
import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
							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 				lowercase		:




							def __init__(				self   ,A__   ,A__=1_3   ,A__=3_0   ,A__=2   ,A__=3   ,A__=True   ,A__=True   ,A__=3_2   ,A__=5   ,A__=4   ,A__=3_7   ,A__="gelu"   ,A__=0.1   ,A__=0.1   ,A__=1_0   ,A__=0.02   ,A__=None   ,A__=2   ,):
												lowercase							=						parent
												lowercase							=						batch_size
												lowercase							=						image_size
												lowercase							=						patch_size
												lowercase							=						num_channels
												lowercase							=						is_training
												lowercase							=						use_labels
												lowercase							=						hidden_size
												lowercase							=						num_hidden_layers
												lowercase							=						num_attention_heads
												lowercase							=						intermediate_size
												lowercase							=						hidden_act
												lowercase							=						hidden_dropout_prob
												lowercase							=						attention_probs_dropout_prob
												lowercase							=						type_sequence_label_size
												lowercase							=						initializer_range
												lowercase							=						scope
												lowercase							=						encoder_stride

												# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
												lowercase							=						(image_size // patch_size) ** 2
												lowercase							=						num_patches + 1




							def  A__      (				self):
												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.type_sequence_label_size)

												lowercase							=						self.get_config()

												return config, pixel_values, labels




							def  A__      (				self):
												return ViTConfig(
												    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=A__   ,initializer_range=self.initializer_range   ,encoder_stride=self.encoder_stride   ,)




							def  A__      (				self   ,A__   ,A__   ,A__):
												lowercase							=						ViTModel(config=A__)
												model.to(A__)
												model.eval()
												lowercase							=						model(A__)
												self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size))




							def  A__      (				self   ,A__   ,A__   ,A__):
												lowercase							=						ViTForMaskedImageModeling(config=A__)
												model.to(A__)
												model.eval()
												lowercase							=						model(A__)
												self.parent.assertEqual(
												    result.reconstruction.shape   ,(self.batch_size, self.num_channels, self.image_size, self.image_size))

												# test greyscale images
												lowercase							=						1
												lowercase							=						ViTForMaskedImageModeling(A__)
												model.to(A__)
												model.eval()

												lowercase							=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
												lowercase							=						model(A__)
												self.parent.assertEqual(result.reconstruction.shape   ,(self.batch_size, 1, self.image_size, self.image_size))




							def  A__      (				self   ,A__   ,A__   ,A__):
												lowercase							=						self.type_sequence_label_size
												lowercase							=						ViTForImageClassification(A__)
												model.to(A__)
												model.eval()
												lowercase							=						model(A__   ,labels=A__)
												self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.type_sequence_label_size))

												# test greyscale images
												lowercase							=						1
												lowercase							=						ViTForImageClassification(A__)
												model.to(A__)
												model.eval()

												lowercase							=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
												lowercase							=						model(A__)
												self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.type_sequence_label_size))





							def  A__      (				self):
												lowercase							=						self.prepare_config_and_inputs()
												(
												    (
												    lowercase
												)	,						(
												    lowercase
												)	,						(
												    lowercase
												)	,						
												)							=						config_and_inputs
												lowercase							=						{'''pixel_values''': pixel_values}
												return config, inputs_dict







@require_torch
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Tuple       					=(
							    (
							        ViTModel,
							        ViTForImageClassification,
							        ViTForMaskedImageModeling,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase_       :       List[Any]       					=(
							    {'''feature-extraction''': ViTModel, '''image-classification''': ViTForImageClassification}
							    if is_torch_available()
							    else {}
							)
							lowercase_       :       Dict       					=True

							lowercase_       :       Tuple       					=False
							lowercase_       :       Any       					=False
							lowercase_       :       str       					=False




							def  A__      (				self):
												lowercase							=						ViTModelTester(self)
												lowercase							=						ConfigTester(self   ,config_class=A__   ,has_text_modality=A__   ,hidden_size=3_7)




							def  A__      (				self):
												self.config_tester.run_common_tests()




							@unittest.skip(reason='''ViT does not use inputs_embeds''')
							def  A__      (				self):
												pass




							def  A__      (				self):
												lowercase	,						lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()

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




							def  A__      (				self):
												lowercase	,						lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	lowercase							=						model_class(A__)
																	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]   ,A__)




							def  A__      (				self):
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*A__)




							def  A__      (				self):
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_image_modeling(*A__)




							def  A__      (				self):
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*A__)





							@slow
							def  A__      (				self):
												for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase							=						ViTModel.from_pretrained(A__)
																	self.assertIsNotNone(A__)



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
					return image







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




							@cached_property
							def  A__      (				self):
												return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224''') if is_vision_available() else None




