




'''simple docstring'''





import inspect
import unittest

from transformers import MobileViTConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
			from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import MobileViTImageProcessor
class 		SCREAMING_SNAKE_CASE			(     __a							):




				'''simple docstring'''





				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       List[str]        =       self.config_class(**self.inputs_dict				)
							self.parent.assertTrue(hasattr(_lowerCamelCase  ,						'hidden_sizes'				)				)
							self.parent.assertTrue(hasattr(_lowerCamelCase  ,						'neck_hidden_sizes'				)				)
							self.parent.assertTrue(hasattr(_lowerCamelCase  ,						'num_attention_heads'				)				)






class 		SCREAMING_SNAKE_CASE			:




				'''simple docstring'''





				def __init__(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__=13  ,						SCREAMING_SNAKE_CASE__=32  ,						SCREAMING_SNAKE_CASE__=2  ,						SCREAMING_SNAKE_CASE__=3  ,						SCREAMING_SNAKE_CASE__=6_40  ,						SCREAMING_SNAKE_CASE__=4  ,						SCREAMING_SNAKE_CASE__="silu"  ,						SCREAMING_SNAKE_CASE__=3  ,						SCREAMING_SNAKE_CASE__=32  ,						SCREAMING_SNAKE_CASE__=0.1  ,						SCREAMING_SNAKE_CASE__=0.1  ,						SCREAMING_SNAKE_CASE__=0.1  ,						SCREAMING_SNAKE_CASE__=0.02  ,						SCREAMING_SNAKE_CASE__=True  ,						SCREAMING_SNAKE_CASE__=True  ,						SCREAMING_SNAKE_CASE__=10  ,						SCREAMING_SNAKE_CASE__=None  ,						):
							'''simple docstring'''







							snake_case:       Optional[Any]        =       parent
							snake_case:       Dict        =       batch_size
							snake_case:       List[Any]        =       image_size
							snake_case:       Optional[Any]        =       patch_size
							snake_case:       Dict        =       num_channels
							snake_case:       Tuple        =       last_hidden_size
							snake_case:       Dict        =       num_attention_heads
							snake_case:       str        =       hidden_act
							snake_case:       str        =       conv_kernel_size
							snake_case:       Dict        =       output_stride
							snake_case:       Tuple        =       hidden_dropout_prob
							snake_case:       Optional[int]        =       attention_probs_dropout_prob
							snake_case:       List[Any]        =       classifier_dropout_prob
							snake_case:       Optional[int]        =       use_labels
							snake_case:       str        =       is_training
							snake_case:       Union[str, Any]        =       num_labels
							snake_case:       List[Any]        =       initializer_range
							snake_case:       Dict        =       scope







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       Dict        =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

							snake_case:       Any        =       None
							snake_case:       Optional[Any]        =       None
							if self.use_labels:
										snake_case:       Tuple        =       ids_tensor([self.batch_size]  ,						self.num_labels				)
										snake_case:       List[Any]        =       ids_tensor([self.batch_size, self.image_size, self.image_size]  ,						self.num_labels				)

							snake_case:       Union[str, Any]        =       self.get_config()

							return config, pixel_values, labels, pixel_labels







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							return MobileViTConfig(
							    image_size=self.image_size  ,						patch_size=self.patch_size  ,						num_channels=self.num_channels  ,						num_attention_heads=self.num_attention_heads  ,						hidden_act=self.hidden_act  ,						conv_kernel_size=self.conv_kernel_size  ,						output_stride=self.output_stride  ,						hidden_dropout_prob=self.hidden_dropout_prob  ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,						classifier_dropout_prob=self.classifier_dropout_prob  ,						initializer_range=self.initializer_range  ,						)







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       Optional[Any]        =       MobileViTModel(config=_lowerCamelCase				)
							model.to(_lowerCamelCase				)
							model.eval()
							snake_case:       Dict        =       model(_lowerCamelCase				)
							self.parent.assertEqual(
							    result.last_hidden_state.shape  ,						(
							        self.batch_size,
							        self.last_hidden_size,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )  ,						)







