


'''simple docstring'''
import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
					lowerCAmelCase   : Optional[Any]             =				argparse.ArgumentParser(
					    description=(
					        """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
					        """ Distillation"""
					    )
					)
					parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
					parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
					parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
					parser.add_argument("""--vocab_transform""", action="""store_true""")
					lowerCAmelCase   : Optional[int]             =				parser.parse_args()

					if args.model_type == "bert":
										lowerCAmelCase   : Any             =				BertForMaskedLM.from_pretrained(args.model_name)
										lowerCAmelCase   : Optional[int]             =				"""bert"""
					else:
										raise ValueError("""args.model_type should be \"bert\".""")

					lowerCAmelCase   : str             =				model.state_dict()
					lowerCAmelCase   : str             =				{}

					for w in ["word_embeddings", "position_embeddings"]:
										lowerCAmelCase   : Optional[int]             =				state_dict[F'''{prefix}.embeddings.{w}.weight''']
					for w in ["weight", "bias"]:
										lowerCAmelCase   : Optional[int]             =				state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

					lowerCAmelCase   : Union[str, Any]             =				0
					for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
										for w in ["weight", "bias"]:
															lowerCAmelCase   : str             =				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
															]
															lowerCAmelCase   : List[Any]             =				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
															]
															lowerCAmelCase   : str             =				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
															]

															lowerCAmelCase   : Dict             =				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
															]
															lowerCAmelCase   : Union[str, Any]             =				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
															]

															lowerCAmelCase   : Union[str, Any]             =				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
															]
															lowerCAmelCase   : Union[str, Any]             =				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
															]
															lowerCAmelCase   : Optional[Any]             =				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
															]
										std_idx += 1

					lowerCAmelCase   : Optional[Any]             =				state_dict["""cls.predictions.decoder.weight"""]
					lowerCAmelCase   : Tuple             =				state_dict["""cls.predictions.bias"""]
					if args.vocab_transform:
										for w in ["weight", "bias"]:
															lowerCAmelCase   : int             =				state_dict[F'''cls.predictions.transform.dense.{w}''']
															lowerCAmelCase   : Optional[int]             =				state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']

					print(F'''N layers selected for distillation: {std_idx}''')
					print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

					print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
					torch.save(compressed_sd, args.dump_checkpoint)






'''simple docstring'''
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	:


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=1_3							,      a_=3							,      a_=True							,      a_=True							,      a_=0.1							,      a_=0.1							,      a_=2_2_4							,      a_=1_0_0_0							,      a_=[3, 3, 6, 4]							,      a_=[4_8, 5_6, 1_1_2, 2_2_0]							,      )    ->  Union[str, Any]:
					lowercase						:		Optional[Any]   					=		parent
					lowercase						:		List[Any]   					=		batch_size
					lowercase						:		Union[str, Any]   					=		num_channels
					lowercase						:		str   					=		is_training
					lowercase						:		Any   					=		use_labels
					lowercase						:		Optional[Any]   					=		hidden_dropout_prob
					lowercase						:		List[str]   					=		attention_probs_dropout_prob
					lowercase						:		Optional[Any]   					=		num_labels
					lowercase						:		str   					=		image_size
					lowercase						:		Dict   					=		layer_depths
					lowercase						:		List[str]   					=		embed_dims

				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		int   					=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

					lowercase						:		int   					=		None
					if self.use_labels:
						lowercase						:		Optional[Any]   					=		ids_tensor([self.batch_size]							,      self.num_labels							)

					lowercase						:		List[Any]   					=		self.get_config()

					return config, pixel_values, labels

				def 	a__		(     self							)    ->  List[Any]:
					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=a_							,      layer_scale_init_value=1e-5							,      )

				def 	a__		(     self							,      a_							,      a_							,      a_							)    ->  Optional[Any]:
					lowercase						:		Tuple   					=		SwiftFormerModel(config=a_							)
					model.to(a_							)
					model.eval()
					lowercase						:		Union[str, Any]   					=		model(a_							)
					self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, self.embed_dims[-1], 7, 7)							)

				def 	a__		(     self							,      a_							,      a_							,      a_							)    ->  Any:
					lowercase						:		Dict   					=		self.num_labels
					lowercase						:		Optional[int]   					=		SwiftFormerForImageClassification(a_							)
					model.to(a_							)
					model.eval()
					lowercase						:		Tuple   					=		model(a_							,      labels=a_							)
					self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.num_labels)							)

					lowercase						:		Tuple   					=		SwiftFormerForImageClassification(a_							)
					model.to(a_							)
					model.eval()

					lowercase						:		List[str]   					=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
					lowercase						:		str   					=		model(a_							)
					self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.num_labels)							)


				def 	a__		(     self							)    ->  Dict:
					((lowercase)					,		(lowercase)					,		(lowercase))						:		Dict   					=		self.prepare_config_and_inputs()
					lowercase						:		Dict   					=		{"pixel_values": pixel_values}
					return config, inputs_dict




