



import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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 torch import nn

		from transformers import ViTMAEForPreTraining, ViTMAEModel
		from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor




class 							lowerCAmelCase__  :







					def __init__(       self      :     List[str]			,		_A      :     Any			,		_A      :     List[Any]=13			,		_A      :     List[str]=30			,		_A      :     Dict=2			,		_A      :     Optional[Any]=3			,		_A      :     List[Any]=True			,		_A      :     Tuple=True			,		_A      :     Union[str, Any]=32			,		_A      :     List[Any]=5			,		_A      :     Any=4			,		_A      :     List[str]=37			,		_A      :     str="gelu"			,		_A      :     List[Any]=0.1			,		_A      :     Optional[Any]=0.1			,		_A      :     Optional[int]=10			,		_A      :     Any=0.02			,		_A      :     Union[str, Any]=3			,		_A      :     List[Any]=0.6			,		_A      :     Any=None			,		):
											A__	:							List[str]						    = parent
											A__	:							Optional[Any]						    = batch_size
											A__	:							Any						    = image_size
											A__	:							Union[str, Any]						    = patch_size
											A__	:							Dict						    = num_channels
											A__	:							Dict						    = is_training
											A__	:							int						    = use_labels
											A__	:							str						    = hidden_size
											A__	:							Tuple						    = num_hidden_layers
											A__	:							int						    = num_attention_heads
											A__	:							int						    = intermediate_size
											A__	:							Union[str, Any]						    = hidden_act
											A__	:							Optional[Any]						    = hidden_dropout_prob
											A__	:							Tuple						    = attention_probs_dropout_prob
											A__	:							List[str]						    = type_sequence_label_size
											A__	:							int						    = initializer_range
											A__	:							str						    = mask_ratio
											A__	:							Optional[int]						    = scope

											# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
											# (we add 1 for the [CLS] token)
											A__	:							Tuple						    = (image_size // patch_size) ** 2
											A__	:							Dict						    = int(math.ceil((1 - mask_ratio) * (num_patches + 1)))







					def  _lowercase			(       self      :     Optional[Any]):
											A__	:							Any						    = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

											A__	:							Dict						    = None
											if self.use_labels:
																	A__	:							Tuple						    = ids_tensor([self.batch_size]			,		self.type_sequence_label_size)

											A__	:							Dict						    = self.get_config()

											return config, pixel_values, labels







					def  _lowercase			(       self      :     List[str]):
											return ViTMAEConfig(
											    image_size=self.image_size			,		patch_size=self.patch_size			,		num_channels=self.num_channels			,		hidden_size=self.hidden_size			,		num_hidden_layers=self.num_hidden_layers			,		num_attention_heads=self.num_attention_heads			,		intermediate_size=self.intermediate_size			,		hidden_act=self.hidden_act			,		hidden_dropout_prob=self.hidden_dropout_prob			,		attention_probs_dropout_prob=self.attention_probs_dropout_prob			,		is_decoder=__UpperCamelCase			,		initializer_range=self.initializer_range			,		mask_ratio=self.mask_ratio			,		)







					def  _lowercase			(       self      :     Dict			,		_A      :     Optional[Any]			,		_A      :     Optional[Any]			,		_A      :     Union[str, Any]):
											A__	:							List[str]						    = ViTMAEModel(config=__UpperCamelCase)
											model.to(__UpperCamelCase)
											model.eval()
											A__	:							Any						    = model(__UpperCamelCase)
											self.parent.assertEqual(result.last_hidden_state.shape			,		(self.batch_size, self.seq_length, self.hidden_size))







					def  _lowercase			(       self      :     str			,		_A      :     Union[str, Any]			,		_A      :     int			,		_A      :     int):
											A__	:							Tuple						    = ViTMAEForPreTraining(__UpperCamelCase)
											model.to(__UpperCamelCase)
											model.eval()
											A__	:							int						    = model(__UpperCamelCase)
											A__	:							Dict						    = (self.image_size // self.patch_size) ** 2
											A__	:							Any						    = self.patch_size**2 * self.num_channels
											self.parent.assertEqual(result.logits.shape			,		(self.batch_size, num_patches, expected_num_channels))

											# test greyscale images
											A__	:							Tuple						    = 1
											A__	:							List[Any]						    = ViTMAEForPreTraining(__UpperCamelCase)
											model.to(__UpperCamelCase)
											model.eval()
											A__	:							Tuple						    = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
											A__	:							Optional[Any]						    = model(__UpperCamelCase)
											A__	:							Any						    = self.patch_size**2
											self.parent.assertEqual(result.logits.shape			,		(self.batch_size, num_patches, expected_num_channels))







					def  _lowercase			(       self      :     List[str]):
											A__	:							Tuple						    = self.prepare_config_and_inputs()
											A__    ,       A__    ,       A__	:							Optional[int]						    = config_and_inputs
											A__	:							List[str]						    = {"pixel_values": pixel_values}
											return config, inputs_dict




