        """ 'retriever' checkpoints."""
	    ),
	)
	parser.add_argument("""--dest""", type=str, default=None, help="""Path to the output PyTorch model directory.""")
	UpperCAmelCase							=       parser.parse_args()

	UpperCAmelCase							=       Path(args.src)
	UpperCAmelCase							=       f"""converted-{src_file.name}""" if args.dest is None else args.dest
	UpperCAmelCase							=       Path(dest_dir)
	assert src_file.exists()
	assert (
	    args.type is not None
	), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
	convert(args.type, src_file, dest_dir)







import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class   UpperCAmelCase    (      A ,       unittest.TestCase ):
   __SCREAMING_SNAKE_CASE		:				int      					=       KandinskyPipeline
   __SCREAMING_SNAKE_CASE		:				Union[str, Any]      					=       [
       "prompt",
       "image_embeds",
       "negative_image_embeds",
   ]
   __SCREAMING_SNAKE_CASE		:				Tuple      					=       ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
   __SCREAMING_SNAKE_CASE		:				Tuple      					=       [
       "generator",
       "height",
       "width",
       "latents",
       "guidance_scale",
       "negative_prompt",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   __SCREAMING_SNAKE_CASE		:				Dict      					=       False






   @property
   def 			_a    (						self :							str  )->      Dict:
     return 32






   @property
   def 			_a    (						self :							Tuple  )->      Any:
     return 32






   @property
   def 			_a    (						self :							Union[str, Any]  )->      List[str]:
     return self.time_input_dim






   @property
   def 			_a    (						self :							Optional[Any]  )->      Tuple:
     return self.time_input_dim * 4






   @property
   def 			_a    (						self :							int  )->      Dict:
     return 1_00






   @property
   def 			_a    (						self :							Tuple  )->      Tuple:
     _lowercase:			List[Any]      =     XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
     return tokenizer






   @property
   def 			_a    (						self :							List[str]  )->      Optional[int]:
     torch.manual_seed(0  )
     _lowercase:			int      =     MCLIPConfig(
         numDims=self.cross_attention_dim    ,       transformerDimensions=self.text_embedder_hidden_size    ,       hidden_size=self.text_embedder_hidden_size    ,       intermediate_size=37    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       vocab_size=10_05    ,       )

     _lowercase:			Optional[Any]      =     MultilingualCLIP(UpperCamelCase_  )
     _lowercase:			int      =     text_encoder.eval()

     return text_encoder






   @property
   def 			_a    (						self :							Optional[int]  )->      Optional[Any]:
     torch.manual_seed(0  )

     _lowercase:			Optional[Any]      =     {
         'in_channels': 4,
         # Out channels is double in channels because predicts mean and variance
         'out_channels': 8,
         'addition_embed_type': 'text_image',
         'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
         'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
         'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
         'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
         'layers_per_block': 1,
         'encoder_hid_dim': self.text_embedder_hidden_size,
         'encoder_hid_dim_type': 'text_image_proj',
         'cross_attention_dim': self.cross_attention_dim,
         'attention_head_dim': 4,
         'resnet_time_scale_shift': 'scale_shift',
         'class_embed_type': None,
     }

     _lowercase:			str      =     UNetaDConditionModel(**UpperCamelCase_  )
     return model






   @property
   def 			_a    (						self :							Dict  )->      Optional[Any]:
     return {
         "block_out_channels": [32, 64],
         "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
         "in_channels": 3,
         "latent_channels": 4,
         "layers_per_block": 1,
         "norm_num_groups": 8,
         "norm_type": "spatial",
         "num_vq_embeddings": 12,
         "out_channels": 3,
         "up_block_types": [
             "AttnUpDecoderBlock2D",
             "UpDecoderBlock2D",
         ],
         "vq_embed_dim": 4,
     }






   @property
   def 			_a    (						self :							List[str]  )->      int:
     torch.manual_seed(0  )
     _lowercase:			List[str]      =     VQModel(**self.dummy_movq_kwargs  )
     return model






   def 			_a    (						self :							int  )->      List[Any]:
     _lowercase:			List[str]      =     self.dummy_text_encoder
     _lowercase:			List[Any]      =     self.dummy_tokenizer
     _lowercase:			Optional[Any]      =     self.dummy_unet
     _lowercase:			List[str]      =     self.dummy_movq

