
from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


lowercase__		:Optional[int]			  =   logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase__		:Dict			  =   "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")\n        >>> pipe.to(\"cuda\")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save(\"cat.png\")\n        ```\n"



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__=8		):
					'''simple docstring'''
					lowercase						     =						h // scale_factor**2
					if h % scale_factor**2 != 0:
										new_h += 1
					lowercase						     =						w // scale_factor**2
					if w % scale_factor**2 != 0:
										new_w += 1
					return new_h * scale_factor, new_w * scale_factor







class     lowercase		( SCREAMING_SNAKE_CASE__		):




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

												self.register_modules(
												    text_encoder=A__       ,tokenizer=A__       ,unet=A__       ,scheduler=A__       ,movq=A__       ,)
												lowercase						     =						2 ** (len(self.movq.config.block_out_channels) - 1)




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												if latents is None:
																	lowercase						     =						randn_tensor(A__       ,generator=A__       ,device=A__       ,dtype=A__)
												else:
																	if latents.shape != shape:
																						raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}')
																	lowercase						     =						latents.to(A__)

												lowercase						     =						latents * scheduler.init_noise_sigma
												return latents




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__=None       ,):
												lowercase						     =						len(A__) if isinstance(A__       ,A__) else 1
												# get prompt text embeddings
												lowercase						     =						self.tokenizer(
												    A__       ,padding='''max_length'''       ,truncation=A__       ,max_length=7_7       ,return_attention_mask=A__       ,add_special_tokens=A__       ,return_tensors='''pt'''       ,)

												lowercase						     =						text_inputs.input_ids
												lowercase						     =						self.tokenizer(A__       ,padding='''longest'''       ,return_tensors='''pt''').input_ids

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

												lowercase						     =						text_input_ids.to(A__)
												lowercase						     =						text_inputs.attention_mask.to(A__)

												lowercase     ,						lowercase						     =						self.text_encoder(
												    input_ids=A__       ,attention_mask=A__)

												lowercase						     =						prompt_embeds.repeat_interleave(A__       ,dim=0)
												lowercase						     =						text_encoder_hidden_states.repeat_interleave(A__       ,dim=0)
												lowercase						     =						text_mask.repeat_interleave(A__       ,dim=0)

												if do_classifier_free_guidance:
																	lowercase						     =						42
																	if negative_prompt is None:
																						lowercase						     =						[''''''] * batch_size
																	elif type(A__) is not type(A__):
																						raise TypeError(
																						    f'`negative_prompt` should be the same type to `prompt`, but got {type(A__)} !='
																						    f' {type(A__)}.')
																	elif isinstance(A__       ,A__):
																						lowercase						     =						[negative_prompt]
																	elif batch_size != len(A__):
																						raise ValueError(
																						    f'`negative_prompt`: {negative_prompt} has batch size {len(A__)}, but `prompt`:'
																						    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
																						    ''' the batch size of `prompt`.''')
																	else:
																						lowercase						     =						negative_prompt

																	lowercase						     =						self.tokenizer(
																	    A__       ,padding='''max_length'''       ,max_length=7_7       ,truncation=A__       ,return_attention_mask=A__       ,add_special_tokens=A__       ,return_tensors='''pt'''       ,)
																	lowercase						     =						uncond_input.input_ids.to(A__)
																	lowercase						     =						uncond_input.attention_mask.to(A__)

																	lowercase     ,						lowercase						     =						self.text_encoder(
																	    input_ids=A__       ,attention_mask=A__)

																	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

																	lowercase						     =						negative_prompt_embeds.shape[1]
																	lowercase						     =						negative_prompt_embeds.repeat(1       ,A__)
																	lowercase						     =						negative_prompt_embeds.view(batch_size * num_images_per_prompt       ,A__)

																	lowercase						     =						uncond_text_encoder_hidden_states.shape[1]
																	lowercase						     =						uncond_text_encoder_hidden_states.repeat(1       ,A__       ,1)
																	lowercase						     =						uncond_text_encoder_hidden_states.view(
																	    batch_size * num_images_per_prompt       ,A__       ,-1)
																	lowercase						     =						uncond_text_mask.repeat_interleave(A__       ,dim=0)

																	# done duplicates

																	# 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
																	lowercase						     =						torch.cat([negative_prompt_embeds, prompt_embeds])
																	lowercase						     =						torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])

																	lowercase						     =						torch.cat([uncond_text_mask, text_mask])

												return prompt_embeds, text_encoder_hidden_states, text_mask




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

												lowercase						     =						torch.device(f'cuda:{gpu_id}')

												lowercase						     =						[
												    self.unet,
												    self.text_encoder,
												    self.movq,
												]
												for cpu_offloaded_model in models:
																	if cpu_offloaded_model is not None:
																						cpu_offload(A__       ,A__)




							def 			A__				(    self       ,A__=0):
												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.''')

												lowercase						     =						torch.device(f'cuda:{gpu_id}')

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

												lowercase						     =						None
												for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
																	lowercase     ,						lowercase						     =						cpu_offload_with_hook(A__       ,A__       ,prev_module_hook=A__)

												if self.safety_checker is not None:
																	lowercase     ,						lowercase						     =						cpu_offload_with_hook(self.safety_checker       ,A__       ,prev_module_hook=A__)

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




							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def 			A__				(    self):
												if not hasattr(self.unet       ,'''_hf_hook'''):
																	return self.device
												for module in self.unet.modules():
																	if (
																	    hasattr(A__       ,'''_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





