
"""simple docstring"""







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	"""▁"""

lowerCamelCase__           =	{"""vocab_file""": """sentencepiece.bpe.model"""}

lowerCamelCase__           =	{
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowerCamelCase__           =	{
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}




class 				A__ (			_lowerCamelCase):
		A_		: List[Any]            =			VOCAB_FILES_NAMES
		A_		: List[str]            =			PRETRAINED_VOCAB_FILES_MAP
		A_		: Optional[Any]            =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_		: Tuple            =			['input_ids', 'attention_mask']

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE="<s>" ,	_SCREAMING_SNAKE_CASE="</s>" ,	_SCREAMING_SNAKE_CASE="</s>" ,	_SCREAMING_SNAKE_CASE="<s>" ,	_SCREAMING_SNAKE_CASE="<unk>" ,	_SCREAMING_SNAKE_CASE="<pad>" ,	_SCREAMING_SNAKE_CASE="<mask>" ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			# Mask token behave like a normal word, i.e. include the space before it
			__lowerCAmelCase    :		Dict		    =   AddedToken(_SCREAMING_SNAKE_CASE ,	lstrip=_SCREAMING_SNAKE_CASE ,	rstrip=_SCREAMING_SNAKE_CASE				) if isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) else mask_token

			__lowerCAmelCase    :		Tuple		    =   {} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    bos_token=_SCREAMING_SNAKE_CASE ,	eos_token=_SCREAMING_SNAKE_CASE ,	unk_token=_SCREAMING_SNAKE_CASE ,	sep_token=_SCREAMING_SNAKE_CASE ,	cls_token=_SCREAMING_SNAKE_CASE ,	pad_token=_SCREAMING_SNAKE_CASE ,	mask_token=_SCREAMING_SNAKE_CASE ,	sp_model_kwargs=self.sp_model_kwargs ,	**_SCREAMING_SNAKE_CASE ,	)

			__lowerCAmelCase    :		Tuple		    =   spm.SentencePieceProcessor(**self.sp_model_kwargs				)
			self.sp_model.Load(str(_SCREAMING_SNAKE_CASE				)				)
			__lowerCAmelCase    :		List[str]		    =   vocab_file

			# Original fairseq vocab and spm vocab must be "aligned":
			# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
			# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
			# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
			# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

			# Mimic fairseq token-to-id alignment for the first 4 token
			__lowerCAmelCase    :		Any		    =   {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

			# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
			__lowerCAmelCase    :		Union[str, Any]		    =   1

			__lowerCAmelCase    :		List[str]		    =   len(self.sp_model				) + self.fairseq_offset
			__lowerCAmelCase    :		Union[str, Any]		    =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}

		def __getstate__(   self				):
			__lowerCAmelCase    :		Dict		    =   self.__dict__.copy()
			__lowerCAmelCase    :		Any		    =   None
			__lowerCAmelCase    :		List[str]		    =   self.sp_model.serialized_model_proto()
			return state

		def __setstate__(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Union[str, Any]		    =   d

			# for backward compatibility
			if not hasattr(self ,	'sp_model_kwargs'				):
				__lowerCAmelCase    :		Optional[int]		    =   {}

			__lowerCAmelCase    :		Any		    =   spm.SentencePieceProcessor(**self.sp_model_kwargs				)
			self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):

			if token_ids_a is None:
				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			__lowerCAmelCase    :		str		    =   [self.cls_token_id]
			__lowerCAmelCase    :		Union[str, Any]		    =   [self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = False				):

			if already_has_special_tokens:
				return super().get_special_tokens_mask(
				    token_ids_a=_SCREAMING_SNAKE_CASE ,	token_ids_a=_SCREAMING_SNAKE_CASE ,	already_has_special_tokens=_SCREAMING_SNAKE_CASE				)

			if token_ids_a is None:
				return [1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1]
			return [1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1, 1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1]

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
			__lowerCAmelCase    :		Tuple		    =   [self.sep_token_id]
			__lowerCAmelCase    :		Optional[Any]		    =   [self.cls_token_id]

			if token_ids_a is None:
				return len(cls + token_ids_a + sep				) * [0]
			return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]

