


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


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

__UpperCamelCase      						=    '\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     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			_UpperCamelCase						:   Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			def __init__(							self ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  ):

						'''simple docstring'''


						super().__init__()

						self.register_modules(
						    prior=snake_case ,  text_encoder=snake_case ,  tokenizer=snake_case ,  scheduler=snake_case ,  renderer=snake_case ,  )




			def 			snake_case__			(							self ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						if latents is None:
									UpperCamelCase__   =    randn_tensor(snake_case ,  generator=snake_case ,  device=snake_case ,  dtype=snake_case       )
						else:
									if latents.shape != shape:
												raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''       )
									UpperCamelCase__   =    latents.to(snake_case       )

						UpperCamelCase__   =    latents * scheduler.init_noise_sigma
						return latents




			def 			snake_case__			(							self ,  snake_case=0       ):

						'''simple docstring'''


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

						UpperCamelCase__   =    torch.device(F'''cuda:{gpu_id}'''       )

						UpperCamelCase__   =    [self.text_encoder, self.prior]
						for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
												cpu_offload(snake_case ,  snake_case       )




			def 			snake_case__			(							self ,  snake_case=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."       )

						UpperCamelCase__   =    torch.device(F'''cuda:{gpu_id}'''       )

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

						UpperCamelCase__   =    None
						for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
									UpperCamelCase__, UpperCamelCase__   =    cpu_offload_with_hook(snake_case ,  snake_case ,  prev_module_hook=snake_case       )

						if self.safety_checker is not None:
									UpperCamelCase__, UpperCamelCase__   =    cpu_offload_with_hook(self.safety_checker ,  snake_case ,  prev_module_hook=snake_case       )

						# We'll offload the last model manually.
						UpperCamelCase__   =    hook




			@property
			def 			snake_case__			(							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(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 			snake_case__			(							self ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  ):

						'''simple docstring'''


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

						# YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
						UpperCamelCase__   =    0
						# get prompt text embeddings
						UpperCamelCase__   =    self.tokenizer(
						    snake_case ,  padding="max_length" ,  max_length=self.tokenizer.model_max_length ,  truncation=snake_case ,  return_tensors="pt" ,  )
						UpperCamelCase__   =    text_inputs.input_ids
						UpperCamelCase__   =    self.tokenizer(snake_case ,  padding="longest" ,  return_tensors="pt"       ).input_ids

						if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(snake_case ,  snake_case       ):
									UpperCamelCase__   =    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}'''       )

						UpperCamelCase__   =    self.text_encoder(text_input_ids.to(snake_case       )       )
						UpperCamelCase__   =    text_encoder_output.text_embeds

						UpperCamelCase__   =    prompt_embeds.repeat_interleave(snake_case ,  dim=0       )
						# in Shap-E it normalize the prompt_embeds and then later rescale it
						UpperCamelCase__   =    prompt_embeds / torch.linalg.norm(snake_case ,  dim=-1 ,  keepdim=snake_case       )

						if do_classifier_free_guidance:
									UpperCamelCase__   =    torch.zeros_like(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
									UpperCamelCase__   =    torch.cat([negative_prompt_embeds, prompt_embeds]       )

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

						return prompt_embeds







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

						'''simple docstring'''


						if isinstance(snake_case ,  snake_case       ):
									UpperCamelCase__   =    1
						elif isinstance(snake_case ,  snake_case       ):
									UpperCamelCase__   =    len(snake_case       )
						else:
									raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(snake_case       )}'''       )

						UpperCamelCase__   =    self._execution_device

						UpperCamelCase__   =    batch_size * num_images_per_prompt

						UpperCamelCase__   =    guidance_scale > 1.0
						UpperCamelCase__   =    self._encode_prompt(snake_case ,  snake_case ,  snake_case ,  snake_case       )

						# prior

						self.scheduler.set_timesteps(snake_case ,  device=snake_case       )
						UpperCamelCase__   =    self.scheduler.timesteps

						UpperCamelCase__   =    self.prior.config.num_embeddings
						UpperCamelCase__   =    self.prior.config.embedding_dim

						UpperCamelCase__   =    self.prepare_latents(
						    (batch_size, num_embeddings * embedding_dim) ,  prompt_embeds.dtype ,  snake_case ,  snake_case ,  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
						UpperCamelCase__   =    latents.reshape(latents.shape[0] ,  snake_case ,  snake_case       )

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

									UpperCamelCase__   =    self.prior(
									    snake_case ,  timestep=snake_case ,  proj_embedding=snake_case ,  ).predicted_image_embedding

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

									if do_classifier_free_guidance is not None:
												UpperCamelCase__, UpperCamelCase__   =    noise_pred.chunk(2       )
												UpperCamelCase__   =    noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

									UpperCamelCase__   =    self.scheduler.step(
									    snake_case ,  timestep=snake_case ,  sample=snake_case ,  ).prev_sample

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

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

						UpperCamelCase__   =    torch.stack(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}'''       )

