
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    a_ (			_a					,			_a    ):






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

									_lowercase					       =					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"

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

									_lowercase					       =					torch.nn.Parameter(__UpperCamelCase  )







class    a_ (			_a    ):
			a	:			VQModel
			a	:			CLIPTextModel
			a	:			CLIPTokenizer
			a	:			TransformeraDModel
			a	:			LearnedClassifierFreeSamplingEmbeddings
			a	:			VQDiffusionScheduler






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			):
									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				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					len(__UpperCamelCase  ) if isinstance(__UpperCamelCase				,			__UpperCamelCase  ) else 1

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

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

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

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

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

															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
															_lowercase					       =					negative_prompt_embeds.shape[1]
															_lowercase					       =					negative_prompt_embeds.repeat(1				,			__UpperCamelCase				,			1  )
															_lowercase					       =					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
															_lowercase					       =					torch.cat([negative_prompt_embeds, prompt_embeds]  )

									return prompt_embeds






			@torch.no_grad()
			def __call__(      self				,			__UpperCamelCase				,			__UpperCamelCase = 100				,			__UpperCamelCase = 5.0				,			__UpperCamelCase = 1.0				,			__UpperCamelCase = 1				,			__UpperCamelCase = None				,			__UpperCamelCase = None				,			__UpperCamelCase = "pil"				,			__UpperCamelCase = True				,			__UpperCamelCase = None				,			__UpperCamelCase = 1				,			):
									if isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					1
									elif isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					len(__UpperCamelCase  )
									else:
															raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase  )}"""  )

									_lowercase					       =					batch_size * num_images_per_prompt

									_lowercase					       =					guidance_scale > 1.0

									_lowercase					       =					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

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

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

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

									_lowercase					       =					latents

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

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

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

															_lowercase					       =					self.truncate(__UpperCamelCase				,			__UpperCamelCase  )

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

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

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

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

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

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase       ,						_lowercase					       =					torch.sort(__UpperCamelCase				,			1				,			descending=__UpperCamelCase  )
									_lowercase					       =					torch.exp(__UpperCamelCase  )
									_lowercase					       =					sorted_p_x_0.cumsum(dim=1  ) < truncation_rate

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

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

									_lowercase					       =					log_p_x_0.clone()

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

									return rv

import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


A		: int								=     ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
A		: Any								=     {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
		raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
A		: Optional[int]								=     logging.get_logger(__name__)

A		: int								=     ''' Hello world! cécé herlolip'''

A		: List[Any]								=     [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     Tuple:
						_lowercase					       =					[
						    """encoder.version""",
						    """decoder.version""",
						    """model.encoder.version""",
						    """model.decoder.version""",
						    """_float_tensor""",
						]
						for k in ignore_keys:
												state_dict.pop(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]		,   SCREAMING_SNAKE_CASE_	:	Optional[int]		,   SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     List[str]:
						_lowercase					       =					dct.pop(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					val




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any       )  ->     Any:
						_lowercase					       =					torch.load(SCREAMING_SNAKE_CASE_		,   map_location="""cpu"""       )
						_lowercase					       =					torch.hub.load("""pytorch/fairseq"""		,   """bart.large.cnn"""       ).eval()
						hub_interface.model.load_state_dict(sd["""model"""]       )
						return hub_interface




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     Optional[int]:
						_lowercase       ,						_lowercase					       =					emb.weight.shape
						_lowercase					       =					nn.Linear(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   bias=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					emb.weight.data
						return lin_layer




@torch.no_grad()
def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any		,   SCREAMING_SNAKE_CASE_	:	Tuple		,   SCREAMING_SNAKE_CASE_	:	int=None       )  ->     int:
						if not os.path.exists(SCREAMING_SNAKE_CASE_       ):
												_lowercase					       =					torch.hub.load("""pytorch/fairseq"""		,   SCREAMING_SNAKE_CASE_       ).eval()
						else:
												_lowercase					       =					load_xsum_checkpoint(SCREAMING_SNAKE_CASE_       )

						bart.model.upgrade_state_dict(bart.model.state_dict()       )
						if hf_checkpoint_name is None:
												_lowercase					       =					checkpoint_path.replace("""."""		,   """-"""       )
						_lowercase					       =					BartConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					bart.encode(SCREAMING_SNAKE_CASE_       ).unsqueeze(0       )
						_lowercase					       =					BartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       ).encode(SCREAMING_SNAKE_CASE_		,   return_tensors="""pt"""       ).unsqueeze(0       )
						if not torch.eq(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       ).all():
												raise ValueError(
												    f"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""       )

						if checkpoint_path == "bart.large.mnli":
												_lowercase					       =					bart.state_dict()
												remove_ignore_keys_(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					state_dict["""model.decoder.embed_tokens.weight"""]
												for src, dest in mnli_rename_keys:
																		rename_key(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					BartForSequenceClassification(SCREAMING_SNAKE_CASE_       ).eval()
												model.load_state_dict(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					bart.predict("""mnli"""		,   SCREAMING_SNAKE_CASE_		,   return_logits=SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					model(SCREAMING_SNAKE_CASE_       )[0]  # logits
						else:  # no classification heads to worry about
												_lowercase					       =					bart.model.state_dict()
												remove_ignore_keys_(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					state_dict["""decoder.embed_tokens.weight"""]
												_lowercase					       =					bart.extract_features(SCREAMING_SNAKE_CASE_       )
												if hf_checkpoint_name == "facebook/bart-large":
																		_lowercase					       =					BartModel(SCREAMING_SNAKE_CASE_       ).eval()
																		model.load_state_dict(SCREAMING_SNAKE_CASE_       )
																		_lowercase					       =					model(SCREAMING_SNAKE_CASE_       ).model[0]
												else:
																		_lowercase					       =					BartForConditionalGeneration(SCREAMING_SNAKE_CASE_       ).eval()  # an existing summarization ckpt
																		model.model.load_state_dict(SCREAMING_SNAKE_CASE_       )
																		if hasattr(SCREAMING_SNAKE_CASE_		,   """lm_head"""       ):
																								_lowercase					       =					make_linear_from_emb(model.model.shared       )
																		_lowercase					       =					model.model(SCREAMING_SNAKE_CASE_       )[0]

    # Check results
						if fairseq_output.shape != new_model_outputs.shape:
												raise ValueError(
												    f"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""       )
						if (fairseq_output != new_model_outputs).any().item():
												raise ValueError("""Some values in `fairseq_output` are different from `new_model_outputs`"""       )
						Path(SCREAMING_SNAKE_CASE_       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_       )
						model.save_pretrained(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
		A		: List[Any]								=     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=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
		)
		A		: Any								=     parser.parse_args()
		convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)