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       Optional[int]        =       self.num_labels
							snake_case:       List[Any]        =       MobileViTForImageClassification(_lowerCamelCase				)
							model.to(_lowerCamelCase				)
							model.eval()
							snake_case:       Optional[int]        =       model(_lowerCamelCase  ,						labels=_lowerCamelCase				)
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels)				)







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       Union[str, Any]        =       self.num_labels
							snake_case:       List[Any]        =       MobileViTForSemanticSegmentation(_lowerCamelCase				)
							model.to(_lowerCamelCase				)
							model.eval()
							snake_case:       List[str]        =       model(_lowerCamelCase				)
							self.parent.assertEqual(
							    result.logits.shape  ,						(
							        self.batch_size,
							        self.num_labels,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )  ,						)
							snake_case:       int        =       model(_lowerCamelCase  ,						labels=_lowerCamelCase				)
							self.parent.assertEqual(
							    result.logits.shape  ,						(
							        self.batch_size,
							        self.num_labels,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )  ,						)







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       List[Any]        =       self.prepare_config_and_inputs()
							snake_case    ,						snake_case    ,						snake_case    ,						snake_case:       List[Any]        =       config_and_inputs
							snake_case:       str        =       {'pixel_values': pixel_values}
							return config, inputs_dict






@require_torch
class 		SCREAMING_SNAKE_CASE			(     __a    ,      __a    ,      unittest.TestCase							):




				'''simple docstring'''





				__UpperCamelCase     				=   (
				    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
				    if is_torch_available()
				    else ()
				)
				__UpperCamelCase     				=   (
				    {
				        """feature-extraction""": MobileViTModel,
				        """image-classification""": MobileViTForImageClassification,
				        """image-segmentation""": MobileViTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				__UpperCamelCase     				=   False
				__UpperCamelCase     				=   False
				__UpperCamelCase     				=   False
				__UpperCamelCase     				=   False







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       List[str]        =       MobileViTModelTester(self				)
							snake_case:       Optional[int]        =       MobileViTConfigTester(self  ,						config_class=_lowerCamelCase  ,						has_text_modality=_lowerCamelCase				)







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							self.config_tester.run_common_tests()







				@unittest.skip(reason='MobileViT does not use inputs_embeds'				)
				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							pass







				@unittest.skip(reason='MobileViT does not support input and output embeddings'				)
				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							pass







				@unittest.skip(reason='MobileViT does not output attentions'				)
				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							pass







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case    ,						snake_case:       int        =       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										snake_case:       Tuple        =       model_class(_lowerCamelCase				)
										snake_case:       List[Any]        =       inspect.signature(model.forward				)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										snake_case:       int        =       [*signature.parameters.keys()]

										snake_case:       Dict        =       ['pixel_values']
										self.assertListEqual(arg_names[:1]  ,						_lowerCamelCase				)







				@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'				)
				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							pass







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       int        =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_lowerCamelCase				)







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							def check_hidden_states_output(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				):
										snake_case:       List[str]        =       model_class(_lowerCamelCase				)
										model.to(_lowerCamelCase				)
										model.eval()

										with torch.no_grad():
													snake_case:       Optional[Any]        =       model(**self._prepare_for_class(_lowerCamelCase  ,						_lowerCamelCase				)				)

										snake_case:       List[Any]        =       outputs.hidden_states

										snake_case:       Optional[Any]        =       5
										self.assertEqual(len(_lowerCamelCase				)  ,						_lowerCamelCase				)

										# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
										# with the width and height being successively divided by 2.
										snake_case:       Union[str, Any]        =       2
										for i in range(len(_lowerCamelCase				)				):
													self.assertListEqual(
													    list(hidden_states[i].shape[-2:]				)  ,						[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]  ,						)
													divisor *= 2

										self.assertEqual(self.model_tester.output_stride  ,						divisor // 2				)

							snake_case    ,						snake_case:       List[Any]        =       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										snake_case:       int        =       True
										check_hidden_states_output(_lowerCamelCase  ,						_lowerCamelCase  ,						_lowerCamelCase				)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										snake_case:       List[str]        =       True

										check_hidden_states_output(_lowerCamelCase  ,						_lowerCamelCase  ,						_lowerCamelCase				)







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       List[str]        =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase				)







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       List[str]        =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCamelCase				)







