code: 
  
  
  '''simple docstring'''
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
  
  from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
  from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
  from diffusers.utils import load_numpy, nightly, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
  
  
  enable_full_determinism()
  class 						__lowerCAmelCase		(lowercase_						,  unittest.TestCase  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  List[Any]						      =							UnCLIPPipeline
        lowerCAmelCase__      :  Dict						      =							TEXT_TO_IMAGE_PARAMS - {
            """negative_prompt""",
            """height""",
            """width""",
            """negative_prompt_embeds""",
            """guidance_scale""",
            """prompt_embeds""",
            """cross_attention_kwargs""",
        }
        lowerCAmelCase__      :  int						      =							TEXT_TO_IMAGE_BATCH_PARAMS
        lowerCAmelCase__      :  Optional[Any]						      =							[
            """generator""",
            """return_dict""",
            """prior_num_inference_steps""",
            """decoder_num_inference_steps""",
            """super_res_num_inference_steps""",
        ]
        lowerCAmelCase__      :  Union[str, Any]						      =							False
  
  
  
        @property
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return 32
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return 32
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Dict				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self.time_input_dim
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Optional[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self.time_input_dim * 4
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Optional[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return 100
  
  
  
        @property
        def    UpperCamelCase__	(self					:					str				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)
               return tokenizer
  
  
  
        @property
        def    UpperCamelCase__	(self					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               torch.manual_seed(0				)
               lowercase__       =				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=1000     ,		)
               return CLIPTextModelWithProjection(UpperCamelCase				)
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               torch.manual_seed(0				)
  
               lowercase__       =				{
                   '''num_attention_heads''': 2,
                   '''attention_head_dim''': 12,
                   '''embedding_dim''': self.text_embedder_hidden_size,
                   '''num_layers''': 1,
               }
  
               lowercase__       =				PriorTransformer(**UpperCamelCase				)
               return model
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               torch.manual_seed(0				)
  
               lowercase__       =				{
                   '''clip_embeddings_dim''': self.text_embedder_hidden_size,
                   '''time_embed_dim''': self.time_embed_dim,
                   '''cross_attention_dim''': self.cross_attention_dim,
               }
  
               lowercase__       =				UnCLIPTextProjModel(**UpperCamelCase				)
               return model
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               torch.manual_seed(0				)
  
               lowercase__       =				{
                   '''sample_size''': 32,
                   # RGB in channels
                   '''in_channels''': 3,
                   # Out channels is double in channels because predicts mean and variance
                   '''out_channels''': 6,
                   '''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,
                   '''cross_attention_dim''': self.cross_attention_dim,
                   '''attention_head_dim''': 4,
                   '''resnet_time_scale_shift''': '''scale_shift''',
                   '''class_embed_type''': '''identity''',
               }
  
               lowercase__       =				UNetaDConditionModel(**UpperCamelCase				)
               return model
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return {
                   "sample_size": 64,
                   "layers_per_block": 1,
                   "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
                   "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
                   "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
                   "in_channels": 6,
                   "out_channels": 3,
               }
  
  
  
        @property
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               torch.manual_seed(0				)
  
               lowercase__       =				UNetaDModel(**self.dummy_super_res_kwargs				)
               return model
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               torch.manual_seed(1				)
  
               lowercase__       =				UNetaDModel(**self.dummy_super_res_kwargs				)
               return model
  
  
  
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.dummy_prior
               lowercase__       =				self.dummy_decoder
               lowercase__       =				self.dummy_text_proj
               lowercase__       =				self.dummy_text_encoder
               lowercase__       =				self.dummy_tokenizer
               lowercase__       =				self.dummy_super_res_first
               lowercase__       =				self.dummy_super_res_last
  
               lowercase__       =				UnCLIPScheduler(
                   variance_type='''fixed_small_log'''     ,		prediction_type='''sample'''     ,		num_train_timesteps=1000     ,		clip_sample_range=5.0     ,		)
  
               lowercase__       =				UnCLIPScheduler(
                   variance_type='''learned_range'''     ,		prediction_type='''epsilon'''     ,		num_train_timesteps=1000     ,		)
  
               lowercase__       =				UnCLIPScheduler(
                   variance_type='''fixed_small_log'''     ,		prediction_type='''epsilon'''     ,		num_train_timesteps=1000     ,		)
  
               lowercase__       =				{
                   '''prior''': prior,
                   '''decoder''': decoder,
                   '''text_proj''': text_proj,
                   '''text_encoder''': text_encoder,
                   '''tokenizer''': tokenizer,
                   '''super_res_first''': super_res_first,
                   '''super_res_last''': super_res_last,
                   '''prior_scheduler''': prior_scheduler,
                   '''decoder_scheduler''': decoder_scheduler,
                   '''super_res_scheduler''': super_res_scheduler,
               }
  
