


"""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						,     UpperCamelCase__						,     UpperCamelCase__=13						,     UpperCamelCase__=30						,     UpperCamelCase__=2						,     UpperCamelCase__=3						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=32						,     UpperCamelCase__=2						,     UpperCamelCase__=4						,     UpperCamelCase__=37						,     UpperCamelCase__="gelu"						,     UpperCamelCase__=0.1						,     UpperCamelCase__=0.1						,     UpperCamelCase__=10						,     UpperCamelCase__=0.02						,     UpperCamelCase__=3						,     UpperCamelCase__=None						,     )     ->		Union[str, Any]:

       '''simple docstring'''




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

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







   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




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

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

       snake_case   :				str				 =   self.get_config()

       return config, pixel_values, labels







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

       '''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 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		List[str]:

       '''simple docstring'''




       snake_case   :				str				 =   TFViTModel(config=UpperCamelCase__						)
       snake_case   :				List[Any]				 =   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.
       snake_case   :				Dict				 =   self.image_size // 2
       snake_case   :				Optional[Any]				 =   pixel_values[:, :, :image_size, :image_size]
       snake_case   :				str				 =   model(UpperCamelCase__						,     interpolate_pos_encoding=UpperCamelCase__						,     training=UpperCamelCase__						)
       snake_case   :				Optional[int]				 =   (image_size // self.patch_size) ** 2 + 1
       self.parent.assertEqual(result.last_hidden_state.shape						,     (self.batch_size, seq_length, self.hidden_size)						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				List[str]				 =   self.type_sequence_label_size
       snake_case   :				Optional[Any]				 =   TFViTForImageClassification(UpperCamelCase__						)
       snake_case   :				Union[str, Any]				 =   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.
       snake_case   :				Optional[Any]				 =   self.image_size // 2
       snake_case   :				List[Any]				 =   pixel_values[:, :, :image_size, :image_size]
       snake_case   :				Any				 =   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
       snake_case   :				Tuple				 =   1
       snake_case   :				Optional[Any]				 =   TFViTForImageClassification(UpperCamelCase__						)
       snake_case   :				List[Any]				 =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
       snake_case   :				Optional[int]				 =   model(UpperCamelCase__						)
       self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.type_sequence_label_size)						)







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				Tuple				 =   self.prepare_config_and_inputs()
       snake_case ,snake_case ,snake_case   :				List[Any]				 =   config_and_inputs
       snake_case   :				int				 =   {"pixel_values": pixel_values}
       return config, inputs_dict







@require_tf
class _lowerCAmelCase    (				snake_case_   ,							snake_case_   ,							unittest.TestCase     ):
   __UpperCAmelCase			:	List[str]							  =  (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
   __UpperCAmelCase			:	List[Any]							  =  (
       {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
       if is_tf_available()
       else {}
   )

   __UpperCAmelCase			:	Tuple							  =  False
   __UpperCAmelCase			:	List[Any]							  =  False
   __UpperCAmelCase			:	Dict							  =  False







   def 			lowerCamelCase		(     self						)     ->		Optional[int]:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   TFViTModelTester(self						)
       snake_case   :				Optional[Any]				 =   ConfigTester(self						,     config_class=UpperCamelCase__						,     has_text_modality=UpperCamelCase__						,     hidden_size=37						)







   def 			lowerCamelCase		(     self						)     ->		Optional[int]:

       '''simple docstring'''




       self.config_tester.run_common_tests()







   @unittest.skip(reason="ViT does not use inputs_embeds"						)
   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




       pass







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

       '''simple docstring'''




       pass







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case ,snake_case   :				List[Any]				 =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           snake_case   :				Dict				 =   model_class(UpperCamelCase__						)
           self.assertIsInstance(model.get_input_embeddings()						,     (tf.keras.layers.Layer)						)
           snake_case   :				Optional[int]				 =   model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(UpperCamelCase__						,     tf.keras.layers.Layer						)						)







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case ,snake_case   :				Any				 =   self.model_tester.prepare_config_and_inputs_for_common()

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

           snake_case   :				Optional[int]				 =   ["pixel_values"]
           self.assertListEqual(arg_names[:1]						,     UpperCamelCase__						)







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

       '''simple docstring'''




       snake_case   :				int				 =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__						)







   @slow
   def 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

       '''simple docstring'''




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







def   __lowerCAmelCase						(						)     ->						Any:


    """simple docstring"""





    snake_case   :				Optional[Any]				 =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
    return image







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







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

       '''simple docstring'''




