






import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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 SwiftFormerForImageClassification, SwiftFormerModel
						from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor


class        __UpperCamelCase    :







	def __init__(  self      :							Tuple       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Optional[Any]=13       ,				_lowerCAmelCase      :							str=3       ,				_lowerCAmelCase      :							str=True       ,				_lowerCAmelCase      :							Union[str, Any]=True       ,				_lowerCAmelCase      :							str=0.1       ,				_lowerCAmelCase      :							int=0.1       ,				_lowerCAmelCase      :							str=224       ,				_lowerCAmelCase      :							Tuple=1000       ,				_lowerCAmelCase      :							str=[3, 3, 6, 4]       ,				_lowerCAmelCase      :							str=[48, 56, 112, 220]       ,				)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  parent
		__lowercase             =  batch_size
		__lowercase             =  num_channels
		__lowercase             =  is_training
		__lowercase             =  use_labels
		__lowercase             =  hidden_dropout_prob
		__lowercase             =  attention_probs_dropout_prob
		__lowercase             =  num_labels
		__lowercase             =  image_size
		__lowercase             =  layer_depths
		__lowercase             =  embed_dims







	def  _a   (  self      :							Any			)		-> Tuple:
		"""simple docstring"""







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

		__lowercase             =  None
		if self.use_labels:
			__lowercase             =  ids_tensor([self.batch_size]       ,				self.num_labels			)

		__lowercase             =  self.get_config()

		return config, pixel_values, labels







	def  _a   (  self      :							Any			)		-> List[Any]:
		"""simple docstring"""







		return SwiftFormerConfig(
		    depths=self.layer_depths       ,				embed_dims=self.embed_dims       ,				mlp_ratio=4       ,				downsamples=[True, True, True, True]       ,				hidden_act="""gelu"""       ,				num_labels=self.num_labels       ,				down_patch_size=3       ,				down_stride=2       ,				down_pad=1       ,				drop_rate=0.0       ,				drop_path_rate=0.0       ,				use_layer_scale=_lowerCAmelCase       ,				layer_scale_init_value=1e-5       ,				)







	def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							List[Any]       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Optional[Any]			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  SwiftFormerModel(config=_lowerCAmelCase			)
		model.to(_lowerCAmelCase			)
		model.eval()
		__lowercase             =  model(_lowerCAmelCase			)
		self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.embed_dims[-1], 7, 7)			)







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							Optional[Any]       ,				_lowerCAmelCase      :							Optional[Any]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  self.num_labels
		__lowercase             =  SwiftFormerForImageClassification(_lowerCAmelCase			)
		model.to(_lowerCAmelCase			)
		model.eval()
		__lowercase             =  model(_lowerCAmelCase       ,				labels=_lowerCAmelCase			)
		self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)			)

		__lowercase             =  SwiftFormerForImageClassification(_lowerCAmelCase			)
		model.to(_lowerCAmelCase			)
		model.eval()

		__lowercase             =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
		__lowercase             =  model(_lowerCAmelCase			)
		self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)			)







	def  _a   (  self      :							int			)		-> Any:
		"""simple docstring"""







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


@require_torch
class        __UpperCamelCase    (     _lowerCAmelCase   ,			_lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Union[str, Any]   =       (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	__snake_case	:List[Any]   =       (
	    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__snake_case	:Tuple   =       False
	__snake_case	:List[Any]   =       False
	__snake_case	:List[str]   =       False
	__snake_case	:Tuple   =       False
	__snake_case	:int   =       False







	def  _a   (  self      :							Union[str, Any]			)		-> str:
		"""simple docstring"""







		__lowercase             =  SwiftFormerModelTester(self			)
		__lowercase             =  ConfigTester(
		    self       ,				config_class=_lowerCAmelCase       ,				has_text_modality=_lowerCAmelCase       ,				hidden_size=37       ,				num_attention_heads=12       ,				num_hidden_layers=12       ,				)







	def  _a   (  self      :							Optional[int]			)		-> str:
		"""simple docstring"""







		self.config_tester.run_common_tests()







	@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""			)
	def  _a   (  self      :							Tuple			)		-> str:
		"""simple docstring"""







		pass







	def  _a   (  self      :							int			)		-> Tuple:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__lowercase             =  model_class(_lowerCAmelCase			)
			__lowercase             =  model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(_lowerCAmelCase       ,				nn.Linear			)			)







	def  _a   (  self      :							Optional[int]			)		-> Any:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

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

			__lowercase             =  ["""pixel_values"""]
			self.assertListEqual(arg_names[:1]       ,				_lowerCAmelCase			)







	def  _a   (  self      :							Optional[int]			)		-> int:
		"""simple docstring"""







		__lowercase             =  self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*_lowerCAmelCase			)







	def  _a   (  self      :							Any			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase			)







