



"""simple docstring"""
import inspect
import unittest

from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaModel
  from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor
class 				_UpperCAmelCase				(					_lowercase ):





     '''simple docstring'''



     def 					__lowerCAmelCase      (   self						)				->   List[Any]:
         _UpperCAmelCase				:    int											=       self.config_class(**self.inputs_dict						)
         self.parent.assertTrue(hasattr(A_			,      '''tf_padding'''						)						)
         self.parent.assertTrue(hasattr(A_			,      '''depth_multiplier'''						)						)



class 				_UpperCAmelCase				:





     '''simple docstring'''



     def __init__(   self			,      A			,      A=1_3			,      A=3			,      A=3_2			,      A=0.25			,      A=8			,      A=True			,      A=1_0_2_4			,      A=3_2			,      A="relu6"			,      A=0.1			,      A=0.02			,      A=True			,      A=True			,      A=1_0			,      A=None			,      )				->   List[Any]:
         _UpperCAmelCase				:    Dict											=       parent
         _UpperCAmelCase				:    str											=       batch_size
         _UpperCAmelCase				:    Dict											=       num_channels
         _UpperCAmelCase				:    List[Any]											=       image_size
         _UpperCAmelCase				:    Union[str, Any]											=       depth_multiplier
         _UpperCAmelCase				:    Optional[Any]											=       min_depth
         _UpperCAmelCase				:    List[Any]											=       tf_padding
         _UpperCAmelCase				:    Any											=       int(last_hidden_size * depth_multiplier						)
         _UpperCAmelCase				:    Optional[Any]											=       output_stride
         _UpperCAmelCase				:    Union[str, Any]											=       hidden_act
         _UpperCAmelCase				:    Optional[Any]											=       classifier_dropout_prob
         _UpperCAmelCase				:    Optional[Any]											=       use_labels
         _UpperCAmelCase				:    List[Any]											=       is_training
         _UpperCAmelCase				:    Dict											=       num_labels
         _UpperCAmelCase				:    List[Any]											=       initializer_range
         _UpperCAmelCase				:    Union[str, Any]											=       scope





     def 					__lowerCAmelCase      (   self						)				->   Any:
         _UpperCAmelCase				:    Union[str, Any]											=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

         _UpperCAmelCase				:    str											=       None
         _UpperCAmelCase				:    List[Any]											=       None
         if self.use_labels:
             _UpperCAmelCase				:    int											=       ids_tensor([self.batch_size]			,      self.num_labels						)
             _UpperCAmelCase				:    Dict											=       ids_tensor([self.batch_size, self.image_size, self.image_size]			,      self.num_labels						)

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

         return config, pixel_values, labels, pixel_labels





     def 					__lowerCAmelCase      (   self						)				->   List[Any]:
         return MobileNetVaConfig(
             num_channels=self.num_channels			,      image_size=self.image_size			,      depth_multiplier=self.depth_multiplier			,      min_depth=self.min_depth			,      tf_padding=self.tf_padding			,      hidden_act=self.hidden_act			,      classifier_dropout_prob=self.classifier_dropout_prob			,      initializer_range=self.initializer_range			,      )





     def 					__lowerCAmelCase      (   self			,      A			,      A			,      A			,      A						)				->   List[Any]:
         _UpperCAmelCase				:    Tuple											=       MobileNetVaModel(config=A_						)
         model.to(A_						)
         model.eval()
         _UpperCAmelCase				:    Optional[int]											=       model(A_						)
         self.parent.assertEqual(
             result.last_hidden_state.shape			,      (
                 self.batch_size,
                 self.last_hidden_size,
                 self.image_size // self.output_stride,
                 self.image_size // self.output_stride,
             )			,      )





     def 					__lowerCAmelCase      (   self			,      A			,      A			,      A			,      A						)				->   Tuple:
         _UpperCAmelCase				:    Dict											=       self.num_labels
         _UpperCAmelCase				:    Any											=       MobileNetVaForImageClassification(A_						)
         model.to(A_						)
         model.eval()
         _UpperCAmelCase				:    Union[str, Any]											=       model(A_			,      labels=A_						)
         self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.num_labels)						)





     def 					__lowerCAmelCase      (   self						)				->   Optional[Any]:
         _UpperCAmelCase				:    Optional[Any]											=       self.prepare_config_and_inputs()
         _UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				:    int											=       config_and_inputs
         _UpperCAmelCase				:    List[Any]											=       {'''pixel_values''': pixel_values}
         return config, inputs_dict



