






import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
	from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor


class        lowercase__       :

	def __init__(							self						,      __UpperCAmelCase						,      __UpperCAmelCase=13						,      __UpperCAmelCase=32						,      __UpperCAmelCase=2						,      __UpperCAmelCase=3						,      __UpperCAmelCase=16						,      __UpperCAmelCase=[1, 2, 1]						,      __UpperCAmelCase=[2, 2, 4]						,      __UpperCAmelCase=2						,      __UpperCAmelCase=2.0						,      __UpperCAmelCase=True						,      __UpperCAmelCase=0.0						,      __UpperCAmelCase=0.0						,      __UpperCAmelCase=0.1						,      __UpperCAmelCase="gelu"						,      __UpperCAmelCase=False						,      __UpperCAmelCase=True						,      __UpperCAmelCase=0.02						,      __UpperCAmelCase=1E-5						,      __UpperCAmelCase=True						,      __UpperCAmelCase=None						,      __UpperCAmelCase=True						,      __UpperCAmelCase=10						,      __UpperCAmelCase=8						,      __UpperCAmelCase=["stage1", "stage2"]						,      __UpperCAmelCase=[1, 2]						,      )->   List[str]:







							'''simple docstring'''





							lowerCAmelCase__	 =  parent
							lowerCAmelCase__	 =  batch_size
							lowerCAmelCase__	 =  image_size
							lowerCAmelCase__	 =  patch_size
							lowerCAmelCase__	 =  num_channels
							lowerCAmelCase__	 =  embed_dim
							lowerCAmelCase__	 =  depths
							lowerCAmelCase__	 =  num_heads
							lowerCAmelCase__	 =  window_size
							lowerCAmelCase__	 =  mlp_ratio
							lowerCAmelCase__	 =  qkv_bias
							lowerCAmelCase__	 =  hidden_dropout_prob
							lowerCAmelCase__	 =  attention_probs_dropout_prob
							lowerCAmelCase__	 =  drop_path_rate
							lowerCAmelCase__	 =  hidden_act
							lowerCAmelCase__	 =  use_absolute_embeddings
							lowerCAmelCase__	 =  patch_norm
							lowerCAmelCase__	 =  layer_norm_eps
							lowerCAmelCase__	 =  initializer_range
							lowerCAmelCase__	 =  is_training
							lowerCAmelCase__	 =  scope
							lowerCAmelCase__	 =  use_labels
							lowerCAmelCase__	 =  type_sequence_label_size
							lowerCAmelCase__	 =  encoder_stride
							lowerCAmelCase__	 =  out_features
							lowerCAmelCase__	 =  out_indices

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







							'''simple docstring'''





							lowerCAmelCase__	 =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

							lowerCAmelCase__	 =  None
							if self.use_labels:
													lowerCAmelCase__	 =  ids_tensor([self.batch_size]						,      self.type_sequence_label_size    )

							lowerCAmelCase__	 =  self.get_config()

							return config, pixel_values, labels

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







							'''simple docstring'''





							return SwinConfig(
							    image_size=self.image_size						,      patch_size=self.patch_size						,      num_channels=self.num_channels						,      embed_dim=self.embed_dim						,      depths=self.depths						,      num_heads=self.num_heads						,      window_size=self.window_size						,      mlp_ratio=self.mlp_ratio						,      qkv_bias=self.qkv_bias						,      hidden_dropout_prob=self.hidden_dropout_prob						,      attention_probs_dropout_prob=self.attention_probs_dropout_prob						,      drop_path_rate=self.drop_path_rate						,      hidden_act=self.hidden_act						,      use_absolute_embeddings=self.use_absolute_embeddings						,      path_norm=self.patch_norm						,      layer_norm_eps=self.layer_norm_eps						,      initializer_range=self.initializer_range						,      encoder_stride=self.encoder_stride						,      out_features=self.out_features						,      out_indices=self.out_indices						,      )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  SwinModel(config=__UpperCAmelCase    )
							model.to(__UpperCAmelCase    )
							model.eval()
							lowerCAmelCase__	 =  model(__UpperCAmelCase    )

							lowerCAmelCase__	 =  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
							lowerCAmelCase__	 =  int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

							self.parent.assertEqual(result.last_hidden_state.shape						,      (self.batch_size, expected_seq_len, expected_dim)    )

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







							'''simple docstring'''





							lowerCAmelCase__	 =  SwinBackbone(config=__UpperCAmelCase    )
							model.to(__UpperCAmelCase    )
							model.eval()
							lowerCAmelCase__	 =  model(__UpperCAmelCase    )

