





'''simple docstring'''

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
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 (
          TFDeiTForImageClassification,
          TFDeiTForImageClassificationWithTeacher,
          TFDeiTForMaskedImageModeling,
          TFDeiTModel,
      )
      from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import DeiTImageProcessor


class 					a_     :

    '''simple docstring'''


    def __init__(		self ,		A ,		A=13 ,		A=30 ,		A=2 ,		A=3 ,		A=True ,		A=True ,		A=32 ,		A=2 ,		A=4 ,		A=37 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=10 ,		A=0.02 ,		A=3 ,		A=None ,		A=2 ,		)		->   Any:
        _SCREAMING_SNAKE_CASE									=			parent
        _SCREAMING_SNAKE_CASE									=			batch_size
        _SCREAMING_SNAKE_CASE									=			image_size
        _SCREAMING_SNAKE_CASE									=			patch_size
        _SCREAMING_SNAKE_CASE									=			num_channels
        _SCREAMING_SNAKE_CASE									=			is_training
        _SCREAMING_SNAKE_CASE									=			use_labels
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			type_sequence_label_size
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			scope
        _SCREAMING_SNAKE_CASE									=			encoder_stride

        # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
        _SCREAMING_SNAKE_CASE									=			(image_size // patch_size) ** 2
        _SCREAMING_SNAKE_CASE									=			num_patches + 2


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

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

        _SCREAMING_SNAKE_CASE									=			self.get_config()

        return config, pixel_values, labels


    def 			snake_case_(		self       )		->   int:
        return DeiTConfig(
            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=A ,		initializer_range=self.initializer_range ,		encoder_stride=self.encoder_stride ,		)


    def 			snake_case_(		self ,		A ,		A ,		A       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			TFDeiTModel(config=A       )
        _SCREAMING_SNAKE_CASE									=			model(A       )
        self.parent.assertEqual(result.last_hidden_state.shape ,		(self.batch_size, self.seq_length, self.hidden_size)       )


    def 			snake_case_(		self ,		A ,		A ,		A       )		->   int:
        _SCREAMING_SNAKE_CASE									=			TFDeiTForMaskedImageModeling(config=A       )
        _SCREAMING_SNAKE_CASE									=			model(A       )
        self.parent.assertEqual(
            result.reconstruction.shape ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

        # test greyscale images
        _SCREAMING_SNAKE_CASE									=			1
        _SCREAMING_SNAKE_CASE									=			TFDeiTForMaskedImageModeling(A       )

        _SCREAMING_SNAKE_CASE									=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
        _SCREAMING_SNAKE_CASE									=			model(A       )
        self.parent.assertEqual(result.reconstruction.shape ,		(self.batch_size, 1, self.image_size, self.image_size)       )


    def 			snake_case_(		self ,		A ,		A ,		A       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			self.type_sequence_label_size
        _SCREAMING_SNAKE_CASE									=			TFDeiTForImageClassification(A       )
        _SCREAMING_SNAKE_CASE									=			model(A ,		labels=A       )
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.type_sequence_label_size)       )

        # test greyscale images
        _SCREAMING_SNAKE_CASE									=			1
        _SCREAMING_SNAKE_CASE									=			TFDeiTForImageClassification(A       )

        _SCREAMING_SNAKE_CASE									=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
        _SCREAMING_SNAKE_CASE									=			model(A ,		labels=A       )
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.type_sequence_label_size)       )





    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			self.prepare_config_and_inputs()
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			config_and_inputs
        _SCREAMING_SNAKE_CASE									=			{"""pixel_values""": pixel_values}
        return config, inputs_dict




@require_tf
class 					a_     (				snake_case_      ,    snake_case_      ,    unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = (
        (
            TFDeiTModel,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    UpperCamelCase     = (
        {
            '''feature-extraction''': TFDeiTModel,
            '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
        }
        if is_tf_available()
        else {}
    )

    UpperCamelCase     = False
    UpperCamelCase     = False
    UpperCamelCase     = False
    UpperCamelCase     = False


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			TFDeiTModelTester(self       )
        _SCREAMING_SNAKE_CASE									=			ConfigTester(self ,		config_class=A ,		has_text_modality=A ,		hidden_size=37       )


    def 			snake_case_(		self       )		->   Optional[int]:
        self.config_tester.run_common_tests()


    @unittest.skip(reason="""DeiT does not use inputs_embeds"""       )
    def 			snake_case_(		self       )		->   Optional[int]:
        pass


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs_for_common()

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


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs_for_common()

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

            _SCREAMING_SNAKE_CASE									=			["""pixel_values"""]
            self.assertListEqual(arg_names[:1] ,		A       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A       )


    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*A       )


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*A       )


    def 			snake_case_(		self ,		A ,		A ,		A=False       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			super()._prepare_for_class(A ,		A ,		return_labels=A       )

        if return_labels:
            if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call       ).parameters:
                del inputs_dict["labels"]

        return inputs_dict





    @slow
    def 			snake_case_(		self       )		->   Union[str, Any]:
        for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _SCREAMING_SNAKE_CASE									=			TFDeiTModel.from_pretrained(A       )
            self.assertIsNotNone(A       )





def lowerCamelCase						(     )						->Union[str, Any]:
    _SCREAMING_SNAKE_CASE									=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
    return image






