import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

_lowerCAmelCase        =       '\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = "a shark"\n\n        >>> images = pipe(\n        ...     prompt,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], "shark_3d.gif")\n        ```\n'






@dataclass
class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 __magic_name__          =   42






class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 def __init__(    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       ):

        """simple docstring"""

        super().__init__()

        self.register_modules(
            prior=__magic_name__ ,       text_encoder=__magic_name__ ,       tokenizer=__magic_name__ ,       scheduler=__magic_name__ ,       renderer=__magic_name__ ,       )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__      ):

        """simple docstring"""

        if latents is None:
               A_							:       Optional[int]	      =				randn_tensor(__magic_name__ ,       generator=__magic_name__ ,       device=__magic_name__ ,       dtype=__magic_name__      )
        else:
               if latents.shape != shape:
                      raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""      )
               A_							:       str	      =				latents.to(__magic_name__      )

        A_							:       List[Any]	      =				latents * scheduler.init_noise_sigma
        return latents
 def  UpperCAmelCase   (    self ,       __magic_name__=0      ):

        """simple docstring"""

        if is_accelerate_available():
               from accelerate import cpu_offload
        else:
               raise ImportError('''Please install accelerate via `pip install accelerate`'''      )

        A_							:       Optional[Any]	      =				torch.device(f"""cuda:{gpu_id}"""      )

        A_							:       Tuple	      =				[self.text_encoder, self.prior]
        for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                      cpu_offload(__magic_name__ ,       __magic_name__      )
 def  UpperCAmelCase   (    self ,       __magic_name__=0      ):

        """simple docstring"""

        if is_accelerate_available() and is_accelerate_version('''>=''' ,       '''0.17.0.dev0'''      ):
               from accelerate import cpu_offload_with_hook
        else:
               raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''      )

        A_							:       str	      =				torch.device(f"""cuda:{gpu_id}"""      )

        if self.device.type != "cpu":
               self.to('''cpu''' ,       silence_dtype_warnings=__magic_name__      )
               torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        A_							:       Optional[Any]	      =				None
        for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
               A_      ,							A_							:       List[str]	      =				cpu_offload_with_hook(__magic_name__ ,       __magic_name__ ,       prev_module_hook=__magic_name__      )

        if self.safety_checker is not None:
               A_      ,							A_							:       List[Any]	      =				cpu_offload_with_hook(self.safety_checker ,       __magic_name__ ,       prev_module_hook=__magic_name__      )

        # We'll offload the last model manually.
        A_							:       Tuple	      =				hook
 @property
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        if self.device != torch.device('''meta'''      ) or not hasattr(self.text_encoder ,       '''_hf_hook'''      ):
               return self.device
        for module in self.text_encoder.modules():
               if (
                   hasattr(__magic_name__ ,       '''_hf_hook'''      )
                   and hasattr(module._hf_hook ,       '''execution_device'''      )
                   and module._hf_hook.execution_device is not None
               ):
                      return torch.device(module._hf_hook.execution_device      )
        return self.device
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       ):

        """simple docstring"""

        len(__magic_name__      ) if isinstance(__magic_name__ ,       __magic_name__      ) else 1

        # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
        A_							:       Union[str, Any]	      =				0
        # get prompt text embeddings
        A_							:       int	      =				self.tokenizer(
            __magic_name__ ,       padding='''max_length''' ,       max_length=self.tokenizer.model_max_length ,       truncation=__magic_name__ ,       return_tensors='''pt''' ,       )
        A_							:       Union[str, Any]	      =				text_inputs.input_ids
        A_							:       Optional[Any]	      =				self.tokenizer(__magic_name__ ,       padding='''longest''' ,       return_tensors='''pt'''      ).input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(__magic_name__ ,       __magic_name__      ):
               A_							:       str	      =				self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]      )
               logger.warning(
                   '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                   f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""      )

        A_							:       Tuple	      =				self.text_encoder(text_input_ids.to(__magic_name__      )      )
        A_							:       str	      =				text_encoder_output.text_embeds

        A_							:       Tuple	      =				prompt_embeds.repeat_interleave(__magic_name__ ,       dim=0      )
        # in Shap-E it normalize the prompt_embeds and then later rescale it
        A_							:       int	      =				prompt_embeds / torch.linalg.norm(__magic_name__ ,       dim=-1 ,       keepdim=__magic_name__      )

        if do_classifier_free_guidance:
               A_							:       Any	      =				torch.zeros_like(__magic_name__      )