							# verify hidden states
							self.parent.assertEqual(len(result.feature_maps    )						,      len(config.out_features    )    )
							self.parent.assertListEqual(list(result.feature_maps[0].shape    )						,      [self.batch_size, model.channels[0], 16, 16]    )

							# verify channels
							self.parent.assertEqual(len(model.channels    )						,      len(config.out_features    )    )

							# verify backbone works with out_features=None
							lowerCAmelCase__	 =  None
							lowerCAmelCase__	 =  SwinBackbone(config=__UpperCAmelCase    )
							model.to(__UpperCAmelCase    )
							model.eval()
							lowerCAmelCase__	 =  model(__UpperCAmelCase    )

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps    )						,      1    )
							self.parent.assertListEqual(list(result.feature_maps[0].shape    )						,      [self.batch_size, model.channels[-1], 4, 4]    )

							# verify channels
							self.parent.assertEqual(len(model.channels    )						,      1    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   Tuple:







							'''simple docstring'''





							lowerCAmelCase__	 =  SwinForMaskedImageModeling(config=__UpperCAmelCase    )
							model.to(__UpperCAmelCase    )
							model.eval()
							lowerCAmelCase__	 =  model(__UpperCAmelCase    )
							self.parent.assertEqual(
							    result.logits.shape						,      (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

							# test greyscale images
							lowerCAmelCase__	 =  1
							lowerCAmelCase__	 =  SwinForMaskedImageModeling(__UpperCAmelCase    )
							model.to(__UpperCAmelCase    )
							model.eval()

							lowerCAmelCase__	 =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
							lowerCAmelCase__	 =  model(__UpperCAmelCase    )
							self.parent.assertEqual(result.logits.shape						,      (self.batch_size, 1, self.image_size, self.image_size)    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.type_sequence_label_size
							lowerCAmelCase__	 =  SwinForImageClassification(__UpperCAmelCase    )
							model.to(__UpperCAmelCase    )
							model.eval()
							lowerCAmelCase__	 =  model(__UpperCAmelCase						,      labels=__UpperCAmelCase    )
							self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.type_sequence_label_size)    )

							# test greyscale images
							lowerCAmelCase__	 =  1
							lowerCAmelCase__	 =  SwinForImageClassification(__UpperCAmelCase    )
							model.to(__UpperCAmelCase    )
							model.eval()

							lowerCAmelCase__	 =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
							lowerCAmelCase__	 =  model(__UpperCAmelCase    )
							self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.type_sequence_label_size)    )


	def 						UpperCAmelCase					(							self    )->   str:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase__
							) ,							(
							    lowerCAmelCase__
							) ,							(
							    lowerCAmelCase__
							) ,							
							)	 =  config_and_inputs
							lowerCAmelCase__	 =  {"pixel_values": pixel_values}
							return config, inputs_dict





@require_torch
class        lowercase__       (     _UpperCAmelCase,   _UpperCAmelCase,   unittest.TestCase		):
	a_             =(
	    (
	        SwinModel,
	        SwinBackbone,
	        SwinForImageClassification,
	        SwinForMaskedImageModeling,
	    )
	    if is_torch_available()
	    else ()
	)
	a_             =(
	    {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
	    if is_torch_available()
	    else {}
	)
	a_             =True

	a_             =False
	a_             =False
	a_             =False

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







							'''simple docstring'''





							lowerCAmelCase__	 =  SwinModelTester(self    )
							lowerCAmelCase__	 =  ConfigTester(self						,      config_class=__UpperCAmelCase						,      embed_dim=37    )

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







							'''simple docstring'''





							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							return

	def 						UpperCAmelCase					(							self    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCAmelCase    )

	@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."    )
	def 						UpperCAmelCase					(							self    )->   Optional[int]:







							'''simple docstring'''





							pass

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







							'''simple docstring'''





							super().test_training_gradient_checkpointing()

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







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*__UpperCAmelCase    )

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







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase    )

	def 						UpperCAmelCase					(							self    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase    )

	@unittest.skip(reason="Swin does not use inputs_embeds"    )
	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							pass

	@unittest.skip(reason="Swin Transformer does not use feedforward chunking"    )
	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





							pass

	def 						UpperCAmelCase					(							self    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__ ,							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowerCAmelCase__	 =  model_class(__UpperCAmelCase    )
													self.assertIsInstance(model.get_input_embeddings()						,      (nn.Module)    )
													lowerCAmelCase__	 =  model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(__UpperCAmelCase						,      nn.Linear    )    )

