


"""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_	   =     {
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """git_vision_model"""




      def __init__(    self:	Tuple						,				a:	Tuple=768						,				a:	Optional[int]=3072						,				a:	Dict=12						,				a:	Tuple=12						,				a:	Optional[int]=3						,				a:	Any=224						,				a:	Dict=16						,				a:	Optional[int]="quick_gelu"						,				a:	Tuple=1e-5						,				a:	Dict=0.0						,				a:	List[Any]=0.0_2						,				**a:	List[str]						,				):
       super().__init__(**a    )

       __lowerCamelCase		:							List[str]		 =						hidden_size
       __lowerCamelCase		:							Tuple		 =						intermediate_size
       __lowerCamelCase		:							Optional[Any]		 =						num_hidden_layers
       __lowerCamelCase		:							str		 =						num_attention_heads
       __lowerCamelCase		:							List[str]		 =						num_channels
       __lowerCamelCase		:							Optional[Any]		 =						patch_size
       __lowerCamelCase		:							str		 =						image_size
       __lowerCamelCase		:							Optional[int]		 =						initializer_range
       __lowerCamelCase		:							Union[str, Any]		 =						attention_dropout
       __lowerCamelCase		:							str		 =						layer_norm_eps
       __lowerCamelCase		:							Optional[Any]		 =						hidden_act




      @classmethod
      def 					_snake_case     (    cls:	Union[str, Any]						,				a:	Union[str, os.PathLike]						,				**a:	Union[str, Any]    ):
       cls._set_token_in_kwargs(a    )

       __lowerCamelCase		:							Optional[Any]		 =						cls.get_config_dict(a						,				**a    )

       # get the vision config dict if we are loading from GITConfig
       if config_dict.get('model_type'    ) == "git":
        __lowerCamelCase		:							str		 =						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(a						,				**a    )



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """git"""




      def __init__(    self:	List[Any]						,				a:	Tuple=None						,				a:	Any=3_0522						,				a:	List[Any]=768						,				a:	Union[str, Any]=6						,				a:	Tuple=12						,				a:	Union[str, Any]=3072						,				a:	List[Any]="gelu"						,				a:	Optional[int]=0.1						,				a:	str=0.1						,				a:	str=1024						,				a:	List[Any]=0.0_2						,				a:	Optional[Any]=1e-12						,				a:	Tuple=0						,				a:	Dict="absolute"						,				a:	List[str]=True						,				a:	Tuple=False						,				a:	int=101						,				a:	Any=102						,				a:	Union[str, Any]=None						,				**a:	Any						,				):
       super().__init__(bos_token_id=a						,				eos_token_id=a						,				pad_token_id=a						,				**a    )

       if vision_config is None:
        __lowerCamelCase		:							List[Any]		 =						{}
        logger.info('vision_config is None. initializing the GitVisionConfig with default values.'    )

       __lowerCamelCase		:							str		 =						GitVisionConfig(**a    )
       __lowerCamelCase		:							Any		 =						vocab_size
       __lowerCamelCase		:							List[Any]		 =						hidden_size
       __lowerCamelCase		:							List[Any]		 =						num_hidden_layers
       __lowerCamelCase		:							Any		 =						num_attention_heads
       __lowerCamelCase		:							Dict		 =						hidden_act
       __lowerCamelCase		:							Optional[int]		 =						intermediate_size
       __lowerCamelCase		:							Tuple		 =						hidden_dropout_prob
       __lowerCamelCase		:							Dict		 =						attention_probs_dropout_prob
       __lowerCamelCase		:							List[Any]		 =						max_position_embeddings
       __lowerCamelCase		:							List[Any]		 =						initializer_range
       __lowerCamelCase		:							List[Any]		 =						layer_norm_eps
       __lowerCamelCase		:							Dict		 =						position_embedding_type
       __lowerCamelCase		:							Optional[int]		 =						use_cache
       __lowerCamelCase		:							int		 =						tie_word_embeddings
       __lowerCamelCase		:							str		 =						num_image_with_embedding

