import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=0.999  ,      SCREAMING_SNAKE_CASE="cosine"  ,      ):
    '''simple docstring'''







    if alpha_transform_type == "cosine":

        def alpha_bar_fn(SCREAMING_SNAKE_CASE     ):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

    elif alpha_transform_type == "exp":

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

    else:
        raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""     )

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


class      lowerCamelCase_					(       UpperCAmelCase_						,     UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Union[str, Any]      =				[e.name for e in KarrasDiffusionSchedulers]
    a__     :						Dict      =				2







    @register_to_config
    def __init__(     self			,			__lowercase = 1_000			,			__lowercase = 0.0_00_85			,			__lowercase = 0.0_12			,			__lowercase = "linear"			,			__lowercase = None			,			__lowercase = "epsilon"			,			__lowercase = "linspace"			,			__lowercase = 0			,			)	->     Optional[int]:
        if trained_betas is not None:
            __UpperCamelCase					:Any        =		torch.tensor(__lowercase			,			dtype=torch.floataa)
        elif beta_schedule == "linear":
            __UpperCamelCase					:str        =		torch.linspace(__lowercase			,			__lowercase			,			__lowercase			,			dtype=torch.floataa)
        elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            __UpperCamelCase					:List[str]        =		(
                torch.linspace(beta_start**0.5			,			beta_end**0.5			,			__lowercase			,			dtype=torch.floataa) ** 2
            )
        elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            __UpperCamelCase					:Optional[int]        =		betas_for_alpha_bar(__lowercase)
        else:
            raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""")

        __UpperCamelCase					:Any        =		1.0 - self.betas
        __UpperCamelCase					:Optional[int]        =		torch.cumprod(self.alphas			,			dim=0)

        #  set all values
        self.set_timesteps(__lowercase			,			__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase=None)	->     Any:
        if schedule_timesteps is None:
            __UpperCamelCase					:Dict        =		self.timesteps

        __UpperCamelCase					:List[str]        =		(schedule_timesteps == timestep).nonzero()

        # The sigma index that is taken for the **very** first `step`
        # is always the second index (or the last index if there is only 1)
        # This way we can ensure we don't accidentally skip a sigma in
        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
        if len(self._index_counter) == 0:
            __UpperCamelCase					:int        =		1 if len(__lowercase) > 1 else 0
        else:
            __UpperCamelCase					:str        =		timestep.cpu().item() if torch.is_tensor(__lowercase) else timestep
            __UpperCamelCase					:Union[str, Any]        =		self._index_counter[timestep_int]

        return indices[pos].item()







    @property
    def       UpperCamelCase__       (     self)	->     Tuple:
        # standard deviation of the initial noise distribution
        if self.config.timestep_spacing in ["linspace", "trailing"]:
            return self.sigmas.max()

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







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			)	->     torch.FloatTensor:
        __UpperCamelCase					:Dict        =		self.index_for_timestep(__lowercase)

        if self.state_in_first_order:
            __UpperCamelCase					:Dict        =		self.sigmas[step_index]
        else:
            __UpperCamelCase					:int        =		self.sigmas_interpol[step_index]

        __UpperCamelCase					:Tuple        =		sample / ((sigma**2 + 1) ** 0.5)
        return sample







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None			,			__lowercase = None			,			)	->     Tuple:
        __UpperCamelCase					:Optional[Any]        =		num_inference_steps

        __UpperCamelCase					:Any        =		num_train_timesteps or self.config.num_train_timesteps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
            __UpperCamelCase					:List[Any]        =		np.linspace(0			,			num_train_timesteps - 1			,			__lowercase			,			dtype=__lowercase)[::-1].copy()
        elif self.config.timestep_spacing == "leading":
            __UpperCamelCase					:Dict        =		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					:List[str]        =		(np.arange(0			,			__lowercase) * step_ratio).round()[::-1].copy().astype(__lowercase)
            timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
            __UpperCamelCase					:Optional[Any]        =		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					:Dict        =		(np.arange(__lowercase			,			0			,			-step_ratio)).round().copy().astype(__lowercase)
            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					:List[str]        =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
        __UpperCamelCase					:Tuple        =		torch.from_numpy(np.log(__lowercase)).to(__lowercase)

