




'''simple docstring'''







import collections
import inspect
import unittest

from transformers import SwinvaConfig
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_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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
   from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor


class 			_a  :
       def __init__(	self						:	Optional[Any]   ,				lowercase						:	Tuple   ,				lowercase						:	Union[str, Any]=13   ,				lowercase						:	int=32   ,				lowercase						:	Optional[Any]=2   ,				lowercase						:	Union[str, Any]=3   ,				lowercase						:	Union[str, Any]=16   ,				lowercase						:	List[str]=[1, 2, 1]   ,				lowercase						:	Dict=[2, 2, 4]   ,				lowercase						:	Optional[Any]=2   ,				lowercase						:	Dict=2.0   ,				lowercase						:	Optional[int]=True   ,				lowercase						:	List[str]=0.0   ,				lowercase						:	Tuple=0.0   ,				lowercase						:	Union[str, Any]=0.1   ,				lowercase						:	Union[str, Any]="gelu"   ,				lowercase						:	Any=False   ,				lowercase						:	Optional[int]=True   ,				lowercase						:	Optional[Any]=0.02   ,				lowercase						:	Optional[int]=1E-5   ,				lowercase						:	Optional[int]=True   ,				lowercase						:	Optional[int]=None   ,				lowercase						:	List[Any]=True   ,				lowercase						:	int=10   ,				lowercase						:	Optional[int]=8   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		parent
           UpperCAmelCase			       =		batch_size
           UpperCAmelCase			       =		image_size
           UpperCAmelCase			       =		patch_size
           UpperCAmelCase			       =		num_channels
           UpperCAmelCase			       =		embed_dim
           UpperCAmelCase			       =		depths
           UpperCAmelCase			       =		num_heads
           UpperCAmelCase			       =		window_size
           UpperCAmelCase			       =		mlp_ratio
           UpperCAmelCase			       =		qkv_bias
           UpperCAmelCase			       =		hidden_dropout_prob
           UpperCAmelCase			       =		attention_probs_dropout_prob
           UpperCAmelCase			       =		drop_path_rate
           UpperCAmelCase			       =		hidden_act
           UpperCAmelCase			       =		use_absolute_embeddings
           UpperCAmelCase			       =		patch_norm
           UpperCAmelCase			       =		layer_norm_eps
           UpperCAmelCase			       =		initializer_range
           UpperCAmelCase			       =		is_training
           UpperCAmelCase			       =		scope
           UpperCAmelCase			       =		use_labels
           UpperCAmelCase			       =		type_sequence_label_size
           UpperCAmelCase			       =		encoder_stride
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

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

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

           UpperCAmelCase			       =		self.get_config()

           return config, pixel_values, labels
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           return SwinvaConfig(
               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   ,				)
       def     A      (	self						:	Tuple   ,				lowercase						:	str   ,				lowercase						:	Tuple   ,				lowercase						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		SwinvaModel(config=_a				)
           model.to(_a				)
           model.eval()
           UpperCAmelCase			       =		model(_a				)

           UpperCAmelCase			       =		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
           UpperCAmelCase			       =		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     A      (	self						:	Tuple   ,				lowercase						:	Union[str, Any]   ,				lowercase						:	Dict   ,				lowercase						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		SwinvaForMaskedImageModeling(config=_a				)
           model.to(_a				)
           model.eval()
           UpperCAmelCase			       =		model(_a				)
           self.parent.assertEqual(
               result.logits.shape   ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)				)

           # test greyscale images
           UpperCAmelCase			       =		1
           UpperCAmelCase			       =		SwinvaForMaskedImageModeling(_a				)
           model.to(_a				)
           model.eval()

           UpperCAmelCase			       =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
           UpperCAmelCase			       =		model(_a				)
           self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, 1, self.image_size, self.image_size)				)
       def     A      (	self						:	Tuple   ,				lowercase						:	Optional[Any]   ,				lowercase						:	Optional[int]   ,				lowercase						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.type_sequence_label_size
           UpperCAmelCase			       =		SwinvaForImageClassification(_a				)
           model.to(_a				)
           model.eval()
           UpperCAmelCase			       =		model(_a   ,				labels=_a				)
           self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)				)







