




# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			42
			snake_case_  						=			None







def      lowerCamelCase_						(			UpperCamelCase__							: Optional[int]					,	UpperCamelCase__							: List[str]=0.9_99					,	UpperCamelCase__							: Any="cosine"					,	)	->			Tuple:





				"""simple docstring"""


				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

				__lowerCamelCase									=       []
				for i in range(UpperCamelCase__ ):
								__lowerCamelCase									=       i / num_diffusion_timesteps
								__lowerCamelCase									=       (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        __lowerCAmelCase					(			__magic_name__     ,							__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			1


			@register_to_config
			def __init__( self     ,		lowerCamelCase__ = 1_000     ,		lowerCamelCase__ = 0.00_01     ,		lowerCamelCase__ = 0.02     ,		lowerCamelCase__ = "linear"     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = 0     ,		lowerCamelCase__ = "epsilon"     ,		lowerCamelCase__ = 1.0     ,		**lowerCamelCase__     ,		)       -> str:



							'''simple docstring'''
							if kwargs.get('set_alpha_to_one'     ,		lowerCamelCase__				) is not None:
											__lowerCamelCase									=       (
											    'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
											)
											deprecate('set_alpha_to_one'     ,		'1.0.0'     ,		lowerCamelCase__     ,		standard_warn=lowerCamelCase__				)
											__lowerCamelCase									=       kwargs['set_alpha_to_one']
							if trained_betas is not None:
											__lowerCamelCase									=       torch.tensor(lowerCamelCase__     ,		dtype=torch.floataa				)
							elif beta_schedule == "linear":
											__lowerCamelCase									=       torch.linspace(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		dtype=torch.floataa				)
							elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											__lowerCamelCase									=       (
											    torch.linspace(beta_start**0.5     ,		beta_end**0.5     ,		lowerCamelCase__     ,		dtype=torch.floataa				) ** 2
											)
							elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											__lowerCamelCase									=       betas_for_alpha_bar(lowerCamelCase__				)
							else:
											raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""				)

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

							# At every step in inverted ddim, we are looking into the next alphas_cumprod
							# For the final step, there is no next alphas_cumprod, and the index is out of bounds
							# `set_alpha_to_zero` decides whether we set this parameter simply to zero
							# in this case, self.step() just output the predicted noise
							# or whether we use the final alpha of the "non-previous" one.
							__lowerCamelCase									=       torch.tensor(0.0				) if set_alpha_to_zero else self.alphas_cumprod[-1]

							# standard deviation of the initial noise distribution
							__lowerCamelCase									=       1.0

							# setable values
							__lowerCamelCase									=       None
							__lowerCamelCase									=       torch.from_numpy(np.arange(0     ,		lowerCamelCase__				).copy().astype(np.intaa				)				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None				)       -> torch.FloatTensor:



							'''simple docstring'''
							return sample


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None				)       -> Any:



							'''simple docstring'''

							if num_inference_steps > self.config.num_train_timesteps:
											raise ValueError(
											    f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
											    f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
											    f""" maximal {self.config.num_train_timesteps} timesteps."""				)

							__lowerCamelCase									=       num_inference_steps
							__lowerCamelCase									=       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
							__lowerCamelCase									=       (np.arange(0     ,		lowerCamelCase__				) * step_ratio).round().copy().astype(np.intaa				)
							__lowerCamelCase									=       torch.from_numpy(lowerCamelCase__				).to(lowerCamelCase__				)
							self.timesteps += self.config.steps_offset


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = 0.0     ,		lowerCamelCase__ = False     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = True     ,		)       -> Union[DDIMSchedulerOutput, Tuple]:



							'''simple docstring'''
							# 1. get previous step value (=t+1)
							__lowerCamelCase									=       timestep + self.config.num_train_timesteps // self.num_inference_steps

							# 2. compute alphas, betas
							# change original implementation to exactly match noise levels for analogous forward process
							__lowerCamelCase									=       self.alphas_cumprod[timestep]
							__lowerCamelCase									=       (
							    self.alphas_cumprod[prev_timestep]
							    if prev_timestep < self.config.num_train_timesteps
							    else self.final_alpha_cumprod
							)

