



from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)

class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_	,	a_	,	a_	,	a_	,	a_ = 0.93	,	a_ = True	,	):
											a__					   =		mixing_coeff
											a__					   =		leapfrog_steps

											super().__init__()
											self.register_modules(
											    vae=a_	,	text_encoder=a_	,	tokenizer=a_	,	unet=a_	,	scheduler=a_	,	)

											a__					   =		2 ** (len(self.vae.config.block_out_channels		) - 1)
											a__					   =		VaeImageProcessor(vae_scale_factor=self.vae_scale_factor		)


							def _a   (				self	,	a_	,	a_ = None	,	a_ = False		):
											a__					   =		self.tokenizer(
											    a_	,	padding="""max_length"""	,	max_length=self.tokenizer.model_max_length	,	truncation=a_	,	return_tensors="""pt"""	,	)

											a__					   =		self.text_encoder(text_inputs.input_ids.to(self.device		)		).last_hidden_state

											a__					   =		prompt_embeds.to(dtype=self.text_encoder.dtype	,	device=self.device		)

											if do_classifier_free_guidance:
															a__					   =		"""""" if negative_prompt is None else negative_prompt

															a__					   =		self.tokenizer(
															    a_	,	padding="""max_length"""	,	max_length=self.tokenizer.model_max_length	,	truncation=a_	,	return_tensors="""pt"""	,	)

															a__					   =		self.text_encoder(uncond_input.input_ids.to(self.device		)		).last_hidden_state

															a__					   =		torch.cat([negative_prompt_embeds, prompt_embeds]		)

											return prompt_embeds


							def _a   (				self	,	a_	,	a_		):
											a__					   =		self.mixing_coeff * x + (1 - self.mixing_coeff) * y
											a__					   =		self.mixing_coeff * y + (1 - self.mixing_coeff) * x

											return [x, y]


							def _a   (				self	,	a_	,	a_		):
											a__					   =		(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
											a__					   =		(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

											return [x, y]


							def _a   (				self	,	a_		):
											# as self.alphas_cumprod is always in cpu
											a__					   =		int(a_		)

											a__					   =		self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

											return alpha_prod, 1 - alpha_prod


							def _a   (				self	,	a_	,	a_	,	a_	,	a_	,	):
											a__					   =		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

											a__   ,      a__					   =		self._get_alpha_and_beta(a_		)
											a__   ,      a__					   =		self._get_alpha_and_beta(a_		)

											a__					   =		(alpha_prod_t_prev / alpha_prod_t) ** 0.5
											a__					   =		-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

											a__					   =		(base - b_t * model_output) / a_t

											return model_input, next_model_input.to(base.dtype		)


							def _a   (				self	,	a_	,	a_	,	a_	,	a_	,	):
											a__					   =		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

											a__   ,      a__					   =		self._get_alpha_and_beta(a_		)
											a__   ,      a__					   =		self._get_alpha_and_beta(a_		)

											a__					   =		(alpha_prod_t_prev / alpha_prod_t) ** 0.5
											a__					   =		-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
											a__					   =		a_t * base + b_t * model_output

											return model_input, next_model_input.to(base.dtype		)


							@torch.no_grad()
							def _a   (				self	,	a_		):
											a__					   =		1 / self.vae.config.scaling_factor * latents
											a__					   =		self.vae.decode(a_		).sample
											a__					   =		(image / 2 + 0.5).clamp(0	,	1		)
											return image


							@torch.no_grad()
							def _a   (				self	,	a_	,	a_	,	a_	,	a_	,	a_ = None	,	):
											a__					   =		guidance_scale > 1.0

											a__					   =		image.to(device=self.device	,	dtype=text_embeds.dtype		)
											a__					   =		self.vae.encode(a_		).latent_dist.sample(a_		)

											a__					   =		self.vae.config.scaling_factor * latent

											a__					   =		[latent.clone(), latent.clone()]

											for i, t in tqdm(enumerate(a_		)	,	total=len(a_		)		):
															a__					   =		self.noise_mixing_layer(x=coupled_latents[0]	,	y=coupled_latents[1]		)