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







							'''simple docstring'''





							lowerCAmelCase__ ,							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowerCAmelCase__	 =  model_class(__UpperCAmelCase    )
													lowerCAmelCase__	 =  inspect.signature(model.forward    )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowerCAmelCase__	 =  [*signature.parameters.keys()]

													lowerCAmelCase__	 =  ["pixel_values"]
													self.assertListEqual(arg_names[:1]						,      __UpperCAmelCase    )

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







							'''simple docstring'''





							lowerCAmelCase__ ,							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase__	 =  True

							for model_class in self.all_model_classes:
													lowerCAmelCase__	 =  True
													lowerCAmelCase__	 =  False
													lowerCAmelCase__	 =  True
													lowerCAmelCase__	 =  model_class(__UpperCAmelCase    )
													model.to(__UpperCAmelCase    )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__	 =  model(**self._prepare_for_class(__UpperCAmelCase						,      __UpperCAmelCase    )    )
													lowerCAmelCase__	 =  outputs.attentions
													lowerCAmelCase__	 =  len(self.model_tester.depths    )
													self.assertEqual(len(__UpperCAmelCase    )						,      __UpperCAmelCase    )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													lowerCAmelCase__	 =  True
													lowerCAmelCase__	 =  config.window_size**2
													lowerCAmelCase__	 =  model_class(__UpperCAmelCase    )
													model.to(__UpperCAmelCase    )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__	 =  model(**self._prepare_for_class(__UpperCAmelCase						,      __UpperCAmelCase    )    )
													lowerCAmelCase__	 =  outputs.attentions
													self.assertEqual(len(__UpperCAmelCase    )						,      __UpperCAmelCase    )

													self.assertListEqual(
													    list(attentions[0].shape[-3:]    )						,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,      )
													lowerCAmelCase__	 =  len(__UpperCAmelCase    )

													# Check attention is always last and order is fine
													lowerCAmelCase__	 =  True
													lowerCAmelCase__	 =  True
													lowerCAmelCase__	 =  model_class(__UpperCAmelCase    )
													model.to(__UpperCAmelCase    )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__	 =  model(**self._prepare_for_class(__UpperCAmelCase						,      __UpperCAmelCase    )    )

													# also another +1 for reshaped_hidden_states
													lowerCAmelCase__	 =  1 if model_class.__name__ == "SwinBackbone" else 2
													self.assertEqual(out_len + added_hidden_states						,      len(__UpperCAmelCase    )    )

													lowerCAmelCase__	 =  outputs.attentions

													self.assertEqual(len(__UpperCAmelCase    )						,      __UpperCAmelCase    )

													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]    )						,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,      )

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







							'''simple docstring'''





							lowerCAmelCase__	 =  model_class(__UpperCAmelCase    )
							model.to(__UpperCAmelCase    )
							model.eval()

							with torch.no_grad():
													lowerCAmelCase__	 =  model(**self._prepare_for_class(__UpperCAmelCase						,      __UpperCAmelCase    )    )

							lowerCAmelCase__	 =  outputs.hidden_states

							lowerCAmelCase__	 =  getattr(
							    self.model_tester						,      "expected_num_hidden_layers"						,      len(self.model_tester.depths    ) + 1    )
							self.assertEqual(len(__UpperCAmelCase    )						,      __UpperCAmelCase    )

							# Swin has a different seq_length
							lowerCAmelCase__	 =  (
							    config.patch_size
							    if isinstance(config.patch_size						,      collections.abc.Iterable    )
							    else (config.patch_size, config.patch_size)
							)

							lowerCAmelCase__	 =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]    )						,      [num_patches, self.model_tester.embed_dim]						,      )

							if not model_class.__name__ == "SwinBackbone":
													lowerCAmelCase__	 =  outputs.reshaped_hidden_states
													self.assertEqual(len(__UpperCAmelCase    )						,      __UpperCAmelCase    )

													lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__	 =  reshaped_hidden_states[0].shape
													lowerCAmelCase__	 =  (
													    reshaped_hidden_states[0].view(__UpperCAmelCase						,      __UpperCAmelCase						,      height * width    ).permute(0						,      2						,      1    )
													)
													self.assertListEqual(
													    list(reshaped_hidden_states.shape[-2:]    )						,      [num_patches, self.model_tester.embed_dim]						,      )

	def 						UpperCAmelCase					(							self    )->   Tuple:







							'''simple docstring'''





							lowerCAmelCase__ ,							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs_for_common()

							lowerCAmelCase__	 =  (
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size						,      collections.abc.Iterable    )
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)

							for model_class in self.all_model_classes:
													lowerCAmelCase__	 =  True
													self.check_hidden_states_output(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowerCAmelCase__	 =  True

													self.check_hidden_states_output(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )

	def 						UpperCAmelCase					(							self    )->   str:







							'''simple docstring'''





							lowerCAmelCase__ ,							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase__	 =  3

							lowerCAmelCase__	 =  (
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size						,      collections.abc.Iterable    )
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)
							lowerCAmelCase__	 =  (
							    config.patch_size
							    if isinstance(config.patch_size						,      collections.abc.Iterable    )
							    else (config.patch_size, config.patch_size)
							)

							lowerCAmelCase__	 =  image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							lowerCAmelCase__	 =  image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes:
													lowerCAmelCase__	 =  True
													self.check_hidden_states_output(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      (padded_height, padded_width)    )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowerCAmelCase__	 =  True
													self.check_hidden_states_output(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      (padded_height, padded_width)    )

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







							'''simple docstring'''





							for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCAmelCase__	 =  SwinModel.from_pretrained(__UpperCAmelCase    )
													self.assertIsNotNone(__UpperCAmelCase    )


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







							'''simple docstring'''





							lowerCAmelCase__ ,							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs_for_common()

							lowerCAmelCase__	 =  _config_zero_init(__UpperCAmelCase    )
							for model_class in self.all_model_classes:
													lowerCAmelCase__	 =  model_class(config=__UpperCAmelCase    )
													for name, param in model.named_parameters():
																			if "embeddings" not in name and param.requires_grad:
																									self.assertIn(
																									    ((param.data.mean() * 1E9).round() / 1E9).item()						,      [0.0, 1.0]						,      msg=F"Parameter {name} of model {model_class} seems not properly initialized"						,      )





@require_vision
@require_torch
class        lowercase__       (     unittest.TestCase		):

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







							'''simple docstring'''





							return (
							    AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"    )
							    if is_vision_available()
							    else None
							)


	@slow
	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"    ).to(__UpperCAmelCase    )
							lowerCAmelCase__	 =  self.default_image_processor

							lowerCAmelCase__	 =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
							lowerCAmelCase__	 =  image_processor(images=__UpperCAmelCase						,      return_tensors="pt"    ).to(__UpperCAmelCase    )

							# forward pass
							with torch.no_grad():
													lowerCAmelCase__	 =  model(**__UpperCAmelCase    )

							# verify the logits
							lowerCAmelCase__	 =  torch.Size((1, 1000)    )
							self.assertEqual(outputs.logits.shape						,      __UpperCAmelCase    )
							lowerCAmelCase__	 =  torch.tensor([-0.0_948, -0.6_454, -0.0_921]    ).to(__UpperCAmelCase    )
							self.assertTrue(torch.allclose(outputs.logits[0, :3]						,      __UpperCAmelCase						,      atol=1E-4    )    )





@require_torch
class        lowercase__       (     unittest.TestCase,   _UpperCAmelCase		):
	a_             =(SwinBackbone,) if is_torch_available() else ()
	a_             =SwinConfig

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  SwinModelTester(self    )














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     _a					(       UpperCamelCase_   :			str				)	->				CheckpointState:
						"""simple docstring"""







						print(F"Reading saved model from {model_file}"				)
						lowerCAmelCase__	 =  torch.load(UpperCamelCase_      ,				map_location=lambda UpperCamelCase_      ,				UpperCamelCase_				: default_restore_location(UpperCamelCase_      ,				"cpu"				)				)
						return CheckpointState(**UpperCamelCase_				)


class        lowercase__       :

	def __init__(							self						,      __UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  src_file

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







							'''simple docstring'''





							raise NotImplementedError


	@staticmethod
	def 						UpperCAmelCase					(							__UpperCAmelCase						,      *__UpperCAmelCase						,      **__UpperCAmelCase    )->   "DPRState":







							'''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        lowercase__       (     _UpperCAmelCase		):

	def 						UpperCAmelCase					(							self    )->   str:







							'''simple docstring'''





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





class        lowercase__       (     _UpperCAmelCase		):

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





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





class        lowercase__       (     _UpperCAmelCase		):

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







							'''simple docstring'''





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





def     _a					(       UpperCamelCase_   :			str      ,				UpperCamelCase_   :			Path      ,				UpperCamelCase_   :			Path				)	->				Any:
						"""simple docstring"""







						lowerCAmelCase__	 =  Path(UpperCamelCase_				)
						dest_dir.mkdir(exist_ok=UpperCamelCase_				)

						lowerCAmelCase__	 =  DPRState.from_type(UpperCamelCase_      ,				src_file=UpperCamelCase_				)
						lowerCAmelCase__	 =  dpr_state.load_dpr_model()
						model.save_pretrained(UpperCamelCase_				)
						model.from_pretrained(UpperCamelCase_				)  # 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)