							__lowerCamelCase									=       1 - alpha_prod_t

							# 3. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							if self.config.prediction_type == "epsilon":
											__lowerCamelCase									=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											__lowerCamelCase									=       model_output
							elif self.config.prediction_type == "sample":
											__lowerCamelCase									=       model_output
											__lowerCamelCase									=       (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
							elif self.config.prediction_type == "v_prediction":
											__lowerCamelCase									=       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											__lowerCamelCase									=       (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
							else:
											raise ValueError(
											    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
											    ' `v_prediction`'				)

							# 4. Clip or threshold "predicted x_0"
							if self.config.clip_sample:
											__lowerCamelCase									=       pred_original_sample.clamp(
											    -self.config.clip_sample_range     ,		self.config.clip_sample_range				)

							# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							__lowerCamelCase									=       (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

							# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							__lowerCamelCase									=       alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if not return_dict:
											return (prev_sample, pred_original_sample)
							return DDIMSchedulerOutput(prev_sample=lowerCamelCase__     ,		pred_original_sample=lowerCamelCase__				)






			def __len__( self				)       -> Dict:



							'''simple docstring'''
							return self.config.num_train_timesteps








from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


__A								=       logging.get_logger(__name__)  # pylint: disable=invalid-name







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			Tuple:





				"""simple docstring"""


				if not path:
								return "pipe"

				for ext in PipelineDataFormat.SUPPORTED_FORMATS:
								if path.endswith(UpperCamelCase__ ):
												return ext

				raise Exception(
				    F"""Unable to determine file format from file extension {path}. """
				    F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}""" )







def      lowerCamelCase_						(			UpperCamelCase__							: Dict )	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       pipeline(
				    task=args.task					,	model=args.model if args.model else None					,	config=args.config					,	tokenizer=args.tokenizer					,	device=args.device					,	)
				__lowerCamelCase									=       try_infer_format_from_ext(args.input ) if args.format == 'infer' else args.format
				__lowerCamelCase									=       PipelineDataFormat.from_str(
				    format=UpperCamelCase__					,	output_path=args.output					,	input_path=args.input					,	column=args.column if args.column else nlp.default_input_names					,	overwrite=args.overwrite					,	)
				return RunCommand(UpperCamelCase__					,	UpperCamelCase__ )







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       nlp
							__lowerCamelCase									=       reader


			@staticmethod
			def  lowercase_       ( lowerCamelCase__				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       parser.add_parser('run'     ,		help='Run a pipeline through the CLI'				)
							run_parser.add_argument('--task'     ,		choices=get_supported_tasks()     ,		help='Task to run'				)
							run_parser.add_argument('--input'     ,		type=lowerCamelCase__     ,		help='Path to the file to use for inference'				)
							run_parser.add_argument('--output'     ,		type=lowerCamelCase__     ,		help='Path to the file that will be used post to write results.'				)
							run_parser.add_argument('--model'     ,		type=lowerCamelCase__     ,		help='Name or path to the model to instantiate.'				)
							run_parser.add_argument('--config'     ,		type=lowerCamelCase__     ,		help='Name or path to the model\'s config to instantiate.'				)
							run_parser.add_argument(
							    '--tokenizer'     ,		type=lowerCamelCase__     ,		help='Name of the tokenizer to use. (default: same as the model name)'				)
							run_parser.add_argument(
							    '--column'     ,		type=lowerCamelCase__     ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'     ,		)
							run_parser.add_argument(
							    '--format'     ,		type=lowerCamelCase__     ,		default='infer'     ,		choices=PipelineDataFormat.SUPPORTED_FORMATS     ,		help='Input format to read from'     ,		)
							run_parser.add_argument(
							    '--device'     ,		type=lowerCamelCase__     ,		default=-1     ,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'     ,		)
							run_parser.add_argument('--overwrite'     ,		action='store_true'     ,		help='Allow overwriting the output file.'				)
							run_parser.set_defaults(func=lowerCamelCase__				)






			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase			,		__lowerCamelCase									=       self._nlp, []

							for entry in self._reader:
											__lowerCamelCase									=       nlp(**lowerCamelCase__				) if self._reader.is_multi_columns else nlp(lowerCamelCase__				)
											if isinstance(lowerCamelCase__     ,		lowerCamelCase__				):
															outputs.append(lowerCamelCase__				)
											else:
															outputs += output

        # Saving data
							if self._nlp.binary_output:
											__lowerCamelCase									=       self._reader.save_binary(lowerCamelCase__				)
											logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""				)
							else:
											self._reader.save(lowerCamelCase__				)