															# j - model_input index, k - base index
															for j in range(2		):
																			a__					   =		j ^ 1

																			if self.leapfrog_steps:
																							if i % 2 == 0:
																											a__   ,      a__					   =		j, k

																			a__					   =		coupled_latents[j]
																			a__					   =		coupled_latents[k]

																			a__					   =		torch.cat([model_input] * 2		) if do_classifier_free_guidance else model_input

																			a__					   =		self.unet(a_	,	a_	,	encoder_hidden_states=a_		).sample

																			if do_classifier_free_guidance:
																							a__   ,      a__					   =		noise_pred.chunk(2		)
																							a__					   =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																			a__   ,      a__					   =		self.noise_step(
																			    base=a_	,	model_input=a_	,	model_output=a_	,	timestep=a_	,	)

																			a__					   =		model_input

											return coupled_latents







							@torch.no_grad()
							def __call__(				self	,	a_	,	a_	,	a_	,	a_ = 3.0	,	a_ = 50	,	a_ = 0.8	,	a_ = None	,	a_ = None	,	a_ = "pil"	,	):
											a__					   =		guidance_scale > 1.0

											a__					   =		self.image_processor.preprocess(a_		)

											a__					   =		self._encode_prompt(a_	,	a_	,	a_		)
											a__					   =		self._encode_prompt(a_	,	a_	,	a_		)

											self.scheduler.set_timesteps(a_	,	self.device		)

											a__					   =		num_inference_steps - int(num_inference_steps * strength		)
											a__					   =		self.scheduler.timesteps[t_limit:]
											a__					   =		fwd_timesteps.flip(0		)

											a__					   =		self.prepare_latents(a_	,	a_	,	a_	,	a_	,	a_		)

											for i, t in tqdm(enumerate(a_		)	,	total=len(a_		)		):
															# j - model_input index, k - base index
															for k in range(2		):
																			a__					   =		k ^ 1

																			if self.leapfrog_steps:
																							if i % 2 == 1:
																											a__   ,      a__					   =		j, k

																			a__					   =		coupled_latents[j]
																			a__					   =		coupled_latents[k]

																			a__					   =		torch.cat([model_input] * 2		) if do_classifier_free_guidance else model_input

																			a__					   =		self.unet(a_	,	a_	,	encoder_hidden_states=a_		).sample

																			if do_classifier_free_guidance:
																							a__   ,      a__					   =		noise_pred.chunk(2		)
																							a__					   =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																			a__   ,      a__					   =		self.denoise_step(
																			    base=a_	,	model_input=a_	,	model_output=a_	,	timestep=a_	,	)

																			a__					   =		model_input

															a__					   =		self.denoise_mixing_layer(x=coupled_latents[0]	,	y=coupled_latents[1]		)

											# either one is fine
											a__					   =		coupled_latents[0]

											if output_type not in ["latent", "pt", "np", "pil"]:
															a__					   =		(
															    F'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
															    """`pil`, `np`, `pt`, `latent`"""
															)
															deprecate("""Unsupported output_type"""	,	"""1.0.0"""	,	a_	,	standard_warn=a_		)
															a__					   =		"""np"""

											if output_type == "latent":
															a__					   =		final_latent
											else:
															a__					   =		self.decode_latents(a_		)
															a__					   =		self.image_processor.postprocess(a_	,	output_type=a_		)

											return image






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()
UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       """Hello world! cécé herlolip"""

def       A_   (						__a  :					str			,		__a  :					str			,		__a  :					bool		):

				"""simple docstring"""
				a__   ,      a__					   =		os.path.split(__a		)
				a__					   =		DataaVecTextModel.from_pretrained(
				    __a			,		checkpoint_file=__a		)
				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=514			,		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:"""			,		__a		)

				a__					   =		DataaVecTextForSequenceClassification(__a		) if classification_head else DataaVecTextForMaskedLM(__a		)
				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(__a		).unsqueeze(0		)  # batch of size 1

				a__					   =		model(__a		)[0]
				if classification_head:
								a__					   =		dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__a		)		)
				else:
								a__					   =		dataavec_model(__a		)[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(__a			,		__a			,		atol=1e-3		)
				print("""Do both models output the same tensors?"""			,		"""🔥""" if success else """💩"""		)
				if not success:
								raise Exception("""Something went wRoNg"""		)

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


if __name__ == "__main__":
	UpperCAmelCase							=       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."""
	)
	UpperCAmelCase							=       parser.parse_args()
	convert_dataavec_checkpoint_to_pytorch(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)