               return components
  
  
  
        def    UpperCamelCase__	(self					:					List[str]     ,		UpperCamelCase					:					Tuple     ,		UpperCamelCase					:					Union[str, Any]=0				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if str(UpperCamelCase				).startswith('''mps'''				):
                      lowercase__       =				torch.manual_seed(UpperCamelCase				)
               else:
                      lowercase__       =				torch.Generator(device=UpperCamelCase				).manual_seed(UpperCamelCase				)
               lowercase__       =				{
                   '''prompt''': '''horse''',
                   '''generator''': generator,
                   '''prior_num_inference_steps''': 2,
                   '''decoder_num_inference_steps''': 2,
                   '''super_res_num_inference_steps''': 2,
                   '''output_type''': '''numpy''',
               }
               return inputs
  
  
  
        def    UpperCamelCase__	(self					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				'''cpu'''
  
               lowercase__       =				self.get_dummy_components()
  
               lowercase__       =				self.pipeline_class(**UpperCamelCase				)
               lowercase__       =				pipe.to(UpperCamelCase				)
  
               pipe.set_progress_bar_config(disable=UpperCamelCase				)
  
               lowercase__       =				pipe(**self.get_dummy_inputs(UpperCamelCase				)				)
               lowercase__       =				output.images
  
               lowercase__       =				pipe(
                   **self.get_dummy_inputs(UpperCamelCase				)     ,		return_dict=UpperCamelCase     ,		)[0]
  
               lowercase__       =				image[0, -3:, -3:, -1]
               lowercase__       =				image_from_tuple[0, -3:, -3:, -1]
  
               assert image.shape == (1, 64, 64, 3)
  
               lowercase__       =				np.array(
                   [
                       0.99_97,
                       0.99_88,
                       0.00_28,
                       0.99_97,
                       0.99_84,
                       0.99_65,
                       0.00_29,
                       0.99_86,
                       0.00_25,
                   ]				)
  
               assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
               assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				torch.device('''cpu'''				)
               class 						__lowerCAmelCase		:
  
  
  
  
  
                     '''simple docstring'''
  
  
  
  
  
  
                     lowerCAmelCase__      :  Optional[Any]						      =							1
  
  
  
  
  
  
               lowercase__       =				self.get_dummy_components()
  
               lowercase__       =				self.pipeline_class(**UpperCamelCase				)
               lowercase__       =				pipe.to(UpperCamelCase				)
  
               lowercase__       =				components['''prior''']
               lowercase__       =				components['''decoder''']
               lowercase__       =				components['''super_res_first''']
               lowercase__       =				components['''tokenizer''']
               lowercase__       =				components['''text_encoder''']
  
               lowercase__       =				torch.Generator(device=UpperCamelCase				).manual_seed(0				)
               lowercase__       =				prior.dtype
               lowercase__       =				1
  
               lowercase__       =				(batch_size, prior.config.embedding_dim)
               lowercase__       =				pipe.prepare_latents(
                   UpperCamelCase     ,		dtype=UpperCamelCase     ,		device=UpperCamelCase     ,		generator=UpperCamelCase     ,		latents=UpperCamelCase     ,		scheduler=DummyScheduler()				)
               lowercase__       =				(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
               lowercase__       =				pipe.prepare_latents(
                   UpperCamelCase     ,		dtype=UpperCamelCase     ,		device=UpperCamelCase     ,		generator=UpperCamelCase     ,		latents=UpperCamelCase     ,		scheduler=DummyScheduler()				)
  
               lowercase__       =				(
                   batch_size,
                   super_res_first.config.in_channels // 2,
                   super_res_first.config.sample_size,
                   super_res_first.config.sample_size,
               )
               lowercase__       =				pipe.prepare_latents(
                   UpperCamelCase     ,		dtype=UpperCamelCase     ,		device=UpperCamelCase     ,		generator=UpperCamelCase     ,		latents=UpperCamelCase     ,		scheduler=DummyScheduler()				)
  
               pipe.set_progress_bar_config(disable=UpperCamelCase				)
  
               lowercase__       =				'''this is a prompt example'''
  
               lowercase__       =				torch.Generator(device=UpperCamelCase				).manual_seed(0				)
               lowercase__       =				pipe(
                   [prompt]     ,		generator=UpperCamelCase     ,		prior_num_inference_steps=2     ,		decoder_num_inference_steps=2     ,		super_res_num_inference_steps=2     ,		prior_latents=UpperCamelCase     ,		decoder_latents=UpperCamelCase     ,		super_res_latents=UpperCamelCase     ,		output_type='''np'''     ,		)
               lowercase__       =				output.images
  