@require_torch
class 							lowerCAmelCase__  (	UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase						):
					__A     :    Dict           =						(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
					__A     :    Optional[int]           =						{'feature-extraction': ViTMAEModel} if is_torch_available() else {}

					__A     :    str           =						False
					__A     :    Optional[int]           =						False
					__A     :    int           =						False
					__A     :    int           =						False







					def  _lowercase			(       self      :     Tuple):
											A__	:							Dict						    = ViTMAEModelTester(self)
											A__	:							Union[str, Any]						    = ConfigTester(self			,		config_class=__UpperCamelCase			,		has_text_modality=__UpperCamelCase			,		hidden_size=37)







					def  _lowercase			(       self      :     Optional[int]):
											self.config_tester.run_common_tests()







					@unittest.skip(reason="ViTMAE does not use inputs_embeds")
					def  _lowercase			(       self      :     str):
											pass







					def  _lowercase			(       self      :     Dict):
											A__    ,       A__	:							List[str]						    = self.model_tester.prepare_config_and_inputs_for_common()

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







					def  _lowercase			(       self      :     str):
											A__    ,       A__	:							int						    = self.model_tester.prepare_config_and_inputs_for_common()

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

																	A__	:							str						    = ["pixel_values"]
																	self.assertListEqual(arg_names[:1]			,		__UpperCamelCase)







					def  _lowercase			(       self      :     Optional[Any]):
											A__	:							int						    = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__UpperCamelCase)







					def  _lowercase			(       self      :     Tuple):
											A__	:							Dict						    = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*__UpperCamelCase)







					def  _lowercase			(       self      :     Union[str, Any]			,		_A      :     Optional[int]			,		_A      :     Tuple			,		_A      :     Union[str, Any]):
											# make masks reproducible
											np.random.seed(2)

											A__	:							List[Any]						    = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2)
											A__	:							str						    = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
											A__	:							Union[str, Any]						    = torch.from_numpy(__UpperCamelCase)

											# Add `noise` argument.
											# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
											A__	:							Union[str, Any]						    = pt_noise

											super().check_pt_tf_models(__UpperCamelCase			,		__UpperCamelCase			,		__UpperCamelCase)







					def  _lowercase			(       self      :     List[str]):
											A__    ,       A__	:							Tuple						    = self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	A__	:							Dict						    = model_class(__UpperCamelCase)
																	model.to(__UpperCamelCase)
																	model.eval()
																	# make random mask reproducible
																	torch.manual_seed(2)
																	with torch.no_grad():
																							A__	:							Dict						    = model(**self._prepare_for_class(__UpperCamelCase			,		__UpperCamelCase))

																	A__	:							Union[str, Any]						    = outputs[0].cpu().numpy()
																	A__	:							str						    = 0

																	with tempfile.TemporaryDirectory() as tmpdirname:
																							model.save_pretrained(__UpperCamelCase)
																							A__	:							List[str]						    = model_class.from_pretrained(__UpperCamelCase)
																							model.to(__UpperCamelCase)
																							# make random mask reproducible
																							torch.manual_seed(2)
																							with torch.no_grad():
																													A__	:							Tuple						    = model(**self._prepare_for_class(__UpperCamelCase			,		__UpperCamelCase))

																							# Make sure we don't have nans
																							A__	:							List[str]						    = after_outputs[0].cpu().numpy()
																							A__	:							Optional[int]						    = 0
																							A__	:							Any						    = np.amax(np.abs(out_a - out_a))
																							self.assertLessEqual(__UpperCamelCase			,		1e-5)







					@unittest.skip(
					    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.")
					def  _lowercase			(       self      :     Any):
											pass







					@unittest.skip(
					    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.")
					def  _lowercase			(       self      :     Any):
											pass







					@unittest.skip(
					    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.")
					def  _lowercase			(       self      :     str):
											pass







					@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load")
					def  _lowercase			(       self      :     Optional[Any]):
											pass







					@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
					def  _lowercase			(       self      :     str):
											pass







					@slow
					def  _lowercase			(       self      :     Any):
											for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	A__	:							List[Any]						    = ViTMAEModel.from_pretrained(__UpperCamelCase)
																	self.assertIsNotNone(__UpperCamelCase)




def 	snake_case__       (					)		->		Optional[Any]:







						"""simple docstring"""

						A__	:							List[str]						    = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
						return image




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







					@cached_property
					def  _lowercase			(       self      :     Any):
											return ViTImageProcessor.from_pretrained("facebook/vit-mae-base") if is_vision_available() else None







					@slow
					def  _lowercase			(       self      :     Optional[int]):
											# make random mask reproducible across the PT and TF model
											np.random.seed(2)

											A__	:							int						    = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base").to(__UpperCamelCase)

