import copy
import os
from typing import Union

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


lowerCamelCase_    :		Optional[int]							=	logging.get_logger(__name__)

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


class 						a__					(       __snake_case		):
			A__    :       Optional[Any]        =      'git_vision_model'



			def __init__(						self				,						UpperCAmelCase=7_6_8				,						UpperCAmelCase=3_0_7_2				,						UpperCAmelCase=1_2				,						UpperCAmelCase=1_2				,						UpperCAmelCase=3				,						UpperCAmelCase=2_2_4				,						UpperCAmelCase=1_6				,						UpperCAmelCase="quick_gelu"				,						UpperCAmelCase=1e-5				,						UpperCAmelCase=0.0				,						UpperCAmelCase=0.02				,						**UpperCAmelCase				,						)			->   List[str]:
								super().__init__(**UpperCAmelCase   )

								__a		 =       hidden_size
								__a		 =       intermediate_size
								__a		 =       num_hidden_layers
								__a		 =       num_attention_heads
								__a		 =       num_channels
								__a		 =       patch_size
								__a		 =       image_size
								__a		 =       initializer_range
								__a		 =       attention_dropout
								__a		 =       layer_norm_eps
								__a		 =       hidden_act







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						UpperCAmelCase				,						**UpperCAmelCase   )			->   "PretrainedConfig":
								cls._set_token_in_kwargs(UpperCAmelCase   )

								__a ,				__a		 =       cls.get_config_dict(UpperCAmelCase				,						**UpperCAmelCase   )

								# get the vision config dict if we are loading from GITConfig
								if config_dict.get('model_type'   ) == "git":
													__a		 =       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 						a__					(       __snake_case		):
			A__    :       Dict        =      'git'



			def __init__(						self				,						UpperCAmelCase=None				,						UpperCAmelCase=3_0_5_2_2				,						UpperCAmelCase=7_6_8				,						UpperCAmelCase=6				,						UpperCAmelCase=1_2				,						UpperCAmelCase=3_0_7_2				,						UpperCAmelCase="gelu"				,						UpperCAmelCase=0.1				,						UpperCAmelCase=0.1				,						UpperCAmelCase=1_0_2_4				,						UpperCAmelCase=0.02				,						UpperCAmelCase=1e-12				,						UpperCAmelCase=0				,						UpperCAmelCase="absolute"				,						UpperCAmelCase=True				,						UpperCAmelCase=False				,						UpperCAmelCase=1_0_1				,						UpperCAmelCase=1_0_2				,						UpperCAmelCase=None				,						**UpperCAmelCase				,						)			->   Any:
								super().__init__(bos_token_id=UpperCAmelCase				,						eos_token_id=UpperCAmelCase				,						pad_token_id=UpperCAmelCase				,						**UpperCAmelCase   )

								if vision_config is None:
													__a		 =       {}
													logger.info('vision_config is None. initializing the GitVisionConfig with default values.'   )

								__a		 =       GitVisionConfig(**UpperCAmelCase   )
								__a		 =       vocab_size
								__a		 =       hidden_size
								__a		 =       num_hidden_layers
								__a		 =       num_attention_heads
								__a		 =       hidden_act
								__a		 =       intermediate_size
								__a		 =       hidden_dropout_prob
								__a		 =       attention_probs_dropout_prob
								__a		 =       max_position_embeddings
								__a		 =       initializer_range
								__a		 =       layer_norm_eps
								__a		 =       position_embedding_type
								__a		 =       use_cache
								__a		 =       tie_word_embeddings
								__a		 =       num_image_with_embedding

								__a		 =       bos_token_id
								__a		 =       eos_token_id







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       copy.deepcopy(self.__dict__   )
								__a		 =       self.vision_config.to_dict()
								__a		 =       self.__class__.model_type
								return output





from datetime import datetime

import matplotlib.pyplot as plt
import torch
def 							lowerCAmelCase(     __lowerCamelCase						):
					for param in module.parameters():
										__a		 =       False
def 							lowerCAmelCase(     ):
					__a		 =       'cuda' if torch.cuda.is_available() else 'cpu'
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										__a		 =       'mps'
					if device == "mps":
										print(
										    'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
										    ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
										    ' with generations.'						)
					return device
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       plt.imshow(__lowerCamelCase						)
					fig.axes.get_xaxis().set_visible(__lowerCamelCase						)
					fig.axes.get_yaxis().set_visible(__lowerCamelCase						)
					plt.show()



def 							lowerCAmelCase(     ):
					__a		 =       datetime.now()
					__a		 =       current_time.strftime('%H:%M:%S'						)
					return timestamp




