





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







 '''simple docstring'''
 lowercase_		=      CTRLTokenizer
 lowercase_		=      False
 lowercase_		=      False

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



   '''simple docstring'''



   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   lowerCamelCase__:				Optional[Any]        =["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
   lowerCamelCase__:				Optional[Any]        =dict(zip(UpperCAmelCase_	,			range(len(UpperCAmelCase_))))
   lowerCamelCase__:				Union[str, Any]        =["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
   lowerCamelCase__:				Union[str, Any]        ={"unk_token": "<unk>"}

   lowerCamelCase__:				Union[str, Any]        =os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES["vocab_file"])
   lowerCamelCase__:				Optional[Any]        =os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES["merges_file"])
   with open(self.vocab_file	,			"w"	,			encoding="utf-8") as fp:
     fp.write(json.dumps(UpperCAmelCase_) + "\n")
   with open(self.merges_file	,			"w"	,			encoding="utf-8") as fp:
     fp.write("\n".join(UpperCAmelCase_))

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			**UpperCAmelCase_    :       str)	->List[Any]:



   '''simple docstring'''



   kwargs.update(self.special_tokens_map)
   return CTRLTokenizer.from_pretrained(self.tmpdirname	,			**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       Optional[int])	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				int        ="adapt react readapt apt"
   lowerCamelCase__:				int        ="adapt react readapt apt"
   return input_text, output_text







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



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =CTRLTokenizer(self.vocab_file	,			self.merges_file	,			**self.special_tokens_map)
   lowerCamelCase__:				Union[str, Any]        ="adapt react readapt apt"
   lowerCamelCase__:				Tuple        ="adapt re@@ a@@ c@@ t re@@ adapt apt".split()
   lowerCamelCase__:				Dict        =tokenizer.tokenize(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_	,			UpperCAmelCase_)

   lowerCamelCase__:				Optional[int]        =tokens + [tokenizer.unk_token]

   lowerCamelCase__:				List[str]        =[0, 1, 2, 4, 5, 1, 0, 3, 6]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_)	,			UpperCAmelCase_)








from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self    :       Optional[Any]	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       List[Any]=13	,			UpperCAmelCase_    :       Dict=30	,			UpperCAmelCase_    :       str=2	,			UpperCAmelCase_    :       Union[str, Any]=3	,			UpperCAmelCase_    :       Union[str, Any]=True	,			UpperCAmelCase_    :       List[str]=True	,			UpperCAmelCase_    :       Dict=32	,			UpperCAmelCase_    :       List[Any]=2	,			UpperCAmelCase_    :       List[str]=4	,			UpperCAmelCase_    :       List[str]=37	,			UpperCAmelCase_    :       List[str]="gelu"	,			UpperCAmelCase_    :       List[Any]=0.1	,			UpperCAmelCase_    :       Optional[Any]=0.1	,			UpperCAmelCase_    :       str=10	,			UpperCAmelCase_    :       str=0.02	,			UpperCAmelCase_    :       Optional[int]=3	,			UpperCAmelCase_    :       Dict=None	,			)	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				int        =parent
   lowerCamelCase__:				Any        =batch_size
   lowerCamelCase__:				List[str]        =image_size
   lowerCamelCase__:				Tuple        =patch_size
   lowerCamelCase__:				Tuple        =num_channels
   lowerCamelCase__:				Tuple        =is_training
   lowerCamelCase__:				Optional[int]        =use_labels
   lowerCamelCase__:				Dict        =hidden_size
   lowerCamelCase__:				Dict        =num_hidden_layers
   lowerCamelCase__:				Optional[Any]        =num_attention_heads
   lowerCamelCase__:				Union[str, Any]        =intermediate_size
   lowerCamelCase__:				List[str]        =hidden_act
   lowerCamelCase__:				Optional[Any]        =hidden_dropout_prob
   lowerCamelCase__:				str        =attention_probs_dropout_prob
   lowerCamelCase__:				str        =type_sequence_label_size
   lowerCamelCase__:				Optional[Any]        =initializer_range
   lowerCamelCase__:				Optional[int]        =scope

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

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



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

   lowerCamelCase__:				List[str]        =self.get_config()

   return config, pixel_values, labels

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->List[str]:



   '''simple docstring'''



   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=UpperCAmelCase_	,			initializer_range=self.initializer_range	,			)

 def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       Tuple)	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =TFViTModel(config=UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =model(UpperCAmelCase_	,			training=UpperCAmelCase_)
   self.parent.assertEqual(result.last_hidden_state.shape	,			(self.batch_size, self.seq_length, self.hidden_size))

