

import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


A__						=							logging.get_logger(__name__)

A__						=							{
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}




class 				__UpperCamelCase				(					__A			):
	_lowercase  :   Tuple       				=  "segformer"







	def __init__(   self: Dict  , __UpperCamelCase: Union[str, Any]=3  , __UpperCamelCase: Optional[int]=4  , __UpperCamelCase: Any=[2, 2, 2, 2]  , __UpperCamelCase: Any=[8, 4, 2, 1]  , __UpperCamelCase: Dict=[32, 64, 1_60, 2_56]  , __UpperCamelCase: List[str]=[7, 3, 3, 3]  , __UpperCamelCase: Optional[Any]=[4, 2, 2, 2]  , __UpperCamelCase: Optional[Any]=[1, 2, 5, 8]  , __UpperCamelCase: Optional[int]=[4, 4, 4, 4]  , __UpperCamelCase: List[str]="gelu"  , __UpperCamelCase: List[Any]=0.0  , __UpperCamelCase: List[str]=0.0  , __UpperCamelCase: Optional[Any]=0.1  , __UpperCamelCase: List[Any]=0.02  , __UpperCamelCase: Union[str, Any]=0.1  , __UpperCamelCase: Union[str, Any]=1E-6  , __UpperCamelCase: Any=2_56  , __UpperCamelCase: Dict=2_55  , **__UpperCamelCase: Any  , ):

						'''simple docstring'''





						super().__init__(**__UpperCamelCase      )

						if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
											warnings.warn(
											    'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
											    ' removed, as the behaviour will default to that of reshape_last_stage = True.'  , __UpperCamelCase  , )

						__magic_name__							=    num_channels
						__magic_name__							=    num_encoder_blocks
						__magic_name__							=    depths
						__magic_name__							=    sr_ratios
						__magic_name__							=    hidden_sizes
						__magic_name__							=    patch_sizes
						__magic_name__							=    strides
						__magic_name__							=    mlp_ratios
						__magic_name__							=    num_attention_heads
						__magic_name__							=    hidden_act
						__magic_name__							=    hidden_dropout_prob
						__magic_name__							=    attention_probs_dropout_prob
						__magic_name__							=    classifier_dropout_prob
						__magic_name__							=    initializer_range
						__magic_name__							=    drop_path_rate
						__magic_name__							=    layer_norm_eps
						__magic_name__							=    decoder_hidden_size
						__magic_name__							=    kwargs.get('reshape_last_stage'  , __UpperCamelCase      )
						__magic_name__							=    semantic_loss_ignore_index




class 				__UpperCamelCase				(					__A			):
	_lowercase  :   List[str]       				=  version.parse("1.11"			)







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						return OrderedDict(
						    [
						        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
						    ]      )







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						return 1E-4







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						return 12







import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			):







	@register_to_config
	def __init__(   self: Optional[Any]  , *,
	    __UpperCamelCase: int = 4  , __UpperCamelCase: int = 7_68  , __UpperCamelCase: int  , __UpperCamelCase: Any  , ):

						'''simple docstring'''





						super().__init__()

						__magic_name__							=    nn.Parameter(torch.zeros(__UpperCamelCase      )      )

						# parameters for additional clip time embeddings
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )

						# parameters for encoder hidden states
						__magic_name__							=    clip_extra_context_tokens
						__magic_name__							=    nn.Linear(
						    __UpperCamelCase  , self.clip_extra_context_tokens * cross_attention_dim      )
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    nn.LayerNorm(__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , *, __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: Any      ):

						'''simple docstring'''





						if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__magic_name__							=    image_embeddings.shape[0]
											__magic_name__							=    self.learned_classifier_free_guidance_embeddings.unsqueeze(0      )
											__magic_name__							=    classifier_free_guidance_embeddings.expand(
											    __UpperCamelCase  , -1      )
											__magic_name__							=    torch.cat([classifier_free_guidance_embeddings, image_embeddings]  , dim=0      )

						# The image embeddings batch size and the text embeddings batch size are equal
						assert image_embeddings.shape[0] == prompt_embeds.shape[0]

						__magic_name__							=    prompt_embeds.shape[0]

						# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
						# adding CLIP embeddings to the existing timestep embedding, ...
						__magic_name__							=    self.embedding_proj(__UpperCamelCase      )
						__magic_name__							=    self.clip_image_embeddings_project_to_time_embeddings(__UpperCamelCase      )
						__magic_name__							=    time_projected_image_embeddings + time_projected_prompt_embeds

						# ... and by projecting CLIP embeddings into four
						# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
						__magic_name__							=    self.clip_extra_context_tokens_proj(__UpperCamelCase      )
						__magic_name__							=    clip_extra_context_tokens.reshape(__UpperCamelCase  , -1  , self.clip_extra_context_tokens      )
						__magic_name__							=    clip_extra_context_tokens.permute(0  , 2  , 1      )

						__magic_name__							=    self.encoder_hidden_states_proj(__UpperCamelCase      )
						__magic_name__							=    self.text_encoder_hidden_states_norm(__UpperCamelCase      )
						__magic_name__							=    torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]  , dim=1      )

						return text_encoder_hidden_states, additive_clip_time_embeddings




