






'''simple docstring'''

import copy
import os
from typing import Union

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


UpperCamelCase							=logging.get_logger(__name__)

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




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Dict      =						'''git_vision_model'''
 def __init__(							self			,     __lowerCAmelCase=7_68			,     __lowerCAmelCase=30_72			,     __lowerCAmelCase=12			,     __lowerCAmelCase=12			,     __lowerCAmelCase=3			,     __lowerCAmelCase=2_24			,     __lowerCAmelCase=16			,     __lowerCAmelCase="quick_gelu"			,     __lowerCAmelCase=1E-5			,     __lowerCAmelCase=0.0			,     __lowerCAmelCase=0.02			,     **__lowerCAmelCase			,     ):
       super().__init__(**__lowerCAmelCase )

       UpperCamelCase_				:   Dict					    =							hidden_size
       UpperCamelCase_				:   Optional[int]					    =							intermediate_size
       UpperCamelCase_				:   str					    =							num_hidden_layers
       UpperCamelCase_				:   Dict					    =							num_attention_heads
       UpperCamelCase_				:   Optional[Any]					    =							num_channels
       UpperCamelCase_				:   Tuple					    =							patch_size
       UpperCamelCase_				:   str					    =							image_size
       UpperCamelCase_				:   Optional[int]					    =							initializer_range
       UpperCamelCase_				:   Optional[Any]					    =							attention_dropout
       UpperCamelCase_				:   Tuple					    =							layer_norm_eps
       UpperCamelCase_				:   Optional[int]					    =							hidden_act





 @classmethod
 def   _UpperCAmelCase       (							cls			,     __lowerCAmelCase			,     **__lowerCAmelCase ):
       cls._set_token_in_kwargs(__lowerCAmelCase )

       UpperCamelCase_    ,			UpperCamelCase_				:   Union[str, Any]					    =							cls.get_config_dict(__lowerCAmelCase			,     **__lowerCAmelCase )

       # get the vision config dict if we are loading from GITConfig
       if config_dict.get("""model_type""" ) == "git":
             UpperCamelCase_				:   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(__lowerCAmelCase			,     **__lowerCAmelCase )






class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Optional[Any]      =						'''git'''
 def __init__(							self			,     __lowerCAmelCase=None			,     __lowerCAmelCase=3_05_22			,     __lowerCAmelCase=7_68			,     __lowerCAmelCase=6			,     __lowerCAmelCase=12			,     __lowerCAmelCase=30_72			,     __lowerCAmelCase="gelu"			,     __lowerCAmelCase=0.1			,     __lowerCAmelCase=0.1			,     __lowerCAmelCase=10_24			,     __lowerCAmelCase=0.02			,     __lowerCAmelCase=1E-12			,     __lowerCAmelCase=0			,     __lowerCAmelCase="absolute"			,     __lowerCAmelCase=True			,     __lowerCAmelCase=False			,     __lowerCAmelCase=1_01			,     __lowerCAmelCase=1_02			,     __lowerCAmelCase=None			,     **__lowerCAmelCase			,     ):
       super().__init__(bos_token_id=__lowerCAmelCase			,     eos_token_id=__lowerCAmelCase			,     pad_token_id=__lowerCAmelCase			,     **__lowerCAmelCase )

       if vision_config is None:
             UpperCamelCase_				:   Union[str, Any]					    =							{}
             logger.info("""vision_config is None. initializing the GitVisionConfig with default values.""" )

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

       UpperCamelCase_				:   str					    =							bos_token_id
       UpperCamelCase_				:   Tuple					    =							eos_token_id





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Dict					    =							copy.deepcopy(self.__dict__ )
       UpperCamelCase_				:   Tuple					    =							self.vision_config.to_dict()
       UpperCamelCase_				:   str					    =							self.__class__.model_type
       return output














'''simple docstring'''

import torch

from diffusers import StableDiffusionPipeline


UpperCamelCase							="path-to-your-trained-model"
UpperCamelCase							=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

UpperCamelCase							="A photo of sks dog in a bucket"
UpperCamelCase							=pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")






