





import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
     import jax

     from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel



class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 def __init__(self    :       List[str]	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       Tuple=13	,			UpperCAmelCase_    :       List[str]=30	,			UpperCAmelCase_    :       int=2	,			UpperCAmelCase_    :       List[str]=3	,			UpperCAmelCase_    :       int=True	,			UpperCAmelCase_    :       Tuple=True	,			UpperCAmelCase_    :       Union[str, Any]=32	,			UpperCAmelCase_    :       str=5	,			UpperCAmelCase_    :       Optional[int]=4	,			UpperCAmelCase_    :       Tuple=37	,			UpperCAmelCase_    :       Optional[int]="gelu"	,			UpperCAmelCase_    :       Optional[Any]=0.1	,			UpperCAmelCase_    :       str=0.1	,			UpperCAmelCase_    :       Dict=10	,			UpperCAmelCase_    :       List[Any]=0.02	,			)	->Optional[int]:



   '''simple docstring'''



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

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

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



   '''simple docstring'''



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

   lowerCamelCase__:				str        =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	,			)

   return config, pixel_values

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



   '''simple docstring'''



   lowerCamelCase__:				str        =FlaxViTModel(config=UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =model(UpperCAmelCase_)
   # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
   lowerCamelCase__:				Tuple        =(self.image_size, self.image_size)
   lowerCamelCase__:				Dict        =(self.patch_size, self.patch_size)
   lowerCamelCase__:				str        =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
   self.parent.assertEqual(result.last_hidden_state.shape	,			(self.batch_size, num_patches + 1, self.hidden_size))

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



   '''simple docstring'''



   lowerCamelCase__:				str        =self.type_sequence_label_size
   lowerCamelCase__:				Optional[int]        =FlaxViTForImageClassification(config=UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =model(UpperCAmelCase_)
   self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.type_sequence_label_size))

   # test greyscale images
   lowerCamelCase__:				str        =1
   lowerCamelCase__:				List[str]        =FlaxViTForImageClassification(UpperCAmelCase_)

   lowerCamelCase__:				str        =floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
   lowerCamelCase__:				Optional[Any]        =model(UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->str:



   '''simple docstring'''



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



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







 '''simple docstring'''
 lowercase_		=      (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->None:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->str:



   '''simple docstring'''



   self.config_tester.run_common_tests()

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



   '''simple docstring'''



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

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



   '''simple docstring'''



   lowerCamelCase__:				Any        =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:				Any        =self.model_tester.prepare_config_and_inputs_for_common()

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

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

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



   '''simple docstring'''



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

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__):
       lowerCamelCase__:				str        =self._prepare_for_class(UpperCAmelCase_	,			UpperCAmelCase_)
       lowerCamelCase__:				Tuple        =model_class(UpperCAmelCase_)

       @jax.jit
       def model_jitted(UpperCAmelCase_    :       List[str]	,			**UpperCAmelCase_    :       List[Any]):
         return model(pixel_values=UpperCAmelCase_	,			**UpperCAmelCase_)

       with self.subTest("JIT Enabled"):
         lowerCamelCase__:				Dict        =model_jitted(**UpperCAmelCase_).to_tuple()

       with self.subTest("JIT Disabled"):
         with jax.disable_jit():
           lowerCamelCase__:				str        =model_jitted(**UpperCAmelCase_).to_tuple()

       self.assertEqual(len(UpperCAmelCase_)	,			len(UpperCAmelCase_))
       for jitted_output, output in zip(UpperCAmelCase_	,			UpperCAmelCase_):
         self.assertEqual(jitted_output.shape	,			output.shape)







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



   '''simple docstring'''



   for model_class_name in self.all_model_classes:
     lowerCamelCase__:				Optional[int]        =model_class_name.from_pretrained("google/vit-base-patch16-224")
     lowerCamelCase__:				Optional[int]        =model(np.ones((1, 3, 224, 224)))
     self.assertIsNotNone(UpperCAmelCase_)








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_)

