




'''simple docstring'''







import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class       _a  (     __a							,							unittest.TestCase			):
       __a    :					List[str]           = KandinskyPriorPipeline
       __a    :					Union[str, Any]           = ["""prompt"""]
       __a    :					Union[str, Any]           = ["""prompt""", """negative_prompt"""]
       __a    :					List[Any]           = [
           """num_images_per_prompt""",
           """generator""",
           """num_inference_steps""",
           """latents""",
           """negative_prompt""",
           """guidance_scale""",
           """output_type""",
           """return_dict""",
       ]
       __a    :					List[Any]           = False
       @property
       def    A				(     self						:     Optional[Any]    ):



           '''simple docstring'''

           return 32
       @property
       def    A				(     self						:     Optional[Any]    ):



           '''simple docstring'''

           return 32
       @property
       def    A				(     self						:     Optional[int]    ):



           '''simple docstring'''

           return self.time_input_dim
       @property
       def    A				(     self						:     List[str]    ):



           '''simple docstring'''

           return self.time_input_dim * 4
       @property
       def    A				(     self						:     Dict    ):



           '''simple docstring'''

           return 100
       @property
       def    A				(     self						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )
           return tokenizer
       @property
       def    A				(     self						:     List[str]    ):



           '''simple docstring'''

           torch.manual_seed(0    )
           UpperCAmelCase      	=		CLIPTextConfig(
               bos_token_id=0       ,    eos_token_id=2       ,    hidden_size=self.text_embedder_hidden_size       ,    projection_dim=self.text_embedder_hidden_size       ,    intermediate_size=37       ,    layer_norm_eps=1E-05       ,    num_attention_heads=4       ,    num_hidden_layers=5       ,    pad_token_id=1       ,    vocab_size=1_000       ,    )
           return CLIPTextModelWithProjection(lowercase    )
       @property
       def    A				(     self						:     int    ):



           '''simple docstring'''

           torch.manual_seed(0    )

           UpperCAmelCase      	=		{
               '''num_attention_heads''': 2,
               '''attention_head_dim''': 12,
               '''embedding_dim''': self.text_embedder_hidden_size,
               '''num_layers''': 1,
           }

           UpperCAmelCase      	=		PriorTransformer(**lowercase    )
           # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
           UpperCAmelCase      	=		nn.Parameter(torch.ones(model.clip_std.shape    )    )
           return model
       @property
       def    A				(     self						:     List[Any]    ):



           '''simple docstring'''

           torch.manual_seed(0    )
           UpperCAmelCase      	=		CLIPVisionConfig(
               hidden_size=self.text_embedder_hidden_size       ,    image_size=224       ,    projection_dim=self.text_embedder_hidden_size       ,    intermediate_size=37       ,    num_attention_heads=4       ,    num_channels=3       ,    num_hidden_layers=5       ,    patch_size=14       ,    )

           UpperCAmelCase      	=		CLIPVisionModelWithProjection(lowercase    )
           return model
       @property
       def    A				(     self						:     Optional[int]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		CLIPImageProcessor(
               crop_size=224       ,    do_center_crop=lowercase       ,    do_normalize=lowercase       ,    do_resize=lowercase       ,    image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]       ,    image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]       ,    resample=3       ,    size=224       ,    )

           return image_processor
       def    A				(     self						:     List[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.dummy_prior
           UpperCAmelCase      	=		self.dummy_image_encoder
           UpperCAmelCase      	=		self.dummy_text_encoder
           UpperCAmelCase      	=		self.dummy_tokenizer
           UpperCAmelCase      	=		self.dummy_image_processor

           UpperCAmelCase      	=		UnCLIPScheduler(
               variance_type='''fixed_small_log'''       ,    prediction_type='''sample'''       ,    num_train_timesteps=1_000       ,    clip_sample=lowercase       ,    clip_sample_range=10.0       ,    )

           UpperCAmelCase      	=		{
               '''prior''': prior,
               '''image_encoder''': image_encoder,
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''scheduler''': scheduler,
               '''image_processor''': image_processor,
           }

           return components
       def    A				(     self						:     Tuple       ,    lowercase						:     Dict       ,    lowercase						:     str=0    ):



           '''simple docstring'''

           if str(lowercase    ).startswith('''mps'''    ):
               UpperCAmelCase      	=		torch.manual_seed(lowercase    )
           else:
               UpperCAmelCase      	=		torch.Generator(device=lowercase    ).manual_seed(lowercase    )
           UpperCAmelCase      	=		{
               '''prompt''': '''horse''',
               '''generator''': generator,
               '''guidance_scale''': 4.0,
               '''num_inference_steps''': 2,
               '''output_type''': '''np''',
           }
           return inputs
       def    A				(     self						:     Any    ):



           '''simple docstring'''

           UpperCAmelCase      	=		'''cpu'''

           UpperCAmelCase      	=		self.get_dummy_components()