        __UpperCamelCase					:Union[str, Any]        =		np.interp(__lowercase			,			np.arange(0			,			len(__lowercase))			,			__lowercase)
        __UpperCamelCase					:Any        =		np.concatenate([sigmas, [0.0]]).astype(np.floataa)
        __UpperCamelCase					:Optional[Any]        =		torch.from_numpy(__lowercase).to(device=__lowercase)

        # interpolate sigmas
        __UpperCamelCase					:Optional[int]        =		sigmas.log().lerp(sigmas.roll(1).log()			,			0.5).exp()

        __UpperCamelCase					:Tuple        =		torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
        __UpperCamelCase					:str        =		torch.cat(
            [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])

        if str(__lowercase).startswith('''mps'''):
            # mps does not support float64
            __UpperCamelCase					:Any        =		torch.from_numpy(__lowercase).to(__lowercase			,			dtype=torch.floataa)
        else:
            __UpperCamelCase					:List[str]        =		torch.from_numpy(__lowercase).to(__lowercase)

        # interpolate timesteps
        __UpperCamelCase					:int        =		self.sigma_to_t(__lowercase).to(__lowercase			,			dtype=timesteps.dtype)
        __UpperCamelCase					:Union[str, Any]        =		torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])			,			dim=-1).flatten()

        __UpperCamelCase					:Optional[Any]        =		torch.cat([timesteps[:1], interleaved_timesteps])

        __UpperCamelCase					:Union[str, Any]        =		None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        __UpperCamelCase					:str        =		defaultdict(__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[str]:
        # get log sigma
        __UpperCamelCase					:Optional[Any]        =		sigma.log()

        # get distribution
        __UpperCamelCase					:Optional[Any]        =		log_sigma - self.log_sigmas[:, None]

        # get sigmas range
        __UpperCamelCase					:Tuple        =		dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
        __UpperCamelCase					:List[str]        =		low_idx + 1

        __UpperCamelCase					:str        =		self.log_sigmas[low_idx]
        __UpperCamelCase					:List[str]        =		self.log_sigmas[high_idx]

        # interpolate sigmas
        __UpperCamelCase					:Tuple        =		(low - log_sigma) / (low - high)
        __UpperCamelCase					:List[str]        =		w.clamp(0			,			1)

        # transform interpolation to time range
        __UpperCamelCase					:Tuple        =		(1 - w) * low_idx + w * high_idx
        __UpperCamelCase					:Optional[int]        =		t.view(sigma.shape)
        return t







    @property
    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        return self.sample is None







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase = True			,			)	->     Union[SchedulerOutput, Tuple]:
        __UpperCamelCase					:Union[str, Any]        =		self.index_for_timestep(__lowercase)

        # advance index counter by 1
        __UpperCamelCase					:int        =		timestep.cpu().item() if torch.is_tensor(__lowercase) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
            __UpperCamelCase					:List[Any]        =		self.sigmas[step_index]
            __UpperCamelCase					:List[str]        =		self.sigmas_interpol[step_index + 1]
            __UpperCamelCase					:Optional[Any]        =		self.sigmas[step_index + 1]
        else:
            # 2nd order / KDPM2's method
            __UpperCamelCase					:List[Any]        =		self.sigmas[step_index - 1]
            __UpperCamelCase					:List[Any]        =		self.sigmas_interpol[step_index]
            __UpperCamelCase					:str        =		self.sigmas[step_index]

        # currently only gamma=0 is supported. This usually works best anyways.
        # We can support gamma in the future but then need to scale the timestep before
        # passing it to the model which requires a change in API
        __UpperCamelCase					:str        =		0
        __UpperCamelCase					:Optional[int]        =		sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
            __UpperCamelCase					:Optional[Any]        =		sigma_hat if self.state_in_first_order else sigma_interpol
            __UpperCamelCase					:List[Any]        =		sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
            __UpperCamelCase					:Dict        =		sigma_hat if self.state_in_first_order else sigma_interpol
            __UpperCamelCase					:List[Any]        =		model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                sample / (sigma_input**2 + 1)
            )
        elif self.config.prediction_type == "sample":
            raise NotImplementedError('''prediction_type not implemented yet: sample''')
        else:
            raise ValueError(
                f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""")

        if self.state_in_first_order:
            # 2. Convert to an ODE derivative for 1st order
            __UpperCamelCase					:List[Any]        =		(sample - pred_original_sample) / sigma_hat
            # 3. delta timestep
            __UpperCamelCase					:Union[str, Any]        =		sigma_interpol - sigma_hat