               # For classifier free guidance, we need to do two forward passes.
               # Here we concatenate the unconditional and text embeddings into a single batch
               # to avoid doing two forward passes
               A_							:       int	      =				torch.cat([negative_prompt_embeds, prompt_embeds]      )

        # Rescale the features to have unit variance
        A_							:       int	      =				math.sqrt(prompt_embeds.shape[1]      ) * prompt_embeds

        return prompt_embeds
 @torch.no_grad()
 @replace_example_docstring(__magic_name__      )
 def __call__(    self ,       __magic_name__ ,       __magic_name__ = 1 ,       __magic_name__ = 25 ,       __magic_name__ = None ,       __magic_name__ = None ,       __magic_name__ = 4.0 ,       __magic_name__ = 64 ,       __magic_name__ = "pil" ,       __magic_name__ = True ,       ):

        """simple docstring"""

        if isinstance(__magic_name__ ,       __magic_name__      ):
               A_							:       Tuple	      =				1
        elif isinstance(__magic_name__ ,       __magic_name__      ):
               A_							:       Optional[Any]	      =				len(__magic_name__      )
        else:
               raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__magic_name__      )}"""      )

        A_							:       Optional[Any]	      =				self._execution_device

        A_							:       Optional[int]	      =				batch_size * num_images_per_prompt

        A_							:       Dict	      =				guidance_scale > 1.0
        A_							:       Optional[Any]	      =				self._encode_prompt(__magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__      )

        # prior

        self.scheduler.set_timesteps(__magic_name__ ,       device=__magic_name__      )
        A_							:       Union[str, Any]	      =				self.scheduler.timesteps

        A_							:       List[str]	      =				self.prior.config.num_embeddings
        A_							:       int	      =				self.prior.config.embedding_dim

        A_							:       List[str]	      =				self.prepare_latents(
            (batch_size, num_embeddings * embedding_dim) ,       prompt_embeds.dtype ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       self.scheduler ,       )

        # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
        A_							:       str	      =				latents.reshape(latents.shape[0] ,       __magic_name__ ,       __magic_name__      )

        for i, t in enumerate(self.progress_bar(__magic_name__      )      ):
               # expand the latents if we are doing classifier free guidance
               A_							:       Optional[int]	      =				torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents
               A_							:       List[Any]	      =				self.scheduler.scale_model_input(__magic_name__ ,       __magic_name__      )

               A_							:       str	      =				self.prior(
                   __magic_name__ ,       timestep=__magic_name__ ,       proj_embedding=__magic_name__ ,       ).predicted_image_embedding

               # remove the variance
               A_      ,							A_							:       List[str]	      =				noise_pred.split(
                   scaled_model_input.shape[2] ,       dim=2      )  # batch_size, num_embeddings, embedding_dim

               if do_classifier_free_guidance is not None:
                      A_      ,							A_							:       Optional[int]	      =				noise_pred.chunk(2      )
                      A_							:       Optional[int]	      =				noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

               A_							:       Dict	      =				self.scheduler.step(
                   __magic_name__ ,       timestep=__magic_name__ ,       sample=__magic_name__ ,       ).prev_sample

        if output_type == "latent":
               return ShapEPipelineOutput(images=__magic_name__      )

        A_							:       List[Any]	      =				[]
        for i, latent in enumerate(__magic_name__      ):
               A_							:       Any	      =				self.renderer.decode(
                   latent[None, :] ,       __magic_name__ ,       size=__magic_name__ ,       ray_batch_size=4096 ,       n_coarse_samples=64 ,       n_fine_samples=128 ,       )
               images.append(__magic_name__      )

        A_							:       List[Any]	      =				torch.stack(__magic_name__      )

        if output_type not in ["np", "pil"]:
               raise ValueError(f"""Only the output types `pil` and `np` are supported not output_type={output_type}"""      )

        A_							:       Optional[Any]	      =				images.cpu().numpy()

        if output_type == "pil":
               A_							:       Union[str, Any]	      =				[self.numpy_to_pil(__magic_name__      ) for image in images]