       __lowerCamelCase		:							Optional[int]		 =						bos_token_id
       __lowerCamelCase		:							List[Any]		 =						eos_token_id




      def 					_snake_case     (    self:	Any    ):
       __lowerCamelCase		:							List[str]		 =						copy.deepcopy(self.__dict__    )
       __lowerCamelCase		:							Optional[int]		 =						self.vision_config.to_dict()
       __lowerCamelCase		:							int		 =						self.__class__.model_type
       return output





import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
       import tensorflow as tf
       from tensorflow.python.eager import context
       from tensorflow.python.framework import ops

       from transformers import GradientAccumulator, create_optimizer


@require_tf
class 	A_ (	unittest.TestCase   ):




      '''simple docstring'''


      def 					_snake_case     (    self:	Optional[int]						,				a:	int						,				a:	List[Any]						,				a:	Any    ):
       self.assertEqual(len(a    )						,				len(a    )    )
       for a, b in zip(a						,				a    ):
        self.assertAlmostEqual(a						,				a						,				delta=a    )




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							Union[str, Any]		 =						GradientAccumulator()
       accumulator([tf.constant([1.0, 2.0]    )]    )
       accumulator([tf.constant([-2.0, 1.0]    )]    )
       accumulator([tf.constant([-1.0, 2.0]    )]    )
       with self.assertRaises(a    ):
        accumulator([tf.constant([1.0, 1.0]    ), tf.constant([2.0, 2.0]    )]    )
       self.assertEqual(accumulator.step						,				3    )
       self.assertEqual(len(accumulator.gradients    )						,				1    )
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()						,				[-2.0, 5.0]						,				tol=1e-2    )
       accumulator.reset()
       self.assertEqual(accumulator.step						,				0    )
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()						,				[0.0, 0.0]						,				tol=1e-2    )




      def 					_snake_case     (    self:	str    ):
       __lowerCamelCase		:							Any		 =						None
       ops.enable_eager_execution_internal()
       __lowerCamelCase		:							List[str]		 =						tf.config.list_physical_devices('CPU'    )
       if len(a    ) == 1:
        tf.config.set_logical_device_configuration(
            physical_devices[0]						,				[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]    )
       __lowerCamelCase		:							Dict		 =						tf.config.list_logical_devices(device_type='CPU'    )
       __lowerCamelCase		:							List[Any]		 =						tf.distribute.MirroredStrategy(devices=devices[:2]    )

       with strategy.scope():
        __lowerCamelCase		:							Optional[Any]		 =						GradientAccumulator()
        __lowerCamelCase		:							int		 =						tf.Variable([4.0, 3.0]    )
        __lowerCamelCase ,					__lowerCamelCase		:							Any		 =						create_optimizer(5e-5						,				10						,				5    )
        __lowerCamelCase		:							Any		 =						tf.Variable([0.0, 0.0]						,				trainable=a    )

       def accumulate_on_replica(a:	str    ):
        accumulator([gradient]    )

       def apply_on_replica():
        optimizer.apply_gradients(list(zip(accumulator.gradients						,				[variable]    )    )    )

       @tf.function
       def accumulate(a:	List[Any]						,				a:	List[str]    ):
        with strategy.scope():
         __lowerCamelCase		:							Optional[int]		 =						strategy.experimental_local_results(a    )
         local_variables[0].assign(a    )
         local_variables[1].assign(a    )
         strategy.run(a						,				args=(gradient_placeholder,)    )

       @tf.function
       def apply_grad():
        with strategy.scope():
         strategy.run(a    )

       def _check_local_values(a:	str						,				a:	Union[str, Any]    ):
        __lowerCamelCase		:							Optional[Any]		 =						strategy.experimental_local_results(accumulator._gradients[0]    )
        self.assertListAlmostEqual(values[0].value()						,				a						,				tol=1e-2    )
        self.assertListAlmostEqual(values[1].value()						,				a						,				tol=1e-2    )

       accumulate([1.0, 2.0]						,				[-1.0, 1.0]    )
       accumulate([3.0, -1.0]						,				[-1.0, -1.0]    )
       accumulate([-2.0, 2.0]						,				[3.0, -2.0]    )
       self.assertEqual(accumulator.step						,				3    )
       _check_local_values([2.0, 3.0]						,				[1.0, -2.0]    )
       apply_grad()
       self.assertListAlmostEqual(variable.value()						,				[4.0, 3.0]						,				tol=1e-2    )
       accumulator.reset()
       self.assertEqual(accumulator.step						,				0    )
       _check_local_values([0.0, 0.0]						,				[0.0, 0.0]    )