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





     '''simple docstring'''



     a__   							=(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
     a__   							=(
         {'''feature-extraction''': MobileNetVaModel, '''image-classification''': MobileNetVaForImageClassification}
         if is_torch_available()
         else {}
     )

     a__   							=False
     a__   							=False
     a__   							=False
     a__   							=False





     def 					__lowerCAmelCase      (   self						)				->   str:
         _UpperCAmelCase				:    Dict											=       MobileNetVaModelTester(self						)
         _UpperCAmelCase				:    List[str]											=       MobileNetVaConfigTester(self			,      config_class=A_			,      has_text_modality=A_						)





     def 					__lowerCAmelCase      (   self						)				->   Tuple:
         self.config_tester.run_common_tests()





     @unittest.skip(reason='''MobileNetV1 does not use inputs_embeds'''						)
     def 					__lowerCAmelCase      (   self						)				->   Union[str, Any]:
         pass





     @unittest.skip(reason='''MobileNetV1 does not support input and output embeddings'''						)
     def 					__lowerCAmelCase      (   self						)				->   Union[str, Any]:
         pass





     @unittest.skip(reason='''MobileNetV1 does not output attentions'''						)
     def 					__lowerCAmelCase      (   self						)				->   List[str]:
         pass





     def 					__lowerCAmelCase      (   self						)				->   List[Any]:
         _UpperCAmelCase				,							_UpperCAmelCase				:    int											=       self.model_tester.prepare_config_and_inputs_for_common()

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

             _UpperCAmelCase				:    Optional[int]											=       ['''pixel_values''']
             self.assertListEqual(arg_names[:1]			,      A_						)





     def 					__lowerCAmelCase      (   self						)				->   Tuple:
         _UpperCAmelCase				:    Tuple											=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_						)





     def 					__lowerCAmelCase      (   self						)				->   List[Any]:
         def check_hidden_states_output(A			,      A			,      A						):
             _UpperCAmelCase				:    int											=       model_class(A_						)
             model.to(A_						)
             model.eval()

             with torch.no_grad():
                 _UpperCAmelCase				:    int											=       model(**self._prepare_for_class(A_			,      A_						)						)

             _UpperCAmelCase				:    Dict											=       outputs.hidden_states

             _UpperCAmelCase				:    Dict											=       2_6
             self.assertEqual(len(A_						)			,      A_						)

         _UpperCAmelCase				,							_UpperCAmelCase				:    str											=       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             _UpperCAmelCase				:    Tuple											=       True
             check_hidden_states_output(A_			,      A_			,      A_						)

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

             check_hidden_states_output(A_			,      A_			,      A_						)





     def 					__lowerCAmelCase      (   self						)				->   List[str]:
         _UpperCAmelCase				:    Tuple											=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*A_						)





     @slow
     def 					__lowerCAmelCase      (   self						)				->   int:
         for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _UpperCAmelCase				:    str											=       MobileNetVaModel.from_pretrained(A_						)
             self.assertIsNotNone(A_						)





def 					lowerCamelCase_		():
    _UpperCAmelCase				:    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 					__lowerCAmelCase      (   self						)				->   Dict:
         return (
             MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224'''						) if is_vision_available() else None
         )





     @slow
     def 					__lowerCAmelCase      (   self						)				->   str:
         _UpperCAmelCase				:    Optional[Any]											=       MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224'''						).to(A_						)

         _UpperCAmelCase				:    Optional[int]											=       self.default_image_processor
         _UpperCAmelCase				:    Tuple											=       prepare_img()
         _UpperCAmelCase				:    Union[str, Any]											=       image_processor(images=A_			,      return_tensors='''pt'''						).to(A_						)

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

         # verify the logits
         _UpperCAmelCase				:    Optional[Any]											=       torch.Size((1, 1_0_0_1)						)
         self.assertEqual(outputs.logits.shape			,      A_						)

         _UpperCAmelCase				:    Tuple											=       torch.tensor([-4.1_739, -1.1_233, 3.1_205]						).to(A_						)

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




"""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   )