       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.prepare_config_and_inputs()
           UpperCAmelCase			       =		config_and_inputs
           UpperCAmelCase			       =		{'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
class 			_a  (	lowercase__							,							lowercase__							,							unittest.TestCase ):
       __a				:     Optional[int]   				=					(
           (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
       )
       __a				:     Tuple   				=					(
           {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
           if is_torch_available()
           else {}
       )

       __a				:     Optional[Any]   				=					False
       __a				:     List[Any]   				=					False
       __a				:     List[str]   				=					False
       __a				:     Tuple   				=					False
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		SwinvaModelTester(self				)
           UpperCAmelCase			       =		ConfigTester(self   ,				config_class=_a   ,				embed_dim=37				)
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           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     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_a				)
       @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           pass
       @unittest.skip(reason='''Swinv2 does not use inputs_embeds'''				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               UpperCAmelCase			       =		model_class(_a				)
               self.assertIsInstance(model.get_input_embeddings()   ,				(nn.Module)				)
               UpperCAmelCase			       =		model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(_a   ,				nn.Linear				)				)
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()

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

               UpperCAmelCase			       =		['pixel_values']
               self.assertListEqual(arg_names[:1]   ,				_a				)
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()
           UpperCAmelCase			       =		True

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

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

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

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

               if hasattr(self.model_tester   ,				'''num_hidden_states_types'''				):
                   UpperCAmelCase			       =		self.model_tester.num_hidden_states_types
               else:
                   # also another +1 for reshaped_hidden_states
                   UpperCAmelCase			       =		2
               self.assertEqual(out_len + added_hidden_states   ,				len(_a				)				)

               UpperCAmelCase			       =		outputs.attentions

               self.assertEqual(len(_a				)   ,				_a				)

               self.assertListEqual(
                   list(self_attentions[0].shape[-3:]				)   ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,				)
       def     A      (	self						:	Dict   ,				lowercase						:	List[str]   ,				lowercase						:	Any   ,				lowercase						:	List[str]   ,				lowercase						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		model_class(_a				)
           model.to(_a				)
           model.eval()

           with torch.no_grad():
               UpperCAmelCase			       =		model(**self._prepare_for_class(_a   ,				_a				)				)

           UpperCAmelCase			       =		outputs.hidden_states

           UpperCAmelCase			       =		getattr(
               self.model_tester   ,				'''expected_num_hidden_layers'''   ,				len(self.model_tester.depths				) + 1				)
           self.assertEqual(len(_a				)   ,				_a				)

           # Swinv2 has a different seq_length
           UpperCAmelCase			       =		(
               config.patch_size
               if isinstance(config.patch_size   ,				collections.abc.Iterable				)
               else (config.patch_size, config.patch_size)
           )

           UpperCAmelCase			       =		(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]   ,				)

           UpperCAmelCase			       =		outputs.reshaped_hidden_states
           self.assertEqual(len(_a				)   ,				_a				)

           UpperCAmelCase			       =		reshaped_hidden_states[0].shape
           UpperCAmelCase			       =		(
               reshaped_hidden_states[0].view(_a   ,				_a   ,				height * width				).permute(0   ,				2   ,				1				)
           )
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:]				)   ,				[num_patches, self.model_tester.embed_dim]   ,				)
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()

           UpperCAmelCase			       =		(
               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:
               UpperCAmelCase			       =		True
               self.check_hidden_states_output(_a   ,				_a   ,				_a   ,				_a				)

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

               self.check_hidden_states_output(_a   ,				_a   ,				_a   ,				_a				)
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()
           UpperCAmelCase			       =		3

