
"""simple docstring"""



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       lowerCAmelCase__				:







     def __init__(							self			:			int    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			List[Any]=13    ,      _lowerCamelCase			:			Optional[int]=30    ,      _lowerCamelCase			:			Optional[Any]=2    ,      _lowerCamelCase			:			Optional[int]=3    ,      _lowerCamelCase			:			List[Any]=True    ,      _lowerCamelCase			:			List[str]=True    ,      _lowerCamelCase			:			Union[str, Any]=32    ,      _lowerCamelCase			:			List[Any]=2    ,      _lowerCamelCase			:			List[str]=4    ,      _lowerCamelCase			:			int=37    ,      _lowerCamelCase			:			Any="gelu"    ,      _lowerCamelCase			:			int=0.1    ,      _lowerCamelCase			:			Tuple=0.1    ,      _lowerCamelCase			:			Tuple=10    ,      _lowerCamelCase			:			Dict=0.0_2    ,      _lowerCamelCase			:			Optional[int]=3    ,      _lowerCamelCase			:			Dict=None    ,      ):
       _snake_case					     =       parent
       _snake_case					     =       batch_size
       _snake_case					     =       image_size
       _snake_case					     =       patch_size
       _snake_case					     =       num_channels
       _snake_case					     =       is_training
       _snake_case					     =       use_labels
       _snake_case					     =       hidden_size
       _snake_case					     =       num_hidden_layers
       _snake_case					     =       num_attention_heads
       _snake_case					     =       intermediate_size
       _snake_case					     =       hidden_act
       _snake_case					     =       hidden_dropout_prob
       _snake_case					     =       attention_probs_dropout_prob
       _snake_case					     =       type_sequence_label_size
       _snake_case					     =       initializer_range
       _snake_case					     =       scope

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







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

       _snake_case					     =       self.get_config()

       return config, pixel_values, labels







     def      lowercase	(							self			:			Dict			):
       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=_lowerCamelCase    ,      initializer_range=self.initializer_range    ,      )







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			Any			):
       _snake_case					     =       TFViTModel(config=_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase    ,      training=_lowerCamelCase			)
       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.
       _snake_case					     =       self.image_size // 2
       _snake_case					     =       pixel_values[:, :, :image_size, :image_size]
       _snake_case					     =       model(_lowerCamelCase    ,      interpolate_pos_encoding=_lowerCamelCase    ,      training=_lowerCamelCase			)
       _snake_case					     =       (image_size // self.patch_size) ** 2 + 1
       self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, seq_length, self.hidden_size)			)







     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			List[Any]    ,      _lowerCamelCase			:			Tuple    ,      _lowerCamelCase			:			str			):
       _snake_case					     =       self.type_sequence_label_size
       _snake_case					     =       TFViTForImageClassification(_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase    ,      labels=_lowerCamelCase    ,      training=_lowerCamelCase			)
       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.
       _snake_case					     =       self.image_size // 2
       _snake_case					     =       pixel_values[:, :, :image_size, :image_size]
       _snake_case					     =       model(_lowerCamelCase    ,      interpolate_pos_encoding=_lowerCamelCase    ,      training=_lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.type_sequence_label_size)			)

       # test greyscale images
       _snake_case					     =       1
       _snake_case					     =       TFViTForImageClassification(_lowerCamelCase			)
       _snake_case					     =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
       _snake_case					     =       model(_lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.type_sequence_label_size)			)







     def      lowercase	(							self			:			Optional[Any]			):
       _snake_case					     =       self.prepare_config_and_inputs()
       _snake_case     ,					_snake_case     ,					_snake_case					     =       config_and_inputs
       _snake_case					     =       {'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_tf
class       lowerCAmelCase__				(						A_ ,	A_ ,	unittest.TestCase	):
     __a						=					(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
     __a						=					(
         {"""feature-extraction""": TFViTModel, """image-classification""": TFViTForImageClassification}
         if is_tf_available()
         else {}
     )

