




'''simple docstring'''
from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TFCvtForImageClassification, TFCvtModel
							from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					def     __snake_case						(	self     :						List[str])    ->							str:
								A_									= self.config_class(**self.inputs_dict)
								self.parent.assertTrue(hasattr(_lowercase      ,       'embed_dim'))
								self.parent.assertTrue(hasattr(_lowercase      ,       'num_heads'))





class   __UpperCAmelCase				:



					'''simple docstring'''





					def __init__(	self     :						List[Any]      ,       _lowercase     :						int      ,       _lowercase     :						Dict=13      ,       _lowercase     :						Optional[int]=64      ,       _lowercase     :						str=3      ,       _lowercase     :						List[str]=[16, 48, 96]      ,       _lowercase     :						List[Any]=[1, 3, 6]      ,       _lowercase     :						Tuple=[1, 2, 10]      ,       _lowercase     :						Optional[int]=[7, 3, 3]      ,       _lowercase     :						Dict=[4, 2, 2]      ,       _lowercase     :						List[str]=[2, 1, 1]      ,       _lowercase     :						Optional[int]=[2, 2, 2]      ,       _lowercase     :						Tuple=[False, False, True]      ,       _lowercase     :						str=[0.0, 0.0, 0.0]      ,       _lowercase     :						int=0.02      ,       _lowercase     :						int=1E-12      ,       _lowercase     :						int=True      ,       _lowercase     :						List[str]=True      ,       _lowercase     :						Any=2      ,       )    ->							Any:
								A_									= parent
								A_									= batch_size
								A_									= image_size
								A_									= patch_sizes
								A_									= patch_stride
								A_									= patch_padding
								A_									= is_training
								A_									= use_labels
								A_									= num_labels
								A_									= num_channels
								A_									= embed_dim
								A_									= num_heads
								A_									= stride_kv
								A_									= depth
								A_									= cls_token
								A_									= attention_drop_rate
								A_									= initializer_range
								A_									= layer_norm_eps



					def     __snake_case						(	self     :						int)    ->							Tuple:
								A_									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								A_									= None
								if self.use_labels:
											# create a random int32 tensor of given shape
											A_									= ids_tensor([self.batch_size]      ,       self.num_labels)

								A_									= self.get_config()
								return config, pixel_values, labels



					def     __snake_case						(	self     :						List[Any])    ->							str:
								return CvtConfig(
								    image_size=self.image_size      ,       num_labels=self.num_labels      ,       num_channels=self.num_channels      ,       embed_dim=self.embed_dim      ,       num_heads=self.num_heads      ,       patch_sizes=self.patch_sizes      ,       patch_padding=self.patch_padding      ,       patch_stride=self.patch_stride      ,       stride_kv=self.stride_kv      ,       depth=self.depth      ,       cls_token=self.cls_token      ,       attention_drop_rate=self.attention_drop_rate      ,       initializer_range=self.initializer_range      ,       )



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						Optional[int]      ,       _lowercase     :						Optional[int]      ,       _lowercase     :						List[str])    ->							Union[str, Any]:
								A_									= TFCvtModel(config=_lowercase)
								A_									= model(_lowercase      ,       training=_lowercase)
								A_									= (self.image_size, self.image_size)
								A_		, A_									= image_size[0], image_size[1]
								for i in range(len(self.depth)):
											A_									= floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
											A_									= floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
								self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.embed_dim[-1], height, width))



					def     __snake_case						(	self     :						List[str]      ,       _lowercase     :						Any      ,       _lowercase     :						Any      ,       _lowercase     :						Optional[Any])    ->							List[str]:
								A_									= self.num_labels
								A_									= TFCvtForImageClassification(_lowercase)
								A_									= model(_lowercase      ,       labels=_lowercase      ,       training=_lowercase)
								self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels))



					def     __snake_case						(	self     :						Optional[Any])    ->							Optional[int]:
								A_									= self.prepare_config_and_inputs()
								A_		, A_		, A_									= config_and_inputs
								A_									= {'pixel_values': pixel_values}
								return config, inputs_dict





@require_tf
class   __UpperCAmelCase				(     lowerCAmelCase			,lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
					_UpperCamelCase							=		(
					    {"""feature-extraction""": TFCvtModel, """image-classification""": TFCvtForImageClassification}
					    if is_tf_available()
					    else {}
					)
					_UpperCamelCase							=		False
					_UpperCamelCase							=		False
					_UpperCamelCase							=		False
					_UpperCamelCase							=		False
					_UpperCamelCase							=		False



					def     __snake_case						(	self     :						Dict)    ->							Tuple:
								A_									= TFCvtModelTester(self)
								A_									= TFCvtConfigTester(self      ,       config_class=_lowercase      ,       has_text_modality=_lowercase      ,       hidden_size=37)



					def     __snake_case						(	self     :						Dict)    ->							int:
								self.config_tester.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()



