






import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

	if is_vision_available():
		from transformers import MaskaFormerImageProcessor

if is_vision_available():
	from PIL import Image


class    _lowerCAmelCase			:



				"""simple docstring"""



				def __init__(      self    :    Tuple			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    str=2			,		UpperCamelCase__    :    str=True			,		UpperCamelCase__    :    List[str]=False			,		UpperCamelCase__    :    List[Any]=1_0			,		UpperCamelCase__    :    Any=3			,		UpperCamelCase__    :    str=3_2 * 8			,		UpperCamelCase__    :    Union[str, Any]=3_2 * 8			,		UpperCamelCase__    :    List[Any]=4			,		UpperCamelCase__    :    Any=6_4			,		):






											'''simple docstring'''







											snake_case__	  =       parent
											snake_case__	  =       batch_size
											snake_case__	  =       is_training
											snake_case__	  =       use_auxiliary_loss
											snake_case__	  =       num_queries
											snake_case__	  =       num_channels
											snake_case__	  =       min_size
											snake_case__	  =       max_size
											snake_case__	  =       num_labels
											snake_case__	  =       hidden_dim
											snake_case__	  =       hidden_dim



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
											    UpperCamelCase__)

											snake_case__	  =       torch.ones([self.batch_size, self.min_size, self.max_size]			,		device=UpperCamelCase__)

											snake_case__	  =       (
											    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]			,		device=UpperCamelCase__) > 0.5
											).float()
											snake_case__	  =       (torch.rand((self.batch_size, self.num_labels)			,		device=UpperCamelCase__) > 0.5).long()

											snake_case__	  =       self.get_config()
											return config, pixel_values, pixel_mask, mask_labels, class_labels



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											snake_case__	  =       MaskaFormerConfig(
											    hidden_size=self.hidden_dim			,		)
											snake_case__	  =       self.num_queries
											snake_case__	  =       self.num_labels

											snake_case__	  =       [1, 1, 1, 1]
											snake_case__	  =       self.num_channels

											snake_case__	  =       6_4
											snake_case__	  =       1_2_8
											snake_case__	  =       self.hidden_dim
											snake_case__	  =       self.hidden_dim
											snake_case__	  =       self.hidden_dim
											return config



				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											snake_case__  , snake_case__  , snake_case__  , snake_case__  , snake_case__	  =       self.prepare_config_and_inputs()
											snake_case__	  =       {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
											return config, inputs_dict



				def 					__magic_name__							(      self    :    Dict			,		UpperCamelCase__    :    Optional[int]			,		UpperCamelCase__    :    str):






											'''simple docstring'''







											snake_case__	  =       output.encoder_hidden_states
											snake_case__	  =       output.pixel_decoder_hidden_states
											snake_case__	  =       output.transformer_decoder_hidden_states

											self.parent.assertTrue(len(UpperCamelCase__)			,		len(config.backbone_config.depths))
											self.parent.assertTrue(len(UpperCamelCase__)			,		len(config.backbone_config.depths))
											self.parent.assertTrue(len(UpperCamelCase__)			,		config.decoder_layers)



				def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    List[str]			,		UpperCamelCase__    :    Dict			,		UpperCamelCase__    :    str=False):






											'''simple docstring'''







											with torch.no_grad():
																		snake_case__	  =       MaskaFormerModel(config=UpperCamelCase__)
																		model.to(UpperCamelCase__)
																		model.eval()

																		snake_case__	  =       model(pixel_values=UpperCamelCase__			,		pixel_mask=UpperCamelCase__)
																		snake_case__	  =       model(UpperCamelCase__			,		output_hidden_states=UpperCamelCase__)

											self.parent.assertEqual(
											    output.transformer_decoder_last_hidden_state.shape			,		(self.batch_size, self.num_queries, self.hidden_dim)			,		)
											# let's ensure the other two hidden state exists
											self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None)
											self.parent.assertTrue(output.encoder_last_hidden_state is not None)

											if output_hidden_states:
																		self.check_output_hidden_state(UpperCamelCase__			,		UpperCamelCase__)



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    List[str]			,		UpperCamelCase__    :    Tuple			,		UpperCamelCase__    :    Dict			,		UpperCamelCase__    :    Union[str, Any]			,		UpperCamelCase__    :    List[str]):