     _lowercase:			Optional[Any]      =     DDIMScheduler(
         num_train_timesteps=10_00    ,       beta_schedule='linear'    ,       beta_start=0.00_085    ,       beta_end=0.012    ,       clip_sample=UpperCamelCase_    ,       set_alpha_to_one=UpperCamelCase_    ,       steps_offset=1    ,       prediction_type='epsilon'    ,       thresholding=UpperCamelCase_    ,       )

     _lowercase:			List[Any]      =     {
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'unet': unet,
         'scheduler': scheduler,
         'movq': movq,
     }
     return components






   def 			_a    (						self :							str    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							Optional[int]=0  )->      Union[str, Any]:
     _lowercase:			str      =     floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(UpperCamelCase_  )  ).to(UpperCamelCase_  )
     _lowercase:			Optional[int]      =     floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(seed + 1  )  ).to(UpperCamelCase_  )
     if str(UpperCamelCase_  ).startswith('mps'  ):
       _lowercase:			Any      =     torch.manual_seed(UpperCamelCase_  )
     else:
       _lowercase:			Tuple      =     torch.Generator(device=UpperCamelCase_  ).manual_seed(UpperCamelCase_  )
     _lowercase:			List[str]      =     {
         'prompt': 'horse',
         'image_embeds': image_embeds,
         'negative_image_embeds': negative_image_embeds,
         'generator': generator,
         'height': 64,
         'width': 64,
         'guidance_scale': 4.0,
         'num_inference_steps': 2,
         'output_type': 'np',
     }
     return inputs






   def 			_a    (						self :							List[str]  )->      List[str]:
     _lowercase:			str      =     'cpu'

     _lowercase:			Optional[Any]      =     self.get_dummy_components()

     _lowercase:			int      =     self.pipeline_class(**UpperCamelCase_  )
     _lowercase:			List[str]      =     pipe.to(UpperCamelCase_  )

     pipe.set_progress_bar_config(disable=UpperCamelCase_  )

     _lowercase:			Tuple      =     pipe(**self.get_dummy_inputs(UpperCamelCase_  )  )
     _lowercase:			Union[str, Any]      =     output.images

     _lowercase:			Union[str, Any]      =     pipe(
         **self.get_dummy_inputs(UpperCamelCase_  )    ,       return_dict=UpperCamelCase_    ,       )[0]

     _lowercase:			int      =     image[0, -3:, -3:, -1]
     _lowercase:			List[str]      =     image_from_tuple[0, -3:, -3:, -1]

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

     _lowercase:			Union[str, Any]      =     np.array(
         [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]  )

     assert (
         np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
     ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2
     ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'

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






   def 			_a    (						self :							Optional[int]  )->      Any:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






   def 			_a    (						self :							List[Any]  )->      Optional[int]:
     _lowercase:			Optional[int]      =     load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinsky/kandinsky_text2img_cat_fp16.npy'  )

     _lowercase:			List[Any]      =     KandinskyPriorPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-1-prior'    ,       torch_dtype=torch.floataa  )
     pipe_prior.to(UpperCamelCase_  )

     _lowercase:			str      =     KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'    ,       torch_dtype=torch.floataa  )
     _lowercase:			List[str]      =     pipeline.to(UpperCamelCase_  )
     pipeline.set_progress_bar_config(disable=UpperCamelCase_  )

     _lowercase:			Dict      =     'red cat, 4k photo'

     _lowercase:			List[Any]      =     torch.Generator(device='cuda'  ).manual_seed(0  )
     _lowercase  ,       _lowercase:			Dict      =     pipe_prior(
         UpperCamelCase_    ,       generator=UpperCamelCase_    ,       num_inference_steps=5    ,       negative_prompt=''    ,       ).to_tuple()

     _lowercase:			str      =     torch.Generator(device='cuda'  ).manual_seed(0  )
     _lowercase:			Optional[int]      =     pipeline(
         UpperCamelCase_    ,       image_embeds=UpperCamelCase_    ,       negative_image_embeds=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       num_inference_steps=1_00    ,       output_type='np'    ,       )

     _lowercase:			Optional[int]      =     output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(UpperCamelCase_    ,       UpperCamelCase_  )