           UpperCAmelCase			       =		(
               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)
           )
           UpperCAmelCase			       =		(
               config.patch_size
               if isinstance(config.patch_size   ,				collections.abc.Iterable				)
               else (config.patch_size, config.patch_size)
           )

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

           for model_class in self.all_model_classes:
               UpperCAmelCase			       =		True
               self.check_hidden_states_output(_a   ,				_a   ,				_a   ,				(padded_height, padded_width)				)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               UpperCAmelCase			       =		True
               self.check_hidden_states_output(_a   ,				_a   ,				_a   ,				(padded_height, padded_width)				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*_a				)
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*_a				)
       @slow
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase			       =		SwinvaModel.from_pretrained(_a				)
               self.assertIsNotNone(_a				)







       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()

           UpperCAmelCase			       =		_config_zero_init(_a				)
           for model_class in self.all_model_classes:
               UpperCAmelCase			       =		model_class(config=_a				)
               for name, param in model.named_parameters():
                   if "embeddings" not in name and "logit_scale" 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 			_a  (	unittest.TestCase ):
       @cached_property
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           return (
               AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''				)
               if is_vision_available()
               else None
           )







       @slow
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''				).to(
               _a				)
           UpperCAmelCase			       =		self.default_image_processor

           UpperCAmelCase			       =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
           UpperCAmelCase			       =		image_processor(images=_a   ,				return_tensors='''pt'''				).to(_a				)

           # forward pass
           with torch.no_grad():
               UpperCAmelCase			       =		model(**_a				)

           # verify the logits
           UpperCAmelCase			       =		torch.Size((1, 1_000)				)
           self.assertEqual(outputs.logits.shape   ,				_a				)
           UpperCAmelCase			       =		torch.tensor([-0.3947, -0.4306, 0.0026]				).to(_a				)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,				_a   ,				atol=1E-4				)				)






'''simple docstring'''







import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()





def 					UpperCAmelCase_		(__a      :    Optional[Any] ,	__a      :    str ,	__a      :    Optional[Any]=None ):






   """simple docstring"""

   assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
   _a				:	str			=   nn.Parameter(__a )
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
      _a				:	Any			=   nn.Parameter(__a )





def 					UpperCAmelCase_		(__a      :    int ,	__a      :    Optional[Any] ,	__a      :    int ):






   """simple docstring"""

   _a				:	Tuple			=   np.asarray(weights[0] )
   _a				:	Union[str, Any]			=   np.asarray(weights[1] )
   _a				:	Dict			=   np.asarray(weights[2] )

   set_param(
       torch_layer.self_attention.query_key ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.self_attention.value ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.output.dense ,	torch.tensor(__a ).view(-1 ,	__a ).contiguous().transpose(0 ,	1 ) ,	)





def 					UpperCAmelCase_		(__a      :    Optional[Any] ,	__a      :    Optional[int] ,	__a      :    List[str] ):






   """simple docstring"""

   _a				:	Dict			=   np.asarray(weights[0] )
   _a				:	Union[str, Any]			=   np.asarray(weights[1] )
   _a				:	str			=   np.asarray(weights[2] )
   _a				:	int			=   np.asarray(weights[3] )

   set_param(
       torch_layer.self_attention.query ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.self_attention.key ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.self_attention.value ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.output.dense ,	torch.tensor(__a ).view(-1 ,	__a ).contiguous().transpose(0 ,	1 ) ,	)





def 					UpperCAmelCase_		(__a      :    Any ,	__a      :    Any ,	__a      :    Optional[Any] ):






   """simple docstring"""

   _a				:	List[str]			=   weights[0][0][0]
   _a				:	List[Any]			=   np.asarray(layer_norm_a[0] )
   _a				:	List[str]			=   np.asarray(layer_norm_a[1] )
   set_param(
       torch_block.attention.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # lsh weights + output
   _a				:	List[str]			=   weights[0][1]
   if len(__a ) < 4:
      set_layer_weights_in_torch_lsh(__a ,	torch_block.attention ,	__a )
   else:
      set_layer_weights_in_torch_local(__a ,	torch_block.attention ,	__a )

