
"""simple docstring"""


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 						lowerCamelCase__   (   __magic_name__							):





			'''simple docstring'''



			def __init__(				self	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase = 0.9_3	,	__UpperCAmelCase = True	,	)					->	Union[str, Any]:
				_lowerCAmelCase								=mixing_coeff
				_lowerCAmelCase								=leapfrog_steps

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

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



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase	,	__UpperCAmelCase = None	,	__UpperCAmelCase = False    )					->	str:
				_lowerCAmelCase								=self.tokenizer(
				    __UpperCAmelCase	,	padding="""max_length"""	,	max_length=self.tokenizer.model_max_length	,	truncation=__UpperCAmelCase	,	return_tensors="""pt"""	,	)

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

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

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

					_lowerCAmelCase								=self.tokenizer(
					    __UpperCAmelCase	,	padding="""max_length"""	,	max_length=self.tokenizer.model_max_length	,	truncation=__UpperCAmelCase	,	return_tensors="""pt"""	,	)

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

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

				return prompt_embeds



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase	,	__UpperCAmelCase    )					->	Tuple:
				_lowerCAmelCase								=self.mixing_coeff * x + (1 - self.mixing_coeff) * y
				_lowerCAmelCase								=self.mixing_coeff * y + (1 - self.mixing_coeff) * x

				return [x, y]



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase	,	__UpperCAmelCase    )					->	Union[str, Any]:
				_lowerCAmelCase								=(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
				_lowerCAmelCase								=(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

				return [x, y]



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase    )					->	Optional[int]:
				# as self.alphas_cumprod is always in cpu
				_lowerCAmelCase								=int(__UpperCAmelCase    )

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

				return alpha_prod, 1 - alpha_prod



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase	,	)					->	Any:
				_lowerCAmelCase								=timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

				_lowerCAmelCase		,				_lowerCAmelCase								=self._get_alpha_and_beta(__UpperCAmelCase    )
				_lowerCAmelCase		,				_lowerCAmelCase								=self._get_alpha_and_beta(__UpperCAmelCase    )

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

				_lowerCAmelCase								=(base - b_t * model_output) / a_t

				return model_input, next_model_input.to(base.dtype    )



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase	,	)					->	Optional[Any]:
				_lowerCAmelCase								=timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

				_lowerCAmelCase		,				_lowerCAmelCase								=self._get_alpha_and_beta(__UpperCAmelCase    )
				_lowerCAmelCase		,				_lowerCAmelCase								=self._get_alpha_and_beta(__UpperCAmelCase    )

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

				return model_input, next_model_input.to(base.dtype    )



			@torch.no_grad()
			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase    )					->	Any:
				_lowerCAmelCase								=1 / self.vae.config.scaling_factor * latents
				_lowerCAmelCase								=self.vae.decode(__UpperCAmelCase    ).sample
				_lowerCAmelCase								=(image / 2 + 0.5).clamp(0	,	1    )
				return image



			@torch.no_grad()
			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase = None	,	)					->	int:
				_lowerCAmelCase								=guidance_scale > 1.0

				_lowerCAmelCase								=image.to(device=self.device	,	dtype=text_embeds.dtype    )
				_lowerCAmelCase								=self.vae.encode(__UpperCAmelCase    ).latent_dist.sample(__UpperCAmelCase    )

				_lowerCAmelCase								=self.vae.config.scaling_factor * latent

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

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

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

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

						_lowerCAmelCase								=coupled_latents[j]
						_lowerCAmelCase								=coupled_latents[k]

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

						_lowerCAmelCase								=self.unet(__UpperCAmelCase	,	__UpperCAmelCase	,	encoder_hidden_states=__UpperCAmelCase    ).sample

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

						_lowerCAmelCase		,				_lowerCAmelCase								=self.noise_step(
						    base=__UpperCAmelCase	,	model_input=__UpperCAmelCase	,	model_output=__UpperCAmelCase	,	timestep=__UpperCAmelCase	,	)

						_lowerCAmelCase								=model_input

				return coupled_latents



			@torch.no_grad()
			def __call__(				self	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase = 3.0	,	__UpperCAmelCase = 50	,	__UpperCAmelCase = 0.8	,	__UpperCAmelCase = None	,	__UpperCAmelCase = None	,	__UpperCAmelCase = "pil"	,	)					->	Optional[int]:
				_lowerCAmelCase								=guidance_scale > 1.0

				_lowerCAmelCase								=self.image_processor.preprocess(__UpperCAmelCase    )

				_lowerCAmelCase								=self._encode_prompt(__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase    )
				_lowerCAmelCase								=self._encode_prompt(__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase    )

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

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

				_lowerCAmelCase								=self.prepare_latents(__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase    )

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

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

						_lowerCAmelCase								=coupled_latents[j]
						_lowerCAmelCase								=coupled_latents[k]

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

						_lowerCAmelCase								=self.unet(__UpperCAmelCase	,	__UpperCAmelCase	,	encoder_hidden_states=__UpperCAmelCase    ).sample

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

						_lowerCAmelCase		,				_lowerCAmelCase								=self.denoise_step(
						    base=__UpperCAmelCase	,	model_input=__UpperCAmelCase	,	model_output=__UpperCAmelCase	,	timestep=__UpperCAmelCase	,	)

						_lowerCAmelCase								=model_input

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

				# either one is fine
				_lowerCAmelCase								=coupled_latents[0]

				if output_type not in ["latent", "pt", "np", "pil"]:
					_lowerCAmelCase								=(
					    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"""	,	__UpperCAmelCase	,	standard_warn=__UpperCAmelCase    )
					_lowerCAmelCase								="""np"""

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

				return image





"""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()
__A					=			logging.get_logger(__name__)

__A					=			'Hello world! cécé herlolip'



def        _lowerCamelCase(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			)							->					List[Any]:
	_lowerCAmelCase		,				_lowerCAmelCase								=os.path.split(__UpperCamelCase			)
	_lowerCAmelCase								=DataaVecTextModel.from_pretrained(
	    __UpperCamelCase       ,					checkpoint_file=__UpperCamelCase			)
	dataavec.eval()  # disable dropout
	_lowerCAmelCase								=dataavec.models[0]
	_lowerCAmelCase								=dataavec_model.encoder.sentence_encoder
	_lowerCAmelCase								=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:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
	print("""Our BERT config:"""       ,					__UpperCamelCase			)

	_lowerCAmelCase								=DataaVecTextForSequenceClassification(__UpperCamelCase			) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase			)
	model.eval()

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

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

		# self attention
		_lowerCAmelCase								=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)			)}'''
		)

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

		# self-attention output
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias

		# output
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.bias
		# end of layer

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

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

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

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


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