        # Offload last model to CPU
        if hasattr(self ,       '''final_offload_hook'''      ) and self.final_offload_hook is not None:
               self.final_offload_hook.offload()

        if not return_dict:
               return (images,)

        return ShapEPipelineOutput(images=__magic_name__      )



def       a__      (					a  ,   a  ,   a  ,   a  ,   a  ,   a  ,   )		->	tuple[list[list[int]], list[list[str]]]:
       A_							:       Optional[Any]	      =				list(a							)
       A_							:       int	      =				list(a							)
       A_							:       Union[str, Any]	      =				len(a							)
       A_							:       Any	      =				len(a							)

       A_							:       Union[str, Any]	      =				[
           [0 for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
       ]
       A_							:       Optional[int]	      =				[
           ['''0''' for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
       ]

       for i in range(1  ,   len_source_seq + 1							):
              A_							:       List[Any]	      =				i * delete_cost
              A_							:       List[Any]	      =				f"""D{source_seq[i - 1]:c}"""

       for i in range(1  ,   len_destination_seq + 1							):
              A_							:       Optional[Any]	      =				i * insert_cost
              A_							:       str	      =				f"""I{destination_seq[i - 1]:c}"""

       for i in range(1  ,   len_source_seq + 1							):
              for j in range(1  ,   len_destination_seq + 1							):
                     if source_seq[i - 1] == destination_seq[j - 1]:
                            A_							:       List[Any]	      =				costs[i - 1][j - 1] + copy_cost
                            A_							:       Tuple	      =				f"""C{source_seq[i - 1]:c}"""
                     else:
                            A_							:       Union[str, Any]	      =				costs[i - 1][j - 1] + replace_cost
                            A_							:       Dict	      =				f"""R{source_seq[i - 1]:c}""" + str(destination_seq[j - 1]							)

                     if costs[i - 1][j] + delete_cost < costs[i][j]:
                            A_							:       Dict	      =				costs[i - 1][j] + delete_cost
                            A_							:       Optional[int]	      =				f"""D{source_seq[i - 1]:c}"""

                     if costs[i][j - 1] + insert_cost < costs[i][j]:
                            A_							:       List[Any]	      =				costs[i][j - 1] + insert_cost
                            A_							:       Tuple	      =				f"""I{destination_seq[j - 1]:c}"""

       return costs, ops


def       a__      (					a  ,   a  ,   a							)		->	list[str]:
       if i == 0 and j == 0:
              return []
       else:
              if ops[i][j][0] == "C" or ops[i][j][0] == "R":
                     A_							:       Tuple	      =				assemble_transformation(a  ,   i - 1  ,   j - 1							)
                     seq.append(ops[i][j]							)
                     return seq
              elif ops[i][j][0] == "D":
                     A_							:       Any	      =				assemble_transformation(a  ,   i - 1  ,   a							)
                     seq.append(ops[i][j]							)
                     return seq
              else:
                     A_							:       Tuple	      =				assemble_transformation(a  ,   a  ,   j - 1							)
                     seq.append(ops[i][j]							)
                     return seq


if __name__ == "__main__":
    _lowerCAmelCase  ,			_lowerCAmelCase        =       compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

    _lowerCAmelCase        =       len(operations)
    _lowerCAmelCase        =       len(operations[0])
    _lowerCAmelCase        =       assemble_transformation(operations, m - 1, n - 1)

    _lowerCAmelCase        =       list('Python')
    _lowerCAmelCase        =       0
    _lowerCAmelCase        =       0

    with open('min_cost.txt', 'w') as file:
        for op in sequence:
            print(''.join(string))

            if op[0] == "C":
                file.write('%-16s' % 'Copy %c' % op[1])
                file.write('\t\t\t' + ''.join(string))
                file.write('\r\n')

                cost -= 1
            elif op[0] == "R":
                _lowerCAmelCase        =       op[2]

                file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
                file.write('\t\t' + ''.join(string))
                file.write('\r\n')

                cost += 1
            elif op[0] == "D":
                string.pop(i)

                file.write('%-16s' % 'Delete %c' % op[1])
                file.write('\t\t\t' + ''.join(string))
                file.write('\r\n')

                cost += 2
            else:
                string.insert(i, op[1])

                file.write('%-16s' % 'Insert %c' % op[1])
                file.write('\t\t\t' + ''.join(string))
                file.write('\r\n')

                cost += 2

            i += 1

        print(''.join(string))
        print('Cost: ', cost)

        file.write('\r\nMinimum cost: ' + str(cost))


