


"""simple docstring"""







from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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




class  UpperCamelCase_	( UpperCamelCase			,  UpperCamelCase):

	"""simple docstring"""





	@register_to_config
	def __init__( self			:				Tuple			,			UpperCAmelCase__			:				bool			,			UpperCAmelCase__			:				Optional[int] = None			,			UpperCAmelCase__			:				Optional[int] = None   )       ->	Tuple:
						super().__init__()

						__SCREAMING_SNAKE_CASE						   =				learnable

						if self.learnable:
											assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
											assert length is not None, "learnable=True requires `length` to be set"

											__SCREAMING_SNAKE_CASE						   =				torch.zeros(UpperCAmelCase__			,			UpperCAmelCase__   )
						else:
											__SCREAMING_SNAKE_CASE						   =				None

						__SCREAMING_SNAKE_CASE						   =				torch.nn.Parameter(UpperCAmelCase__   )




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  VQModel
	snake_case__				:  CLIPTextModel
	snake_case__				:  CLIPTokenizer
	snake_case__				:  TransformeraDModel
	snake_case__				:  LearnedClassifierFreeSamplingEmbeddings
	snake_case__				:  VQDiffusionScheduler




	def __init__( self			:				Union[str, Any]			,			UpperCAmelCase__			:				VQModel			,			UpperCAmelCase__			:				CLIPTextModel			,			UpperCAmelCase__			:				CLIPTokenizer			,			UpperCAmelCase__			:				TransformeraDModel			,			UpperCAmelCase__			:				VQDiffusionScheduler			,			UpperCAmelCase__			:				LearnedClassifierFreeSamplingEmbeddings			,			)       ->	int:
						super().__init__()

						self.register_modules(
						    vqvae=UpperCAmelCase__			,			transformer=UpperCAmelCase__			,			text_encoder=UpperCAmelCase__			,			tokenizer=UpperCAmelCase__			,			scheduler=UpperCAmelCase__			,			learned_classifier_free_sampling_embeddings=UpperCAmelCase__			,			)




	def   UpperCAmelCase_      ( self			:				List[Any]			,			UpperCAmelCase__			:				List[Any]			,			UpperCAmelCase__			:				int			,			UpperCAmelCase__			:				Any   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				len(UpperCAmelCase__   ) if isinstance(UpperCAmelCase__			,			UpperCAmelCase__   ) else 1

						# get prompt text embeddings
						__SCREAMING_SNAKE_CASE						   =				self.tokenizer(
						    UpperCAmelCase__			,			padding="max_length"			,			max_length=self.tokenizer.model_max_length			,			return_tensors="pt"			,			)
						__SCREAMING_SNAKE_CASE						   =				text_inputs.input_ids

						if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
											__SCREAMING_SNAKE_CASE						   =				self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]   )
											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}"""   )
											__SCREAMING_SNAKE_CASE						   =				text_input_ids[:, : self.tokenizer.model_max_length]
						__SCREAMING_SNAKE_CASE						   =				self.text_encoder(text_input_ids.to(self.device   )   )[0]

						# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
						# While CLIP does normalize the pooled output of the text transformer when combining
						# the image and text embeddings, CLIP does not directly normalize the last hidden state.
						#
						# CLIP normalizing the pooled output.
						# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
						__SCREAMING_SNAKE_CASE						   =				prompt_embeds / prompt_embeds.norm(dim=-1			,			keepdim=UpperCAmelCase__   )

						# duplicate text embeddings for each generation per prompt
						__SCREAMING_SNAKE_CASE						   =				prompt_embeds.repeat_interleave(UpperCAmelCase__			,			dim=0   )

						if do_classifier_free_guidance:
											if self.learned_classifier_free_sampling_embeddings.learnable:
																__SCREAMING_SNAKE_CASE						   =				self.learned_classifier_free_sampling_embeddings.embeddings
																__SCREAMING_SNAKE_CASE						   =				negative_prompt_embeds.unsqueeze(0   ).repeat(UpperCAmelCase__			,			1			,			1   )
											else:
																__SCREAMING_SNAKE_CASE						   =				[""] * batch_size