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

    '''simple docstring'''


    @cached_property
    def 			snake_case_(		self       )		->   Dict:
        return (
            DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""       )
            if is_vision_available()
            else None
        )





    @slow
    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""       )

        _SCREAMING_SNAKE_CASE									=			self.default_image_processor
        _SCREAMING_SNAKE_CASE									=			prepare_img()
        _SCREAMING_SNAKE_CASE									=			image_processor(images=A ,		return_tensors="""tf"""       )

        # forward pass
        _SCREAMING_SNAKE_CASE									=			model(**A       )

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

        _SCREAMING_SNAKE_CASE									=			tf.constant([-1.0266, 0.1912, -1.2861]       )

        self.assertTrue(np.allclose(outputs.logits[0, :3] ,		A ,		atol=1e-4       )       )










'''simple docstring'''

import copy
import os
from typing import Union

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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json""",
    """BridgeTower/bridgetower-base-itm-mlm""": (
        """https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"""
    ),
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''bridgetower_vision_model'''


    def __init__(		self ,		A=768 ,		A=12 ,		A=3 ,		A=16 ,		A=288 ,		A=1 ,		A=1e-05 ,		A=False ,		A=True ,		A=False ,		**A ,		)		->   Dict:
        super().__init__(**A       )
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_channels
        _SCREAMING_SNAKE_CASE									=			patch_size
        _SCREAMING_SNAKE_CASE									=			image_size
        _SCREAMING_SNAKE_CASE									=			initializer_factor
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			stop_gradient
        _SCREAMING_SNAKE_CASE									=			share_layernorm
        _SCREAMING_SNAKE_CASE									=			remove_last_layer





    @classmethod
    def 			snake_case_(		cls ,		A ,		**A       )		->   "PretrainedConfig":
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			cls.get_config_dict(A ,		**A       )

        if config_dict.get("""model_type"""       ) == "bridgetower":
            _SCREAMING_SNAKE_CASE									=			config_dict["""text_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(A ,		**A       )




class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''bridgetower_text_model'''


    def __init__(		self ,		A=5_0265 ,		A=768 ,		A=12 ,		A=12 ,		A=1 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=514 ,		A=1 ,		A=1e-05 ,		A=1 ,		A=0 ,		A=2 ,		A="absolute" ,		A=True ,		**A ,		)		->   Union[str, Any]:
        super().__init__(**A       )

        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			initializer_factor
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			max_position_embeddings
        _SCREAMING_SNAKE_CASE									=			type_vocab_size
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			position_embedding_type
        _SCREAMING_SNAKE_CASE									=			use_cache
        _SCREAMING_SNAKE_CASE									=			pad_token_id
        _SCREAMING_SNAKE_CASE									=			bos_token_id
        _SCREAMING_SNAKE_CASE									=			eos_token_id





    @classmethod
    def 			snake_case_(		cls ,		A ,		**A       )		->   "PretrainedConfig":
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			cls.get_config_dict(A ,		**A       )

        if config_dict.get("""model_type"""       ) == "bridgetower":
            _SCREAMING_SNAKE_CASE									=			config_dict["""text_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(A ,		**A       )




class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''bridgetower'''


    def __init__(		self ,		A=True ,		A="gelu" ,		A=768 ,		A=1 ,		A=1e-05 ,		A=False ,		A="add" ,		A=12 ,		A=6 ,		A=False ,		A=False ,		A=None ,		A=None ,		**A ,		)		->   Tuple:
        # TODO: remove this once the Hub files are updated.
        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""text_config_dict""" ,		A       )
        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""vision_config_dict""" ,		A       )

        super().__init__(**A       )
        _SCREAMING_SNAKE_CASE									=			share_cross_modal_transformer_layers
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			initializer_factor
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			share_link_tower_layers
        _SCREAMING_SNAKE_CASE									=			link_tower_type
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			tie_word_embeddings
        _SCREAMING_SNAKE_CASE									=			init_layernorm_from_vision_encoder

        if text_config is None:
            _SCREAMING_SNAKE_CASE									=			{}
            logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."""       )

        if vision_config is None:
            _SCREAMING_SNAKE_CASE									=			{}
            logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."""       )

        _SCREAMING_SNAKE_CASE									=			BridgeTowerTextConfig(**A       )
        _SCREAMING_SNAKE_CASE									=			BridgeTowerVisionConfig(**A       )


    @classmethod
    def 			snake_case_(		cls ,		A ,		A ,		**A       )		->   int:
        return cls(text_config=text_config.to_dict() ,		vision_config=vision_config.to_dict() ,		**A       )





    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			copy.deepcopy(self.__dict__       )
        _SCREAMING_SNAKE_CASE									=			self.text_config.to_dict()
        _SCREAMING_SNAKE_CASE									=			self.vision_config.to_dict()
        _SCREAMING_SNAKE_CASE									=			self.__class__.model_type
        return output