	@slow
	def  _a   (  self      :							Dict			)		-> Union[str, Any]:
		"""simple docstring"""







		for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			__lowercase             =  SwiftFormerModel.from_pretrained(_lowerCAmelCase			)
			self.assertIsNotNone(_lowerCAmelCase			)







	@unittest.skip(reason="""SwiftFormer does not output attentions"""			)
	def  _a   (  self      :							Dict			)		-> Union[str, Any]:
		"""simple docstring"""







		pass







	def  _a   (  self      :							int			)		-> Optional[int]:
		"""simple docstring"""







		def check_hidden_states_output(_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							Optional[Any]       ,				_lowerCAmelCase      :							int			):
			__lowercase             =  model_class(_lowerCAmelCase			)
			model.to(_lowerCAmelCase			)
			model.eval()

			with torch.no_grad():
				__lowercase             =  model(**self._prepare_for_class(_lowerCAmelCase       ,				_lowerCAmelCase			)			)

			__lowercase             =  outputs.hidden_states

			__lowercase             =  8
			self.assertEqual(len(_lowerCAmelCase			)       ,				_lowerCAmelCase			)  # TODO

			# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
			# with the width and height being successively divided by 2, after every 2 blocks
			for i in range(len(_lowerCAmelCase			)			):
				self.assertEqual(
				    hidden_states[i].shape       ,				torch.Size(
				        [
				            self.model_tester.batch_size,
				            self.model_tester.embed_dims[i // 2],
				            (self.model_tester.image_size // 4) // 2 ** (i // 2),
				            (self.model_tester.image_size // 4) // 2 ** (i // 2),
				        ]			)       ,				)

		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__lowercase             =  True
			check_hidden_states_output(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)

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

			check_hidden_states_output(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)







	def  _a   (  self      :							Dict			)		-> Optional[Any]:
		"""simple docstring"""







		def _config_zero_init(_lowerCAmelCase      :							Union[str, Any]			):
			__lowercase             =  copy.deepcopy(_lowerCAmelCase			)
			for key in configs_no_init.__dict__.keys():
				if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
					setattr(_lowerCAmelCase       ,				_lowerCAmelCase       ,				1e-10			)
				if isinstance(getattr(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)       ,				_lowerCAmelCase			):
					__lowercase             =  _config_zero_init(getattr(_lowerCAmelCase       ,				_lowerCAmelCase			)			)
					setattr(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)
			return configs_no_init

		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

		__lowercase             =  _config_zero_init(_lowerCAmelCase			)
		for model_class in self.all_model_classes:
			__lowercase             =  model_class(config=_lowerCAmelCase			)
			for name, param in model.named_parameters():
				if param.requires_grad:
					self.assertIn(
					    ((param.data.mean() * 1e9) / 1e9).round().item()       ,				[0.0, 1.0]       ,				msg=F'Parameter {name} of model {model_class} seems not properly initialized'       ,				)







	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
	def  _a   (  self      :							Optional[Any]			)		-> Union[str, Any]:
		"""simple docstring"""







		pass





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
	return image




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







	@cached_property
	def  _a   (  self      :							int			)		-> Tuple:
		"""simple docstring"""







		return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""			) if is_vision_available() else None







	@slow
	def  _a   (  self      :							Optional[int]			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""			).to(_lowerCAmelCase			)

		__lowercase             =  self.default_image_processor
		__lowercase             =  prepare_img()
		__lowercase             =  image_processor(images=_lowerCAmelCase       ,				return_tensors="""pt"""			).to(_lowerCAmelCase			)

		# forward pass
		with torch.no_grad():
			__lowercase             =  model(**_lowerCAmelCase			)

		# verify the logits
		__lowercase             =  torch.Size((1, 1000)			)
		self.assertEqual(outputs.logits.shape       ,				_lowerCAmelCase			)

		__lowercase             =  torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]			).to(_lowerCAmelCase			)
		self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				_lowerCAmelCase       ,				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      :      str     						=						logging.get_logger(__name__)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  RobertaPreLayerNormConfig.from_pretrained(
	    lowerCamelCase				,  architectures=["""RobertaPreLayerNormForMaskedLM"""]					)

	# convert state_dict
	__lowercase             =  torch.load(hf_hub_download(repo_id=lowerCamelCase				,  filename="""pytorch_model.bin"""					)					)
	__lowercase             =  {}
	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."""					):
			__lowercase             =  """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

		__lowercase             =  tensor_value

	__lowercase             =  RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=lowerCamelCase				,  config=lowerCamelCase				,  state_dict=lowerCamelCase					)
	model.save_pretrained(lowerCamelCase					)

	# convert tokenizer
	__lowercase             =  AutoTokenizer.from_pretrained(lowerCamelCase					)
	tokenizer.save_pretrained(lowerCamelCase					)


if __name__ == "__main__":
						__UpperCamelCase      :      List[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      :      Dict     						=						parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