   # intermediate weighs
   _a				:	Optional[Any]			=   weights[2][0][1][2]

   # Chunked Feed Forward
   if len(__a ) == 4:
      _a				:	Union[str, Any]			=   intermediate_weights[2]

   # layernorm 2
   _a				:	Any			=   np.asarray(intermediate_weights[0][0] )
   _a				:	List[Any]			=   np.asarray(intermediate_weights[0][1] )
   set_param(
       torch_block.feed_forward.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # intermediate dense
   _a				:	Any			=   np.asarray(intermediate_weights[1][0] )
   _a				:	Any			=   np.asarray(intermediate_weights[1][1] )
   set_param(
       torch_block.feed_forward.dense.dense ,	torch.tensor(__a ).transpose(0 ,	1 ).contiguous() ,	torch.tensor(__a ) ,	)

   # intermediate out
   _a				:	Optional[int]			=   np.asarray(intermediate_weights[4][0] )
   _a				:	int			=   np.asarray(intermediate_weights[4][1] )
   set_param(
       torch_block.feed_forward.output.dense ,	torch.tensor(__a ).transpose(0 ,	1 ).contiguous() ,	torch.tensor(__a ) ,	)





def 					UpperCAmelCase_		(__a      :    Dict ,	__a      :    Dict ,	__a      :    List[Any] ):






   """simple docstring"""

   _a				:	Optional[int]			=   torch_model.reformer

   # word embeds
   _a				:	Tuple			=   np.asarray(weights[1] )
   set_param(
       torch_model_reformer.embeddings.word_embeddings ,	torch.tensor(__a ) ,	)

   if isinstance(weights[3] ,	__a ):
      _a				:	Any			=   torch_model_reformer.embeddings.position_embeddings
      for emb_idx in range(len(position_embeddings.weights ) ):
         _a				:	List[Any]			=   np.asarray(weights[3][emb_idx][0] )
         assert (
             position_embeddings.weights[emb_idx].shape == emb_weights.shape
         ), f"""{position_embeddings[emb_idx]} emb does not match"""
         _a				:	Any			=   nn.Parameter(torch.tensor(__a ) )

   _a				:	List[str]			=   weights[5]
   assert len(torch_model_reformer.encoder.layers ) * 4 == len(
       __a ), "HF and trax model do not have the same number of layers"
   for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
      _a				:	Tuple			=   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
      set_block_weights_in_torch(__a ,	__a ,	__a )

   # output layer norm
   _a				:	Optional[Any]			=   np.asarray(weights[7][0] )
   _a				:	int			=   np.asarray(weights[7][1] )
   set_param(
       torch_model_reformer.encoder.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # output embeddings
   _a				:	List[str]			=   np.asarray(weights[9][0] )
   _a				:	int			=   np.asarray(weights[9][1] )
   set_param(
       torch_model.lm_head.decoder ,	torch.tensor(__a ).transpose(0 ,	1 ).contiguous() ,	torch.tensor(__a ) ,	)





def 					UpperCAmelCase_		(__a      :    Tuple ,	__a      :    Optional[Any] ,	__a      :    Dict ):






   """simple docstring"""

   _a				:	List[Any]			=   ReformerConfig.from_json_file(__a )
   print(f"""Building PyTorch model from configuration: {config}""" )
   _a				:	int			=   ReformerModelWithLMHead(__a )

   with open(__a ,	'rb' ) as f:
      _a				:	Optional[Any]			=   pickle.load(__a )['weights']

   set_model_weights_in_torch(__a ,	__a ,	config.hidden_size )

   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}""" )
   torch.save(model.state_dict() ,	__a )


if __name__ == "__main__":
  __lowerCAmelCase         =    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained Reformer model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __lowerCAmelCase         =    parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