						UpperCamelCase__   =    images.cpu().numpy()

						if output_type == "pil":
									UpperCamelCase__   =    [self.numpy_to_pil(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=snake_case       )






import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
						if is_torch_available():
												import torch
						if is_tf_available():
												import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


__UpperCamelCase      						=    logging.get_logger(__name__)

__UpperCamelCase      						=    {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__UpperCamelCase      						=    {
    'vocab_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
    },
    'merges_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
    },
}

__UpperCamelCase      						=    {
    'Salesforce/codegen-350M-mono': 2_0_4_8,
}
@lru_cache()
def     UpperCamelCase_(			)->						Tuple:
			UpperCamelCase__   =    (
			    list(range(ord("!"					)			,       ord("~"					) + 1					)					) + list(range(ord("¡"					)			,       ord("¬"					) + 1					)					) + list(range(ord("®"					)			,       ord("ÿ"					) + 1					)					)
			)
			UpperCamelCase__   =    bs[:]
			UpperCamelCase__   =    0
			for b in range(2**8					):
						if b not in bs:
									bs.append(_A					)
									cs.append(2**8 + n					)
									n += 1
			UpperCamelCase__   =    [chr(_A					) for n in cs]
			return dict(zip(_A			,       _A					)					)
def     UpperCamelCase_(			_A				:Tuple					)->						Optional[Any]:
			UpperCamelCase__   =    set()
			UpperCamelCase__   =    word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)					)
						UpperCamelCase__   =    char
			return pairs







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			_UpperCamelCase						:   Optional[Any]					=						VOCAB_FILES_NAMES
			_UpperCamelCase						:   List[Any]					=						PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase						:   Any					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCamelCase						:   int					=						['input_ids', 'attention_mask']




			def __init__(							self ,  snake_case ,  snake_case ,  snake_case="replace" ,  snake_case="<|endoftext|>" ,  snake_case="<|endoftext|>" ,  snake_case="<|endoftext|>" ,  snake_case=None ,  snake_case=False ,  snake_case=False ,  **snake_case ,  ):

						'''simple docstring'''


						UpperCamelCase__   =    AddedToken(snake_case ,  lstrip=snake_case ,  rstrip=snake_case       ) if isinstance(snake_case ,  snake_case       ) else bos_token
						UpperCamelCase__   =    AddedToken(snake_case ,  lstrip=snake_case ,  rstrip=snake_case       ) if isinstance(snake_case ,  snake_case       ) else eos_token
						UpperCamelCase__   =    AddedToken(snake_case ,  lstrip=snake_case ,  rstrip=snake_case       ) if isinstance(snake_case ,  snake_case       ) else unk_token
						UpperCamelCase__   =    AddedToken(snake_case ,  lstrip=snake_case ,  rstrip=snake_case       ) if isinstance(snake_case ,  snake_case       ) else pad_token
						super().__init__(
						    errors=snake_case ,  unk_token=snake_case ,  bos_token=snake_case ,  eos_token=snake_case ,  pad_token=snake_case ,  add_prefix_space=snake_case ,  add_bos_token=snake_case ,  **snake_case ,  )
						UpperCamelCase__   =    add_bos_token

						with open(snake_case ,  encoding="utf-8"       ) as vocab_handle:
									UpperCamelCase__   =    json.load(snake_case       )
						UpperCamelCase__   =    {v: k for k, v in self.encoder.items()}
						UpperCamelCase__   =    errors  # how to handle errors in decoding
						UpperCamelCase__   =    bytes_to_unicode()
						UpperCamelCase__   =    {v: k for k, v in self.byte_encoder.items()}
						with open(snake_case ,  encoding="utf-8"       ) as merges_handle:
									UpperCamelCase__   =    merges_handle.read().split("\n"       )[1:-1]
						UpperCamelCase__   =    [tuple(merge.split()       ) for merge in bpe_merges]
						UpperCamelCase__   =    dict(zip(snake_case ,  range(len(snake_case       )       )       )       )
						UpperCamelCase__   =    {}
						UpperCamelCase__   =    add_prefix_space

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						UpperCamelCase__   =    re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"       )




			@property
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						return len(self.encoder       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						return dict(self.encoder ,  **self.added_tokens_encoder       )




			def 			snake_case__			(							self ,  snake_case       ):

