





'''simple docstring'''


from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin

@dataclass
class    lowerCAmelCase_		(		UpperCamelCase__		):
 __lowerCamelCase						:							List[Any]         =					42







class    lowerCAmelCase_		(		UpperCamelCase__						,UpperCamelCase__		):




 @register_to_config
 def __init__(	self			,     _lowerCAmelCase = 16			,     _lowerCAmelCase = 88			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = 1			,     _lowerCAmelCase = 0.0			,     _lowerCAmelCase = 32			,     _lowerCAmelCase = None			,     _lowerCAmelCase = False			,     _lowerCAmelCase = None			,     _lowerCAmelCase = "geglu"			,     _lowerCAmelCase = True			,     _lowerCAmelCase = True			,     )    ->							List[Any]:
     super().__init__()
     _lowerCAmelCase				=      num_attention_heads
     _lowerCAmelCase				=      attention_head_dim
     _lowerCAmelCase				=      num_attention_heads * attention_head_dim

     _lowerCAmelCase				=      in_channels

     _lowerCAmelCase				=      torch.nn.GroupNorm(num_groups=__A			,     num_channels=__A			,     eps=1E-6			,     affine=__A		)
     _lowerCAmelCase				=      nn.Linear(__A			,     __A		)

     # 3. Define transformers blocks
     _lowerCAmelCase				=      nn.ModuleList(
         [
             BasicTransformerBlock(
                 __A			,     __A			,     __A			,     dropout=__A			,     cross_attention_dim=__A			,     activation_fn=__A			,     attention_bias=__A			,     double_self_attention=__A			,     norm_elementwise_affine=__A			,     )
             for d in range(__A		)
         ]		)

     _lowerCAmelCase				=      nn.Linear(__A			,     __A		)





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     _lowerCAmelCase=1			,     _lowerCAmelCase=None			,     _lowerCAmelCase = True			,     )    ->							List[str]:
     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      hidden_states.shape
     _lowerCAmelCase				=      batch_frames // num_frames

     _lowerCAmelCase				=      hidden_states

     _lowerCAmelCase				=      hidden_states[None, :].reshape(__A			,     __A			,     __A			,     __A			,     __A		)
     _lowerCAmelCase				=      hidden_states.permute(0			,     2			,     1			,     3			,     4		)

     _lowerCAmelCase				=      self.norm(__A		)
     _lowerCAmelCase				=      hidden_states.permute(0			,     3			,     4			,     2			,     1		).reshape(batch_size * height * width			,     __A			,     __A		)

     _lowerCAmelCase				=      self.proj_in(__A		)

     # 2. Blocks
     for block in self.transformer_blocks:
         _lowerCAmelCase				=      block(
             __A			,     encoder_hidden_states=__A			,     timestep=__A			,     cross_attention_kwargs=__A			,     class_labels=__A			,     )

     # 3. Output
     _lowerCAmelCase				=      self.proj_out(__A		)
     _lowerCAmelCase				=      (
         hidden_states[None, None, :]
         .reshape(__A			,     __A			,     __A			,     __A			,     __A		)
         .permute(0			,     3			,     4			,     1			,     2		)
         .contiguous()
     )
     _lowerCAmelCase				=      hidden_states.reshape(__A			,     __A			,     __A			,     __A		)

     _lowerCAmelCase				=      hidden_states + residual

     if not return_dict:
         return (output,)

     return TransformerTemporalModelOutput(sample=__A		)











'''simple docstring'''


from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
       from PIL import Image

       from ..image_utils import load_image

if is_torch_available():
       import torch

       from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

@add_end_docstrings(__magic_name__		)
class    lowerCAmelCase_		(		__magic_name__		):




 def __init__(	self			,     *_lowerCAmelCase			,     **_lowerCAmelCase		)    ->							List[Any]:
     super().__init__(*_lowerCAmelCase			,     **_lowerCAmelCase		)
     requires_backends(self			,     "vision"		)
     self.check_model_type(_lowerCAmelCase		)




 def __call__(	self			,     _lowerCAmelCase			,     **_lowerCAmelCase		)    ->							str:
     return super().__call__(_lowerCAmelCase			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							Tuple:
     return {}, {}, {}




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      load_image(_lowerCAmelCase		)
     _lowerCAmelCase				=      image.size
     _lowerCAmelCase				=      self.image_processor(images=_lowerCAmelCase			,     return_tensors=self.framework		)
     return model_inputs




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Tuple:
     _lowerCAmelCase				=      self.model(**_lowerCAmelCase		)
     return model_outputs





 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Optional[Any]:
     _lowerCAmelCase				=      model_outputs.predicted_depth
     _lowerCAmelCase				=      torch.nn.functional.interpolate(
         predicted_depth.unsqueeze(1		)			,     size=self.image_size[::-1]			,     mode="bicubic"			,     align_corners=_lowerCAmelCase		)
     _lowerCAmelCase				=      prediction.squeeze().cpu().numpy()
     _lowerCAmelCase				=      (output * 255 / np.max(_lowerCAmelCase		)).astype("uint8"		)
     _lowerCAmelCase				=      Image.fromarray(_lowerCAmelCase		)
     _lowerCAmelCase				=      {}
     _lowerCAmelCase				=      predicted_depth
     _lowerCAmelCase				=      depth
     return output_dict


