





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


SCREAMING_SNAKE_CASE_      :			List[str]		  =      collections.namedtuple(
    '''CheckpointState''', ['''model_dict''', '''optimizer_dict''', '''scheduler_dict''', '''offset''', '''epoch''', '''encoder_params''']
)



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						CheckpointState:
		print(F"Reading saved model from {model_file}"       )
		__lowercase					=    torch.load(_UpperCAmelCase	,      map_location=lambda snake_case	,      snake_case       : default_restore_location(_UpperCAmelCase	,      'cpu'       )       )
		return CheckpointState(**_UpperCAmelCase       )

class 		snake_case_      :







	'''simple docstring'''




	def __init__(	self       :    int				,			__lowerCamelCase       :    Path      )					->   int:

			'''simple docstring'''




			__lowercase					=    src_file




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

			'''simple docstring'''




			raise NotImplementedError






	@staticmethod
	def       UpperCAmelCase (	__lowerCamelCase       :    str				,			*__lowerCamelCase       :    Any				,			**__lowerCamelCase       :    Optional[Any]      )					->   str:

			'''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 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Optional[int]:

			'''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					=    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 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    Tuple      )					->   List[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					=    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 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    List[Any]      )					->   Union[str, 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							(			snake_case	,      snake_case	,      snake_case       )		->						str:
		__lowercase					=    Path(_UpperCAmelCase       )
		dest_dir.mkdir(exist_ok=_UpperCAmelCase       )

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


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_      :			Optional[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.''')
		SCREAMING_SNAKE_CASE_      :			Optional[int]		  =      parser.parse_args()

		SCREAMING_SNAKE_CASE_      :			Any		  =      Path(args.src)
		SCREAMING_SNAKE_CASE_      :			Optional[Any]		  =      f"""converted-{src_file.name}""" if args.dest is None else args.dest
		SCREAMING_SNAKE_CASE_      :			Dict		  =      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 unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin

class 		snake_case_      (       UpperCAmelCase_    ,    UpperCAmelCase_    ,    unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		UNetaDModel
	__UpperCamelCase        =		'''sample'''




	@property
	def       UpperCAmelCase (	self       :    str      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    4
			__lowercase					=    14
			__lowercase					=    16

			__lowercase					=    floats_tensor((batch_size, num_features, seq_len)      ).to(__lowerCamelCase      )
			__lowercase					=    torch.tensor([10] * batch_size      ).to(__lowerCamelCase      )

			return {"sample": noise, "timestep": time_step}




	@property
	def       UpperCAmelCase (	self       :    List[str]      )					->   Union[str, Any]:

			'''simple docstring'''




			return (4, 14, 16)




	@property
	def       UpperCAmelCase (	self       :    Dict      )					->   List[Any]:

			'''simple docstring'''




			return (4, 14, 16)




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Dict:

			'''simple docstring'''




			pass




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

			'''simple docstring'''




			pass




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[Any]      )					->   Optional[Any]:

			'''simple docstring'''




			super().test_determinism()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			super().test_outputs_equivalence()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   List[Any]:

			'''simple docstring'''




			super().test_from_save_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Any:

			'''simple docstring'''




			super().test_from_save_pretrained_variant()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   int:

			'''simple docstring'''




			super().test_model_from_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Tuple:

			'''simple docstring'''




			super().test_output()




	def       UpperCAmelCase (	self       :    List[Any]      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    {
			    'block_out_channels': (32, 64, 128, 256),
			    'in_channels': 14,
			    'out_channels': 14,
			    'time_embedding_type': 'positional',
			    'use_timestep_embedding': True,
			    'flip_sin_to_cos': False,
			    'freq_shift': 1.0,
			    'out_block_type': 'OutConv1DBlock',
			    'mid_block_type': 'MidResTemporalBlock1D',
			    'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
			    'up_block_types': ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D'),
			    'act_fn': 'mish',
			}
			__lowercase					=    self.dummy_input
			return init_dict, inputs_dict




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Any:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='unet'      )
			self.assertIsNotNone(__lowerCamelCase      )
			self.assertEqual(len(loading_info['missing_keys']      )				,			0      )

			model.to(__lowerCamelCase      )
			__lowercase					=    model(**self.dummy_input      )

			assert image is not None, "Make sure output is not None"




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			__lowercase					=    UNetaDModel.from_pretrained('bglick13/hopper-medium-v2-value-function-hor32'				,			subfolder='unet'      )
			torch.manual_seed(0      )
			if torch.cuda.is_available():
					torch.cuda.manual_seed_all(0      )

			__lowercase					=    model.config.in_channels
			__lowercase					=    16
			__lowercase					=    torch.randn((1, seq_len, num_features)      ).permute(
			    0				,			2				,			1      )  # match original, we can update values and remove
			__lowercase					=    torch.full((num_features,)				,			0      )

			with torch.no_grad():
					__lowercase					=    model(__lowerCamelCase				,			__lowerCamelCase      ).sample.permute(0				,			2				,			1      )

			__lowercase					=    output[0, -3:, -3:].flatten()
			# fmt: off
			__lowercase					=    torch.tensor([-2.137172, 1.1426016, 0.3688687, -0.766922, 0.7303146, 0.11038864, -0.4760633, 0.13270172, 0.02591348]      )
			# fmt: on
			self.assertTrue(torch.allclose(__lowerCamelCase				,			__lowerCamelCase				,			rtol=1E-3      )      )