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







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

       '''simple docstring'''




       snake_case   :				Any				 =   TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224"						)

       snake_case   :				Optional[int]				 =   self.default_image_processor
       snake_case   :				int				 =   prepare_img()
       snake_case   :				List[str]				 =   image_processor(images=UpperCamelCase__						,     return_tensors="tf"						)

       # forward pass
       snake_case   :				str				 =   model(**UpperCamelCase__						)

       # verify the logits
       snake_case   :				Any				 =   tf.TensorShape((1, 1000)						)
       self.assertEqual(outputs.logits.shape						,     UpperCamelCase__						)

       snake_case   :				List[str]				 =   tf.constant([-0.2744, 0.8215, -0.0836]						)

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









"""simple docstring"""



import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case             =   logging.get_logger(__name__)

__snake_case             =   {
    """BAAI/AltCLIP""": """https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json""",
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Tuple							  =  '''altclip_text_model'''







   def __init__(     self						,     UpperCamelCase__=25_0002						,     UpperCamelCase__=1024						,     UpperCamelCase__=24						,     UpperCamelCase__=16						,     UpperCamelCase__=4096						,     UpperCamelCase__="gelu"						,     UpperCamelCase__=0.1						,     UpperCamelCase__=0.1						,     UpperCamelCase__=514						,     UpperCamelCase__=1						,     UpperCamelCase__=0.02						,     UpperCamelCase__=0.02						,     UpperCamelCase__=1e-05						,     UpperCamelCase__=1						,     UpperCamelCase__=0						,     UpperCamelCase__=2						,     UpperCamelCase__="absolute"						,     UpperCamelCase__=True						,     UpperCamelCase__=768						,     **UpperCamelCase__						,     )     ->		Optional[int]:

       '''simple docstring'''




       super().__init__(pad_token_id=UpperCamelCase__						,     bos_token_id=UpperCamelCase__						,     eos_token_id=UpperCamelCase__						,     **UpperCamelCase__						)

       snake_case   :				Any				 =   vocab_size
       snake_case   :				List[Any]				 =   hidden_size
       snake_case   :				Optional[int]				 =   num_hidden_layers
       snake_case   :				Optional[Any]				 =   num_attention_heads
       snake_case   :				Dict				 =   hidden_act
       snake_case   :				Dict				 =   intermediate_size
       snake_case   :				int				 =   hidden_dropout_prob
       snake_case   :				Optional[int]				 =   attention_probs_dropout_prob
       snake_case   :				Union[str, Any]				 =   max_position_embeddings
       snake_case   :				Optional[int]				 =   type_vocab_size
       snake_case   :				Dict				 =   initializer_range
       snake_case   :				int				 =   initializer_factor
       snake_case   :				Union[str, Any]				 =   layer_norm_eps
       snake_case   :				List[Any]				 =   position_embedding_type
       snake_case   :				Any				 =   use_cache
       snake_case   :				str				 =   project_dim







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Tuple							  =  '''altclip_vision_model'''







   def __init__(     self						,     UpperCamelCase__=768						,     UpperCamelCase__=3072						,     UpperCamelCase__=512						,     UpperCamelCase__=12						,     UpperCamelCase__=12						,     UpperCamelCase__=3						,     UpperCamelCase__=224						,     UpperCamelCase__=32						,     UpperCamelCase__="quick_gelu"						,     UpperCamelCase__=1e-5						,     UpperCamelCase__=0.0						,     UpperCamelCase__=0.02						,     UpperCamelCase__=1.0						,     **UpperCamelCase__						,     )     ->		str:

       '''simple docstring'''




       super().__init__(**UpperCamelCase__						)

       snake_case   :				Optional[int]				 =   hidden_size
       snake_case   :				str				 =   intermediate_size
       snake_case   :				List[str]				 =   projection_dim
       snake_case   :				Optional[Any]				 =   num_hidden_layers
       snake_case   :				Optional[int]				 =   num_attention_heads
       snake_case   :				str				 =   num_channels
       snake_case   :				List[str]				 =   patch_size
       snake_case   :				List[Any]				 =   image_size
       snake_case   :				Union[str, Any]				 =   initializer_range
       snake_case   :				Optional[Any]				 =   initializer_factor
       snake_case   :				Any				 =   attention_dropout
       snake_case   :				Dict				 =   layer_norm_eps
       snake_case   :				List[str]				 =   hidden_act







   @classmethod
   def 			lowerCamelCase		(     cls						,     UpperCamelCase__						,     **UpperCamelCase__						)     ->		"PretrainedConfig":