   # Test with an image with different size than the one specified in config.
   lowerCamelCase__:				Dict        =self.image_size // 2
   lowerCamelCase__:				List[str]        =pixel_values[:, :, :image_size, :image_size]
   lowerCamelCase__:				List[str]        =model(UpperCAmelCase_	,			interpolate_pos_encoding=UpperCAmelCase_	,			training=UpperCAmelCase_)
   lowerCamelCase__:				List[str]        =(image_size // self.patch_size) ** 2 + 1
   self.parent.assertEqual(result.last_hidden_state.shape	,			(self.batch_size, seq_length, self.hidden_size))

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       Tuple	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       Any)	->str:



   '''simple docstring'''



   lowerCamelCase__:				Any        =self.type_sequence_label_size
   lowerCamelCase__:				str        =TFViTForImageClassification(UpperCAmelCase_)
   lowerCamelCase__:				List[str]        =model(UpperCAmelCase_	,			labels=UpperCAmelCase_	,			training=UpperCAmelCase_)
   self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.type_sequence_label_size))

   # Test with an image with different size than the one specified in config.
   lowerCamelCase__:				Union[str, Any]        =self.image_size // 2
   lowerCamelCase__:				Dict        =pixel_values[:, :, :image_size, :image_size]
   lowerCamelCase__:				Tuple        =model(UpperCAmelCase_	,			interpolate_pos_encoding=UpperCAmelCase_	,			training=UpperCAmelCase_)
   self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.type_sequence_label_size))

   # test greyscale images
   lowerCamelCase__:				int        =1
   lowerCamelCase__:				List[str]        =TFViTForImageClassification(UpperCAmelCase_)
   lowerCamelCase__:				List[str]        =floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
   lowerCamelCase__:				Any        =model(UpperCAmelCase_)
   self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.type_sequence_label_size))







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



   '''simple docstring'''



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



@require_tf
class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







 '''simple docstring'''
 lowercase_		=      (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
 lowercase_		=      (
     {"feature-extraction": TFViTModel, "image-classification": TFViTForImageClassification}
     if is_tf_available()
     else {}
 )

 lowercase_		=      False
 lowercase_		=      False
 lowercase_		=      False

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				int        =TFViTModelTester(self)
   lowerCamelCase__:				List[str]        =ConfigTester(self	,			config_class=UpperCAmelCase_	,			has_text_modality=UpperCAmelCase_	,			hidden_size=37)

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Tuple:



   '''simple docstring'''



   self.config_tester.run_common_tests()

 @unittest.skip(reason="ViT does not use inputs_embeds")
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Any:



   '''simple docstring'''



   pass

 @unittest.skip(reason="ViT does not use inputs_embeds")
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->str:



   '''simple docstring'''



   pass

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



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				int        =self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowerCamelCase__:				int        =model_class(UpperCAmelCase_)
     self.assertIsInstance(model.get_input_embeddings()	,			(tf.keras.layers.Layer))
     lowerCamelCase__:				Tuple        =model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(UpperCAmelCase_	,			tf.keras.layers.Layer))

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



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Optional[Any]        =self.model_tester.prepare_config_and_inputs_for_common()

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

     lowerCamelCase__:				str        =["pixel_values"]
     self.assertListEqual(arg_names[:1]	,			UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->Any:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_)







 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				Any        =TFViTModel.from_pretrained("google/vit-base-patch16-224")
   self.assertIsNotNone(UpperCAmelCase_)





def 					lowerCAmelCase_			(	)					->		Tuple:







  """simple docstring"""





  lowerCamelCase__:				Any        =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
  return image






@require_tf
@require_vision
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 @cached_property
 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->List[Any]:



   '''simple docstring'''



   return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224") if is_vision_available() else None







 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224")

   lowerCamelCase__:				List[str]        =self.default_image_processor
   lowerCamelCase__:				Union[str, Any]        =prepare_img()
   lowerCamelCase__:				List[Any]        =image_processor(images=UpperCAmelCase_	,			return_tensors="tf")

   # forward pass
   lowerCamelCase__:				Dict        =model(**UpperCAmelCase_)

   # verify the logits
   lowerCamelCase__:				List[Any]        =tf.TensorShape((1, 1_000))
   self.assertEqual(outputs.logits.shape	,			UpperCAmelCase_)

   lowerCamelCase__:				List[Any]        =tf.constant([-0.2744, 0.8215, -0.0836])

   tf.debugging.assert_near(outputs.logits[0, :3]	,			UpperCAmelCase_	,			atol=1E-4)