				@slow
				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										snake_case:       List[str]        =       MobileViTModel.from_pretrained(_lowerCamelCase				)
										self.assertIsNotNone(_lowerCamelCase				)






def       lowerCAmelCase_     (					):




			'''simple docstring'''


			snake_case:       int        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
			return image




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




				'''simple docstring'''





				@cached_property
				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'				) if is_vision_available() else None







				@slow
				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       Tuple        =       MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'				).to(_lowerCamelCase				)

							snake_case:       Optional[Any]        =       self.default_image_processor
							snake_case:       Union[str, Any]        =       prepare_img()
							snake_case:       List[Any]        =       image_processor(images=_lowerCamelCase  ,						return_tensors='pt'				).to(_lowerCamelCase				)

							# forward pass
							with torch.no_grad():
										snake_case:       Tuple        =       model(**_lowerCamelCase				)

							# verify the logits
							snake_case:       List[Any]        =       torch.Size((1, 10_00)				)
							self.assertEqual(outputs.logits.shape  ,						_lowerCamelCase				)

							snake_case:       Tuple        =       torch.tensor([-1.93_64, -1.23_27, -0.46_53]				).to(_lowerCamelCase				)

							self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,						_lowerCamelCase  ,						atol=1E-4				)				)







				@slow
				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       Dict        =       MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'				)
							snake_case:       int        =       model.to(_lowerCamelCase				)

							snake_case:       List[str]        =       MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'				)

							snake_case:       str        =       prepare_img()
							snake_case:       List[Any]        =       image_processor(images=_lowerCamelCase  ,						return_tensors='pt'				).to(_lowerCamelCase				)

							# forward pass
							with torch.no_grad():
										snake_case:       Any        =       model(**_lowerCamelCase				)
							snake_case:       Dict        =       outputs.logits

							# verify the logits
							snake_case:       Optional[int]        =       torch.Size((1, 21, 32, 32)				)
							self.assertEqual(logits.shape  ,						_lowerCamelCase				)

							snake_case:       int        =       torch.tensor(
							    [
							        [[6.97_13, 6.97_86, 7.24_22], [7.28_93, 7.28_25, 7.44_46], [7.65_80, 7.87_97, 7.94_20]],
							        [[-10.68_69, -10.32_50, -10.34_71], [-10.42_28, -9.98_68, -9.71_32], [-11.04_05, -11.02_21, -10.73_18]],
							        [[-3.30_89, -2.85_39, -2.67_40], [-3.27_06, -2.56_21, -2.51_08], [-3.25_34, -2.66_15, -2.66_51]],
							    ]  ,						device=_lowerCamelCase  ,						)

							self.assertTrue(torch.allclose(logits[0, :3, :3, :3]  ,						_lowerCamelCase  ,						atol=1E-4				)				)







				@slow
				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       Union[str, Any]        =       MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'				)
							snake_case:       List[Any]        =       model.to(_lowerCamelCase				)

							snake_case:       Optional[int]        =       MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'				)

							snake_case:       Any        =       prepare_img()
							snake_case:       List[str]        =       image_processor(images=_lowerCamelCase  ,						return_tensors='pt'				).to(_lowerCamelCase				)

							# forward pass
							with torch.no_grad():
										snake_case:       str        =       model(**_lowerCamelCase				)

							snake_case:       List[Any]        =       outputs.logits.detach().cpu()

							snake_case:       Optional[int]        =       image_processor.post_process_semantic_segmentation(outputs=_lowerCamelCase  ,						target_sizes=[(50, 60)]				)
							snake_case:       Dict        =       torch.Size((50, 60)				)
							self.assertEqual(segmentation[0].shape  ,						_lowerCamelCase				)

							snake_case:       Optional[int]        =       image_processor.post_process_semantic_segmentation(outputs=_lowerCamelCase				)
							snake_case:       Optional[int]        =       torch.Size((32, 32)				)
							self.assertEqual(segmentation[0].shape  ,						_lowerCamelCase				)

"""simple docstring"""






import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase							=				logging.get_logger(__name__)