											'''simple docstring'''







											snake_case__	  =       MaskaFormerForUniversalSegmentation(config=UpperCamelCase__)
											model.to(UpperCamelCase__)
											model.eval()

											def comm_check_on_output(UpperCamelCase__    :    Optional[Any]):
																		# let's still check that all the required stuff is there
																		self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None)
																		self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None)
																		self.parent.assertTrue(result.encoder_last_hidden_state is not None)
																		# okay, now we need to check the logits shape
																		# due to the encoder compression, masks have a //4 spatial size
																		self.parent.assertEqual(
																		    result.masks_queries_logits.shape			,		(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)			,		)
																		# + 1 for null class
																		self.parent.assertEqual(
																		    result.class_queries_logits.shape			,		(self.batch_size, self.num_queries, self.num_labels + 1))

											with torch.no_grad():
																		snake_case__	  =       model(pixel_values=UpperCamelCase__			,		pixel_mask=UpperCamelCase__)
																		snake_case__	  =       model(UpperCamelCase__)

																		comm_check_on_output(UpperCamelCase__)

																		snake_case__	  =       model(
																		    pixel_values=UpperCamelCase__			,		pixel_mask=UpperCamelCase__			,		mask_labels=UpperCamelCase__			,		class_labels=UpperCamelCase__)

											comm_check_on_output(UpperCamelCase__)

											self.parent.assertTrue(result.loss is not None)
											self.parent.assertEqual(result.loss.shape			,		torch.Size([1]))


@require_torch
class    _lowerCAmelCase			(		lowercase_     ,      lowercase_     ,      unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   int      =  (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
				_lowercase				:   Dict      =  {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

				_lowercase				:   Optional[Any]      =  False
				_lowercase				:   Any      =  False
				_lowercase				:   Optional[Any]      =  False
				_lowercase				:   Optional[Any]      =  False



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											snake_case__	  =       MaskaFormerModelTester(self)
											snake_case__	  =       ConfigTester(self			,		config_class=UpperCamelCase__			,		has_text_modality=UpperCamelCase__)



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											self.config_tester.run_common_tests()



				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											snake_case__  , snake_case__	  =       self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskaformer_model(UpperCamelCase__			,		**UpperCamelCase__			,		output_hidden_states=UpperCamelCase__)



				def 					__magic_name__							(      self    :    Union[str, Any]):






											'''simple docstring'''







											snake_case__	  =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*UpperCamelCase__)



				@unittest.skip(reason="""Mask2Former does not use inputs_embeds""")
				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											pass



				@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""")
				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											pass



				@unittest.skip(reason="""Mask2Former is not a generative model""")
				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											pass



				@unittest.skip(reason="""Mask2Former does not use token embeddings""")
				def 					__magic_name__							(      self    :    Union[str, Any]):






											'''simple docstring'''







											pass



				@require_torch_multi_gpu
				@unittest.skip(
				    reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""")
				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											pass



				@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""")
				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											pass



				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											snake_case__  , snake_case__	  =       self.model_tester.prepare_config_and_inputs_for_common()

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

																		snake_case__	  =       ["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]			,		UpperCamelCase__)



				@slow
				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																		snake_case__	  =       MaskaFormerModel.from_pretrained(UpperCamelCase__)
																		self.assertIsNotNone(UpperCamelCase__)



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__	  =       (self.model_tester.min_size,) * 2
											snake_case__	  =       {
											    """pixel_values""": torch.randn((2, 3, *size)			,		device=UpperCamelCase__),
											    """mask_labels""": torch.randn((2, 1_0, *size)			,		device=UpperCamelCase__),
											    """class_labels""": torch.zeros(2			,		1_0			,		device=UpperCamelCase__).long(),
											}
											snake_case__	  =       self.model_tester.get_config()

											snake_case__	  =       MaskaFormerForUniversalSegmentation(UpperCamelCase__).to(UpperCamelCase__)
											snake_case__	  =       model(**UpperCamelCase__)
											self.assertTrue(outputs.loss is not None)



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											snake_case__  , snake_case__	  =       self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskaformer_model(UpperCamelCase__			,		**UpperCamelCase__			,		output_hidden_states=UpperCamelCase__)



				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											snake_case__  , snake_case__	  =       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		snake_case__	  =       model_class(UpperCamelCase__).to(UpperCamelCase__)
																		snake_case__	  =       model(**UpperCamelCase__			,		output_attentions=UpperCamelCase__)
																		self.assertTrue(outputs.attentions is not None)



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											if not self.model_tester.is_training:
																		return

											snake_case__	  =       self.all_model_classes[1]
											snake_case__  , snake_case__  , snake_case__  , snake_case__  , snake_case__	  =       self.model_tester.prepare_config_and_inputs()

											snake_case__	  =       model_class(UpperCamelCase__)
											model.to(UpperCamelCase__)
											model.train()

											snake_case__	  =       model(UpperCamelCase__			,		mask_labels=UpperCamelCase__			,		class_labels=UpperCamelCase__).loss
											loss.backward()



