






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 						lowerCAmelCase   :

					'''simple docstring'''

					def __init__(     self :		int						,   __a :		List[str]						,   __a :		Optional[int]=13						,   __a :		Dict=64						,   __a :		int=2						,   __a :		Optional[Any]=3						,   __a :		Dict=True						,   __a :		Any=True						,   __a :		Optional[int]=32						,   __a :		int=5						,   __a :		Optional[Any]=4						,   __a :		Optional[int]=37						,   __a :		int="gelu"						,   __a :		str=0.1						,   __a :		List[str]=0.1						,   __a :		Dict=10						,   __a :		Tuple=0.02						,   __a :		Any=[1, 16, 4, 4]						,   __a :		int=None						,   )	->     Optional[int]:


												"""simple docstring"""

												__lowercase					:							str			=   parent
												__lowercase					:							Tuple			=   batch_size
												__lowercase					:							List[Any]			=   image_size
												__lowercase					:							Dict			=   patch_size
												__lowercase					:							Optional[Any]			=   num_channels
												__lowercase					:							Any			=   is_training
												__lowercase					:							int			=   use_labels
												__lowercase					:							Union[str, Any]			=   hidden_size
												__lowercase					:							List[str]			=   num_hidden_layers
												__lowercase					:							Dict			=   num_attention_heads
												__lowercase					:							Dict			=   intermediate_size
												__lowercase					:							str			=   hidden_act
												__lowercase					:							Optional[Any]			=   hidden_dropout_prob
												__lowercase					:							Tuple			=   attention_probs_dropout_prob
												__lowercase					:							str			=   type_sequence_label_size
												__lowercase					:							Optional[int]			=   initializer_range
												__lowercase					:							str			=   scope
												__lowercase					:							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
												__lowercase					:							int			=   (self.image_size // 32) ** 2
												__lowercase					:							List[Any]			=   num_patches + 1



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     Union[str, Any]:


												"""simple docstring"""

												__lowercase					:							Tuple			=   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.type_sequence_label_size							)

												__lowercase					:							str			=   self.get_config()

												return config, pixel_values, labels



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     Tuple:


												"""simple docstring"""

												__lowercase					:							Tuple			=   {
												    """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=__a						,   initializer_range=self.initializer_range						,   backbone_featmap_shape=self.backbone_featmap_shape						,   backbone_config=__a						,   )



					def 					lowerCAmelCase (     self :		List[Any]						,   __a :		Optional[int]						,   __a :		List[str]						,   __a :		Any							)	->     List[str]:


												"""simple docstring"""

												__lowercase					:							Any			=   ViTHybridModel(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.seq_length, self.hidden_size)							)



					def 					lowerCAmelCase (     self :		Dict						,   __a :		Optional[int]						,   __a :		Tuple						,   __a :		Optional[int]							)	->     List[Any]:


												"""simple docstring"""

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



					def 					lowerCAmelCase (     self :		Any							)	->     Any:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   self.prepare_config_and_inputs()
												__lowercase	,	__lowercase	,	__lowercase					:							Optional[int]			=   config_and_inputs
												__lowercase					:							Dict			=   {"""pixel_values""": pixel_values}
												return config, inputs_dict







@require_torch
class 						lowerCAmelCase   (       __a					,				__a					,				unittest.TestCase	):

					'''simple docstring'''

					_A  :	Union[str, Any]     =					(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
					_A  :	Any     =					(
					    {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
					    if is_torch_available()
					    else {}
					)
					_A  :	Optional[Any]     =					False
					_A  :	List[str]     =					False
					_A  :	Union[str, Any]     =					False



					def 					lowerCAmelCase (     self :		Union[str, Any]							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   ViTHybridModelTester(self							)
												__lowercase					:							Optional[Any]			=   ConfigTester(self						,   config_class=__a						,   has_text_modality=__a						,   hidden_size=37							)



					def 					lowerCAmelCase (     self :		Union[str, Any]							)	->     Optional[int]:


												"""simple docstring"""

												self.config_tester.run_common_tests()



					@unittest.skip(reason="""ViT does not use inputs_embeds"""							)
					def 					lowerCAmelCase (     self :		Any							)	->     Dict:


												"""simple docstring"""

												pass



					def 					lowerCAmelCase (     self :		Tuple							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase	,	__lowercase					:							Tuple			=   self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			__lowercase					:							Tuple			=   model_class(__a							)
																			self.assertIsInstance(model.get_input_embeddings()						,   (nn.Module)							)
																			__lowercase					:							Optional[int]			=   model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(__a						,   nn.Linear							)							)



					def 					lowerCAmelCase (     self :		List[str]							)	->     Tuple:


												"""simple docstring"""

												__lowercase	,	__lowercase					:							Dict			=   self.model_tester.prepare_config_and_inputs_for_common()

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

																			__lowercase					:							Optional[int]			=   ["""pixel_values"""]
																			self.assertListEqual(arg_names[:1]						,   __a							)



					def 					lowerCAmelCase (     self :		List[Any]							)	->     Union[str, Any]:


												"""simple docstring"""

												__lowercase					:							int			=   self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__a							)



					def 					lowerCAmelCase (     self :		Optional[Any]							)	->     List[Any]:


												"""simple docstring"""

												__lowercase					:							List[str]			=   self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__a							)



					def 					lowerCAmelCase (     self :		str							)	->     Union[str, Any]:


												"""simple docstring"""

												__lowercase	,	__lowercase					:							Union[str, 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					:							Dict			=   model_class(config=__a							)
																			# Skip the check for the backbone
																			for name, module in model.named_modules():
																										if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																																	__lowercase					:							Tuple			=   [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 					lowerCAmelCase (     self :		Optional[Any]							)	->     Union[str, Any]:


												"""simple docstring"""

												for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__lowercase					:							List[Any]			=   ViTHybridModel.from_pretrained(__a							)
																			self.assertIsNotNone(__a							)







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



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

					'''simple docstring'''

					@cached_property
					def 					lowerCAmelCase (     self :		Dict							)	->     Optional[int]:


												"""simple docstring"""

												return (
												    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
												    if is_vision_available()
												    else None
												)



					@slow
					def 					lowerCAmelCase (     self :		int							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Any			=   ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(
												    __a							)

												__lowercase					:							str			=   self.default_image_processor
												__lowercase					:							Any			=   prepare_img()
												__lowercase					:							int			=   image_processor(images=__a						,   return_tensors="""pt"""							).to(__a							)

												# forward pass
												with torch.no_grad():
																			__lowercase					:							Optional[int]			=   model(**__a							)

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

												__lowercase					:							List[str]			=   torch.tensor([-1.9090, -0.4993, -0.2389]							).to(__a							)

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



					@slow
					@require_accelerate
					def 					lowerCAmelCase (     self :		Any							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384"""							)
												__lowercase					:							Optional[Any]			=   ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384"""						,   device_map="""auto"""							)

												__lowercase					:							str			=   prepare_img()

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

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







lowerCamelCase       :       Optional[Any]        =				'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def 					snake_case_						(   ):
							__lowercase					:							List[str]			=   input("""Enter message: """					)
							__lowercase					:							int			=   input("""Enter key [alphanumeric]: """					)
							__lowercase					:							Optional[Any]			=   input("""Encrypt/Decrypt [e/d]: """					)

							if mode.lower().startswith("""e"""					):
														__lowercase					:							Optional[int]			=   """encrypt"""
														__lowercase					:							Dict			=   encrypt_message(lowerCAmelCase_				,				lowerCAmelCase_					)
							elif mode.lower().startswith("""d"""					):
														__lowercase					:							Union[str, Any]			=   """decrypt"""
														__lowercase					:							Optional[int]			=   decrypt_message(lowerCAmelCase_				,				lowerCAmelCase_					)

							print(F"\n{mode.title()}ed message:"					)
							print(lowerCAmelCase_					)
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  str					):
							return translate_message(lowerCAmelCase_				,				lowerCAmelCase_				,				"""encrypt"""					)
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  str					):
							return translate_message(lowerCAmelCase_				,				lowerCAmelCase_				,				"""decrypt"""					)
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  str					):
							__lowercase					:							Union[str, Any]			=   []
							__lowercase					:							Tuple			=   0
							__lowercase					:							Dict			=   key.upper()

							for symbol in message:
														__lowercase					:							Optional[Any]			=   LETTERS.find(symbol.upper()					)
														if num != -1:
																					if mode == "encrypt":
																												num += LETTERS.find(key[key_index]					)
																					elif mode == "decrypt":
																												num -= LETTERS.find(key[key_index]					)

																					num %= len(lowerCAmelCase_					)

																					if symbol.isupper():
																												translated.append(LETTERS[num]					)
																					elif symbol.islower():
																												translated.append(LETTERS[num].lower()					)

																					key_index += 1
																					if key_index == len(lowerCAmelCase_					):
																												__lowercase					:							str			=   0
														else:
																					translated.append(lowerCAmelCase_					)
							return "".join(lowerCAmelCase_					)


if __name__ == "__main__":
	main()