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->str:
			__lowercase       =       os.path.abspath(__magic_name__      )
			logger.info(F'''Converting TensorFlow checkpoint from {tf_path}'''      )
			# Load weights from TF model
			__lowercase       =       tf.train.list_variables(__magic_name__      )
			__lowercase       =       []
			__lowercase       =       []
			__lowercase       =       []
			for full_name, shape in init_vars:
						# logger.info(f"Loading TF weight {name} with shape {shape}")
						__lowercase       =       full_name.split("/"      )
						if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
									logger.info(F'''Skipping non-model layer {full_name}'''      )
									continue
						if "optimizer" in full_name:
									logger.info(F'''Skipping optimization layer {full_name}'''      )
									continue
						if name[0] == "model":
									# ignore initial 'model'
									__lowercase       =       name[1:]
						# figure out how many levels deep the name is
						__lowercase       =       0
						for _name in name:
									if _name.startswith("layer_with_weights"      ):
												depth += 1
									else:
												break
						layer_depth.append(__magic_name__      )
						# read data
						__lowercase       =       tf.train.load_variable(__magic_name__			,   __magic_name__      )
						names.append("/".join(__magic_name__      )      )
						arrays.append(__magic_name__      )
			logger.info(F'''Read a total of {len(__magic_name__      ):,} layers'''      )

			# Sanity check
			if len(set(__magic_name__      )      ) != 1:
						raise ValueError(F'''Found layer names with different depths (layer depth {list(set(__magic_name__      )      )})'''      )
			__lowercase       =       list(set(__magic_name__      )      )[0]
			if layer_depth != 1:
						raise ValueError(
						    "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"
						    " heads."      )

