




from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class     __lowerCAmelCase :

			"""simple docstring"""







			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42


			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
							assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
							assert len(self.x.shape							) == len(self.y.shape							) == len(self.z.shape							) == len(self.origin.shape							) == 2


			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							return torch.from_numpy(np.array([self.width, self.height]			,						dtype=np.floataa							)							)


			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''simple docstring'''
							return torch.from_numpy(np.array([self.x_fov, self.y_fov]			,						dtype=np.floataa							)							)


			def       lowercase_    (      self							)    ->      torch.Tensor:



							'''simple docstring'''
							__lowerCamelCase		       =    torch.arange(self.height * self.width							)
							__lowerCamelCase		       =    torch.stack(
							    [
							        pixel_indices % self.width,
							        torch.div(lowerCamelCase__			,						self.width			,						rounding_mode='trunc'							),
							    ]			,						axis=1			,						)
							return coords


			@property
			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase       ,						*__lowerCamelCase		       =    self.shape
							__lowerCamelCase		       =    int(np.prod(lowerCamelCase__							)							)

							__lowerCamelCase		       =    self.get_image_coords()
							__lowerCamelCase		       =    torch.broadcast_to(coords.unsqueeze(0							)			,						[batch_size * inner_batch_size, *coords.shape]							)
							__lowerCamelCase		       =    self.get_camera_rays(lowerCamelCase__							)

							__lowerCamelCase		       =    rays.view(lowerCamelCase__			,						inner_batch_size * self.height * self.width			,						2			,						3							)

							return rays


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      torch.Tensor:



							'''simple docstring'''
							__lowerCamelCase       ,						*__lowerCamelCase       ,						__lowerCamelCase		       =    coords.shape
							assert n_coords == 2
							assert batch_size == self.origin.shape[0]

							__lowerCamelCase		       =    coords.view(lowerCamelCase__			,						-1			,						2							)

							__lowerCamelCase		       =    self.resolution()
							__lowerCamelCase		       =    self.fov()

							__lowerCamelCase		       =    (flat.float() / (res - 1)) * 2 - 1
							__lowerCamelCase		       =    fracs * torch.tan(fov / 2							)

							__lowerCamelCase		       =    fracs.view(lowerCamelCase__			,						-1			,						2							)
							__lowerCamelCase		       =    (
							    self.z.view(lowerCamelCase__			,						1			,						3							)
							    + self.x.view(lowerCamelCase__			,						1			,						3							) * fracs[:, :, :1]
							    + self.y.view(lowerCamelCase__			,						1			,						3							) * fracs[:, :, 1:]
							)
							__lowerCamelCase		       =    directions / directions.norm(dim=-1			,						keepdim=lowerCamelCase__							)
							__lowerCamelCase		       =    torch.stack(
							    [
							        torch.broadcast_to(self.origin.view(lowerCamelCase__			,						1			,						3							)			,						[batch_size, directions.shape[1], 3]							),
							        directions,
							    ]			,						dim=2			,						)
							return rays.view(lowerCamelCase__			,						*lowerCamelCase__			,						2			,						3							)






			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      "DifferentiableProjectiveCamera":



							'''simple docstring'''
							assert width * self.height == height * self.width, "The aspect ratio should not change."
							return DifferentiableProjectiveCamera(
							    origin=self.origin			,						x=self.x			,						y=self.y			,						z=self.z			,						width=lowerCamelCase__			,						height=lowerCamelCase__			,						x_fov=self.x_fov			,						y_fov=self.y_fov			,						)







def 			lowerCamelCase_		(       UpperCamelCase__				:      int      )     ->       DifferentiableProjectiveCamera:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				for theta in np.linspace(0 ,     2 * np.pi ,     num=20      ):
								__lowerCamelCase		       =    np.array([np.sin(UpperCamelCase__      ), np.cos(UpperCamelCase__      ), -0.5]      )
								z /= np.sqrt(np.sum(z**2      )      )
								__lowerCamelCase		       =    -z * 4
								__lowerCamelCase		       =    np.array([np.cos(UpperCamelCase__      ), -np.sin(UpperCamelCase__      ), 0.0]      )
								__lowerCamelCase		       =    np.cross(UpperCamelCase__ ,     UpperCamelCase__      )
								origins.append(UpperCamelCase__      )
								xs.append(UpperCamelCase__      )
								ys.append(UpperCamelCase__      )
								zs.append(UpperCamelCase__      )
				return DifferentiableProjectiveCamera(
				    origin=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     x=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     y=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     z=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     width=UpperCamelCase__ ,     height=UpperCamelCase__ ,     x_fov=0.7 ,     y_fov=0.7 ,     shape=(1, len(UpperCamelCase__      )) ,     )