						'''simple docstring'''


						if token in self.cache:
									return self.cache[token]
						UpperCamelCase__   =    tuple(snake_case       )
						UpperCamelCase__   =    get_pairs(snake_case       )

						if not pairs:
									return token

						while True:
									UpperCamelCase__   =    min(snake_case ,  key=lambda snake_case       : self.bpe_ranks.get(snake_case ,  float("inf"       )       )       )
									if bigram not in self.bpe_ranks:
												break
									UpperCamelCase__, UpperCamelCase__   =    bigram
									UpperCamelCase__   =    []
									UpperCamelCase__   =    0
									while i < len(snake_case       ):
												try:
															UpperCamelCase__   =    word.index(snake_case ,  snake_case       )
												except ValueError:
															new_word.extend(word[i:]       )
															break
												else:
															new_word.extend(word[i:j]       )
															UpperCamelCase__   =    j

												if word[i] == first and i < len(snake_case       ) - 1 and word[i + 1] == second:
															new_word.append(first + second       )
															i += 2
												else:
															new_word.append(word[i]       )
															i += 1
									UpperCamelCase__   =    tuple(snake_case       )
									UpperCamelCase__   =    new_word
									if len(snake_case       ) == 1:
												break
									else:
												UpperCamelCase__   =    get_pairs(snake_case       )
						UpperCamelCase__   =    " ".join(snake_case       )
						UpperCamelCase__   =    word
						return word




			def 			snake_case__			(							self ,  snake_case ,  snake_case=None       ):

						'''simple docstring'''


						if self.add_bos_token:
									UpperCamelCase__   =    [self.bos_token_id]
						else:
									UpperCamelCase__   =    []

						UpperCamelCase__   =    bos_token_ids + token_ids_a

						if token_ids_a is None:
									return output

						return output + bos_token_ids + token_ids_a




			def 			snake_case__			(							self ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    []
						for token in re.findall(self.pat ,  snake_case       ):
									UpperCamelCase__   =    "".join(
									    self.byte_encoder[b] for b in token.encode("utf-8"       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
									bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case       ).split(" "       )       )
						return bpe_tokens




			def 			snake_case__			(							self ,  snake_case       ):

						'''simple docstring'''


						return self.encoder.get(snake_case ,  self.encoder.get(self.unk_token       )       )




			def 			snake_case__			(							self ,  snake_case       ):

						'''simple docstring'''


						return self.decoder.get(snake_case       )




			def 			snake_case__			(							self ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    "".join(snake_case       )
						UpperCamelCase__   =    bytearray([self.byte_decoder[c] for c in text]       ).decode("utf-8" ,  errors=self.errors       )
						return text




			def 			snake_case__			(							self ,  snake_case ,  snake_case = None       ):

						'''simple docstring'''


						if not os.path.isdir(snake_case       ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
									return
						UpperCamelCase__   =    os.path.join(
						    snake_case ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )
						UpperCamelCase__   =    os.path.join(
						    snake_case ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]       )

						with open(snake_case ,  "w" ,  encoding="utf-8"       ) as f:
									f.write(json.dumps(self.encoder ,  indent=2 ,  sort_keys=snake_case ,  ensure_ascii=snake_case       ) + "\n"       )

						UpperCamelCase__   =    0
						with open(snake_case ,  "w" ,  encoding="utf-8"       ) as writer:
									writer.write("#version: 0.2\n"       )
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,  key=lambda snake_case       : kv[1]       ):
												if index != token_index:
															logger.warning(
															    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
															    " Please check that the tokenizer is not corrupted!"       )
															UpperCamelCase__   =    token_index
												writer.write(" ".join(snake_case       ) + "\n"       )
												index += 1

						return vocab_file, merge_file




			def 			snake_case__			(							self ,  snake_case ,  snake_case=False ,  **snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    kwargs.pop("add_prefix_space" ,  self.add_prefix_space       )
						if is_split_into_words or add_prefix_space:
									UpperCamelCase__   =    " " + text
						return (text, kwargs)




			def 			snake_case__			(							self ,  snake_case ,  snake_case = False ,  snake_case = None ,  snake_case = None ,  **snake_case ,  ):

						'''simple docstring'''


						UpperCamelCase__   =    super()._decode(
						    token_ids=snake_case ,  skip_special_tokens=snake_case ,  clean_up_tokenization_spaces=snake_case ,  **snake_case ,  )

						if truncate_before_pattern is not None and len(snake_case       ) > 0:
									UpperCamelCase__   =    self.truncate(snake_case ,  snake_case       )

						return decoded_text







			def 			snake_case__			(							self ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						def find_re(snake_case ,  snake_case ,  snake_case       ):
									UpperCamelCase__   =    pattern.search(snake_case ,  snake_case       )
									return m.start() if m else -1

						UpperCamelCase__   =    [re.compile(snake_case ,  re.MULTILINE       ) for pattern in truncate_before_pattern]

						UpperCamelCase__   =    list(re.finditer("^print" ,  snake_case ,  re.MULTILINE       )       )

						if len(snake_case       ) > 1:
									UpperCamelCase__   =    completion[: prints[1].start()]

						UpperCamelCase__   =    list(re.finditer("^def" ,  snake_case ,  re.MULTILINE       )       )

						if len(snake_case       ) > 1:
									UpperCamelCase__   =    completion[: defs[1].start()]

						UpperCamelCase__   =    0

						UpperCamelCase__   =    [
						    pos for pos in [find_re(snake_case ,  snake_case ,  snake_case       ) for terminal in terminals] if pos != -1
						]

						if len(snake_case       ) > 0:
									return completion[: min(snake_case       )]
						else:
									return completion