		@property
		def 							__lowerCamelCase							(   self				):
			return len(self.sp_model				) + self.fairseq_offset + 1  # Add the <mask> token

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE				): i for i in range(self.vocab_size				)}
			vocab.update(self.added_tokens_encoder				)
			return vocab

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			return self.sp_model.encode(_SCREAMING_SNAKE_CASE ,	out_type=_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			if token in self.fairseq_tokens_to_ids:
				return self.fairseq_tokens_to_ids[token]
			__lowerCAmelCase    :		Dict		    =   self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE				)

			# Need to return unknown token if the SP model returned 0
			return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			if index in self.fairseq_ids_to_tokens:
				return self.fairseq_ids_to_tokens[index]
			return self.sp_model.IdToPiece(index - self.fairseq_offset				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		str		    =   ''.join(_SCREAMING_SNAKE_CASE				).replace(_SCREAMING_SNAKE_CASE ,	' '				).strip()
			return out_string







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
			if not os.path.isdir(_SCREAMING_SNAKE_CASE				):
				logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
				return
			__lowerCAmelCase    :		Optional[int]		    =   os.path.join(
			    _SCREAMING_SNAKE_CASE ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

			if os.path.abspath(self.vocab_file				) != os.path.abspath(_SCREAMING_SNAKE_CASE				) and os.path.isfile(self.vocab_file				):
				copyfile(self.vocab_file ,	_SCREAMING_SNAKE_CASE				)
			elif not os.path.isfile(self.vocab_file				):
				with open(_SCREAMING_SNAKE_CASE ,	'wb'				) as fi:
					__lowerCAmelCase    :		Tuple		    =   self.sp_model.serialized_model_proto()
					fi.write(_SCREAMING_SNAKE_CASE				)

			return (out_vocab_file,)

"""simple docstring"""







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

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

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


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

lowerCamelCase__           =	"""
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e-img2img\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
        >>> image = load_image(image_url).convert(\"RGB\")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
        ```
"""




@dataclass
class 				A__ (			_lowerCamelCase):
		A_		: Union[PIL.Image.Image, np.ndarray]




class 				A__ (			_lowerCamelCase):

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	):
			super().__init__()

			self.register_modules(
			    prior=_SCREAMING_SNAKE_CASE ,	image_encoder=_SCREAMING_SNAKE_CASE ,	image_processor=_SCREAMING_SNAKE_CASE ,	scheduler=_SCREAMING_SNAKE_CASE ,	renderer=_SCREAMING_SNAKE_CASE ,	)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if latents is None:
				__lowerCAmelCase    :		List[str]		    =   randn_tensor(_SCREAMING_SNAKE_CASE ,	generator=_SCREAMING_SNAKE_CASE ,	device=_SCREAMING_SNAKE_CASE ,	dtype=_SCREAMING_SNAKE_CASE				)
			else:
				if latents.shape != shape:
					raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"				)
				__lowerCAmelCase    :		Any		    =   latents.to(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[int]		    =   latents * scheduler.init_noise_sigma
			return latents

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=0				):
			if is_accelerate_available():
				from accelerate import cpu_offload
			else:
				raise ImportError('Please install accelerate via `pip install accelerate`'				)

			__lowerCAmelCase    :		Tuple		    =   torch.device(f"cuda:{gpu_id}"				)

			__lowerCAmelCase    :		Union[str, Any]		    =   [self.image_encoder, self.prior]
			for cpu_offloaded_model in models:
				if cpu_offloaded_model is not None:
					cpu_offload(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		@property
		def 							__lowerCamelCase							(   self				):
			if self.device != torch.device('meta'				) or not hasattr(self.image_encoder ,	'_hf_hook'				):
				return self.device
			for module in self.image_encoder.modules():
				if (
				    hasattr(_SCREAMING_SNAKE_CASE ,	'_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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	):
			if isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) and isinstance(image[0] ,	torch.Tensor				):
				__lowerCAmelCase    :		str		    =   torch.cat(_SCREAMING_SNAKE_CASE ,	axis=0				) if image[0].ndim == 4 else torch.stack(_SCREAMING_SNAKE_CASE ,	axis=0				)

			if not isinstance(_SCREAMING_SNAKE_CASE ,	torch.Tensor				):
				__lowerCAmelCase    :		Optional[int]		    =   self.image_processor(_SCREAMING_SNAKE_CASE ,	return_tensors='pt'				).pixel_values[0].unsqueeze(0				)

