
'''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__ :			Optional[Any]											=		logging.get_logger(__name__)  # pylint: disable=invalid-name






class  A  (						SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__     ):





	@register_to_config
	def __init__(       self      :							str   ,				__magic_name__      :							bool   ,				__magic_name__      :							Optional[int] = None   ,				__magic_name__      :							Optional[int] = None				):


				"""simple docstring"""

				super().__init__()

				lowerCAmelCase__										=				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"

							lowerCAmelCase__										=				torch.zeros(__magic_name__   ,				__magic_name__				)
				else:
							lowerCAmelCase__										=				None

				lowerCAmelCase__										=				torch.nn.Parameter(__magic_name__				)






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :VQModel
	snake_case__   :CLIPTextModel
	snake_case__   :CLIPTokenizer
	snake_case__   :TransformeraDModel
	snake_case__   :LearnedClassifierFreeSamplingEmbeddings
	snake_case__   :VQDiffusionScheduler





	def __init__(       self      :							Union[str, Any]   ,				__magic_name__      :							VQModel   ,				__magic_name__      :							CLIPTextModel   ,				__magic_name__      :							CLIPTokenizer   ,				__magic_name__      :							TransformeraDModel   ,				__magic_name__      :							VQDiffusionScheduler   ,				__magic_name__      :							LearnedClassifierFreeSamplingEmbeddings   ,				):


				"""simple docstring"""

				super().__init__()

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							List[str]   ,				__magic_name__      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				len(__magic_name__				) if isinstance(__magic_name__   ,				__magic_name__				) else 1

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

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

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

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

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

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

				return prompt_embeds





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


				"""simple docstring"""

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

				lowerCAmelCase__										=				batch_size * num_images_per_prompt

				lowerCAmelCase__										=				guidance_scale > 1.0

				lowerCAmelCase__										=				self._encode_prompt(__magic_name__   ,				__magic_name__   ,				__magic_name__				)

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

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

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

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

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

				lowerCAmelCase__										=				latents

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

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

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

							lowerCAmelCase__										=				self.truncate(__magic_name__   ,				__magic_name__				)

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

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

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

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

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

				if output_type == "pil":
							lowerCAmelCase__										=				self.numpy_to_pil(__magic_name__				)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=__magic_name__				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				__magic_name__      :							torch.FloatTensor   ,				__magic_name__      :							float				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				torch.sort(__magic_name__   ,				1   ,				descending=__magic_name__				)
				lowerCAmelCase__										=				torch.exp(__magic_name__				)
				lowerCAmelCase__										=				sorted_p_x_0.cumsum(dim=1				) < truncation_rate

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

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

				lowerCAmelCase__										=				log_p_x_0.clone()

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

				return rv












"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def        lowercase__     (       lowercase_      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							[
          "encoder.version",
          "decoder.version",
          "model.encoder.version",
          "model.decoder.version",
          "_float_tensor",
          "decoder.output_projection.weight",
      ]
      for k in ignore_keys:
            state_dict.pop(lowercase_		,lowercase_      )




def        lowercase__     (       lowercase_      )       ->		List[Any]:




      """simple docstring"""

      _UpperCamelCase,	_UpperCamelCase   :						Union[str, Any]    =							emb.weight.shape
      _UpperCamelCase   :						str    =							nn.Linear(lowercase_		,lowercase_		,bias=lowercase_      )
      _UpperCamelCase   :						List[Any]    =							emb.weight.data
      return lin_layer




def        lowercase__     (       lowercase_		,lowercase_="facebook/mbart-large-en-ro"		,lowercase_=False		,lowercase_=False      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						int    =							torch.load(lowercase_		,map_location="cpu"      )["model"]
      remove_ignore_keys_(lowercase_      )
      _UpperCamelCase   :						str    =							state_dict["encoder.embed_tokens.weight"].shape[0]

      _UpperCamelCase   :						int    =							MBartConfig.from_pretrained(lowercase_		,vocab_size=lowercase_      )
      if mbart_aa and finetuned:
            _UpperCamelCase   :						Union[str, Any]    =							"relu"

      _UpperCamelCase   :						Optional[int]    =							state_dict["decoder.embed_tokens.weight"]
      _UpperCamelCase   :						List[str]    =							MBartForConditionalGeneration(lowercase_      )
      model.model.load_state_dict(lowercase_      )

      if finetuned:
            _UpperCamelCase   :						Optional[Any]    =							make_linear_from_emb(model.model.shared      )

      return model


if __name__ == "__main__":
  lowerCamelCase__						= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
  )
  parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument(
      "--hf_config",
      default="facebook/mbart-large-cc25",
      type=str,
      help="Which huggingface architecture to use: mbart-large",
  )
  parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
  parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
  lowerCamelCase__						= parser.parse_args()
  lowerCamelCase__						= convert_fairseq_mbart_checkpoint_from_disk(
      args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
  )
  model.save_pretrained(args.pytorch_dump_folder_path)