			# convert layers
			logger.info("Converting weights..."      )
			for full_name, array in zip(__magic_name__			,   __magic_name__      ):
						__lowercase       =       full_name.split("/"      )
						__lowercase       =       model
						__lowercase       =       []
						for i, m_name in enumerate(__magic_name__      ):
									if m_name == ".ATTRIBUTES":
												# variable names end with .ATTRIBUTES/VARIABLE_VALUE
												break
									if m_name.startswith("layer_with_weights"      ):
												__lowercase       =       int(m_name.split("-"      )[-1]      )
												if layer_num <= 2:
															# embedding layers
															# layer_num 0: word_embeddings
															# layer_num 1: position_embeddings
															# layer_num 2: token_type_embeddings
															continue
												elif layer_num == 3:
															# embedding LayerNorm
															trace.extend(["embeddings", "LayerNorm"]      )
															__lowercase       =       getattr(__magic_name__			,   "embeddings"      )
															__lowercase       =       getattr(__magic_name__			,   "LayerNorm"      )
												elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
															# encoder layers
															trace.extend(["encoder", "layer", str(layer_num - 4      )]      )
															__lowercase       =       getattr(__magic_name__			,   "encoder"      )
															__lowercase       =       getattr(__magic_name__			,   "layer"      )
															__lowercase       =       pointer[layer_num - 4]
												elif layer_num == config.num_hidden_layers + 4:
															# pooler layer
															trace.extend(["pooler", "dense"]      )
															__lowercase       =       getattr(__magic_name__			,   "pooler"      )
															__lowercase       =       getattr(__magic_name__			,   "dense"      )
									elif m_name == "embeddings":
												trace.append("embeddings"      )
												__lowercase       =       getattr(__magic_name__			,   "embeddings"      )
												if layer_num == 0:
															trace.append("word_embeddings"      )
															__lowercase       =       getattr(__magic_name__			,   "word_embeddings"      )
												elif layer_num == 1:
															trace.append("position_embeddings"      )
															__lowercase       =       getattr(__magic_name__			,   "position_embeddings"      )
												elif layer_num == 2:
															trace.append("token_type_embeddings"      )
															__lowercase       =       getattr(__magic_name__			,   "token_type_embeddings"      )
												else:
															raise ValueError(F'''Unknown embedding layer with name {full_name}'''      )
												trace.append("weight"      )
												__lowercase       =       getattr(__magic_name__			,   "weight"      )
									elif m_name == "_attention_layer":
												# self-attention layer
												trace.extend(["attention", "self"]      )
												__lowercase       =       getattr(__magic_name__			,   "attention"      )
												__lowercase       =       getattr(__magic_name__			,   "self"      )
									elif m_name == "_attention_layer_norm":
												# output attention norm
												trace.extend(["attention", "output", "LayerNorm"]      )
												__lowercase       =       getattr(__magic_name__			,   "attention"      )
												__lowercase       =       getattr(__magic_name__			,   "output"      )
												__lowercase       =       getattr(__magic_name__			,   "LayerNorm"      )
									elif m_name == "_attention_output_dense":
												# output attention dense
												trace.extend(["attention", "output", "dense"]      )
												__lowercase       =       getattr(__magic_name__			,   "attention"      )
												__lowercase       =       getattr(__magic_name__			,   "output"      )
												__lowercase       =       getattr(__magic_name__			,   "dense"      )
									elif m_name == "_output_dense":
												# output dense
												trace.extend(["output", "dense"]      )
												__lowercase       =       getattr(__magic_name__			,   "output"      )
												__lowercase       =       getattr(__magic_name__			,   "dense"      )
									elif m_name == "_output_layer_norm":
												# output dense
												trace.extend(["output", "LayerNorm"]      )
												__lowercase       =       getattr(__magic_name__			,   "output"      )
												__lowercase       =       getattr(__magic_name__			,   "LayerNorm"      )
									elif m_name == "_key_dense":
												# attention key
												trace.append("key"      )
												__lowercase       =       getattr(__magic_name__			,   "key"      )
									elif m_name == "_query_dense":
												# attention query
												trace.append("query"      )
												__lowercase       =       getattr(__magic_name__			,   "query"      )
									elif m_name == "_value_dense":
												# attention value
												trace.append("value"      )
												__lowercase       =       getattr(__magic_name__			,   "value"      )
									elif m_name == "_intermediate_dense":
												# attention intermediate dense
												trace.extend(["intermediate", "dense"]      )
												__lowercase       =       getattr(__magic_name__			,   "intermediate"      )
												__lowercase       =       getattr(__magic_name__			,   "dense"      )
									elif m_name == "_output_layer_norm":
												# output layer norm
												trace.append("output"      )
												__lowercase       =       getattr(__magic_name__			,   "output"      )
									# weights & biases
									elif m_name in ["bias", "beta"]:
												trace.append("bias"      )
												__lowercase       =       getattr(__magic_name__			,   "bias"      )
									elif m_name in ["kernel", "gamma"]:
												trace.append("weight"      )
												__lowercase       =       getattr(__magic_name__			,   "weight"      )
									else:
												logger.warning(F'''Ignored {m_name}'''      )
        # for certain layers reshape is necessary
						__lowercase       =       ".".join(__magic_name__      )
						if re.match(R"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"			,   __magic_name__      ) or re.match(
						    R"(\S+)\.attention\.output\.dense\.weight"			,   __magic_name__      ):
									__lowercase       =       array.reshape(pointer.data.shape      )
						if "kernel" in full_name:
									__lowercase       =       array.transpose()
						if pointer.shape == array.shape:
									__lowercase       =       torch.from_numpy(__magic_name__      )
						else:
									raise ValueError(
									    F'''Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'''
									    F''' {array.shape}'''      )
						logger.info(F'''Successfully set variable {full_name} to PyTorch layer {trace}'''      )
			return model



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->str:
			# Instantiate model
			logger.info(F'''Loading model based on config from {config_path}...'''      )
			__lowercase       =       BertConfig.from_json_file(__magic_name__      )
			__lowercase       =       BertModel(__magic_name__      )

			# Load weights from checkpoint
			logger.info(F'''Loading weights from checkpoint {tf_checkpoint_path}...'''      )
			load_tfa_weights_in_bert(__magic_name__			,   __magic_name__			,   __magic_name__      )

			# Save pytorch-model
			logger.info(F'''Saving PyTorch model to {pytorch_dump_path}...'''      )
			torch.save(model.state_dict()			,   __magic_name__      )


if __name__ == "__main__":
					_lowercase							=				argparse.ArgumentParser()
					parser.add_argument(
					    '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow 2.x checkpoint path.'''
					)
					parser.add_argument(
					    '''--bert_config_file''',
					    type=str,
					    required=True,
					    help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_path''',
					    type=str,
					    required=True,
					    help='''Path to the output PyTorch model (must include filename).''',
					)
					_lowercase							=				parser.parse_args()
					convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