																__SCREAMING_SNAKE_CASE						   =				text_input_ids.shape[-1]
																__SCREAMING_SNAKE_CASE						   =				self.tokenizer(
																    UpperCAmelCase__			,			padding="max_length"			,			max_length=UpperCAmelCase__			,			truncation=UpperCAmelCase__			,			return_tensors="pt"			,			)
																__SCREAMING_SNAKE_CASE						   =				self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
																# See comment for normalizing text embeddings
																__SCREAMING_SNAKE_CASE						   =				negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1			,			keepdim=UpperCAmelCase__   )

											# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
											__SCREAMING_SNAKE_CASE						   =				negative_prompt_embeds.shape[1]
											__SCREAMING_SNAKE_CASE						   =				negative_prompt_embeds.repeat(1			,			UpperCAmelCase__			,			1   )
											__SCREAMING_SNAKE_CASE						   =				negative_prompt_embeds.view(batch_size * num_images_per_prompt			,			UpperCAmelCase__			,			-1   )

											# 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
											__SCREAMING_SNAKE_CASE						   =				torch.cat([negative_prompt_embeds, prompt_embeds]   )

						return prompt_embeds




	@torch.no_grad()
	def __call__( self			:				Tuple			,			UpperCAmelCase__			:				Union[str, List[str]]			,			UpperCAmelCase__			:				int = 1_0_0			,			UpperCAmelCase__			:				float = 5.0			,			UpperCAmelCase__			:				float = 1.0			,			UpperCAmelCase__			:				int = 1			,			UpperCAmelCase__			:				Optional[Union[torch.Generator, List[torch.Generator]]] = None			,			UpperCAmelCase__			:				Optional[torch.FloatTensor] = None			,			UpperCAmelCase__			:				Optional[str] = "pil"			,			UpperCAmelCase__			:				bool = True			,			UpperCAmelCase__			:				Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,			UpperCAmelCase__			:				int = 1			,			)       ->	Union[ImagePipelineOutput, Tuple]:
						if isinstance(UpperCAmelCase__			,			UpperCAmelCase__   ):
											__SCREAMING_SNAKE_CASE						   =				1
						elif isinstance(UpperCAmelCase__			,			UpperCAmelCase__   ):
											__SCREAMING_SNAKE_CASE						   =				len(UpperCAmelCase__   )
						else:
											raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase__   )}"""   )

						__SCREAMING_SNAKE_CASE						   =				batch_size * num_images_per_prompt

						__SCREAMING_SNAKE_CASE						   =				guidance_scale > 1.0

						__SCREAMING_SNAKE_CASE						   =				self._encode_prompt(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__   )

						if (callback_steps is None) or (
						    callback_steps is not None and (not isinstance(UpperCAmelCase__			,			UpperCAmelCase__   ) or callback_steps <= 0)
						):
											raise ValueError(
											    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
											    F""" {type(UpperCAmelCase__   )}."""   )

						# get the initial completely masked latents unless the user supplied it

						__SCREAMING_SNAKE_CASE						   =				(batch_size, self.transformer.num_latent_pixels)
						if latents is None:
											__SCREAMING_SNAKE_CASE						   =				self.transformer.num_vector_embeds - 1
											__SCREAMING_SNAKE_CASE						   =				torch.full(UpperCAmelCase__			,			UpperCAmelCase__   ).to(self.device   )
						else:
											if latents.shape != latents_shape:
																raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""   )
											if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
																raise ValueError(
																    "Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"
																    F""" {self.transformer.num_vector_embeds - 1} (inclusive)."""   )
											__SCREAMING_SNAKE_CASE						   =				latents.to(self.device   )

						# set timesteps
						self.scheduler.set_timesteps(UpperCAmelCase__			,			device=self.device   )

						__SCREAMING_SNAKE_CASE						   =				self.scheduler.timesteps.to(self.device   )

						__SCREAMING_SNAKE_CASE						   =				latents

						for i, t in enumerate(self.progress_bar(UpperCAmelCase__   )   ):
											# expand the sample if we are doing classifier free guidance
											__SCREAMING_SNAKE_CASE						   =				torch.cat([sample] * 2   ) if do_classifier_free_guidance else sample

											# predict the un-noised image
											# model_output == `log_p_x_0`
											__SCREAMING_SNAKE_CASE						   =				self.transformer(UpperCAmelCase__			,			encoder_hidden_states=UpperCAmelCase__			,			timestep=UpperCAmelCase__   ).sample

											if do_classifier_free_guidance:
																__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				model_output.chunk(2   )
																__SCREAMING_SNAKE_CASE						   =				model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																model_output -= torch.logsumexp(UpperCAmelCase__			,			dim=1			,			keepdim=UpperCAmelCase__   )

