






"""simple docstring"""







import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


lowerCAmelCase_		:			List[str]				   =					logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class      UpperCamelCase_       (				a_     ):
  _A				:    Tuple =		42
  _A				:    Optional[Any] =		None






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase=0.9_99		,					lowerCAmelCase="cosine"		,					):







       '''simple docstring'''
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(lowerCAmelCase						):
                     return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2						) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(lowerCAmelCase						):
                     return math.exp(t * -12.0						)

       else:
              raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''						)

       UpperCAmelCase					=	[]
       for i in range(__UpperCamelCase						):
              UpperCAmelCase					=	i / num_diffusion_timesteps
              UpperCAmelCase					=	(i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(__UpperCamelCase						) / alpha_bar_fn(__UpperCamelCase						)		,					__UpperCamelCase						)						)
       return torch.tensor(__UpperCamelCase		,					dtype=torch.floataa						)





class      UpperCamelCase_       (				a_      ,    a_     ):
  _A				:    str =		[e.name for e in KarrasDiffusionSchedulers]
  _A				:    Any =		1



  @register_to_config
  def __init__(					self       ,						snake_case__ = 10_00       ,						snake_case__ = 0.0_001       ,						snake_case__ = 0.02       ,						snake_case__ = "linear"       ,						snake_case__ = None       ,						snake_case__ = "epsilon"       ,						snake_case__ = "linear"       ,						snake_case__ = False       ,						snake_case__ = "linspace"       ,						snake_case__ = 0       ,						)	->	Optional[int]:



         """simple docstring"""





         if trained_betas is not None:
                UpperCAmelCase					=	torch.tensor(__UpperCAmelCase       ,						dtype=torch.floataa )
         elif beta_schedule == "linear":
                UpperCAmelCase					=	torch.linspace(__UpperCAmelCase       ,						__UpperCAmelCase       ,						__UpperCAmelCase       ,						dtype=torch.floataa )
         elif beta_schedule == "scaled_linear":
                # this schedule is very specific to the latent diffusion model.
                UpperCAmelCase					=	(
                    torch.linspace(beta_start**0.5       ,						beta_end**0.5       ,						__UpperCAmelCase       ,						dtype=torch.floataa ) ** 2
                )
         elif beta_schedule == "squaredcos_cap_v2":
                # Glide cosine schedule
                UpperCAmelCase					=	betas_for_alpha_bar(__UpperCAmelCase )
         else:
                raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

         UpperCAmelCase					=	1.0 - self.betas
         UpperCAmelCase					=	torch.cumprod(self.alphas       ,						dim=0 )

         UpperCAmelCase					=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
         UpperCAmelCase					=	np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
         UpperCAmelCase					=	torch.from_numpy(__UpperCAmelCase )

         # setable values
         UpperCAmelCase					=	None
         UpperCAmelCase					=	np.linspace(0       ,						num_train_timesteps - 1       ,						__UpperCAmelCase       ,						dtype=__UpperCAmelCase )[::-1].copy()
         UpperCAmelCase					=	torch.from_numpy(__UpperCAmelCase )
         UpperCAmelCase					=	False
         UpperCAmelCase					=	use_karras_sigmas



  @property
  def 						UpperCamelCase_ (					self )	->	Union[str, Any]:



         """simple docstring"""





         if self.config.timestep_spacing in ["linspace", "trailing"]:
                return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ )	->	torch.FloatTensor:



         """simple docstring"""





         if isinstance(__UpperCAmelCase       ,						torch.Tensor ):
                UpperCAmelCase					=	timestep.to(self.timesteps.device )
         UpperCAmelCase					=	(self.timesteps == timestep).nonzero().item()
         UpperCAmelCase					=	self.sigmas[step_index]

         UpperCAmelCase					=	sample / ((sigma**2 + 1) ** 0.5)

         UpperCAmelCase					=	True
         return sample



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = None )	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	num_inference_steps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
                UpperCAmelCase					=	np.linspace(0       ,						self.config.num_train_timesteps - 1       ,						__UpperCAmelCase       ,						dtype=__UpperCAmelCase )[
                    ::-1
                ].copy()
         elif self.config.timestep_spacing == "leading":
                UpperCAmelCase					=	self.config.num_train_timesteps // self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                UpperCAmelCase					=	(np.arange(0       ,						__UpperCAmelCase ) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase )
                timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
                UpperCAmelCase					=	self.config.num_train_timesteps / self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                UpperCAmelCase					=	(np.arange(self.config.num_train_timesteps       ,						0       ,						-step_ratio )).round().copy().astype(__UpperCAmelCase )
                timesteps -= 1
         else:
                raise ValueError(
                    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )

         UpperCAmelCase					=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
         UpperCAmelCase					=	np.log(__UpperCAmelCase )

         if self.config.interpolation_type == "linear":
                UpperCAmelCase					=	np.interp(__UpperCAmelCase       ,						np.arange(0       ,						len(__UpperCAmelCase ) )       ,						__UpperCAmelCase )
         elif self.config.interpolation_type == "log_linear":
                UpperCAmelCase					=	torch.linspace(np.log(sigmas[-1] )       ,						np.log(sigmas[0] )       ,						num_inference_steps + 1 ).exp()
         else:
                raise ValueError(
                    f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
                    """ 'linear' or 'log_linear'""" )

         if self.use_karras_sigmas:
                UpperCAmelCase					=	self._convert_to_karras(in_sigmas=__UpperCAmelCase       ,						num_inference_steps=self.num_inference_steps )
                UpperCAmelCase					=	np.array([self._sigma_to_t(__UpperCAmelCase       ,						__UpperCAmelCase ) for sigma in sigmas] )

