
"""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


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



class __lowerCAmelCase					(       _lowercase  ,					_lowercase   ):




			"""simple docstring"""







			@register_to_config
			def __init__(					self    :       Any							, _snake_case    :       bool							, _snake_case    :       Optional[int] = None							, _snake_case    :       Optional[int] = None						)   ->			int:







								"""simple docstring"""
								super().__init__()

								A_								=    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"

													A_								=    torch.zeros(_snake_case							, _snake_case						)
								else:
													A_								=    None

								A_								=    torch.nn.Parameter(_snake_case						)





class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					42
			snake_case						   =					42
			snake_case						   =					42
			snake_case						   =					42
			snake_case						   =					42
			snake_case						   =					42




			def __init__(					self    :       int							, _snake_case    :       VQModel							, _snake_case    :       CLIPTextModel							, _snake_case    :       CLIPTokenizer							, _snake_case    :       TransformeraDModel							, _snake_case    :       VQDiffusionScheduler							, _snake_case    :       LearnedClassifierFreeSamplingEmbeddings							, )   ->			Dict:







								"""simple docstring"""
								super().__init__()

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




			def   lowerCamelCase__  (					self    :       Tuple							, _snake_case    :       Union[str, Any]							, _snake_case    :       Optional[int]							, _snake_case    :       Any						)   ->			Union[str, Any]:







								"""simple docstring"""
								A_								=    len(_snake_case						) if isinstance(_snake_case							, _snake_case						) else 1

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

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
													A_								=    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}'						)
													A_								=    text_input_ids[:, : self.tokenizer.model_max_length]
								A_								=    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
								A_								=    prompt_embeds / prompt_embeds.norm(dim=-1							, keepdim=_snake_case						)

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

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

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

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

								return prompt_embeds




			@torch.no_grad()
			def __call__(					self    :       Optional[int]							, _snake_case    :       Union[str, List[str]]							, _snake_case    :       int = 100							, _snake_case    :       float = 5.0							, _snake_case    :       float = 1.0							, _snake_case    :       int = 1							, _snake_case    :       Optional[Union[torch.Generator, List[torch.Generator]]] = None							, _snake_case    :       Optional[torch.FloatTensor] = None							, _snake_case    :       Optional[str] = "pil"							, _snake_case    :       bool = True							, _snake_case    :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None							, _snake_case    :       int = 1							, )   ->			Union[ImagePipelineOutput, Tuple]:







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

								A_								=    batch_size * num_images_per_prompt

								A_								=    guidance_scale > 1.0

								A_								=    self._encode_prompt(_snake_case							, _snake_case							, _snake_case						)

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

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

								A_								=    (batch_size, self.transformer.num_latent_pixels)
								if latents is None:
													A_								=    self.transformer.num_vector_embeds - 1
													A_								=    torch.full(_snake_case							, _snake_case						).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).'						)
													A_								=    latents.to(self.device						)

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

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

								A_								=    latents

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

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

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

													A_								=    self.truncate(_snake_case							, _snake_case						)

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

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

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

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

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

								if output_type == "pil":
													A_								=    self.numpy_to_pil(_snake_case						)

								if not return_dict:
													return (image,)

								return ImagePipelineOutput(images=_snake_case						)






			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       torch.FloatTensor							, _snake_case    :       float						)   ->			torch.FloatTensor:







								"""simple docstring"""
								A_			,     A_								=    torch.sort(_snake_case							, 1							, descending=_snake_case						)
								A_								=    torch.exp(_snake_case						)
								A_								=    sorted_p_x_0.cumsum(dim=1						) < truncation_rate

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

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

								A_								=    log_p_x_0.clone()

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

								return rv






"""simple docstring"""





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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_mbart import MBartTokenizer
else:
		UpperCamelCase_       :    Dict						      =			None


UpperCamelCase_       :    int						      =			logging.get_logger(__name__)


UpperCamelCase_       :    str						      =			{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase_       :    List[Any]						      =			{
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
        '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
    },
}

UpperCamelCase_       :    Optional[Any]						      =			{
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
UpperCamelCase_       :    str						      =			['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']



class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					VOCAB_FILES_NAMES
			snake_case						   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case						   =					PRETRAINED_VOCAB_FILES_MAP
			snake_case						   =					["input_ids", "attention_mask"]
			snake_case						   =					MBartTokenizer

			snake_case						   =					[]
			snake_case						   =					[]




			def __init__(					self    :       List[str]							, _snake_case    :       Tuple=None							, _snake_case    :       int=None							, _snake_case    :       List[Any]="<s>"							, _snake_case    :       Tuple="</s>"							, _snake_case    :       str="</s>"							, _snake_case    :       List[Any]="<s>"							, _snake_case    :       Dict="<unk>"							, _snake_case    :       str="<pad>"							, _snake_case    :       Any="<mask>"							, _snake_case    :       int=None							, _snake_case    :       Optional[int]=None							, _snake_case    :       Any=None							, **_snake_case    :       Union[str, Any]							, )   ->			List[str]:







								"""simple docstring"""
								# Mask token behave like a normal word, i.e. include the space before it
								A_								=    AddedToken(_snake_case							, lstrip=_snake_case							, rstrip=_snake_case						) if isinstance(_snake_case							, _snake_case						) else mask_token

								super().__init__(
								    vocab_file=_snake_case							, tokenizer_file=_snake_case							, bos_token=_snake_case							, eos_token=_snake_case							, sep_token=_snake_case							, cls_token=_snake_case							, unk_token=_snake_case							, pad_token=_snake_case							, mask_token=_snake_case							, src_lang=_snake_case							, tgt_lang=_snake_case							, additional_special_tokens=_snake_case							, **_snake_case							, )

								A_								=    vocab_file
								A_								=    False if not self.vocab_file else True

								A_								=    FAIRSEQ_LANGUAGE_CODES.copy()

								if additional_special_tokens is not None:
													# Only add those special tokens if they are not already there.
													_additional_special_tokens.extend(
													    [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

								self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}						)
								A_								=    {
								    lang_code: self.convert_tokens_to_ids(_snake_case						) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

								A_								=    src_lang if src_lang is not None else "en_XX"
								A_								=    self.convert_tokens_to_ids(self._src_lang						)
								A_								=    tgt_lang
								self.set_src_lang_special_tokens(self._src_lang						)




			@property
			def   lowerCamelCase__  (					self    :       Dict						)   ->			str:







								"""simple docstring"""
								return self._src_lang




			@src_lang.setter
			def   lowerCamelCase__  (					self    :       Tuple							, _snake_case    :       str						)   ->			None:







								"""simple docstring"""
								A_								=    new_src_lang
								self.set_src_lang_special_tokens(self._src_lang						)




			def   lowerCamelCase__  (					self    :       Optional[Any]							, _snake_case    :       List[int]							, _snake_case    :       Optional[List[int]] = None						)   ->			List[int]:







								"""simple docstring"""
								if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       List[int]							, _snake_case    :       Optional[List[int]] = None						)   ->			List[int]:







								"""simple docstring"""
								A_								=    [self.sep_token_id]
								A_								=    [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]




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       str							, _snake_case    :       str							, _snake_case    :       Optional[str]							, _snake_case    :       Optional[str]							, **_snake_case    :       Optional[int]						)   ->			str:







								"""simple docstring"""
								if src_lang is None or tgt_lang is None:
													raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"						)
								A_								=    src_lang
								A_								=    self(_snake_case							, add_special_tokens=_snake_case							, return_tensors=_snake_case							, **_snake_case						)
								A_								=    self.convert_tokens_to_ids(_snake_case						)
								A_								=    tgt_lang_id
								return inputs




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       List[str]							, _snake_case    :       str = "en_XX"							, _snake_case    :       Optional[List[str]] = None							, _snake_case    :       str = "ro_RO"							, **_snake_case    :       str							, )   ->			BatchEncoding:







								"""simple docstring"""
								A_								=    src_lang
								A_								=    tgt_lang
								return super().prepare_seqaseq_batch(_snake_case							, _snake_case							, **_snake_case						)




			def   lowerCamelCase__  (					self    :       Any						)   ->			Optional[Any]:







								"""simple docstring"""
								return self.set_src_lang_special_tokens(self.src_lang						)




			def   lowerCamelCase__  (					self    :       Dict						)   ->			List[Any]:







								"""simple docstring"""
								return self.set_tgt_lang_special_tokens(self.tgt_lang						)




			def   lowerCamelCase__  (					self    :       Tuple							, _snake_case    :       List[str]						)   ->			None:







								"""simple docstring"""
								A_								=    self.convert_tokens_to_ids(_snake_case						)
								A_								=    []
								A_								=    [self.eos_token_id, self.cur_lang_code]

								A_								=    self.convert_ids_to_tokens(self.prefix_tokens						)
								A_								=    self.convert_ids_to_tokens(self.suffix_tokens						)

								A_								=    processors.TemplateProcessing(
								    single=prefix_tokens_str + ["$A"] + suffix_tokens_str							, pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str							, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							, self.prefix_tokens + self.suffix_tokens						)						)							, )




			def   lowerCamelCase__  (					self    :       List[str]							, _snake_case    :       str						)   ->			None:







								"""simple docstring"""
								A_								=    self.convert_tokens_to_ids(_snake_case						)
								A_								=    []
								A_								=    [self.eos_token_id, self.cur_lang_code]

								A_								=    self.convert_ids_to_tokens(self.prefix_tokens						)
								A_								=    self.convert_ids_to_tokens(self.suffix_tokens						)

								A_								=    processors.TemplateProcessing(
								    single=prefix_tokens_str + ["$A"] + suffix_tokens_str							, pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str							, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							, self.prefix_tokens + self.suffix_tokens						)						)							, )






			def   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       str							, _snake_case    :       Optional[str] = None						)   ->			Tuple[str]:







								"""simple docstring"""
								if not self.can_save_slow_tokenizer:
													raise ValueError(
													    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
													    "tokenizer."						)

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

								if os.path.abspath(self.vocab_file						) != os.path.abspath(_snake_case						):
													copyfile(self.vocab_file							, _snake_case						)

								return (out_vocab_file,)