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											snake_case__	  =       self.all_model_classes[1]
											snake_case__  , snake_case__  , snake_case__  , snake_case__  , snake_case__	  =       self.model_tester.prepare_config_and_inputs()
											snake_case__	  =       True
											snake_case__	  =       True

											snake_case__	  =       model_class(UpperCamelCase__).to(UpperCamelCase__)
											model.train()

											snake_case__	  =       model(UpperCamelCase__			,		mask_labels=UpperCamelCase__			,		class_labels=UpperCamelCase__)

											snake_case__	  =       outputs.encoder_hidden_states[0]
											encoder_hidden_states.retain_grad()

											snake_case__	  =       outputs.pixel_decoder_hidden_states[0]
											pixel_decoder_hidden_states.retain_grad()

											snake_case__	  =       outputs.transformer_decoder_hidden_states[0]
											transformer_decoder_hidden_states.retain_grad()

											snake_case__	  =       outputs.attentions[0]
											attentions.retain_grad()

											outputs.loss.backward(retain_graph=UpperCamelCase__)

											self.assertIsNotNone(encoder_hidden_states.grad)
											self.assertIsNotNone(pixel_decoder_hidden_states.grad)
											self.assertIsNotNone(transformer_decoder_hidden_states.grad)
											self.assertIsNotNone(attentions.grad)


a__       =     1e-4

def _UpperCAmelCase   (	):
							snake_case__	  =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
							return image



@require_vision
@slow
class    _lowerCAmelCase			(		unittest.TestCase      ):



				"""simple docstring"""



				@cached_property
				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											return "facebook/mask2former-swin-small-coco-instance"



				@cached_property
				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints) if is_vision_available() else None



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											snake_case__	  =       MaskaFormerModel.from_pretrained(self.model_checkpoints).to(UpperCamelCase__)
											snake_case__	  =       self.default_image_processor
											snake_case__	  =       prepare_img()
											snake_case__	  =       image_processor(UpperCamelCase__			,		return_tensors="""pt""").to(UpperCamelCase__)
											snake_case__	  =       inputs["""pixel_values"""].shape
											# check size is divisible by 32
											self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0)
											# check size
											self.assertEqual(UpperCamelCase__			,		(1, 3, 3_8_4, 3_8_4))

											with torch.no_grad():
																		snake_case__	  =       model(**UpperCamelCase__)

											snake_case__	  =       torch.tensor(
											    [[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]]).to(UpperCamelCase__)
											self.assertTrue(
											    torch.allclose(
											        outputs.encoder_last_hidden_state[0, 0, :3, :3]			,		UpperCamelCase__			,		atol=UpperCamelCase__))

											snake_case__	  =       torch.tensor(
											    [[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]]).to(UpperCamelCase__)
											self.assertTrue(
											    torch.allclose(
											        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]			,		UpperCamelCase__			,		atol=UpperCamelCase__))

											snake_case__	  =       torch.tensor(
											    [[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]]).to(UpperCamelCase__)
											self.assertTrue(
											    torch.allclose(
											        outputs.transformer_decoder_last_hidden_state[0, :3, :3]			,		UpperCamelCase__			,		atol=UpperCamelCase__))



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											snake_case__	  =       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(UpperCamelCase__).eval()
											snake_case__	  =       self.default_image_processor
											snake_case__	  =       prepare_img()
											snake_case__	  =       image_processor(UpperCamelCase__			,		return_tensors="""pt""").to(UpperCamelCase__)
											snake_case__	  =       inputs["""pixel_values"""].shape
											# check size is divisible by 32
											self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0)
											# check size
											self.assertEqual(UpperCamelCase__			,		(1, 3, 3_8_4, 3_8_4))

											with torch.no_grad():
																		snake_case__	  =       model(**UpperCamelCase__)
											# masks_queries_logits
											snake_case__	  =       outputs.masks_queries_logits
											self.assertEqual(
											    masks_queries_logits.shape			,		(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4))
											snake_case__	  =       [
											    [-8.78_39, -9.00_56, -8.81_21],
											    [-7.41_04, -7.03_13, -6.54_01],
											    [-6.61_05, -6.34_27, -6.46_75],
											]
											snake_case__	  =       torch.tensor(UpperCamelCase__).to(UpperCamelCase__)
											self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,		UpperCamelCase__			,		atol=UpperCamelCase__))
											# class_queries_logits
											snake_case__	  =       outputs.class_queries_logits
											self.assertEqual(class_queries_logits.shape			,		(1, model.config.num_queries, model.config.num_labels + 1))
											snake_case__	  =       torch.tensor(
											    [
											        [1.83_24, -8.08_35, -4.19_22],
											        [0.84_50, -9.00_50, -3.60_53],
											        [0.30_45, -7.72_93, -3.02_75],
											    ]).to(UpperCamelCase__)
											self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]			,		UpperCamelCase__			,		atol=UpperCamelCase__))



