






'''simple docstring'''
import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
						raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
__lowerCAmelCase							=logging.get_logger(__name__)

__lowerCAmelCase							='Hello world! cécé herlolip'

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )				->				int:






					"""simple docstring"""







					a_							,					a_									=   os.path.split(lowercase__ )
					a_									=   DataaVecTextModel.from_pretrained(
					    lowercase__					,  checkpoint_file=lowercase__ )
					dataavec.eval()  # disable dropout
					a_									=   dataavec.models[0]
					a_									=   dataavec_model.encoder.sentence_encoder
					a_									=   DataaVecTextConfig(
					    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings					,  hidden_size=dataavec_model.args.encoder_embed_dim					,  num_hidden_layers=dataavec_model.args.encoder_layers					,  num_attention_heads=dataavec_model.args.encoder_attention_heads					,  intermediate_size=dataavec_model.args.encoder_ffn_embed_dim					,  max_position_embeddings=5_1_4					,  type_vocab_size=1					,  layer_norm_eps=1E-5					,  )
					if classification_head:
										a_									=   dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
					print('Our BERT config:'					,  lowercase__ )

					a_									=   DataaVecTextForSequenceClassification(lowercase__ ) if classification_head else DataaVecTextForMaskedLM(lowercase__ )
					model.eval()

					# Now let's copy all the weights.
					# Embeddings
					a_									=   dataavec_sent_encoder.embed_tokens.weight
					a_									=   dataavec_sent_encoder.embed_positions.weight
					a_									=   torch.zeros_like(
					    model.dataavec_text.embeddings.token_type_embeddings.weight )  # just zero them out b/c data2vec doesn't use them.
					a_									=   dataavec_sent_encoder.layernorm_embedding.weight
					a_									=   dataavec_sent_encoder.layernorm_embedding.bias

					for i in range(config.num_hidden_layers ):
										# Encoder: start of layer
										a_									=   model.dataavec_text.encoder.layer[i]
										a_									=   dataavec_sent_encoder.layers[i]

										# self attention
										a_									=   layer.attention.self
										assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
										    (config.hidden_size, config.hidden_size) ), (
										    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
										    F''' {torch.Size((config.hidden_size, config.hidden_size) )}'''
										)
										assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
										    (config.hidden_size, config.hidden_size) ), (
										    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
										    F''' {torch.Size((config.hidden_size, config.hidden_size) )}'''
										)
										assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
										    (config.hidden_size, config.hidden_size) ), (
										    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
										    F''' {torch.Size((config.hidden_size, config.hidden_size) )}'''
										)

										a_									=   dataavec_layer.self_attn.q_proj.weight
										a_									=   dataavec_layer.self_attn.q_proj.bias
										a_									=   dataavec_layer.self_attn.k_proj.weight
										a_									=   dataavec_layer.self_attn.k_proj.bias
										a_									=   dataavec_layer.self_attn.v_proj.weight
										a_									=   dataavec_layer.self_attn.v_proj.bias

										# self-attention output
										a_									=   layer.attention.output
										assert (
										    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
										), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
										a_									=   dataavec_layer.self_attn.out_proj.weight
										a_									=   dataavec_layer.self_attn.out_proj.bias
										a_									=   dataavec_layer.self_attn_layer_norm.weight
										a_									=   dataavec_layer.self_attn_layer_norm.bias

										# intermediate
										a_									=   layer.intermediate
										assert (
										    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
										), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
										a_									=   dataavec_layer.fca.weight
										a_									=   dataavec_layer.fca.bias

										# output
										a_									=   layer.output
										assert (
										    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
										), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
										a_									=   dataavec_layer.fca.weight
										a_									=   dataavec_layer.fca.bias
										a_									=   dataavec_layer.final_layer_norm.weight
										a_									=   dataavec_layer.final_layer_norm.bias
										# end of layer

					if classification_head:
										a_									=   dataavec.model.classification_heads['mnli'].dense.weight
										a_									=   dataavec.model.classification_heads['mnli'].dense.bias
										a_									=   dataavec.model.classification_heads['mnli'].out_proj.weight
										a_									=   dataavec.model.classification_heads['mnli'].out_proj.bias
					else:
										# LM Head
										a_									=   dataavec_model.encoder.lm_head.dense.weight
										a_									=   dataavec_model.encoder.lm_head.dense.bias
										a_									=   dataavec_model.encoder.lm_head.layer_norm.weight
										a_									=   dataavec_model.encoder.lm_head.layer_norm.bias
										a_									=   dataavec_model.encoder.lm_head.weight
										a_									=   dataavec_model.encoder.lm_head.bias

