



'''simple docstring'''

import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
							from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

class      __magic_name__						:







							def __init__(							self :	List[Any]     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Optional[int]=13     ,_UpperCAmelCase :	List[str]=64     ,_UpperCAmelCase :	Any=2     ,_UpperCAmelCase :	str=3     ,_UpperCAmelCase :	str=True     ,_UpperCAmelCase :	int=True     ,_UpperCAmelCase :	List[str]=32     ,_UpperCAmelCase :	Tuple=5     ,_UpperCAmelCase :	Tuple=4     ,_UpperCAmelCase :	Dict=37     ,_UpperCAmelCase :	List[Any]="gelu"     ,_UpperCAmelCase :	Any=0.1     ,_UpperCAmelCase :	Optional[int]=0.1     ,_UpperCAmelCase :	List[str]=10     ,_UpperCAmelCase :	Optional[Any]=0.02     ,_UpperCAmelCase :	Dict=[1, 16, 4, 4]     ,_UpperCAmelCase :	Dict=None     ,):
									_a					:				List[Any]				=							parent
									_a					:				Union[str, Any]				=							batch_size
									_a					:				Tuple				=							image_size
									_a					:				Any				=							patch_size
									_a					:				str				=							num_channels
									_a					:				Union[str, Any]				=							is_training
									_a					:				List[str]				=							use_labels
									_a					:				Tuple				=							hidden_size
									_a					:				Optional[Any]				=							num_hidden_layers
									_a					:				Any				=							num_attention_heads
									_a					:				Tuple				=							intermediate_size
									_a					:				Union[str, Any]				=							hidden_act
									_a					:				Optional[int]				=							hidden_dropout_prob
									_a					:				str				=							attention_probs_dropout_prob
									_a					:				Optional[Any]				=							type_sequence_label_size
									_a					:				Dict				=							initializer_range
									_a					:				List[Any]				=							scope
									_a					:				Union[str, Any]				=							backbone_featmap_shape

									# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									# the number of patches is based on the feature map of the backbone, which by default uses an output stride
									# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
									_a					:				Union[str, Any]				=							(self.image_size // 32) ** 2
									_a					:				List[Any]				=							num_patches + 1







							def 							__lowercase      (							self :	Any		):
									_a					:				Tuple				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_a					:				Union[str, Any]				=							None
									if self.use_labels:
											_a					:				str				=							ids_tensor([self.batch_size]     ,self.type_sequence_label_size		)

									_a					:				Optional[int]				=							self.get_config()

									return config, pixel_values, labels







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				List[Any]				=							{
									    'global_padding': 'same',
									    'layer_type': 'bottleneck',
									    'depths': [3, 4, 9],
									    'out_features': ['stage1', 'stage2', 'stage3'],
									    'embedding_dynamic_padding': True,
									    'hidden_sizes': [4, 8, 16, 32],
									    'num_groups': 2,
									}

									return ViTHybridConfig(
									    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=lowercase_     ,initializer_range=self.initializer_range     ,backbone_featmap_shape=self.backbone_featmap_shape     ,backbone_config=lowercase_     ,)







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	Optional[Any]		):
									_a					:				Any				=							ViTHybridModel(config=lowercase_		)
									model.to(lowercase_		)
									model.eval()
									_a					:				int				=							model(lowercase_		)
									self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)		)







							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	Any		):
									_a					:				List[str]				=							self.type_sequence_label_size
									_a					:				Any				=							ViTHybridForImageClassification(lowercase_		)
									model.to(lowercase_		)
									model.eval()
									_a					:				Tuple				=							model(lowercase_     ,labels=lowercase_		)
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)		)







							def 							__lowercase      (							self :	int		):
									_a					:				Dict				=							self.prepare_config_and_inputs()
									_a	, _a	, _a					:				Any				=							config_and_inputs
									_a					:				Any				=							{'pixel_values': pixel_values}
									return config, inputs_dict

@require_torch
class      __magic_name__						(   a_      ,						a_      ,						unittest.TestCase       ):
							lowerCAmelCase	:   Optional[int]           = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
							lowerCAmelCase	:   List[str]           = (
							    {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
							    if is_torch_available()
							    else {}
							)
							lowerCAmelCase	:   Optional[Any]           = False
							lowerCAmelCase	:   Optional[int]           = False
							lowerCAmelCase	:   str           = False







							def 							__lowercase      (							self :	Optional[int]		):
									_a					:				Optional[Any]				=							ViTHybridModelTester(self		)
									_a					:				List[Any]				=							ConfigTester(self     ,config_class=lowercase_     ,has_text_modality=lowercase_     ,hidden_size=37		)







							def 							__lowercase      (							self :	Any		):
									self.config_tester.run_common_tests()







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







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a	, _a					:				int				=							self.model_tester.prepare_config_and_inputs_for_common()

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







							def 							__lowercase      (							self :	Optional[int]		):
									_a	, _a					:				Any				=							self.model_tester.prepare_config_and_inputs_for_common()

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

											_a					:				Tuple				=							['pixel_values']
											self.assertListEqual(arg_names[:1]     ,lowercase_		)







							def 							__lowercase      (							self :	Tuple		):
									_a					:				Union[str, Any]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase_		)







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				Union[str, Any]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowercase_		)







							def 							__lowercase      (							self :	int		):
									_a	, _a					:				Optional[int]				=							self.model_tester.prepare_config_and_inputs_for_common()