			__lowerCAmelCase    :		Dict		    =   image.to(dtype=self.image_encoder.dtype ,	device=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		List[str]		    =   self.image_encoder(_SCREAMING_SNAKE_CASE				)['last_hidden_state']
			__lowerCAmelCase    :		Optional[int]		    =   image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

			__lowerCAmelCase    :		Tuple		    =   image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE ,	dim=0				)

			if do_classifier_free_guidance:
				__lowerCAmelCase    :		List[Any]		    =   torch.zeros_like(_SCREAMING_SNAKE_CASE				)

				# 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
				__lowerCAmelCase    :		Any		    =   torch.cat([negative_image_embeds, image_embeds]				)

			return image_embeds







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

			if isinstance(_SCREAMING_SNAKE_CASE ,	PIL.Image.Image				):
				__lowerCAmelCase    :		Union[str, Any]		    =   1
			elif isinstance(_SCREAMING_SNAKE_CASE ,	torch.Tensor				):
				__lowerCAmelCase    :		Tuple		    =   image.shape[0]
			elif isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) and isinstance(image[0] ,	(torch.Tensor, PIL.Image.Image)				):
				__lowerCAmelCase    :		Any		    =   len(_SCREAMING_SNAKE_CASE				)
			else:
				raise ValueError(
				    f"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(_SCREAMING_SNAKE_CASE				)}"				)

			__lowerCAmelCase    :		Optional[Any]		    =   self._execution_device

			__lowerCAmelCase    :		Optional[Any]		    =   batch_size * num_images_per_prompt

			__lowerCAmelCase    :		Any		    =   guidance_scale > 1.0
			__lowerCAmelCase    :		List[Any]		    =   self._encode_image(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			# prior

			self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE ,	device=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[str]		    =   self.scheduler.timesteps

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

			__lowerCAmelCase    :		Any		    =   self.prepare_latents(
			    (batch_size, num_embeddings * embedding_dim) ,	image_embeds.dtype ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	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
			__lowerCAmelCase    :		str		    =   latents.reshape(latents.shape[0] ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

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

				__lowerCAmelCase    :		Tuple		    =   self.prior(
				    _SCREAMING_SNAKE_CASE ,	timestep=_SCREAMING_SNAKE_CASE ,	proj_embedding=_SCREAMING_SNAKE_CASE ,	).predicted_image_embedding

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

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

				__lowerCAmelCase    :		Dict		    =   self.scheduler.step(
				    _SCREAMING_SNAKE_CASE ,	timestep=_SCREAMING_SNAKE_CASE ,	sample=_SCREAMING_SNAKE_CASE ,	).prev_sample

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

			__lowerCAmelCase    :		List[Any]		    =   []
			for i, latent in enumerate(_SCREAMING_SNAKE_CASE				):
				print()
				__lowerCAmelCase    :		int		    =   self.renderer.decode(
				    latent[None, :] ,	_SCREAMING_SNAKE_CASE ,	size=_SCREAMING_SNAKE_CASE ,	ray_batch_size=40_96 ,	n_coarse_samples=64 ,	n_fine_samples=1_28 ,	)

				images.append(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		List[Any]		    =   torch.stack(_SCREAMING_SNAKE_CASE				)

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

			__lowerCAmelCase    :		int		    =   images.cpu().numpy()

			if output_type == "pil":
				__lowerCAmelCase    :		Dict		    =   [self.numpy_to_pil(_SCREAMING_SNAKE_CASE				) 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=_SCREAMING_SNAKE_CASE				)