							@torch.no_grad()
							@replace_example_docstring(A__)
							def __call__(    self       ,A__       ,A__       ,A__       ,A__ = None       ,A__ = 5_1_2       ,A__ = 5_1_2       ,A__ = 1_0_0       ,A__ = 4.0       ,A__ = 1       ,A__ = None       ,A__ = None       ,A__ = "pil"       ,A__ = True       ,):

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

												lowercase						     =						self._execution_device

												lowercase						     =						batch_size * num_images_per_prompt
												lowercase						     =						guidance_scale > 1.0

												lowercase     ,						lowercase     ,						lowercase						     =						self._encode_prompt(
												    A__       ,A__       ,A__       ,A__       ,A__)

												if isinstance(A__       ,A__):
																	lowercase						     =						torch.cat(A__       ,dim=0)
												if isinstance(A__       ,A__):
																	lowercase						     =						torch.cat(A__       ,dim=0)

												if do_classifier_free_guidance:
																	lowercase						     =						image_embeds.repeat_interleave(A__       ,dim=0)
																	lowercase						     =						negative_image_embeds.repeat_interleave(A__       ,dim=0)

												lowercase						     =						torch.cat([negative_image_embeds, image_embeds]       ,dim=0).to(
												    dtype=prompt_embeds.dtype       ,device=A__)

												self.scheduler.set_timesteps(A__       ,device=A__)
												lowercase						     =						self.scheduler.timesteps

												lowercase						     =						self.unet.config.in_channels

												lowercase     ,						lowercase						     =						get_new_h_w(A__       ,A__       ,self.movq_scale_factor)

												# create initial latent
												lowercase						     =						self.prepare_latents(
												    (batch_size, num_channels_latents, height, width)       ,text_encoder_hidden_states.dtype       ,A__       ,A__       ,A__       ,self.scheduler       ,)

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

																	lowercase						     =						{'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
																	lowercase						     =						self.unet(
																	    sample=A__       ,timestep=A__       ,encoder_hidden_states=A__       ,added_cond_kwargs=A__       ,return_dict=A__       ,)[0]

																	if do_classifier_free_guidance:
																						lowercase     ,						lowercase						     =						noise_pred.split(latents.shape[1]       ,dim=1)
																						lowercase     ,						lowercase						     =						noise_pred.chunk(2)
																						lowercase     ,						lowercase						     =						variance_pred.chunk(2)
																						lowercase						     =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						lowercase						     =						torch.cat([noise_pred, variance_pred_text]       ,dim=1)

																	if not (
																	    hasattr(self.scheduler.config       ,'''variance_type''')
																	    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																	):
																						lowercase     ,						lowercase						     =						noise_pred.split(latents.shape[1]       ,dim=1)

																	# compute the previous noisy sample x_t -> x_t-1
																	lowercase						     =						self.scheduler.step(
																	    A__       ,A__       ,A__       ,generator=A__       ,).prev_sample
												# post-processing
												lowercase						     =						self.movq.decode(A__       ,force_not_quantize=A__)['''sample''']

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

												if output_type in ["np", "pil"]:
																	lowercase						     =						image * 0.5 + 0.5
																	lowercase						     =						image.clamp(0       ,1)
																	lowercase						     =						image.cpu().permute(0       ,2       ,3       ,1).float().numpy()

												if output_type == "pil":
																	lowercase						     =						self.numpy_to_pil(A__)

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=A__)






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


lowercase__		:str			  =   logging.get_logger(__name__)

lowercase__		:Any			  =   {"vocab_file": "sentencepiece.bpe.model"}

lowercase__		:Tuple			  =   {
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

lowercase__		:str			  =   {
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

lowercase__		:int			  =   "▁"







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Union[str, Any]	 =VOCAB_FILES_NAMES
							lowercase_	:	List[Any]	 =PRETRAINED_VOCAB_FILES_MAP
							lowercase_	:	Tuple	 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_	:	str	 =['''input_ids''', '''attention_mask''']




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

												lowercase						     =						{} if sp_model_kwargs is None else sp_model_kwargs

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

												lowercase						     =						vocab_file
												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(str(A__))

												lowercase						     =						{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

												lowercase						     =						len(self.sp_model) - 1
												lowercase						     =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}




							def 			A__				(    self       ,A__       ,A__ = None):

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




							def 			A__				(    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, 1] + ([0] * len(A__)) + [1]




							def 			A__				(    self       ,A__       ,A__ = None):
												lowercase						     =						[self.sep_token_id]
												lowercase						     =						[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 			A__				(    self):
												return len(self.sp_model)




							def 			A__				(    self):
												lowercase						     =						{self.convert_ids_to_tokens(A__): i for i in range(self.vocab_size)}
												vocab.update(self.added_tokens_encoder)
												return vocab




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




							def 			A__				(    self       ,A__):
												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												lowercase						     =						self.sp_model.PieceToId(A__)

												return spm_id if spm_id else self.unk_token_id




							def 			A__				(    self       ,A__):
												if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(A__)




							def 			A__				(    self       ,A__):
												lowercase						     =						[]
												lowercase						     =						''''''
												lowercase						     =						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						     =						True
																						lowercase						     =						[]
																	else:
																						current_sub_tokens.append(A__)
																						lowercase						     =						False
												out_string += self.sp_model.decode(A__)
												return out_string.strip()




							def __getstate__(    self):
												lowercase						     =						self.__dict__.copy()
												lowercase						     =						None
												return state




							def __setstate__(    self       ,A__):
												lowercase						     =						d

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

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





							def 			A__				(    self       ,A__       ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return
												lowercase						     =						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						     =						self.sp_model.serialized_model_proto()
																						fi.write(A__)

												return (out_vocab_file,)