											__SCREAMING_SNAKE_CASE						   =				self.truncate(UpperCAmelCase__			,			UpperCAmelCase__   )

											# remove `log(0)`'s (`-inf`s)
											__SCREAMING_SNAKE_CASE						   =				model_output.clamp(-7_0   )

											# compute the previous noisy sample x_t -> x_t-1
											__SCREAMING_SNAKE_CASE						   =				self.scheduler.step(UpperCAmelCase__			,			timestep=UpperCAmelCase__			,			sample=UpperCAmelCase__			,			generator=UpperCAmelCase__   ).prev_sample

											# call the callback, if provided
											if callback is not None and i % callback_steps == 0:
																callback(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				self.vqvae.config.vq_embed_dim
						__SCREAMING_SNAKE_CASE						   =				(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
						__SCREAMING_SNAKE_CASE						   =				self.vqvae.quantize.get_codebook_entry(UpperCAmelCase__			,			shape=UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				self.vqvae.decode(UpperCAmelCase__			,			force_not_quantize=UpperCAmelCase__   ).sample

						__SCREAMING_SNAKE_CASE						   =				(image / 2 + 0.5).clamp(0			,			1   )
						__SCREAMING_SNAKE_CASE						   =				image.cpu().permute(0			,			2			,			3			,			1   ).numpy()

						if output_type == "pil":
											__SCREAMING_SNAKE_CASE						   =				self.numpy_to_pil(UpperCAmelCase__   )

						if not return_dict:
											return (image,)

						return ImagePipelineOutput(images=UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Optional[Any]			,			UpperCAmelCase__			:				torch.FloatTensor			,			UpperCAmelCase__			:				float   )       ->	torch.FloatTensor:
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				torch.sort(UpperCAmelCase__			,			1			,			descending=UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				torch.exp(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				sorted_p_x_0.cumsum(dim=1   ) < truncation_rate

						# Ensure that at least the largest probability is not zeroed out
						__SCREAMING_SNAKE_CASE						   =				torch.full_like(keep_mask[:, 0:1, :]			,			UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				torch.cat((all_true, keep_mask)			,			dim=1   )
						__SCREAMING_SNAKE_CASE						   =				keep_mask[:, :-1, :]

						__SCREAMING_SNAKE_CASE						   =				keep_mask.gather(1			,			indices.argsort(1   )   )

						__SCREAMING_SNAKE_CASE						   =				log_p_x_0.clone()

						__SCREAMING_SNAKE_CASE						   =				-torch.inf  # -inf = log(0)

						return rv


"""simple docstring"""




import os
import re
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


_A			:       List[str]   		=       logging.get_logger(__name__)

_A			:       List[str]   		=       {"""vocab_file""": """spiece.model"""}

_A			:       List[Any]   		=       {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

_A			:       str   		=       {
    """google/bigbird-roberta-base""": 40_96,
    """google/bigbird-roberta-large""": 40_96,
    """google/bigbird-base-trivia-itc""": 40_96,
}

class 		a__						(      a_						):
   __lowerCAmelCase            =				VOCAB_FILES_NAMES
   __lowerCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase            =				["""input_ids""", """attention_mask"""]
   __lowerCAmelCase            =				[]
   def __init__(			self	,    _a	,    _a="<unk>"	,    _a="<s>"	,    _a="</s>"	,    _a="<pad>"	,    _a="[SEP]"	,    _a="[MASK]"	,    _a="[CLS]"	,    _a = None	,    **_a	,    ):
          lowercase    :				Dict       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else bos_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else eos_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else unk_token
          lowercase    :				List[Any]       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else pad_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else cls_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else sep_token

          # Mask token behave like a normal word, i.e. include the space before it
          lowercase    :				List[Any]       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else mask_token

          lowercase    :				Optional[int]       		=							{} if sp_model_kwargs is None else sp_model_kwargs

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

          lowercase    :				Optional[Any]       		=							vocab_file

          lowercase    :				List[str]       		=							spm.SentencePieceProcessor(**self.sp_model_kwargs				)
          self.sp_model.Load(_a				)
   @property
   def 			__magic_name__			(			self				):
          return self.sp_model.get_piece_size()
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							{self.convert_ids_to_tokens(_a				): i for i in range(self.vocab_size				)}
          vocab.update(self.added_tokens_encoder				)
          return vocab
   def __getstate__(			self				):
          lowercase    :				Union[str, Any]       		=							self.__dict__.copy()
          lowercase    :				Dict       		=							None
          return state
   def __setstate__(			self	,    _a				):
          lowercase    :				str       		=							d