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

			'''simple docstring'''




			pass






	@slow
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			__lowercase					=    'harmonai/maestro-150k'
			__lowercase					=    UNetaDModel.from_pretrained(__lowerCamelCase				,			subfolder='unet'      )
			model.to(__lowerCamelCase      )

			__lowercase					=    65_536
			__lowercase					=    torch.sin(torch.arange(__lowerCamelCase      )[None, None, :].repeat(1				,			2				,			1      )      ).to(__lowerCamelCase      )
			__lowercase					=    torch.tensor([1]      ).to(__lowerCamelCase      )

			with torch.no_grad():
					__lowercase					=    model(__lowerCamelCase				,			__lowerCamelCase      ).sample

			__lowercase					=    output.abs().sum()
			__lowercase					=    output.abs().max()

			assert (output_sum - 224.0896).abs() < 0.5
			assert (output_max - 0.0607).abs() < 4E-4





class 		snake_case_      (       UpperCAmelCase_    ,    UpperCAmelCase_    ,    unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		UNetaDModel
	__UpperCamelCase        =		'''sample'''




	@property
	def       UpperCAmelCase (	self       :    int      )					->   Union[str, Any]:

			'''simple docstring'''




			__lowercase					=    4
			__lowercase					=    14
			__lowercase					=    16

			__lowercase					=    floats_tensor((batch_size, num_features, seq_len)      ).to(__lowerCamelCase      )
			__lowercase					=    torch.tensor([10] * batch_size      ).to(__lowerCamelCase      )

			return {"sample": noise, "timestep": time_step}




	@property
	def       UpperCAmelCase (	self       :    List[str]      )					->   Union[str, Any]:

			'''simple docstring'''




			return (4, 14, 16)




	@property
	def       UpperCAmelCase (	self       :    Tuple      )					->   Union[str, Any]:

			'''simple docstring'''




			return (4, 14, 1)




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			super().test_determinism()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    str      )					->   int:

			'''simple docstring'''




			super().test_outputs_equivalence()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    str      )					->   List[Any]:

			'''simple docstring'''




			super().test_from_save_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[Any]      )					->   int:

			'''simple docstring'''




			super().test_from_save_pretrained_variant()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Dict:

			'''simple docstring'''




			super().test_model_from_pretrained()




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    self.prepare_init_args_and_inputs_for_common()
			__lowercase					=    self.model_class(**__lowerCamelCase      )
			model.to(__lowerCamelCase      )
			model.eval()

			with torch.no_grad():
					__lowercase					=    model(**__lowerCamelCase      )

					if isinstance(__lowerCamelCase				,			__lowerCamelCase      ):
							__lowercase					=    output.sample

			self.assertIsNotNone(__lowerCamelCase      )
			__lowercase					=    torch.Size((inputs_dict['sample'].shape[0], 1)      )
			self.assertEqual(output.shape				,			__lowerCamelCase				,			'Input and output shapes do not match'      )




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   str:

			'''simple docstring'''




			pass




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Optional[int]:

			'''simple docstring'''




			pass




	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase					=    {
			    'in_channels': 14,
			    'out_channels': 14,
			    'down_block_types': ['DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'],
			    'up_block_types': [],
			    'out_block_type': 'ValueFunction',
			    'mid_block_type': 'ValueFunctionMidBlock1D',
			    'block_out_channels': [32, 64, 128, 256],
			    'layers_per_block': 1,
			    'downsample_each_block': True,
			    'use_timestep_embedding': True,
			    'freq_shift': 1.0,
			    'flip_sin_to_cos': False,
			    'time_embedding_type': 'positional',
			    'act_fn': 'mish',
			}
			__lowercase					=    self.dummy_input
			return init_dict, inputs_dict




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Any      )					->   Union[str, Any]:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='value_function'      )
			self.assertIsNotNone(__lowerCamelCase      )
			self.assertEqual(len(vf_loading_info['missing_keys']      )				,			0      )

			value_function.to(__lowerCamelCase      )
			__lowercase					=    value_function(**self.dummy_input      )

			assert image is not None, "Make sure output is not None"




	@unittest.skipIf(torch_device == 'mps'				,			'mish op not supported in MPS'      )
	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase	,			__lowercase					=    UNetaDModel.from_pretrained(
			    'bglick13/hopper-medium-v2-value-function-hor32'				,			output_loading_info=__lowerCamelCase				,			subfolder='value_function'      )
			torch.manual_seed(0      )
			if torch.cuda.is_available():
					torch.cuda.manual_seed_all(0      )

			__lowercase					=    value_function.config.in_channels
			__lowercase					=    14
			__lowercase					=    torch.randn((1, seq_len, num_features)      ).permute(
			    0				,			2				,			1      )  # match original, we can update values and remove
			__lowercase					=    torch.full((num_features,)				,			0      )

			with torch.no_grad():
					__lowercase					=    value_function(__lowerCamelCase				,			__lowerCamelCase      ).sample

			# fmt: off
			__lowercase					=    torch.tensor([165.25] * seq_len      )
			# fmt: on
			self.assertTrue(torch.allclose(__lowerCamelCase				,			__lowerCamelCase				,			rtol=1E-3      )      )






	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			pass




