


'''simple docstring'''





import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__lowerCamelCase												=						logging.get_logger(__name__)  # pylint: disable=invalid-name




class 				UpperCAmelCase			(			_snake_case						):


 def __init__(      self							:	Any			,       __lowerCamelCase							:	Optional[Any]			,       __lowerCamelCase							:	Optional[int]=7_6_8 ):
    super().__init__(__lowerCamelCase )
    UpperCAmelCase__	:Tuple      =      proj_size

    UpperCAmelCase__	:str      =      CLIPVisionModel(__lowerCamelCase )
    UpperCAmelCase__	:Optional[int]      =      PaintByExampleMapper(__lowerCamelCase )
    UpperCAmelCase__	:Union[str, Any]      =      nn.LayerNorm(config.hidden_size )
    UpperCAmelCase__	:List[Any]      =      nn.Linear(config.hidden_size			,       self.proj_size )

    # uncondition for scaling
    UpperCAmelCase__	:List[Any]      =      nn.Parameter(torch.randn((1, 1, self.proj_size) ) )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	Optional[Any]=False ):
    UpperCAmelCase__	:Optional[Any]      =      self.model(pixel_values=__lowerCamelCase )
    UpperCAmelCase__	:int      =      clip_output.pooler_output
    UpperCAmelCase__	:Tuple      =      self.mapper(latent_states[:, None] )
    UpperCAmelCase__	:Tuple      =      self.final_layer_norm(__lowerCamelCase )
    UpperCAmelCase__	:Optional[Any]      =      self.proj_out(__lowerCamelCase )
    if return_uncond_vector:
       return latent_states, self.uncond_vector

    return latent_states




class 				UpperCAmelCase			(			nn.Module						):


 def __init__(      self							:	Union[str, Any]			,       __lowerCamelCase							:	Optional[Any] ):
    super().__init__()
    UpperCAmelCase__	:Tuple      =      (config.num_hidden_layers + 1) // 5
    UpperCAmelCase__	:int      =      config.hidden_size
    UpperCAmelCase__	:str      =      1
    UpperCAmelCase__	:List[str]      =      nn.ModuleList(
        [
            BasicTransformerBlock(__lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase			,       activation_fn='''gelu'''			,       attention_bias=__lowerCamelCase )
            for _ in range(__lowerCamelCase )
        ] )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple			,       __lowerCamelCase							:	List[Any] ):
    for block in self.blocks:
       UpperCAmelCase__	:Optional[Any]      =      block(__lowerCamelCase )

    return hidden_states







'''simple docstring'''





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
    from ... import FeatureExtractionMixin, TensorType

__lowerCamelCase												=						logging.get_logger(__name__)

__lowerCamelCase												=						{
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					"imagegpt"
 UpperCAmelCase				=					["past_key_values"]
 UpperCAmelCase				=					{
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }


 def __init__(      self							:	List[Any]			,       __lowerCamelCase							:	List[Any]=5_1_2 + 1			,       __lowerCamelCase							:	Dict=3_2 * 3_2			,       __lowerCamelCase							:	List[str]=5_1_2			,       __lowerCamelCase							:	List[Any]=2_4			,       __lowerCamelCase							:	Any=8			,       __lowerCamelCase							:	Tuple=None			,       __lowerCamelCase							:	Any="quick_gelu"			,       __lowerCamelCase							:	Optional[int]=0.1			,       __lowerCamelCase							:	str=0.1			,       __lowerCamelCase							:	int=0.1			,       __lowerCamelCase							:	Tuple=1e-5			,       __lowerCamelCase							:	Dict=0.02			,       __lowerCamelCase							:	Tuple=True			,       __lowerCamelCase							:	str=True			,       __lowerCamelCase							:	Union[str, Any]=False			,       __lowerCamelCase							:	Union[str, Any]=False			,       __lowerCamelCase							:	Dict=False			,       **__lowerCamelCase							:	str			,       ):
    UpperCAmelCase__	:Dict      =      vocab_size
    UpperCAmelCase__	:str      =      n_positions
    UpperCAmelCase__	:Tuple      =      n_embd
    UpperCAmelCase__	:Dict      =      n_layer
    UpperCAmelCase__	:List[Any]      =      n_head
    UpperCAmelCase__	:str      =      n_inner
    UpperCAmelCase__	:Optional[Any]      =      activation_function
    UpperCAmelCase__	:str      =      resid_pdrop
    UpperCAmelCase__	:Optional[Any]      =      embd_pdrop
    UpperCAmelCase__	:Tuple      =      attn_pdrop
    UpperCAmelCase__	:int      =      layer_norm_epsilon
    UpperCAmelCase__	:List[Any]      =      initializer_range
    UpperCAmelCase__	:List[Any]      =      scale_attn_weights
    UpperCAmelCase__	:List[str]      =      use_cache
    UpperCAmelCase__	:Tuple      =      scale_attn_by_inverse_layer_idx
    UpperCAmelCase__	:Union[str, Any]      =      reorder_and_upcast_attn
    UpperCAmelCase__	:List[Any]      =      tie_word_embeddings

    super().__init__(tie_word_embeddings=__lowerCamelCase			,       **__lowerCamelCase )




class 				UpperCAmelCase			(			_snake_case						):


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    return OrderedDict(
        [
            ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
        ] )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	"FeatureExtractionMixin"			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	int = -1			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	Optional["TensorType"] = None			,       __lowerCamelCase							:	int = 3			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	int = 3_2			,       ):
    UpperCAmelCase__	:Tuple      =      self._generate_dummy_images(__lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase )
    UpperCAmelCase__	:Dict      =      dict(preprocessor(images=__lowerCamelCase			,       return_tensors=__lowerCamelCase ) )

    return inputs