            # store for 2nd order step
            __UpperCamelCase					:Any        =		sample
        else:
            # DPM-Solver-2
            # 2. Convert to an ODE derivative for 2nd order
            __UpperCamelCase					:Union[str, Any]        =		(sample - pred_original_sample) / sigma_interpol

            # 3. delta timestep
            __UpperCamelCase					:Tuple        =		sigma_next - sigma_hat

            __UpperCamelCase					:Tuple        =		self.sample
            __UpperCamelCase					:int        =		None

        __UpperCamelCase					:Optional[Any]        =		sample + derivative * dt

        if not return_dict:
            return (prev_sample,)

        return SchedulerOutput(prev_sample=__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			)	->     torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        __UpperCamelCase					:Any        =		self.sigmas.to(device=original_samples.device			,			dtype=original_samples.dtype)
        if original_samples.device.type == "mps" and torch.is_floating_point(__lowercase):
            # mps does not support float64
            __UpperCamelCase					:Dict        =		self.timesteps.to(original_samples.device			,			dtype=torch.floataa)
            __UpperCamelCase					:Optional[Any]        =		timesteps.to(original_samples.device			,			dtype=torch.floataa)
        else:
            __UpperCamelCase					:Any        =		self.timesteps.to(original_samples.device)
            __UpperCamelCase					:List[str]        =		timesteps.to(original_samples.device)

        __UpperCamelCase					:List[Any]        =		[self.index_for_timestep(__lowercase			,			__lowercase) for t in timesteps]

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

        __UpperCamelCase					:Optional[int]        =		original_samples + noise * sigma
        return noisy_samples







    def __len__(     self)	->     List[Any]:
        return self.config.num_train_timesteps






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		[
        '''encoder.version''',
        '''decoder.version''',
        '''model.encoder.version''',
        '''model.decoder.version''',
        '''decoder.output_projection.weight''',
        '''_float_tensor''',
        '''encoder.embed_positions._float_tensor''',
        '''decoder.embed_positions._float_tensor''',
    ]
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase  ,	__UpperCamelCase					:List[str]        =		emb.weight.shape
    __UpperCamelCase					:str        =		nn.Linear(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      bias=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Any        =		emb.weight.data
    return lin_layer



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Dict        =		torch.load(SCREAMING_SNAKE_CASE  ,      map_location='''cpu'''     )
    __UpperCamelCase					:Tuple        =		mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
    __UpperCamelCase					:Dict        =		mam_aaa['''model''']
    remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Dict        =		state_dict['''encoder.embed_tokens.weight'''].shape[0]

    __UpperCamelCase					:Dict        =		MaMaaaConfig(
        vocab_size=SCREAMING_SNAKE_CASE  ,      max_position_embeddings=1_024  ,      encoder_layers=args.encoder_layers  ,      decoder_layers=args.decoder_layers  ,      encoder_attention_heads=args.encoder_attention_heads  ,      decoder_attention_heads=args.decoder_attention_heads  ,      encoder_ffn_dim=args.encoder_ffn_embed_dim  ,      decoder_ffn_dim=args.decoder_ffn_embed_dim  ,      d_model=args.encoder_embed_dim  ,      encoder_layerdrop=args.encoder_layerdrop  ,      decoder_layerdrop=args.decoder_layerdrop  ,      dropout=args.dropout  ,      attention_dropout=args.attention_dropout  ,      activation_dropout=args.activation_dropout  ,      activation_function='''relu'''  ,      )

    __UpperCamelCase					:Tuple        =		state_dict['''decoder.embed_tokens.weight''']
    __UpperCamelCase					:int        =		MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE     )
    model.model.load_state_dict(SCREAMING_SNAKE_CASE  ,      strict=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Optional[Any]        =		make_linear_from_emb(model.model.shared     )

    return model


if __name__ == "__main__":
    __lowercase										= argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''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.''')
    __lowercase										= parser.parse_args()
    __lowercase										= convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)





