

import os
import unicodedata
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											=     logging.get_logger(__name__)
A											=     {'vocab_file': 'spiece.model'}

A											=     {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    }
}

A											=     {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

A											=     '▁'


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  VOCAB_FILES_NAMES
			__A						  =  PRETRAINED_VOCAB_FILES_MAP
			__A						  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase=True ,				__UpperCamelCase=True ,				__UpperCamelCase=False ,				__UpperCamelCase="[CLS]" ,				__UpperCamelCase="[SEP]" ,				__UpperCamelCase="<unk>" ,				__UpperCamelCase="[SEP]" ,				__UpperCamelCase="<pad>" ,				__UpperCamelCase="[CLS]" ,				__UpperCamelCase="[MASK]" ,				__UpperCamelCase = None ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				snake_case_      		= (
				    AddedToken(__UpperCamelCase ,				lstrip=__UpperCamelCase ,				rstrip=__UpperCamelCase ,				normalized=__UpperCamelCase							)
				    if isinstance(__UpperCamelCase ,				__UpperCamelCase							)
				    else mask_token
				)

				snake_case_      		= {} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    do_lower_case=__UpperCamelCase ,				remove_space=__UpperCamelCase ,				keep_accents=__UpperCamelCase ,				bos_token=__UpperCamelCase ,				eos_token=__UpperCamelCase ,				unk_token=__UpperCamelCase ,				sep_token=__UpperCamelCase ,				pad_token=__UpperCamelCase ,				cls_token=__UpperCamelCase ,				mask_token=__UpperCamelCase ,				sp_model_kwargs=self.sp_model_kwargs ,				**__UpperCamelCase ,				)

				snake_case_      		= do_lower_case
				snake_case_      		= remove_space
				snake_case_      		= keep_accents
				snake_case_      		= vocab_file

				snake_case_      		= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(__UpperCamelCase							)


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return len(self.sp_model							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= {self.convert_ids_to_tokens(__UpperCamelCase							): i for i in range(self.vocab_size							)}
				vocab.update(self.added_tokens_encoder							)
				return vocab


			def __getstate__(    self							):

				"""simple docstring"""


				snake_case_      		= self.__dict__.copy()
				snake_case_      		= None
				return state


			def __setstate__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= d

				# for backward compatibility
				if not hasattr(self ,				'sp_model_kwargs'							):
					snake_case_      		= {}

				snake_case_      		= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(self.vocab_file							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				if self.remove_space:
					snake_case_      		= ' '.join(inputs.strip().split()							)
				else:
					snake_case_      		= inputs
				snake_case_      		= outputs.replace('``' ,				'"'							).replace('\'\'' ,				'"'							)

				if not self.keep_accents:
					snake_case_      		= unicodedata.normalize('NFKD' ,				__UpperCamelCase							)
					snake_case_      		= ''.join([c for c in outputs if not unicodedata.combining(__UpperCamelCase							)]							)
				if self.do_lower_case:
					snake_case_      		= outputs.lower()

				return outputs


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.preprocess_text(__UpperCamelCase							)
				snake_case_      		= self.sp_model.encode(__UpperCamelCase ,				out_type=__UpperCamelCase							)
				snake_case_      		= []
				for piece in pieces:
					if len(__UpperCamelCase							) > 1 and piece[-1] == str(','							) and piece[-2].isdigit():
						snake_case_      		= self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCamelCase ,				''							)							)
						if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
							if len(cur_pieces[0]							) == 1:
								snake_case_      		= cur_pieces[1:]
							else:
								snake_case_      		= cur_pieces[0][1:]
						cur_pieces.append(piece[-1]							)
						new_pieces.extend(__UpperCamelCase							)
					else:
						new_pieces.append(__UpperCamelCase							)

				return new_pieces


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return self.sp_model.PieceToId(__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return self.sp_model.IdToPiece(__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= []
				snake_case_      		= ''
				snake_case_      		= 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(__UpperCamelCase							) + token
						snake_case_      		= True
						snake_case_      		= []
					else:
						current_sub_tokens.append(__UpperCamelCase							)
						snake_case_      		= False
				out_string += self.sp_model.decode(__UpperCamelCase							)
				return out_string.strip()


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = None							):

				"""simple docstring"""


				snake_case_      		= [self.sep_token_id]
				snake_case_      		= [self.cls_token_id]
				if token_ids_a is None:
					return cls + token_ids_a + sep
				return cls + token_ids_a + sep + token_ids_a + sep