					@unittest.skip(reason='Cvt does not output attentions')
					def     __snake_case						(	self     :						List[Any])    ->							Tuple:
								pass



					@unittest.skip(reason='Cvt does not use inputs_embeds')
					def     __snake_case						(	self     :						int)    ->							int:
								pass



					@unittest.skip(reason='Cvt does not support input and output embeddings')
					def     __snake_case						(	self     :						str)    ->							List[str]:
								pass



					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices('GPU')) == 0      ,       reason='TF does not support backprop for grouped convolutions on CPU.'      ,       )
					def     __snake_case						(	self     :						Union[str, Any])    ->							Tuple:
								super().test_dataset_conversion()



					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices('GPU')) == 0      ,       reason='TF does not support backprop for grouped convolutions on CPU.'      ,       )
					@slow
					def     __snake_case						(	self     :						Union[str, Any])    ->							str:
								super().test_keras_fit()



					@unittest.skip(reason='Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8')
					def     __snake_case						(	self     :						List[str])    ->							List[Any]:
								A_									= tf.keras.mixed_precision.Policy('mixed_float16')
								tf.keras.mixed_precision.set_global_policy(_lowercase)
								super().test_keras_fit()
								tf.keras.mixed_precision.set_global_policy('float32')



					def     __snake_case						(	self     :						Optional[Any])    ->							Any:
								A_		, A_									= self.model_tester.prepare_config_and_inputs_for_common()

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

											A_									= ['pixel_values']
											self.assertListEqual(arg_names[:1]      ,       _lowercase)



					def     __snake_case						(	self     :						Any)    ->							Any:







								def check_hidden_states_output(_lowercase     :						List[str]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						int):
											A_									= model_class(_lowercase)

											A_									= model(**self._prepare_for_class(_lowercase      ,       _lowercase))
											A_									= outputs.hidden_states

											A_									= len(self.model_tester.depth)
											self.assertEqual(len(_lowercase)      ,       _lowercase)

											# verify the first hidden states (first block)
											self.assertListEqual(
											    list(hidden_states[0].shape[-3:])      ,       [
											        self.model_tester.embed_dim[0],
											        self.model_tester.image_size // 4,
											        self.model_tester.image_size // 4,
											    ]      ,       )

								A_		, A_									= self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A_									= True
											check_hidden_states_output(_lowercase      ,       _lowercase      ,       _lowercase)

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

											check_hidden_states_output(_lowercase      ,       _lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						Optional[Any])    ->							Dict:
								A_									= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowercase)



					def     __snake_case						(	self     :						Optional[Any])    ->							Optional[Any]:
								A_									= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_lowercase)



					@slow
					def     __snake_case						(	self     :						int)    ->							Any:







								for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A_									= TFCvtModel.from_pretrained(_lowercase)
											self.assertIsNotNone(_lowercase)





def 			lowerCamelCase(							)		->		Optional[int]:
			A_									= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
			return image






@require_tf
@require_vision
class   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					@cached_property
					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[Any]:
								return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0])



					@slow
					def     __snake_case						(	self     :						List[Any])    ->							Optional[int]:
								A_									= TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0])

								A_									= self.default_image_processor
								A_									= prepare_img()
								A_									= image_processor(images=_lowercase      ,       return_tensors='tf')

								# forward pass
								A_									= model(**_lowercase)

								# verify the logits
								A_									= tf.TensorShape((1, 1_000))
								self.assertEqual(outputs.logits.shape      ,       _lowercase)

								A_									= tf.constant([0.92_85, 0.90_15, -0.31_50])
								self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()      ,       _lowercase      ,       atol=1E-4))










import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase   :			Optional[Any]          = logging.get_logger(__name__)





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				RobertaPreLayerNormConfig.from_pretrained(
	    __lowerCamelCase				,     architectures=["""RobertaPreLayerNormForMaskedLM"""]       )

	# convert state_dict
	a									=				torch.load(hf_hub_download(repo_id=__lowerCamelCase				,     filename="""pytorch_model.bin"""       )       )
	a									=				{}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("""roberta."""       ):
			a									=				"""roberta_prelayernorm.""" + tensor_key[len("""roberta."""       ) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(""".self.LayerNorm.weight"""       ) or tensor_key.endswith(""".self.LayerNorm.bias"""       ):
			continue

		a									=				tensor_value

	a									=				RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=__lowerCamelCase				,     config=__lowerCamelCase				,     state_dict=__lowerCamelCase       )
	model.save_pretrained(__lowerCamelCase       )

	# convert tokenizer
	a									=				AutoTokenizer.from_pretrained(__lowerCamelCase       )
	tokenizer.save_pretrained(__lowerCamelCase       )


if __name__ == "__main__":
		__UpperCamelCase   :			Union[str, Any]          = argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--checkpoint-repo",
		    default=None,
		    type=str,
		    required=True,
		    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		__UpperCamelCase   :			Any          = parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