           UpperCAmelCase      	=		self.pipeline_class(**lowercase    )
           UpperCAmelCase      	=		pipe.to(lowercase    )

           pipe.set_progress_bar_config(disable=lowercase    )

           UpperCAmelCase      	=		pipe(**self.get_dummy_inputs(lowercase    )    )
           UpperCAmelCase      	=		output.image_embeds

           UpperCAmelCase      	=		pipe(
               **self.get_dummy_inputs(lowercase    )       ,    return_dict=lowercase       ,    )[0]

           UpperCAmelCase      	=		image[0, -10:]
           UpperCAmelCase      	=		image_from_tuple[0, -10:]

           assert image.shape == (1, 32)

           UpperCAmelCase      	=		np.array(
               [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2
       @skip_mps
       def    A				(     self						:     List[str]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		torch_device == '''cpu'''
           UpperCAmelCase      	=		True
           UpperCAmelCase      	=		False

           self._test_inference_batch_single_identical(
               test_max_difference=lowercase       ,    relax_max_difference=lowercase       ,    test_mean_pixel_difference=lowercase       ,    )







       @skip_mps
       def    A				(     self						:     List[str]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		torch_device == '''cpu'''
           UpperCAmelCase      	=		False

           self._test_attention_slicing_forward_pass(
               test_max_difference=lowercase       ,    test_mean_pixel_difference=lowercase       ,    )










'''simple docstring'''







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


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





def 						snake_case_							(_a     :							str					):
    print(F"Reading saved model from {model_file}"					)
    UpperCAmelCase      	=		torch.load(_a   ,       map_location=lambda _a   ,       _a					: default_restore_location(_a   ,       '''cpu'''					)					)
    return CheckpointState(**_a					)


class       _a  :
       def __init__(     self						:     List[Any]       ,    lowercase						:     Path    ):



           '''simple docstring'''

           UpperCAmelCase      	=		src_file
       def    A				(     self						:     str    ):



           '''simple docstring'''

           raise NotImplementedError







       @staticmethod
       def    A				(     lowercase						:     str       ,    *lowercase						:     Tuple       ,    **lowercase						:     Union[str, Any]    ):



           '''simple docstring'''

           if comp_type.startswith('''c'''    ):
               return DPRContextEncoderState(*lowercase       ,    **lowercase    )
           if comp_type.startswith('''q'''    ):
               return DPRQuestionEncoderState(*lowercase       ,    **lowercase    )
           if comp_type.startswith('''r'''    ):
               return DPRReaderState(*lowercase       ,    **lowercase    )
           else:
               raise ValueError('''Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''    )






class       _a  (     __a			):
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''    )[0]    )    )
           print(f"Loading DPR biencoder from {self.src_file}"    )
           UpperCAmelCase      	=		load_states_from_checkpoint(self.src_file    )
           UpperCAmelCase	,					UpperCAmelCase      	=		model.ctx_encoder, '''ctx_model.'''
           # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
           UpperCAmelCase      	=		{'''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(lowercase    ):
                   UpperCAmelCase      	=		key[len(lowercase    ) :]
                   if not key.startswith('''encode_proj.'''    ):
                       UpperCAmelCase      	=		'''bert_model.''' + key
                   UpperCAmelCase      	=		value
           encoder.load_state_dict(lowercase    )
           return model






class       _a  (     __a			):
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''    )[0]    )    )
           print(f"Loading DPR biencoder from {self.src_file}"    )
           UpperCAmelCase      	=		load_states_from_checkpoint(self.src_file    )
           UpperCAmelCase	,					UpperCAmelCase      	=		model.question_encoder, '''question_model.'''
           # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
           UpperCAmelCase      	=		{'''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(lowercase    ):
                   UpperCAmelCase      	=		key[len(lowercase    ) :]
                   if not key.startswith('''encode_proj.'''    ):
                       UpperCAmelCase      	=		'''bert_model.''' + key
                   UpperCAmelCase      	=		value
           encoder.load_state_dict(lowercase    )
           return model






class       _a  (     __a			):
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''    )[0]    )    )
           print(f"Loading DPR reader from {self.src_file}"    )
           UpperCAmelCase      	=		load_states_from_checkpoint(self.src_file    )
           # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
           UpperCAmelCase      	=		{
               '''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'''    ):
                   UpperCAmelCase      	=		'''encoder.bert_model.''' + key[len('''encoder.'''    ) :]
               UpperCAmelCase      	=		value
           model.span_predictor.load_state_dict(lowercase    )
           return model






def 						snake_case_							(_a     :							str   ,       _a     :							Path   ,       _a     :							Path					):
    UpperCAmelCase      	=		Path(_a					)
    dest_dir.mkdir(exist_ok=_a					)

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


if __name__ == "__main__":
   A			   =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.')
   A			   =parser.parse_args()

   A			   =Path(args.src)
   A			   =f"""converted-{src_file.name}""" if args.dest is None else args.dest
   A			   =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)




