



import copy
import os
from typing import Union

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


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)

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

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  "git_vision_model"







						def __init__(	self      :			Optional[int]	,	__lowerCamelCase      :			str=7_68	,	__lowerCamelCase      :			Dict=30_72	,	__lowerCamelCase      :			str=12	,	__lowerCamelCase      :			int=12	,	__lowerCamelCase      :			Tuple=3	,	__lowerCamelCase      :			Any=2_24	,	__lowerCamelCase      :			Any=16	,	__lowerCamelCase      :			Optional[Any]="quick_gelu"	,	__lowerCamelCase      :			int=1e-5	,	__lowerCamelCase      :			Tuple=0.0	,	__lowerCamelCase      :			List[Any]=0.02	,	**__lowerCamelCase      :			List[str]	,	)      ->				Optional[Any]:
									super().__init__(**__lowerCamelCase )

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







						@classmethod
						def 			SCREAMING_SNAKE_CASE__		(	cls      :			Optional[Any]	,	__lowerCamelCase      :			Union[str, os.PathLike]	,	**__lowerCamelCase      :			Union[str, Any] )      ->				"PretrainedConfig":
									cls._set_token_in_kwargs(__lowerCamelCase )

									A       , A							:					List[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":
												A							:					List[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(__lowerCamelCase	,	**__lowerCamelCase )







class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  "git"







						def __init__(	self      :			int	,	__lowerCamelCase      :			Tuple=None	,	__lowerCamelCase      :			Any=3_05_22	,	__lowerCamelCase      :			Tuple=7_68	,	__lowerCamelCase      :			Tuple=6	,	__lowerCamelCase      :			Optional[Any]=12	,	__lowerCamelCase      :			List[Any]=30_72	,	__lowerCamelCase      :			Optional[int]="gelu"	,	__lowerCamelCase      :			Any=0.1	,	__lowerCamelCase      :			Optional[Any]=0.1	,	__lowerCamelCase      :			Optional[Any]=10_24	,	__lowerCamelCase      :			Union[str, Any]=0.02	,	__lowerCamelCase      :			List[str]=1e-12	,	__lowerCamelCase      :			Any=0	,	__lowerCamelCase      :			Any="absolute"	,	__lowerCamelCase      :			List[str]=True	,	__lowerCamelCase      :			Any=False	,	__lowerCamelCase      :			Any=1_01	,	__lowerCamelCase      :			Union[str, Any]=1_02	,	__lowerCamelCase      :			Tuple=None	,	**__lowerCamelCase      :			Union[str, Any]	,	)      ->				List[Any]:
									super().__init__(bos_token_id=__lowerCamelCase	,	eos_token_id=__lowerCamelCase	,	pad_token_id=__lowerCamelCase	,	**__lowerCamelCase )

									if vision_config is None:
												A							:					Tuple              =						{}
												logger.info("vision_config is None. initializing the GitVisionConfig with default values." )

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

									A							:					List[str]              =						bos_token_id
									A							:					Dict              =						eos_token_id







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				str:
									A							:					Any              =						copy.deepcopy(self.__dict__ )
									A							:					Dict              =						self.vision_config.to_dict()
									A							:					Dict              =						self.__class__.model_type
									return output




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch

@require_torch
class 	lowerCamelCase_    :






						'''simple docstring'''







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				str:
									return self.get_dummy_input()







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[Any]:
									if self.block_type == "down":
												return (4, 32, 16, 16)
									elif self.block_type == "mid":
												return (4, 32, 32, 32)
									elif self.block_type == "up":
												return (4, 32, 64, 64)

									raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.""" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			Union[str, Any]=True	,	__lowerCamelCase      :			int=False	,	__lowerCamelCase      :			int=False	,	__lowerCamelCase      :			Optional[int]=False	,	)      ->				Dict:
									A							:					Optional[Any]              =						4
									A							:					List[str]              =						32
									A							:					Any              =						(32, 32)

									A							:					str              =						torch.manual_seed(0 )
									A							:					int              =						torch.device(__lowerCamelCase )
									A							:					List[str]              =						(batch_size, num_channels) + sizes
									A							:					Dict              =						randn_tensor(__lowerCamelCase	,	generator=__lowerCamelCase	,	device=__lowerCamelCase )
									A							:					int              =						{"hidden_states": hidden_states}

									if include_temb:
												A							:					Any              =						1_28
												A							:					List[str]              =						randn_tensor((batch_size, temb_channels)	,	generator=__lowerCamelCase	,	device=__lowerCamelCase )

									if include_res_hidden_states_tuple:
												A							:					str              =						torch.manual_seed(1 )
												A							:					Tuple              =						(randn_tensor(__lowerCamelCase	,	generator=__lowerCamelCase	,	device=__lowerCamelCase ),)

									if include_encoder_hidden_states:
												A							:					Dict              =						floats_tensor((batch_size, 32, 32) ).to(__lowerCamelCase )

									if include_skip_sample:
												A							:					Optional[int]              =						randn_tensor(((batch_size, 3) + sizes)	,	generator=__lowerCamelCase	,	device=__lowerCamelCase )

									return dummy_input







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Union[str, Any]:
									A							:					Dict              =						{
									    "in_channels": 32,
									    "out_channels": 32,
									    "temb_channels": 1_28,
									}
									if self.block_type == "up":
												A							:					Dict              =						32

									if self.block_type == "mid":
												init_dict.pop("out_channels" )

									A							:					str              =						self.dummy_input
									return init_dict, inputs_dict







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			Optional[int] )      ->				Union[str, Any]:
									A       , A							:					str              =						self.prepare_init_args_and_inputs_for_common()
									A							:					List[Any]              =						self.block_class(**__lowerCamelCase )
									unet_block.to(__lowerCamelCase )
									unet_block.eval()

									with torch.no_grad():
												A							:					int              =						unet_block(**__lowerCamelCase )

									if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												A							:					Union[str, Any]              =						output[0]

									self.assertEqual(output.shape	,	self.output_shape )

									A							:					Any              =						output[0, -1, -3:, -3:]
									A							:					Union[str, Any]              =						torch.tensor(__lowerCamelCase ).to(__lowerCamelCase )
									assert torch_all_close(output_slice.flatten()	,	__lowerCamelCase	,	atol=5e-3 )







						@unittest.skipIf(torch_device == "mps"	,	"Training is not supported in mps" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Dict:
									A       , A							:					Tuple              =						self.prepare_init_args_and_inputs_for_common()
									A							:					str              =						self.block_class(**__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.train()
									A							:					Optional[int]              =						model(**__lowerCamelCase )

									if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												A							:					Optional[Any]              =						output[0]

									A							:					List[str]              =						torch.device(__lowerCamelCase )
									A							:					List[str]              =						randn_tensor(output.shape	,	device=__lowerCamelCase )
									A							:					Dict              =						torch.nn.functional.mse_loss(__lowerCamelCase	,	__lowerCamelCase )
									loss.backward()