     __a						=					False
     __a						=					False
     __a						=					False







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       TFViTModelTester(self			)
       _snake_case					     =       ConfigTester(self    ,      config_class=_lowerCamelCase    ,      has_text_modality=_lowerCamelCase    ,      hidden_size=37			)







     def      lowercase	(							self			:			Union[str, Any]			):
       self.config_tester.run_common_tests()







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







     @unittest.skip(reason='''ViT does not use inputs_embeds'''			)
     def      lowercase	(							self			:			Optional[int]			):
       pass







     def      lowercase	(							self			:			str			):
       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()

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







     def      lowercase	(							self			:			List[str]			):
       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()

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

         _snake_case					     =       ['''pixel_values''']
         self.assertListEqual(arg_names[:1]    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Optional[Any]			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase			)







     def      lowercase	(							self			:			Dict			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase			)







     @slow
     def      lowercase	(							self			:			Tuple			):
       _snake_case					     =       TFViTModel.from_pretrained('''google/vit-base-patch16-224'''			)
       self.assertIsNotNone(_lowerCamelCase			)






def 					_UpperCAmelCase      ( )     ->					Optional[Any]:
  _snake_case					     =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
  return image

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







     @cached_property
     def      lowercase	(							self			:			Optional[int]			):
       return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''			) if is_vision_available() else None







     @slow
     def      lowercase	(							self			:			Any			):
       _snake_case					     =       TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''			)

       _snake_case					     =       self.default_image_processor
       _snake_case					     =       prepare_img()
       _snake_case					     =       image_processor(images=_lowerCamelCase    ,      return_tensors='''tf'''			)

       # forward pass
       _snake_case					     =       model(**_lowerCamelCase			)

       # verify the logits
       _snake_case					     =       tf.TensorShape((1, 1000)			)
       self.assertEqual(outputs.logits.shape    ,      _lowerCamelCase			)

       _snake_case					     =       tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]			)

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





"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import CLIPImageProcessor, CLIPProcessor

@require_vision
class       lowerCAmelCase__				(						unittest.TestCase	):







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       tempfile.mkdtemp()

       # fmt: off
       _snake_case					     =       ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
       # fmt: on
       _snake_case					     =       dict(zip(_lowerCamelCase    ,      range(len(_lowerCamelCase			)			)			)			)
       _snake_case					     =       ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
       _snake_case					     =       {'''unk_token''': '''<unk>'''}

       _snake_case					     =       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''vocab_file''']			)
       _snake_case					     =       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(_lowerCamelCase			) + '''\n'''			)
       with open(self.merges_file    ,      '''w'''    ,      encoding='''utf-8'''			) as fp:
         fp.write('''\n'''.join(_lowerCamelCase			)			)

       _snake_case					     =       {
           '''do_resize''': True,
           '''size''': 20,
           '''do_center_crop''': True,
           '''crop_size''': 18,
           '''do_normalize''': True,
           '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
           '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
       }
       _snake_case					     =       os.path.join(self.tmpdirname    ,      _lowerCamelCase			)
       with open(self.image_processor_file    ,      '''w'''    ,      encoding='''utf-8'''			) as fp:
         json.dump(_lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Tuple    ,      **_lowerCamelCase			:			Any			):
       return CLIPTokenizer.from_pretrained(self.tmpdirname    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			str    ,      **_lowerCamelCase			:			Any			):
       return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			int    ,      **_lowerCamelCase			:			Optional[int]			):
       return CLIPImageProcessor.from_pretrained(self.tmpdirname    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       shutil.rmtree(self.tmpdirname			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       [np.random.randint(255    ,      size=(3, 30, 400)    ,      dtype=np.uinta			)]

       _snake_case					     =       [Image.fromarray(np.moveaxis(_lowerCamelCase    ,      0    ,      -1			)			) for x in image_inputs]

       return image_inputs







     def      lowercase	(							self			:			Optional[Any]			):
       _snake_case					     =       self.get_tokenizer()
       _snake_case					     =       self.get_rust_tokenizer()
       _snake_case					     =       self.get_image_processor()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)
       processor_slow.save_pretrained(self.tmpdirname			)
       _snake_case					     =       CLIPProcessor.from_pretrained(self.tmpdirname    ,      use_fast=_lowerCamelCase			)