					# Let's check that we get the same results.
					a_									=   dataavec.encode(lowercase__ ).unsqueeze(0 )  # batch of size 1

					a_									=   model(lowercase__ )[0]
					if classification_head:
										a_									=   dataavec.model.classification_heads['mnli'](dataavec.extract_features(lowercase__ ) )
					else:
										a_									=   dataavec_model(lowercase__ )[0]
					print(our_output.shape					,  their_output.shape )
					a_									=   torch.max(torch.abs(our_output - their_output ) ).item()
					print(F'''max_absolute_diff = {max_absolute_diff}''' )  # ~ 1e-7
					a_									=   torch.allclose(lowercase__					,  lowercase__					,  atol=1E-3 )
					print('Do both models output the same tensors?'					,  '🔥' if success else '💩' )
					if not success:
										raise Exception('Something went wRoNg' )

					pathlib.Path(lowercase__ ).mkdir(parents=lowercase__					,  exist_ok=lowercase__ )
					print(F'''Saving model to {pytorch_dump_folder_path}''' )
					model.save_pretrained(lowercase__ )


if __name__ == "__main__":
						__lowerCAmelCase							=argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						parser.add_argument(
						    "--classification_head", action="store_true", help="Whether to convert a final classification head."
						)
						__lowerCAmelCase							=parser.parse_args()
						convert_dataavec_checkpoint_to_pytorch(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)









'''simple docstring'''
from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)


class    _snake_case   (				snake_case					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , )      ->	List[str]:
										super().__init__()

										self.register_modules(vae=UpperCAmelCase__   , text_encoder=UpperCAmelCase__   , tokenizer=UpperCAmelCase__   , unet=UpperCAmelCase__   , scheduler=UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	int:
										with torch.no_grad():
															a_									=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase__     ).unsqueeze(0     ).to(self.device     ) * 2 - 1     )
															a_									=   0.1_8_2_1_5 * latent.latent_dist.sample()
										return latent




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	List[str]:
										a_									=   (1 / 0.1_8_2_1_5) * latent
										with torch.no_grad():
															a_									=   self.vae.decode(UpperCAmelCase__     ).sample
										a_									=   (img / 2 + 0.5).clamp(0   , 1     )
										a_									=   img.detach().cpu().permute(0   , 2   , 3   , 1     ).numpy()
										a_									=   (img * 255).round().astype('uint8'     )
										return Image.fromarray(img[0]     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	Any:
										a_									=   self.tokenizer(
										    UpperCAmelCase__   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

										a_									=   self.text_encoder(text_input.input_ids.to(self.device     )     )[0]

										a_									=   self.tokenizer(
										    ''   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

										a_									=   self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]

										return torch.cat([uncond_embedding, text_embedding]     )




					def __call__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__ = 0.3   , UpperCAmelCase__ = 0.6   , UpperCAmelCase__ = 0.5   , UpperCAmelCase__ = 42   , UpperCAmelCase__ = 50   , UpperCAmelCase__ = 7.5   , )      ->	Image.Image:
										a_									=   steps - int(kmin * steps     )
										a_									=   steps - int(kmax * steps     )

										a_									=   self.prep_text(UpperCAmelCase__     )

										self.scheduler.set_timesteps(UpperCAmelCase__     )

										a_							,					a_									=   img.size
										a_									=   self.encode(UpperCAmelCase__     )

										torch.manual_seed(UpperCAmelCase__     )
										a_									=   torch.randn(
										    (1, self.unet.config.in_channels, height // 8, width // 8)   , ).to(self.device     )

										a_									=   self.scheduler.add_noise(
										    UpperCAmelCase__   , UpperCAmelCase__   , timesteps=self.scheduler.timesteps[tmax]   , )

										a_									=   torch.cat([latents] * 2     )

										a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , self.scheduler.timesteps[tmax]     )

										with torch.no_grad():
															a_									=   self.unet(
															    UpperCAmelCase__   , self.scheduler.timesteps[tmax]   , encoder_hidden_states=UpperCAmelCase__   , ).sample

										a_							,					a_									=   pred.chunk(2     )
										a_									=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

										a_									=   self.scheduler.step(UpperCAmelCase__   , self.scheduler.timesteps[tmax]   , UpperCAmelCase__     ).prev_sample

										for i, t in enumerate(tqdm(self.scheduler.timesteps     )     ):
															if i > tmax:
																				if i < tmin:  # layout generation phase
																									a_									=   self.scheduler.add_noise(
																									    UpperCAmelCase__   , UpperCAmelCase__   , timesteps=UpperCAmelCase__   , )

																									a_									=   (mix_factor * latents) + (
																									    1 - mix_factor
																									) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																									a_									=   torch.cat([input] * 2     )

																				else:  # content generation phase
																									a_									=   torch.cat([latents] * 2     )

																				a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , UpperCAmelCase__     )

																				with torch.no_grad():
																									a_									=   self.unet(
																									    UpperCAmelCase__   , UpperCAmelCase__   , encoder_hidden_states=UpperCAmelCase__   , ).sample

																				a_							,					a_									=   pred.chunk(2     )
																				a_									=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

																				a_									=   self.scheduler.step(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     ).prev_sample

										return self.decode(UpperCAmelCase__     )