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


				'''simple docstring'''


				_snake_case						       =				(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
				_snake_case						       =				(
				    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
				    if is_torch_available()
				    else {}
				)

				_snake_case						       =				False
				_snake_case						       =				False
				_snake_case						       =				False
				_snake_case						       =				False
				_snake_case						       =				False

				def 	a__		(     self							)    ->  str:
					lowercase						:		Optional[int]   					=		SwiftFormerModelTester(self							)
					lowercase						:		int   					=		ConfigTester(
					    self							,      config_class=a_							,      has_text_modality=a_							,      hidden_size=3_7							,      num_attention_heads=1_2							,      num_hidden_layers=1_2							,      )

				def 	a__		(     self							)    ->  Any:
					self.config_tester.run_common_tests()

				@unittest.skip(reason="SwiftFormer does not use inputs_embeds"							)
				def 	a__		(     self							)    ->  int:
					pass

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase					,		lowercase						:		Optional[int]   					=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						lowercase						:		Optional[int]   					=		model_class(a_							)
						lowercase						:		Tuple   					=		model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(a_							,      nn.Linear							)							)

				def 	a__		(     self							)    ->  List[Any]:
					lowercase					,		lowercase						:		Optional[Any]   					=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						lowercase						:		str   					=		model_class(a_							)
						lowercase						:		int   					=		inspect.signature(model.forward							)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						lowercase						:		Union[str, Any]   					=		[*signature.parameters.keys()]

						lowercase						:		int   					=		["pixel_values"]
						self.assertListEqual(arg_names[:1]							,      a_							)

				def 	a__		(     self							)    ->  int:
					lowercase						:		Optional[Any]   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*a_							)

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		List[Any]   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*a_							)

				@slow
				def 	a__		(     self							)    ->  Dict:
					for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase						:		Optional[int]   					=		SwiftFormerModel.from_pretrained(a_							)
						self.assertIsNotNone(a_							)

				@unittest.skip(reason="SwiftFormer does not output attentions"							)
				def 	a__		(     self							)    ->  Optional[Any]:
					pass

				def 	a__		(     self							)    ->  str:
					def check_hidden_states_output(a_							,      a_							,      a_							):
						lowercase						:		Union[str, Any]   					=		model_class(a_							)
						model.to(a_							)
						model.eval()

						with torch.no_grad():
							lowercase						:		List[str]   					=		model(**self._prepare_for_class(a_							,      a_							)							)

						lowercase						:		str   					=		outputs.hidden_states

						lowercase						:		str   					=		8
						self.assertEqual(len(a_							)							,      a_							)  # 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(a_							)							):
							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						:		Optional[Any]   					=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						lowercase						:		Dict   					=		True
						check_hidden_states_output(a_							,      a_							,      a_							)

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

						check_hidden_states_output(a_							,      a_							,      a_							)

				def 	a__		(     self							)    ->  Optional[Any]:
					def _config_zero_init(a_							):
						lowercase						:		str   					=		copy.deepcopy(a_							)
						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(a_							,      a_							,      1e-1_0							)
							if isinstance(getattr(a_							,      a_							,      a_							)							,      a_							):
								lowercase						:		List[Any]   					=		_config_zero_init(getattr(a_							,      a_							)							)
								setattr(a_							,      a_							,      a_							)
						return configs_no_init

					lowercase					,		lowercase						:		Any   					=		self.model_tester.prepare_config_and_inputs_for_common()

					lowercase						:		List[str]   					=		_config_zero_init(a_							)
					for model_class in self.all_model_classes:
						lowercase						:		Tuple   					=		model_class(config=a_							)
						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							)    ->  Tuple:
					pass




def 	_A  (       )  ->				List[str]:
	lowercase						:		Optional[int]   					=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
	return image






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


				'''simple docstring'''


				@cached_property
				def 	a__		(     self							)    ->  List[Any]:
					return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"							) if is_vision_available() else None


				@slow
				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Tuple   					=		SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"							).to(a_							)

					lowercase						:		Any   					=		self.default_image_processor
					lowercase						:		Optional[int]   					=		prepare_img()
					lowercase						:		Any   					=		image_processor(images=a_							,      return_tensors="pt"							).to(a_							)

					# forward pass
					with torch.no_grad():
						lowercase						:		List[str]   					=		model(**a_							)

					# verify the logits
					lowercase						:		str   					=		torch.Size((1, 1_0_0_0)							)
					self.assertEqual(outputs.logits.shape							,      a_							)

					lowercase						:		List[Any]   					=		torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]							).to(a_							)
					self.assertTrue(torch.allclose(outputs.logits[0, :3]							,      a_							,      atol=1e-4							)							)