       '''simple docstring'''




       cls._set_token_in_kwargs(UpperCamelCase__						)

       snake_case ,snake_case   :				str				 =   cls.get_config_dict(UpperCamelCase__						,     **UpperCamelCase__						)

       # get the vision config dict if we are loading from AltCLIPConfig
       if config_dict.get("model_type"						) == "altclip":
           snake_case   :				Optional[Any]				 =   config_dict["vision_config"]

       if "model_type" in config_dict and hasattr(cls						,     "model_type"						) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
               F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

       return cls.from_dict(UpperCamelCase__						,     **UpperCamelCase__						)







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	str							  =  '''altclip'''
   __UpperCAmelCase			:	Optional[Any]							  =  True







   def __init__(     self						,     UpperCamelCase__=None						,     UpperCamelCase__=None						,     UpperCamelCase__=768						,     UpperCamelCase__=2.6592						,     **UpperCamelCase__						)     ->		Any:

       '''simple docstring'''




       snake_case   :				List[str]				 =   kwargs.pop("text_config_dict"						,     UpperCamelCase__						)
       snake_case   :				Union[str, Any]				 =   kwargs.pop("vision_config_dict"						,     UpperCamelCase__						)

       super().__init__(**UpperCamelCase__						)

       # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
       # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
       # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
       if text_config_dict is not None:
           if text_config is None:
               snake_case   :				List[str]				 =   {}

           # This is the complete result when using `text_config_dict`.
           snake_case   :				Dict				 =   AltCLIPTextConfig(**UpperCamelCase__						).to_dict()

           # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
           for key, value in _text_config_dict.items():
               if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
                   # If specified in `text_config_dict`
                   if key in text_config_dict:
                       snake_case   :				Optional[Any]				 =   (
                           F'`{key}` is found in both `text_config_dict` and `text_config` but with different values. '
                           F'The value `text_config_dict["{key}"]` will be used instead.'
                       )
                   # If inferred from default argument values (just to be super careful)
                   else:
                       snake_case   :				Any				 =   (
                           F'`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '
                           F'value `text_config["{key}"]` will be overriden.'
                       )
                   logger.warning(UpperCamelCase__						)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
           text_config.update(_text_config_dict						)

       if vision_config_dict is not None:
           if vision_config is None:
               snake_case   :				Union[str, Any]				 =   {}

           # This is the complete result when using `vision_config_dict`.
           snake_case   :				int				 =   AltCLIPVisionConfig(**UpperCamelCase__						).to_dict()
           # convert keys to string instead of integer
           if "id2label" in _vision_config_dict:
               snake_case   :				Optional[int]				 =   {
                   str(UpperCamelCase__						): value for key, value in _vision_config_dict["id2label"].items()
               }

           # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
           for key, value in _vision_config_dict.items():
               if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
                   # If specified in `vision_config_dict`
                   if key in vision_config_dict:
                       snake_case   :				int				 =   (
                           F'`{key}` is found in both `vision_config_dict` and `vision_config` but with different '
                           F'values. The value `vision_config_dict["{key}"]` will be used instead.'
                       )
                   # If inferred from default argument values (just to be super careful)
                   else:
                       snake_case   :				Optional[Any]				 =   (
                           F'`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '
                           F'The value `vision_config["{key}"]` will be overriden.'
                       )
                   logger.warning(UpperCamelCase__						)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
           vision_config.update(_vision_config_dict						)

       if text_config is None:
           snake_case   :				Optional[int]				 =   {}
           logger.info("`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values."						)

       if vision_config is None:
           snake_case   :				Dict				 =   {}
           logger.info("`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values."						)

       snake_case   :				Dict				 =   AltCLIPTextConfig(**UpperCamelCase__						)
       snake_case   :				Tuple				 =   AltCLIPVisionConfig(**UpperCamelCase__						)

       snake_case   :				int				 =   projection_dim
       snake_case   :				List[str]				 =   logit_scale_init_value
       snake_case   :				int				 =   1.0







   @classmethod
   def 			lowerCamelCase		(     cls						,     UpperCamelCase__						,     UpperCamelCase__						,     **UpperCamelCase__						)     ->		Union[str, Any]:

       '''simple docstring'''




       return cls(text_config=text_config.to_dict()						,     vision_config=vision_config.to_dict()						,     **UpperCamelCase__						)







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

       '''simple docstring'''




       snake_case   :				Tuple				 =   copy.deepcopy(self.__dict__						)
       snake_case   :				Optional[int]				 =   self.text_config.to_dict()
       snake_case   :				str				 =   self.vision_config.to_dict()
       snake_case   :				Optional[int]				 =   self.__class__.model_type
       return output