"""simple docstring"""




from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    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_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
     import torch

     from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

_a      :		Dict           =   logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE_		)
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     *a__				,     **a__  ):
       super().__init__(*a__				,     **a__  )
       requires_backends(self				,     """vision"""  )
       self.check_model_type(
           TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING  )






     def     __A  (		self				,     a__=None				,     a__=None				,     a__=None  ):
       _lowerCAmelCase						:       List[str]  			=     {}
       _lowerCAmelCase						:       Union[str, Any]  			=     {}

       if prompt is not None:
         _lowerCAmelCase						:       List[Any]  			=     prompt

       if generate_kwargs is not None:
         _lowerCAmelCase						:       List[str]  			=     generate_kwargs
       if max_new_tokens is not None:
         if "generate_kwargs" not in forward_kwargs:
           _lowerCAmelCase						:       str  			=     {}
         if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
           raise ValueError(
               """'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
               """ please use only one"""  )
         _lowerCAmelCase						:       Optional[Any]  			=     max_new_tokens
       return preprocess_params, forward_kwargs, {}






     def __call__(		self				,     a__				,     **a__  ):
       return super().__call__(a__				,     **a__  )






     def     __A  (		self				,     a__				,     a__=None  ):
       _lowerCAmelCase						:       Tuple  			=     load_image(a__  )

       if prompt is not None:
         if not isinstance(a__				,     a__  ):
           raise ValueError(
               F"Received an invalid text input, got - {type(a__  )} - but expected a single string. "
               """Note also that one single text can be provided for conditional image to text generation."""  )

         _lowerCAmelCase						:       Optional[int]  			=     self.model.config.model_type

         if model_type == "git":
           _lowerCAmelCase						:       Optional[Any]  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )
           _lowerCAmelCase						:       List[str]  			=     self.tokenizer(text=a__				,     add_special_tokens=a__  ).input_ids
           _lowerCAmelCase						:       Union[str, Any]  			=     [self.tokenizer.cls_token_id] + input_ids
           _lowerCAmelCase						:       Dict  			=     torch.tensor(a__  ).unsqueeze(0  )
           model_inputs.update({"""input_ids""": input_ids}  )

         elif model_type == "pix2struct":
           _lowerCAmelCase						:       Tuple  			=     self.image_processor(images=a__				,     header_text=a__				,     return_tensors=self.framework  )

         elif model_type != "vision-encoder-decoder":
           # vision-encoder-decoder does not support conditional generation
           _lowerCAmelCase						:       Optional[int]  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )
           _lowerCAmelCase						:       Optional[int]  			=     self.tokenizer(a__				,     return_tensors=self.framework  )
           model_inputs.update(a__  )

         else:
           raise ValueError(F"Model type {model_type} does not support conditional text generation"  )

       else:
         _lowerCAmelCase						:       Any  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )

       if self.model.config.model_type == "git" and prompt is None:
         _lowerCAmelCase						:       Union[str, Any]  			=     None

       return model_inputs






     def     __A  (		self				,     a__				,     a__=None  ):
       # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
       # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
       if (
           "input_ids" in model_inputs
           and isinstance(model_inputs["""input_ids"""]				,     a__  )
           and all(x is None for x in model_inputs["""input_ids"""]  )
       ):
         _lowerCAmelCase						:       Optional[int]  			=     None

       if generate_kwargs is None:
         _lowerCAmelCase						:       List[str]  			=     {}
       # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
       #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
       #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
       #  in the `_prepare_model_inputs` method.
       _lowerCAmelCase						:       Tuple  			=     model_inputs.pop(self.model.main_input_name  )
       _lowerCAmelCase						:       Union[str, Any]  			=     self.model.generate(a__				,     **a__				,     **a__  )
       return model_outputs






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     []
       for output_ids in model_outputs:
         _lowerCAmelCase						:       Any  			=     {
             """generated_text""": self.tokenizer.decode(
                 a__				,     skip_special_tokens=a__				,     )
         }
         records.append(a__  )
       return records