         UpperCAmelCase					=	np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
         UpperCAmelCase					=	torch.from_numpy(__UpperCAmelCase ).to(device=__UpperCAmelCase )
         if str(__UpperCAmelCase ).startswith("""mps""" ):
                # mps does not support float64
                UpperCAmelCase					=	torch.from_numpy(__UpperCAmelCase ).to(__UpperCAmelCase       ,						dtype=torch.floataa )
         else:
                UpperCAmelCase					=	torch.from_numpy(__UpperCAmelCase ).to(device=__UpperCAmelCase )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ )	->	Optional[int]:



         """simple docstring"""





         UpperCAmelCase					=	np.log(__UpperCAmelCase )

         # get distribution
         UpperCAmelCase					=	log_sigma - log_sigmas[:, np.newaxis]

         # get sigmas range
         UpperCAmelCase					=	np.cumsum((dists >= 0)       ,						axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
         UpperCAmelCase					=	low_idx + 1

         UpperCAmelCase					=	log_sigmas[low_idx]
         UpperCAmelCase					=	log_sigmas[high_idx]

         # interpolate sigmas
         UpperCAmelCase					=	(low - log_sigma) / (low - high)
         UpperCAmelCase					=	np.clip(__UpperCAmelCase       ,						0       ,						1 )

         # transform interpolation to time range
         UpperCAmelCase					=	(1 - w) * low_idx + w * high_idx
         UpperCAmelCase					=	t.reshape(sigma.shape )
         return t



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ )	->	torch.FloatTensor:



         """simple docstring"""





         UpperCAmelCase					=	in_sigmas[-1].item()
         UpperCAmelCase					=	in_sigmas[0].item()

         UpperCAmelCase					=	7.0  # 7.0 is the value used in the paper
         UpperCAmelCase					=	np.linspace(0       ,						1       ,						__UpperCAmelCase )
         UpperCAmelCase					=	sigma_min ** (1 / rho)
         UpperCAmelCase					=	sigma_max ** (1 / rho)
         UpperCAmelCase					=	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = 0.0       ,						snake_case__ = 0.0       ,						snake_case__ = float("""inf""" )       ,						snake_case__ = 1.0       ,						snake_case__ = None       ,						snake_case__ = True       ,						)	->	Union[EulerDiscreteSchedulerOutput, Tuple]:



         """simple docstring"""





         if (
             isinstance(__UpperCAmelCase       ,						__UpperCAmelCase )
             or isinstance(__UpperCAmelCase       ,						torch.IntTensor )
             or isinstance(__UpperCAmelCase       ,						torch.LongTensor )
         ):
                raise ValueError(
                    (
                        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
                        """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
                        """ one of the `scheduler.timesteps` as a timestep."""
                    )       ,						)

         if not self.is_scale_input_called:
                logger.warning(
                    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
                    """See `StableDiffusionPipeline` for a usage example.""" )

         if isinstance(__UpperCAmelCase       ,						torch.Tensor ):
                UpperCAmelCase					=	timestep.to(self.timesteps.device )

         UpperCAmelCase					=	(self.timesteps == timestep).nonzero().item()
         UpperCAmelCase					=	self.sigmas[step_index]

         UpperCAmelCase					=	min(s_churn / (len(self.sigmas ) - 1)       ,						2**0.5 - 1 ) if s_tmin <= sigma <= s_tmax else 0.0

         UpperCAmelCase					=	randn_tensor(
             model_output.shape       ,						dtype=model_output.dtype       ,						device=model_output.device       ,						generator=__UpperCAmelCase )

         UpperCAmelCase					=	noise * s_noise
         UpperCAmelCase					=	sigma * (gamma + 1)

         if gamma > 0:
                UpperCAmelCase					=	sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         # NOTE: "original_sample" should not be an expected prediction_type but is left in for
         # backwards compatibility
         if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
                UpperCAmelCase					=	model_output
         elif self.config.prediction_type == "epsilon":
                UpperCAmelCase					=	sample - sigma_hat * model_output
         elif self.config.prediction_type == "v_prediction":
                # * c_out + input * c_skip
                UpperCAmelCase					=	model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         else:
                raise ValueError(
                    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )

         # 2. Convert to an ODE derivative
         UpperCAmelCase					=	(sample - pred_original_sample) / sigma_hat

         UpperCAmelCase					=	self.sigmas[step_index + 1] - sigma_hat

         UpperCAmelCase					=	sample + derivative * dt

         if not return_dict:
                return (prev_sample,)

         return EulerDiscreteSchedulerOutput(prev_sample=__UpperCAmelCase       ,						pred_original_sample=__UpperCAmelCase )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						)	->	torch.FloatTensor:



         """simple docstring"""





         UpperCAmelCase					=	self.sigmas.to(device=original_samples.device       ,						dtype=original_samples.dtype )
         if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCAmelCase ):
                # mps does not support float64
                UpperCAmelCase					=	self.timesteps.to(original_samples.device       ,						dtype=torch.floataa )
                UpperCAmelCase					=	timesteps.to(original_samples.device       ,						dtype=torch.floataa )
         else:
                UpperCAmelCase					=	self.timesteps.to(original_samples.device )
                UpperCAmelCase					=	timesteps.to(original_samples.device )

         UpperCAmelCase					=	[(schedule_timesteps == t).nonzero().item() for t in timesteps]

         UpperCAmelCase					=	sigmas[step_indices].flatten()
         while len(sigma.shape ) < len(original_samples.shape ):
                UpperCAmelCase					=	sigma.unsqueeze(-1 )

         UpperCAmelCase					=	original_samples + noise * sigma
         return noisy_samples



  def __len__(					self )	->	Any:



         """simple docstring"""





         return self.config.num_train_timesteps


"""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       )				-> 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
			)