							@slow
							def  A__      (				self):
												lowercase							=						ViTForImageClassification.from_pretrained('''google/vit-base-patch16-224''').to(A__)

												lowercase							=						self.default_image_processor
												lowercase							=						prepare_img()
												lowercase							=						image_processor(images=A__   ,return_tensors='''pt''').to(A__)

												# forward pass
												with torch.no_grad():
																	lowercase							=						model(**A__)

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

												lowercase							=						torch.tensor([-0.2744, 0.8215, -0.0836]).to(A__)

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




							@slow
							def  A__      (				self):
												# ViT models have an `interpolate_pos_encoding` argument in their forward method,
												# allowing to interpolate the pre-trained position embeddings in order to use
												# the model on higher resolutions. The DINO model by Facebook AI leverages this
												# to visualize self-attention on higher resolution images.
												lowercase							=						ViTModel.from_pretrained('''facebook/dino-vits8''').to(A__)

												lowercase							=						ViTImageProcessor.from_pretrained('''facebook/dino-vits8'''   ,size=4_8_0)
												lowercase							=						prepare_img()
												lowercase							=						image_processor(images=A__   ,return_tensors='''pt''')
												lowercase							=						inputs.pixel_values.to(A__)

												# forward pass
												with torch.no_grad():
																	lowercase							=						model(A__   ,interpolate_pos_encoding=A__)

												# verify the logits
												lowercase							=						torch.Size((1, 3_6_0_1, 3_8_4))
												self.assertEqual(outputs.last_hidden_state.shape   ,A__)

												lowercase							=						torch.tensor(
												    [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]]).to(A__)

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





							@slow
							@require_accelerate
							@require_torch_gpu
							def  A__      (				self):
												lowercase							=						ViTModel.from_pretrained('''facebook/dino-vits8'''   ,torch_dtype=torch.floataa   ,device_map='''auto''')
												lowercase							=						self.default_image_processor

												lowercase							=						prepare_img()
												lowercase							=						image_processor(images=A__   ,return_tensors='''pt''')
												lowercase							=						inputs.pixel_values.to(A__)

												# forward pass to make sure inference works in fp16
												with torch.no_grad():
																	lowercase							=						model(A__)











import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Union[str, Any]:







			'''simple docstring'''

			if "model" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''model.'''			,			'''''' )
			if "norm1" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm1'''			,			'''attention.output.LayerNorm''' )
			if "norm2" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm2'''			,			'''output.LayerNorm''' )
			if "norm" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm'''			,			'''LayerNorm''' )
			if "transformer" in orig_key:
						UpperCAmelCase   		=				orig_key.split('''.''' )[0].split('''_''' )[-1]
						UpperCAmelCase   		=				orig_key.replace(F"""transformer_{layer_num}"""			,			F"""encoder.layer.{layer_num}""" )
			if "mha.attn" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mha.attn'''			,			'''attention.self''' )
			if "mha" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mha'''			,			'''attention''' )
			if "W_q" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_q'''			,			'''self.query''' )
			if "W_k" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_k'''			,			'''self.key''' )
			if "W_v" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_v'''			,			'''self.value''' )
			if "ff1" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff1'''			,			'''intermediate.dense''' )
			if "ff2" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff2'''			,			'''output.dense''' )
			if "ff" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff'''			,			'''output.dense''' )
			if "mlm_class" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mlm.mlm_class'''			,			'''cls.predictions.decoder''' )
			if "mlm" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mlm'''			,			'''cls.predictions.transform''' )
			if "cls" not in orig_key:
						UpperCAmelCase   		=				'''yoso.''' + orig_key

			return orig_key





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				Dict:







			'''simple docstring'''

			for key in orig_state_dict.copy().keys():
						UpperCAmelCase   		=				orig_state_dict.pop(UpperCamelCase__ )

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									UpperCAmelCase   		=				val

			UpperCAmelCase   		=				orig_state_dict['''cls.predictions.decoder.bias''']
			UpperCAmelCase   		=				torch.arange(UpperCamelCase__ ).expand((1, -1) ) + 2

			return orig_state_dict





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				int:







			'''simple docstring'''

			UpperCAmelCase   		=				torch.load(UpperCamelCase__			,			map_location='''cpu''' )['''model_state_dict''']
			UpperCAmelCase   		=				YosoConfig.from_json_file(UpperCamelCase__ )
			UpperCAmelCase   		=				YosoForMaskedLM(UpperCamelCase__ )

			UpperCAmelCase   		=				convert_checkpoint_helper(config.max_position_embeddings			,			UpperCamelCase__ )

			print(model.load_state_dict(UpperCamelCase__ ) )
			model.eval()
			model.save_pretrained(UpperCamelCase__ )

			print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )


if __name__ == "__main__":
				__A				:    Any       =   argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
				)
				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The json file for YOSO model config.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				__A				:    List[str]       =   parser.parse_args()
				convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