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)
       processor_fast.save_pretrained(self.tmpdirname			)
       _snake_case					     =       CLIPProcessor.from_pretrained(self.tmpdirname			)

       self.assertEqual(processor_slow.tokenizer.get_vocab()    ,      tokenizer_slow.get_vocab()			)
       self.assertEqual(processor_fast.tokenizer.get_vocab()    ,      tokenizer_fast.get_vocab()			)
       self.assertEqual(tokenizer_slow.get_vocab()    ,      tokenizer_fast.get_vocab()			)
       self.assertIsInstance(processor_slow.tokenizer    ,      _lowerCamelCase			)
       self.assertIsInstance(processor_fast.tokenizer    ,      _lowerCamelCase			)

       self.assertEqual(processor_slow.image_processor.to_json_string()    ,      image_processor.to_json_string()			)
       self.assertEqual(processor_fast.image_processor.to_json_string()    ,      image_processor.to_json_string()			)
       self.assertIsInstance(processor_slow.image_processor    ,      _lowerCamelCase			)
       self.assertIsInstance(processor_fast.image_processor    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       CLIPProcessor(tokenizer=self.get_tokenizer()    ,      image_processor=self.get_image_processor()			)
       processor.save_pretrained(self.tmpdirname			)

       _snake_case					     =       self.get_tokenizer(bos_token='''(BOS)'''    ,      eos_token='''(EOS)'''			)
       _snake_case					     =       self.get_image_processor(do_normalize=_lowerCamelCase    ,      padding_value=1.0			)

       _snake_case					     =       CLIPProcessor.from_pretrained(
           self.tmpdirname    ,      bos_token='''(BOS)'''    ,      eos_token='''(EOS)'''    ,      do_normalize=_lowerCamelCase    ,      padding_value=1.0			)

       self.assertEqual(processor.tokenizer.get_vocab()    ,      tokenizer_add_kwargs.get_vocab()			)
       self.assertIsInstance(processor.tokenizer    ,      _lowerCamelCase			)

       self.assertEqual(processor.image_processor.to_json_string()    ,      image_processor_add_kwargs.to_json_string()			)
       self.assertIsInstance(processor.image_processor    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			int			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

       _snake_case					     =       self.prepare_image_inputs()

       _snake_case					     =       image_processor(_lowerCamelCase    ,      return_tensors='''np'''			)
       _snake_case					     =       processor(images=_lowerCamelCase    ,      return_tensors='''np'''			)

       for key in input_image_proc.keys():
         self.assertAlmostEqual(input_image_proc[key].sum()    ,      input_processor[key].sum()    ,      delta=1e-2			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

       _snake_case					     =       '''lower newer'''

       _snake_case					     =       processor(text=_lowerCamelCase			)

       _snake_case					     =       tokenizer(_lowerCamelCase			)

       for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]    ,      encoded_processor[key]			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

       _snake_case					     =       '''lower newer'''
       _snake_case					     =       self.prepare_image_inputs()

       _snake_case					     =       processor(text=_lowerCamelCase    ,      images=_lowerCamelCase			)

       self.assertListEqual(list(inputs.keys()			)    ,      ['''input_ids''', '''attention_mask''', '''pixel_values''']			)

       # test if it raises when no input is passed
       with pytest.raises(_lowerCamelCase			):
         processor()







     def      lowercase	(							self			:			List[str]			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

       _snake_case					     =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       _snake_case					     =       processor.batch_decode(_lowerCamelCase			)
       _snake_case					     =       tokenizer.batch_decode(_lowerCamelCase			)

       self.assertListEqual(_lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

       _snake_case					     =       '''lower newer'''
       _snake_case					     =       self.prepare_image_inputs()

       _snake_case					     =       processor(text=_lowerCamelCase    ,      images=_lowerCamelCase			)

       self.assertListEqual(list(inputs.keys()			)    ,      processor.model_input_names			)



