

# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput






class 	__magic_name__    (			lowerCAmelCase_      ):





			def __init__(		self					,  __snake_case					,  __snake_case					)   -> List[str]:




						'''simple docstring'''
						super().__init__()
						self.register_modules(unet=__snake_case					,  scheduler=__snake_case					)





			@torch.no_grad()
			def __call__(		self					,  __snake_case = 1					,  __snake_case = None					,  __snake_case = 50					,  __snake_case = "pil"					,  __snake_case = True					,  **__snake_case					,  )   -> Union[ImagePipelineOutput, Tuple]:




						'''simple docstring'''
						__a        =torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,  generator=__snake_case					,  )
						__a        =image.to(self.device					)

						# set step values
						self.scheduler.set_timesteps(__snake_case					)

						for t in self.progress_bar(self.scheduler.timesteps					):
									# 1. predict noise model_output
									__a        =self.unet(__snake_case					,  __snake_case					).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									__a        =self.scheduler.step(__snake_case					,  __snake_case					,  __snake_case					).prev_sample

						__a        =(image / 2 + 0.5).clamp(0					,  1					)
						__a        =image.cpu().permute(0					,  2					,  3					,  1					).numpy()
						if output_type == "pil":
									__a        =self.numpy_to_pil(__snake_case					)

						if not return_dict:
									return (image,), "This is a local test"

						return ImagePipelineOutput(images=__snake_case					), "This is a local test"









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					:			List[str]  				=			{
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'git_vision_model'





			def __init__(		self					,  __snake_case=768					,  __snake_case=3072					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3					,  __snake_case=224					,  __snake_case=16					,  __snake_case="quick_gelu"					,  __snake_case=1e-5					,  __snake_case=0.0					,  __snake_case=0.02					,  **__snake_case					,  )   -> int:




						'''simple docstring'''
						super().__init__(**__snake_case					)

						__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 		__magic_name__    (		cls					,  __snake_case					,  **__snake_case					)   -> "PretrainedConfig":




						'''simple docstring'''
						cls._set_token_in_kwargs(__snake_case					)

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

						# 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(__snake_case					,  **__snake_case					)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'git'





			def __init__(		self					,  __snake_case=None					,  __snake_case=3_0522					,  __snake_case=768					,  __snake_case=6					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=1024					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0					,  __snake_case="absolute"					,  __snake_case=True					,  __snake_case=False					,  __snake_case=101					,  __snake_case=102					,  __snake_case=None					,  **__snake_case					,  )   -> Optional[int]:




						'''simple docstring'''
						super().__init__(bos_token_id=__snake_case					,  eos_token_id=__snake_case					,  pad_token_id=__snake_case					,  **__snake_case					)

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

						__a        =GitVisionConfig(**__snake_case					)
						__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 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =copy.deepcopy(self.__dict__					)
						__a        =self.vision_config.to_dict()
						__a        =self.__class__.model_type
						return output