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = False							):

				"""simple docstring"""


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

				if token_ids_a is not None:
					return [1] + ([0] * len(__UpperCamelCase							)) + [1] + ([0] * len(__UpperCamelCase							)) + [1]
				return [1] + ([0] * len(__UpperCamelCase							)) + [1]


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = None							):

				"""simple docstring"""


				snake_case_      		= [self.sep_token_id]
				snake_case_      		= [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]







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = None							):

				"""simple docstring"""


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

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

				return (out_vocab_file,)



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


class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							):






			"""simple docstring"""




			@register_to_config
			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = None							):

				"""simple docstring"""


				super().__init__()

				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"

					snake_case_      		= torch.zeros(__UpperCamelCase ,				__UpperCamelCase							)
				else:
					snake_case_      		= None

				snake_case_      		= torch.nn.Parameter(__UpperCamelCase							)







class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42


			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__()

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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= len(__UpperCamelCase							) if isinstance(__UpperCamelCase ,				__UpperCamelCase							) else 1

				# get prompt text embeddings
				snake_case_      		= self.tokenizer(
				    __UpperCamelCase ,				padding='max_length' ,				max_length=self.tokenizer.model_max_length ,				return_tensors='pt' ,				)
				snake_case_      		= text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
					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}"""							)
					snake_case_      		= text_input_ids[:, : self.tokenizer.model_max_length]
				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
				snake_case_      		= prompt_embeds / prompt_embeds.norm(dim=-1 ,				keepdim=__UpperCamelCase							)

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

				if do_classifier_free_guidance:
					if self.learned_classifier_free_sampling_embeddings.learnable:
						snake_case_      		= self.learned_classifier_free_sampling_embeddings.embeddings
						snake_case_      		= negative_prompt_embeds.unsqueeze(0							).repeat(__UpperCamelCase ,				1 ,				1							)
					else:
						snake_case_      		= [''] * batch_size

						snake_case_      		= text_input_ids.shape[-1]
						snake_case_      		= self.tokenizer(
						    __UpperCamelCase ,				padding='max_length' ,				max_length=__UpperCamelCase ,				truncation=__UpperCamelCase ,				return_tensors='pt' ,				)
						snake_case_      		= self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
						# See comment for normalizing text embeddings
						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
					snake_case_      		= negative_prompt_embeds.shape[1]
					snake_case_      		= negative_prompt_embeds.repeat(1 ,				__UpperCamelCase ,				1							)
					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
					snake_case_      		= torch.cat([negative_prompt_embeds, prompt_embeds]							)

				return prompt_embeds


			@torch.no_grad()
			def __call__(    self ,				__UpperCamelCase ,				__UpperCamelCase = 1_00 ,				__UpperCamelCase = 5.0 ,				__UpperCamelCase = 1.0 ,				__UpperCamelCase = 1 ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = "pil" ,				__UpperCamelCase = True ,				__UpperCamelCase = None ,				__UpperCamelCase = 1 ,				):

				"""simple docstring"""


				if isinstance(__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= 1
				elif isinstance(__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= len(__UpperCamelCase							)
				else:
					raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase							)}"""							)

				snake_case_      		= batch_size * num_images_per_prompt

				snake_case_      		= guidance_scale > 1.0

				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

				snake_case_      		= (batch_size, self.transformer.num_latent_pixels)
				if latents is None:
					snake_case_      		= self.transformer.num_vector_embeds - 1
					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)."""							)
					snake_case_      		= latents.to(self.device							)

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

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

				snake_case_      		= latents

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

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

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

					snake_case_      		= self.truncate(__UpperCamelCase ,				__UpperCamelCase							)

					# remove `log(0)`'s (`-inf`s)
					snake_case_      		= model_output.clamp(-70							)

					# compute the previous noisy sample x_t -> x_t-1
					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							)

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

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

				if output_type == "pil":
					snake_case_      		= self.numpy_to_pil(__UpperCamelCase							)

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=__UpperCamelCase							)







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= torch.sort(__UpperCamelCase ,				1 ,				descending=__UpperCamelCase							)
				snake_case_      		= torch.exp(__UpperCamelCase							)
				snake_case_      		= sorted_p_x_0.cumsum(dim=1							) < truncation_rate

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

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

				snake_case_      		= log_p_x_0.clone()

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

				return rv