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(UpperCamelCase__).eval()
											snake_case__	  =       self.default_image_processor

											snake_case__	  =       image_processor(
											    [np.zeros((3, 8_0_0, 1_3_3_3)), np.zeros((3, 8_0_0, 1_3_3_3))]			,		segmentation_maps=[np.zeros((3_8_4, 3_8_4)).astype(np.floataa), np.zeros((3_8_4, 3_8_4)).astype(np.floataa)]			,		return_tensors="""pt"""			,		)

											snake_case__	  =       inputs["""pixel_values"""].to(UpperCamelCase__)
											snake_case__	  =       [el.to(UpperCamelCase__) for el in inputs["""mask_labels"""]]
											snake_case__	  =       [el.to(UpperCamelCase__) for el in inputs["""class_labels"""]]

											with torch.no_grad():
																		snake_case__	  =       model(**UpperCamelCase__)

											self.assertTrue(outputs.loss is not None)








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						:	Dict  ,					a						:	Union[str, Any]=None ):
							# set parameter of one layer
							assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
							snake_case__	  =       nn.Parameter(a )
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
														snake_case__	  =       nn.Parameter(a )

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	List[str]  ,					a						:	List[Any] ):
							# set torch weights for 1-to-1 comparison
							snake_case__	  =       np.asarray(weights[0] )
							snake_case__	  =       np.asarray(weights[1] )
							snake_case__	  =       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[int]  ,					a						:	Dict  ,					a						:	Optional[int] ):
							# set torch weights for 1-to-1 comparison
							snake_case__	  =       np.asarray(weights[0] )
							snake_case__	  =       np.asarray(weights[1] )
							snake_case__	  =       np.asarray(weights[2] )
							snake_case__	  =       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						:	List[Any]  ,					a						:	Tuple  ,					a						:	List[Any] ):
							# layernorm 1
							snake_case__	  =       weights[0][0][0]
							snake_case__	  =       np.asarray(layer_norm_a[0] )
							snake_case__	  =       np.asarray(layer_norm_a[1] )
							set_param(
							    torch_block.attention.layer_norm  ,					torch.tensor(a )  ,					torch.tensor(a )  ,					)

							# lsh weights + output
							snake_case__	  =       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
							snake_case__	  =       weights[2][0][1][2]

							# Chunked Feed Forward
							if len(a ) == 4:
														snake_case__	  =       intermediate_weights[2]

							# layernorm 2
							snake_case__	  =       np.asarray(intermediate_weights[0][0] )
							snake_case__	  =       np.asarray(intermediate_weights[0][1] )
							set_param(
							    torch_block.feed_forward.layer_norm  ,					torch.tensor(a )  ,					torch.tensor(a )  ,					)

							# intermediate dense
							snake_case__	  =       np.asarray(intermediate_weights[1][0] )
							snake_case__	  =       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
							snake_case__	  =       np.asarray(intermediate_weights[4][0] )
							snake_case__	  =       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						:	List[Any]  ,					a						:	Dict  ,					a						:	Union[str, Any] ):
							# reformer model
							snake_case__	  =       torch_model.reformer

							# word embeds
							snake_case__	  =       np.asarray(weights[1] )
							set_param(
							    torch_model_reformer.embeddings.word_embeddings  ,					torch.tensor(a )  ,					)

							if isinstance(weights[3]  ,					a ):
														snake_case__	  =       torch_model_reformer.embeddings.position_embeddings
														for emb_idx in range(len(position_embeddings.weights ) ):
																					snake_case__	  =       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'''
																					snake_case__	  =       nn.Parameter(torch.tensor(a ) )

							snake_case__	  =       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 ):
														snake_case__	  =       trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
														set_block_weights_in_torch(a  ,					a  ,					a )

							# output layer norm
							snake_case__	  =       np.asarray(weights[7][0] )
							snake_case__	  =       np.asarray(weights[7][1] )
							set_param(
							    torch_model_reformer.encoder.layer_norm  ,					torch.tensor(a )  ,					torch.tensor(a )  ,					)

							# output embeddings
							snake_case__	  =       np.asarray(weights[9][0] )
							snake_case__	  =       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						:	Any  ,					a						:	str  ,					a						:	str ):
							# Initialise PyTorch model
							snake_case__	  =       ReformerConfig.from_json_file(a )
							print(F'''Building PyTorch model from configuration: {config}''' )
							snake_case__	  =       ReformerModelWithLMHead(a )

							with open(a  ,					"""rb""" ) as f:
														snake_case__	  =       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__":
	a__       =     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."""
	)
	a__       =     parser.parse_args()
	convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