               lowercase__       =				tokenizer(
                   UpperCamelCase     ,		padding='''max_length'''     ,		max_length=tokenizer.model_max_length     ,		return_tensors='''pt'''     ,		)
               lowercase__       =				text_encoder(text_inputs.input_ids				)
               lowercase__       =				text_inputs.attention_mask
  
               lowercase__       =				torch.Generator(device=UpperCamelCase				).manual_seed(0				)
               lowercase__       =				pipe(
                   generator=UpperCamelCase     ,		prior_num_inference_steps=2     ,		decoder_num_inference_steps=2     ,		super_res_num_inference_steps=2     ,		prior_latents=UpperCamelCase     ,		decoder_latents=UpperCamelCase     ,		super_res_latents=UpperCamelCase     ,		text_model_output=UpperCamelCase     ,		text_attention_mask=UpperCamelCase     ,		output_type='''np'''     ,		)[0]
  
               # make sure passing text embeddings manually is identical
               assert np.abs(image - image_from_text				).max() < 1E-4
  
  
  
        @skip_mps
        def    UpperCamelCase__	(self					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				torch_device == '''cpu'''
  
               self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase     ,		expected_max_diff=0.01				)
  
  
  
        @skip_mps
        def    UpperCamelCase__	(self					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				torch_device == '''cpu'''
               lowercase__       =				True
               lowercase__       =				[
                   '''prior_num_inference_steps''',
                   '''decoder_num_inference_steps''',
                   '''super_res_num_inference_steps''',
               ]
  
               self._test_inference_batch_single_identical(
                   test_max_difference=UpperCamelCase     ,		relax_max_difference=UpperCamelCase     ,		additional_params_copy_to_batched_inputs=UpperCamelCase     ,		)
  
  
  
        def    UpperCamelCase__	(self					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				[
                   '''prior_num_inference_steps''',
                   '''decoder_num_inference_steps''',
                   '''super_res_num_inference_steps''',
               ]
  
               if torch_device == "mps":
                      # TODO: MPS errors with larger batch sizes
                      lowercase__       =				[2, 3]
                      self._test_inference_batch_consistent(
                          batch_sizes=UpperCamelCase     ,		additional_params_copy_to_batched_inputs=UpperCamelCase     ,		)
               else:
                      self._test_inference_batch_consistent(
                          additional_params_copy_to_batched_inputs=UpperCamelCase				)
  
  
  
        @skip_mps
        def    UpperCamelCase__	(self					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return super().test_dict_tuple_outputs_equivalent()
  
  
  
        @skip_mps
        def    UpperCamelCase__	(self					:					str				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return super().test_save_load_local()
  
  
  
  
  
  
        @skip_mps
        def    UpperCamelCase__	(self					:					Optional[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return super().test_save_load_optional_components()
  
  
  
  
  
  
  @nightly
  class 						__lowerCAmelCase		(unittest.TestCase  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               super().tearDown()
               gc.collect()
               torch.cuda.empty_cache()
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				load_numpy(
                   '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                   '''/unclip/karlo_v1_alpha_horse_cpu.npy'''				)
  
               lowercase__       =				UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''				)
               pipeline.set_progress_bar_config(disable=UpperCamelCase				)
  
               lowercase__       =				torch.manual_seed(0				)
               lowercase__       =				pipeline(
                   '''horse'''     ,		num_images_per_prompt=1     ,		generator=UpperCamelCase     ,		output_type='''np'''     ,		)
  
               lowercase__       =				output.images[0]
  
               assert image.shape == (256, 256, 3)
               assert np.abs(expected_image - image				).max() < 1E-1
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 						__lowerCAmelCase		(unittest.TestCase  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               super().tearDown()
               gc.collect()
               torch.cuda.empty_cache()
  
  
  
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				load_numpy(
                   '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                   '''/unclip/karlo_v1_alpha_horse_fp16.npy'''				)
  
               lowercase__       =				UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''     ,		torch_dtype=torch.floataa				)
               lowercase__       =				pipeline.to(UpperCamelCase				)
               pipeline.set_progress_bar_config(disable=UpperCamelCase				)
  
               lowercase__       =				torch.Generator(device='''cpu'''				).manual_seed(0				)
               lowercase__       =				pipeline(
                   '''horse'''     ,		generator=UpperCamelCase     ,		output_type='''np'''     ,		)
  
               lowercase__       =				output.images[0]
  
               assert image.shape == (256, 256, 3)
  
               assert_mean_pixel_difference(UpperCamelCase     ,		UpperCamelCase				)
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               torch.cuda.empty_cache()
               torch.cuda.reset_max_memory_allocated()
               torch.cuda.reset_peak_memory_stats()
  
