



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

_lowerCAmelCase	:   Dict    =   logging.get_logger(__name__)


@add_end_docstrings(SCREAMING_SNAKE_CASE )
class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,*a_    ,**a_						):







						"""simple docstring"""
						super().__init__(*a_    ,**a_						)
						requires_backends(self    ,'vision'						)
						self.check_model_type(a_						)
		def __call__(							self    ,a_    ,**a_						):







						"""simple docstring"""
						return super().__call__(a_    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,**a_						):







						"""simple docstring"""
						return {}, {}, {}
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  load_image(a_						)
						lowerCAmelCase__		  =  image.size
						lowerCAmelCase__		  =  self.image_processor(images=a_    ,return_tensors=self.framework						)
						return model_inputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model(**a_						)
						return model_outputs



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  model_outputs.predicted_depth
						lowerCAmelCase__		  =  torch.nn.functional.interpolate(
						    predicted_depth.unsqueeze(1						)    ,size=self.image_size[::-1]    ,mode='bicubic'    ,align_corners=a_						)
						lowerCAmelCase__		  =  prediction.squeeze().cpu().numpy()
						lowerCAmelCase__		  =  (output * 255 / np.max(a_						)).astype('uint8'						)
						lowerCAmelCase__		  =  Image.fromarray(a_						)
						lowerCAmelCase__		  =  {}
						lowerCAmelCase__		  =  predicted_depth
						lowerCAmelCase__		  =  depth
						return output_dict





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 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	42



class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE ):
		@register_to_config
		def __init__(							self    ,a_ = 16    ,a_ = 88    ,a_ = None    ,a_ = None    ,a_ = 1    ,a_ = 0.0    ,a_ = 32    ,a_ = None    ,a_ = False    ,a_ = None    ,a_ = "geglu"    ,a_ = True    ,a_ = True    ,):







						"""simple docstring"""
						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 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=None    ,a_=None    ,a_=None    ,a_=1    ,a_=None    ,a_ = True    ,):







						"""simple docstring"""
						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_						)
