
"""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 					lowerCAmelCase_					:



						'''simple docstring'''


						def __init__(			self  :   Tuple	,A_  :   Optional[int]	,A_  :   int=13	,A_  :   List[Any]=64	,A_  :   int=2	,A_  :   Tuple=3	,A_  :   Optional[Any]=True	,A_  :   str=True	,A_  :   Tuple=32	,A_  :   Tuple=5	,A_  :   str=4	,A_  :   List[str]=37	,A_  :   Optional[int]="gelu"	,A_  :   Tuple=0.1	,A_  :   List[Any]=0.1	,A_  :   List[Any]=10	,A_  :   Optional[Any]=0.02	,A_  :   Dict=[1, 16, 4, 4]	,A_  :   List[Any]=None	,)  -> Any:
							A     = parent
							A     = batch_size
							A     = image_size
							A     = patch_size
							A     = num_channels
							A     = is_training
							A     = use_labels
							A     = hidden_size
							A     = num_hidden_layers
							A     = num_attention_heads
							A     = intermediate_size
							A     = hidden_act
							A     = hidden_dropout_prob
							A     = attention_probs_dropout_prob
							A     = type_sequence_label_size
							A     = initializer_range
							A     = scope
							A     = 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     = (self.image_size // 32) ** 2
							A     = num_patches + 1


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Union[str, Any]:
							A     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

							A     = None
							if self.use_labels:
								A     = ids_tensor([self.batch_size]	,self.type_sequence_label_size   )

							A     = self.get_config()

							return config, pixel_values, labels


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Optional[int]:
							A     = {
							    '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     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   Dict	,A_  :   Tuple	,A_  :   Optional[Any]   )  -> Dict:
							A     = ViTHybridModel(config=A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_   )
							self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   Tuple	,A_  :   Dict	,A_  :   List[str]   )  -> List[str]:
							A     = self.type_sequence_label_size
							A     = ViTHybridForImageClassification(A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_	,labels=A_   )
							self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.type_sequence_label_size)   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> Any:
							A     = self.prepare_config_and_inputs()
							A ,   A ,   A     = config_and_inputs
							A     = {'pixel_values': pixel_values}
							return config, inputs_dict







@require_torch
class 					lowerCAmelCase_					(   _lowercase		,       _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> Optional[int]:
							A     = ViTHybridModelTester(self   )
							A     = ConfigTester(self	,config_class=A_	,has_text_modality=A_	,hidden_size=37   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Optional[int]:
							self.config_tester.run_common_tests()


						@unittest.skip(reason='ViT does not use inputs_embeds'   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> int:
							pass


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> List[Any]:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> List[str]:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()

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

								A     = ['pixel_values']
								self.assertListEqual(arg_names[:1]	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Optional[Any]:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Dict:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Union[str, Any]:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()

							A     = _config_zero_init(A_   )
							for model_class in self.all_model_classes:
								A     = model_class(config=A_   )
								# Skip the check for the backbone
								for name, module in model.named_modules():
									if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
										A     = [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     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Dict:
							for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A     = ViTHybridModel.from_pretrained(A_   )
								self.assertIsNotNone(A_   )






def    _snake_case					(		):
	A     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
	return image







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



						'''simple docstring'''


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


						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Any:
							A     = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(
							    A_   )

							A     = self.default_image_processor
							A     = prepare_img()
							A     = image_processor(images=A_	,return_tensors='pt'   ).to(A_   )

							# forward pass
							with torch.no_grad():
								A     = model(**A_   )

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

							A     = torch.tensor([-1.90_90, -0.49_93, -0.23_89]   ).to(A_   )

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




						@slow
						@require_accelerate
						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Optional[Any]:
							A     = ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'   )
							A     = ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'	,device_map='auto'   )

							A     = prepare_img()

							A     = image_processor(images=A_	,return_tensors='pt'   )
							A     = model(**A_   )
							A     = outputs.logits
							# model predicts one of the 1000 ImageNet classes
							A     = logits.argmax(-1   ).item()

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

"""simple docstring"""


import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     BioGptTokenizer
						_lowerCamelCase:     Tuple   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[int]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							A     = [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    'w</w>',
							    'r</w>',
							    't</w>',
							    'lo',
							    'low',
							    'er</w>',
							    'low</w>',
							    'lowest</w>',
							    'newer</w>',
							    'wider</w>',
							    '<unk>',
							]
							A     = dict(zip(A_	,range(len(A_   )   )   )   )
							A     = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']   )
							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['merges_file']   )
							with open(self.vocab_file	,'w'   ) as fp:
								fp.write(json.dumps(A_   )   )
							with open(self.merges_file	,'w'   ) as fp:
								fp.write('\n'.join(A_   )   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   Tuple   )  -> int:
							A     = 'lower newer'
							A     = 'lower newer'
							return input_text, output_text


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Any:
							A     = BioGptTokenizer(self.vocab_file	,self.merges_file   )

							A     = 'lower'
							A     = ['low', 'er</w>']
							A     = tokenizer.tokenize(A_   )
							self.assertListEqual(A_	,A_   )

							A     = tokens + ['<unk>']
							A     = [14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_   )	,A_   )




						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Optional[Any]:
							A     = BioGptTokenizer.from_pretrained('microsoft/biogpt'   )

							A     = tokenizer.encode('sequence builders'	,add_special_tokens=A_   )
							A     = tokenizer.encode('multi-sequence build'	,add_special_tokens=A_   )

							A     = tokenizer.build_inputs_with_special_tokens(A_   )
							A     = tokenizer.build_inputs_with_special_tokens(A_	,A_   )

							self.assertTrue(encoded_sentence == [2] + text   )
							self.assertTrue(encoded_pair == [2] + text + [2] + text_a   )