               lowercase__       =				UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''     ,		torch_dtype=torch.floataa				)
               lowercase__       =				pipe.to(UpperCamelCase				)
               pipe.set_progress_bar_config(disable=UpperCamelCase				)
               pipe.enable_attention_slicing()
               pipe.enable_sequential_cpu_offload()
  
               lowercase__       =				pipe(
                   '''horse'''     ,		num_images_per_prompt=1     ,		prior_num_inference_steps=2     ,		decoder_num_inference_steps=2     ,		super_res_num_inference_steps=2     ,		output_type='''np'''     ,		)
  
               lowercase__       =				torch.cuda.max_memory_allocated()
               # make sure that less than 7 GB is allocated
               assert mem_bytes < 7 * 10**9
  
  
  
  
  
style_context: 
  
  
  '''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
  
  
  lowerCamelCase							:     List[str]								=			collections.namedtuple(
      'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
  )
  
  def _SCREAMING_SNAKE_CASE   (A ) -> CheckpointState:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         print(f"Reading saved model from {model_file}" )
         lowercase__       =				torch.load(A						,				map_location=lambda A						,				A : default_restore_location(A						,				'''cpu''' ) )
         return CheckpointState(**A )
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def __init__(self					:					Any     ,		UpperCamelCase					:					Path				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				src_file
  
  
  
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               raise NotImplementedError
  
  
  
  
  
  
        @staticmethod
        def    UpperCamelCase__	(UpperCamelCase					:					str     ,		*UpperCamelCase					:					Dict     ,		**UpperCamelCase					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if comp_type.startswith('''c'''				):
                      return DPRContextEncoderState(*UpperCamelCase     ,		**UpperCamelCase				)
               if comp_type.startswith('''q'''				):
                      return DPRQuestionEncoderState(*UpperCamelCase     ,		**UpperCamelCase				)
               if comp_type.startswith('''r'''				):
                      return DPRReaderState(*UpperCamelCase     ,		**UpperCamelCase				)
               else:
                      raise ValueError('''Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''				)
  
  
  
  
  
  
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''				)[0]				)				)
               print(f"Loading DPR biencoder from {self.src_file}"				)
               lowercase__       =				load_states_from_checkpoint(self.src_file				)
               lowercase__    ,lowercase__       =				model.ctx_encoder, '''ctx_model.'''
               # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
               lowercase__       =				{'''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(UpperCamelCase				):
                             lowercase__       =				key[len(UpperCamelCase				) :]
                             if not key.startswith('''encode_proj.'''				):
                                    lowercase__       =				'''bert_model.''' + key
                             lowercase__       =				value
               encoder.load_state_dict(UpperCamelCase				)
               return model
  
  
  
  
  
  
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''				)[0]				)				)
               print(f"Loading DPR biencoder from {self.src_file}"				)
               lowercase__       =				load_states_from_checkpoint(self.src_file				)
               lowercase__    ,lowercase__       =				model.question_encoder, '''question_model.'''
               # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
               lowercase__       =				{'''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(UpperCamelCase				):
                             lowercase__       =				key[len(UpperCamelCase				) :]
                             if not key.startswith('''encode_proj.'''				):
                                    lowercase__       =				'''bert_model.''' + key
                             lowercase__       =				value
               encoder.load_state_dict(UpperCamelCase				)
               return model
  
  
  
  
  
  
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Optional[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''				)[0]				)				)
               print(f"Loading DPR reader from {self.src_file}"				)
               lowercase__       =				load_states_from_checkpoint(self.src_file				)
               # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
               lowercase__       =				{
                   '''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'''				):
                             lowercase__       =				'''encoder.bert_model.''' + key[len('''encoder.'''				) :]
                      lowercase__       =				value
               model.span_predictor.load_state_dict(UpperCamelCase				)
               return model
  
  
  
  
  
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> Optional[Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				Path(A )
         dest_dir.mkdir(exist_ok=A )
  
         lowercase__       =				DPRState.from_type(A						,				src_file=A )
         lowercase__       =				dpr_state.load_dpr_model()
         model.save_pretrained(A )
         model.from_pretrained(A )  # sanity check
  
  
  if __name__ == "__main__":
   lowerCamelCase							:     List[Any]								=			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.')
   lowerCamelCase							:     Optional[int]								=			parser.parse_args()
  
   lowerCamelCase							:     List[str]								=			Path(args.src)
   lowerCamelCase							:     List[Any]								=			f"""converted-{src_file.name}""" if args.dest is None else args.dest
   lowerCamelCase							:     Optional[int]								=			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)
  
  
  
  
  