											A__	:							str						    = self.default_image_processor
											A__	:							Optional[int]						    = prepare_img()
											A__	:							str						    = image_processor(images=__UpperCamelCase			,		return_tensors="pt").to(__UpperCamelCase)

											# prepare a noise vector that will be also used for testing the TF model
											# (this way we can ensure that the PT and TF models operate on the same inputs)
											A__	:							int						    = ViTMAEConfig()
											A__	:							int						    = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
											A__	:							List[str]						    = np.random.uniform(size=(1, num_patches))

											# forward pass
											with torch.no_grad():
																	A__	:							str						    = model(**__UpperCamelCase			,		noise=torch.from_numpy(__UpperCamelCase).to(device=__UpperCamelCase))

											# verify the logits
											A__	:							Optional[int]						    = torch.Size((1, 196, 768))
											self.assertEqual(outputs.logits.shape			,		__UpperCamelCase)

											A__	:							Tuple						    = torch.tensor(
											    [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]])

											self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]			,		expected_slice.to(__UpperCamelCase)			,		atol=1e-4))




import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		List[Any]:







						"""simple docstring"""

						def get_masked_lm_array(__lowercase       ):
												A__	:							int						    = F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							Any						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_array(__lowercase       ):
												A__	:							Dict						    = F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Tuple						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							str						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_layer_array(__lowercase							,     __lowercase       ):
												A__	:							int						    = F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							Any						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_attention_layer_array(__lowercase							,     __lowercase							,     __lowercase       ):
												A__	:							List[str]						    = F'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							List[Any]						    = tf.train.load_variable(__lowercase							,     __lowercase       )
												A__	:							int						    = array.reshape(__lowercase       )

												if "kernel" in name:
																		A__	:							Union[str, Any]						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						print(F'Loading model based on config from {config_path}...'       )
						A__	:							Tuple						    = BertConfig.from_json_file(__lowercase       )
						A__	:							List[str]						    = BertForMaskedLM(__lowercase       )

						# Layers
						for layer_index in range(0							,     config.num_hidden_layers       ):
												A__	:							BertLayer						    = model.bert.encoder.layer[layer_index]

												# Self-attention
												A__	:							BertSelfAttention						    = layer.attention.self

												A__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/kernel"							,     self_attn.query.weight.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/bias"							,     self_attn.query.bias.data.shape       )
												A__	:							int						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/kernel"							,     self_attn.key.weight.data.shape       )
												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/bias"							,     self_attn.key.bias.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/kernel"							,     self_attn.value.weight.data.shape       )
												A__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/bias"							,     self_attn.value.bias.data.shape       )

												# Self-attention Output
												A__	:							BertSelfOutput						    = layer.attention.output

												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/kernel"							,     self_output.dense.weight.data.shape       )
												A__	:							Any						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/bias"							,     self_output.dense.bias.data.shape       )

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/gamma"       )
												A__	:							Optional[int]						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/beta"       )

												# Intermediate
												A__	:							BertIntermediate						    = layer.intermediate

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/kernel"       )
												A__	:							Any						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/bias"       )

												# Output
												A__	:							BertOutput						    = layer.output

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_output_dense/kernel"       )
												A__	:							List[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_dense/bias"       )

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_layer_norm/gamma"       )
												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_output_layer_norm/beta"       )

						# Embeddings
						A__	:							str						    = get_encoder_array("_position_embedding_layer/embeddings"       )
						A__	:							List[str]						    = get_encoder_array("_type_embedding_layer/embeddings"       )
						A__	:							List[Any]						    = get_encoder_array("_embedding_norm_layer/gamma"       )
						A__	:							Optional[int]						    = get_encoder_array("_embedding_norm_layer/beta"       )

						# LM Head
						A__	:							Optional[Any]						    = model.cls.predictions.transform

						A__	:							int						    = get_masked_lm_array("dense/kernel"       )
						A__	:							Tuple						    = get_masked_lm_array("dense/bias"       )

						A__	:							Optional[Any]						    = get_masked_lm_array("layer_norm/gamma"       )
						A__	:							Tuple						    = get_masked_lm_array("layer_norm/beta"       )

						A__	:							Any						    = get_masked_lm_array("embedding_table"       )

						# Pooling
						A__	:							Optional[Any]						    = BertPooler(config=__lowercase       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/kernel"       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/bias"       )

						# Export final model
						model.save_pretrained(__lowercase       )

						# Integration test - should load without any errors ;)
						A__	:							Union[str, Any]						    = BertForMaskedLM.from_pretrained(__lowercase       )
						print(new_model.eval()       )

						print("Model conversion was done sucessfully!"       )


if __name__ == "__main__":
		snake_case    :						str  =						argparse.ArgumentParser()
		parser.add_argument(
		    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping 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.',
		)
		snake_case    :						Dict  =						parser.parse_args()
		convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)