									_a					:				List[str]				=							_config_zero_init(lowercase_		)
									for model_class in self.all_model_classes:
											_a					:				Optional[int]				=							model_class(config=lowercase_		)
											# Skip the check for the backbone
											for name, module in model.named_modules():
													if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
															_a					:				List[str]				=							[F"""{name}.{key}""" for key in module.state_dict().keys()]
															break

											for name, param in model.named_parameters():
													if param.requires_grad:
															if name in backbone_params:
																	continue
															self.assertIn(
															    ((param.data.mean() * 1E9).round() / 1E9).item()     ,[0.0, 1.0]     ,msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""     ,)







							@slow
							def 							__lowercase      (							self :	str		):

									for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_a					:				Union[str, Any]				=							ViTHybridModel.from_pretrained(lowercase_		)
											self.assertIsNotNone(lowercase_		)



def 					__lowerCamelCase					(				)      ->				int:
		_a					:				Dict				=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
		return image

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







							@cached_property
							def 							__lowercase      (							self :	str		):
									return (
									    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
									    if is_vision_available()
									    else None
									)







							@slow
							def 							__lowercase      (							self :	int		):
									_a					:				Optional[Any]				=							ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
									    lowercase_		)

									_a					:				Union[str, Any]				=							self.default_image_processor
									_a					:				Optional[int]				=							prepare_img()
									_a					:				Dict				=							image_processor(images=lowercase_     ,return_tensors='pt'		).to(lowercase_		)

									# forward pass
									with torch.no_grad():
											_a					:				Tuple				=							model(**lowercase_		)

									# verify the logits
									_a					:				Union[str, Any]				=							torch.Size((1, 1000)		)
									self.assertEqual(outputs.logits.shape     ,lowercase_		)

									_a					:				Any				=							torch.tensor([-1.90_90, -0.49_93, -0.23_89]		).to(lowercase_		)

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







							@slow
							@require_accelerate
							def 							__lowercase      (							self :	Dict		):
									_a					:				Tuple				=							ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'		)
									_a					:				str				=							ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'     ,device_map='auto'		)

									_a					:				Optional[int]				=							prepare_img()

									_a					:				int				=							image_processor(images=lowercase_     ,return_tensors='pt'		)
									_a					:				List[Any]				=							model(**lowercase_		)
									_a					:				Optional[int]				=							outputs.logits
									# model predicts one of the 1000 ImageNet classes
									_a					:				List[str]				=							logits.argmax(-1		).item()

									self.assertTrue(model.config.idalabel[predicted_class_idx]     ,'tabby, tabby cat'		)









'''simple docstring'''

from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser

@dataclass
class      __magic_name__						:
							lowerCAmelCase	:   str           = field(
							    metadata={'help': 'The output directory where the model will be written.'}      ,						)
							lowerCAmelCase	:   str           = field(
							    metadata={
							        'help': (
							            'The encoder model checkpoint for weights initialization.'
							            'Don\'t set if you want to train an encoder model from scratch.'
							        )
							    }      ,						)
							lowerCAmelCase	:   str           = field(
							    metadata={
							        'help': (
							            'The decoder model checkpoint for weights initialization.'
							            'Don\'t set if you want to train a decoder model from scratch.'
							        )
							    }      ,						)
							lowerCAmelCase	:   Optional[str]           = field(
							    default=_UpperCamelCase      ,						metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'}       )
							lowerCAmelCase	:   Optional[str]           = field(
							    default=_UpperCamelCase      ,						metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'}       )



def 					__lowerCamelCase					(				)      ->				Union[str, Any]:
		_a					:				Any				=							HfArgumentParser((ModelArguments,)  )
		((_a)	, )					:				Dict				=							parser.parse_args_into_dataclasses()

		# Load pretrained model and tokenizer

		# Use explicit specified encoder config
		if model_args.encoder_config_name:
				_a					:				Optional[Any]				=							AutoConfig.from_pretrained(model_args.encoder_config_name  )
		# Use pretrained encoder model's config
		else:
				_a					:				Optional[Any]				=							AutoConfig.from_pretrained(model_args.encoder_model_name_or_path  )

		# Use explicit specified decoder config
		if model_args.decoder_config_name:
				_a					:				List[str]				=							AutoConfig.from_pretrained(model_args.decoder_config_name  )
		# Use pretrained decoder model's config
		else:
				_a					:				Optional[int]				=							AutoConfig.from_pretrained(model_args.decoder_model_name_or_path  )

		# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
		_a					:				List[Any]				=							True
		_a					:				int				=							True

		_a					:				Any				=							FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
		    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path						,      decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path						,      encoder_config=lowerCAmelCase_						,      decoder_config=lowerCAmelCase_						,      )

		# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
		_a					:				List[str]				=							decoder_config.decoder_start_token_id
		_a					:				Optional[int]				=							decoder_config.pad_token_id
		if decoder_start_token_id is None:
				_a					:				Tuple				=							decoder_config.bos_token_id
		if pad_token_id is None:
				_a					:				List[Any]				=							decoder_config.eos_token_id

		# This is necessary to make Flax's generate() work
		_a					:				Any				=							decoder_config.eos_token_id
		_a					:				Tuple				=							decoder_start_token_id
		_a					:				Any				=							pad_token_id

		_a					:				Dict				=							AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path  )

		_a					:				Dict				=							AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path  )
		_a					:				int				=							tokenizer.convert_ids_to_tokens(model.config.pad_token_id  )

		model.save_pretrained(model_args.output_dir  )
		image_processor.save_pretrained(model_args.output_dir  )
		tokenizer.save_pretrained(model_args.output_dir  )


if __name__ == "__main__":
							main()