          # for backward compatibility
          if not hasattr(self	,    "sp_model_kwargs"				):
                 lowercase    :				List[str]       		=							{}

          lowercase    :				Any       		=							spm.SentencePieceProcessor(**self.sp_model_kwargs				)
          self.sp_model.Load(self.vocab_file				)
   def 			__magic_name__			(			self	,    _a				):
          return self.sp_model.encode(_a	,    out_type=_a				)
   def 			__magic_name__			(			self	,    _a				):
          return self.sp_model.piece_to_id(_a				)
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				Union[str, Any]       		=							self.sp_model.IdToPiece(_a				)
          return token
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				List[Any]       		=							[]
          lowercase    :				List[Any]       		=							""
          lowercase    :				List[Any]       		=							False
          for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                        if not prev_is_special:
                               out_string += " "
                        out_string += self.sp_model.decode(_a				) + token
                        lowercase    :				int       		=							True
                        lowercase    :				Union[str, Any]       		=							[]
                 else:
                        current_sub_tokens.append(_a				)
                        lowercase    :				int       		=							False
          out_string += self.sp_model.decode(_a				)
          return out_string.strip()
   def 			__magic_name__			(			self	,    _a	,    _a = False	,    _a = None	,    _a = True	,    **_a	,    ):
          lowercase    :				int       		=							kwargs.pop("use_source_tokenizer"	,    _a				)

          lowercase    :				Union[str, Any]       		=							self.convert_ids_to_tokens(_a	,    skip_special_tokens=_a				)

          # To avoid mixing byte-level and unicode for byte-level BPT
          # we need to build string separately for added tokens and byte-level tokens
          # cf. https://github.com/huggingface/transformers/issues/1133
          lowercase    :				Any       		=							[]
          lowercase    :				Dict       		=							[]
          for token in filtered_tokens:
                 if skip_special_tokens and token in self.all_special_ids:
                        continue
                 if token in self.added_tokens_encoder:
                        if current_sub_text:
                               sub_texts.append(self.convert_tokens_to_string(_a				)				)
                               lowercase    :				int       		=							[]
                        sub_texts.append(_a				)
                 else:
                        current_sub_text.append(_a				)
          if current_sub_text:
                 sub_texts.append(self.convert_tokens_to_string(_a				)				)

          # Mimic the behavior of the Rust tokenizer:
          # No space before [MASK] and [SEP]
          if spaces_between_special_tokens:
                 lowercase    :				Dict       		=							re.sub(R" (\[(MASK|SEP)\])"	,    R"\1"	,    " ".join(_a				)				)
          else:
                 lowercase    :				Union[str, Any]       		=							"".join(_a				)

          lowercase    :				int       		=							(
              clean_up_tokenization_spaces
              if clean_up_tokenization_spaces is not None
              else self.clean_up_tokenization_spaces
          )
          if clean_up_tokenization_spaces:
                 lowercase    :				Union[str, Any]       		=							self.clean_up_tokenization(_a				)
                 return clean_text
          else:
                 return text
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          if not os.path.isdir(_a				):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
                 return
          lowercase    :				List[Any]       		=							os.path.join(
              _a	,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

          if os.path.abspath(self.vocab_file				) != os.path.abspath(_a				) and os.path.isfile(self.vocab_file				):
                 copyfile(self.vocab_file	,    _a				)
          elif not os.path.isfile(self.vocab_file				):
                 with open(_a	,    "wb"				) as fi:
                        lowercase    :				int       		=							self.sp_model.serialized_model_proto()
                        fi.write(_a				)

          return (out_vocab_file,)
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          lowercase    :				List[str]       		=							[self.cls_token_id]
          lowercase    :				Tuple       		=							[self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep
   def 			__magic_name__			(			self	,    _a	,    _a = None	,    _a = False				):
          if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=_a	,    token_ids_a=_a	,    already_has_special_tokens=_a				)

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

   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Tuple       		=							[self.sep_token_id]
          lowercase    :				Tuple       		=							[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				) * [0] + len(token_ids_a + sep				) * [1]


