

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 						UpperCamelCase (lowercase_:			Dict   ,				lowercase_:			List[str]=0.999   ,				lowercase_:			int="cosine"   ,				)					->				List[str]:
	if alpha_transform_type == "cosine":

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

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(lowercase_:			Optional[int] ):
			return math.exp(t * -12.0 )

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

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



class    _a						(__magic_name__		,       __magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       int  							=	[e.name for e in KarrasDiffusionSchedulers]
					UpperCAmelCase__:       Dict  							=	2






					@register_to_config
					def __init__(			self   ,	A__ = 1000   ,	A__ = 0.0_0_0_8_5   ,	A__ = 0.0_1_2   ,	A__ = "linear"   ,	A__ = None   ,	A__ = "epsilon"   ,	A__ = "linspace"   ,	A__ = 0   ,	):
						if trained_betas is not None:
							A__							:   str					       =		torch.tensor(A__   ,	dtype=torch.floataa      )
						elif beta_schedule == "linear":
							A__							:   Tuple					       =		torch.linspace(A__   ,	A__   ,	A__   ,	dtype=torch.floataa      )
						elif beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							A__							:   Optional[int]					       =		(
							    torch.linspace(beta_start**0.5   ,	beta_end**0.5   ,	A__   ,	dtype=torch.floataa      ) ** 2
							)
						elif beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							A__							:   Optional[int]					       =		betas_for_alpha_bar(A__      )
						else:
							raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""      )

						A__							:   str					       =		1.0 - self.betas
						A__							:   Dict					       =		torch.cumprod(self.alphas   ,	dim=0      )

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






					def   __A      (			self   ,	A__   ,	A__=None      ):
						if schedule_timesteps is None:
							A__							:   List[Any]					       =		self.timesteps

						A__							:   List[Any]					       =		(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:
							A__							:   int					       =		1 if len(A__      ) > 1 else 0
						else:
							A__							:   Dict					       =		timestep.cpu().item() if torch.is_tensor(A__      ) else timestep
							A__							:   Optional[Any]					       =		self._index_counter[timestep_int]

						return indices[pos].item()






					@property
					def   __A      (			self      ):
						# 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   __A      (			self   ,	A__   ,	A__   ,	):
						A__							:   Tuple					       =		self.index_for_timestep(A__      )

						if self.state_in_first_order:
							A__							:   List[Any]					       =		self.sigmas[step_index]
						else:
							A__							:   Tuple					       =		self.sigmas_interpol[step_index]

						A__							:   Union[str, Any]					       =		sample / ((sigma**2 + 1) ** 0.5)
						return sample






					def   __A      (			self   ,	A__   ,	A__ = None   ,	A__ = None   ,	):
						A__							:   str					       =		num_inference_steps

						A__							:   Union[str, 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":
							A__							:   List[str]					       =		np.linspace(0   ,	num_train_timesteps - 1   ,	A__   ,	dtype=A__      )[::-1].copy()
						elif self.config.timestep_spacing == "leading":
							A__							:   str					       =		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
							A__							:   Dict					       =		(np.arange(0   ,	A__      ) * step_ratio).round()[::-1].copy().astype(A__      )
							timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
							A__							:   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
							A__							:   Optional[int]					       =		(np.arange(A__   ,	0   ,	-step_ratio      )).round().copy().astype(A__      )
							timesteps -= 1
						else:
							raise ValueError(
							    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""      )

						A__							:   Dict					       =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
						A__							:   Optional[Any]					       =		torch.from_numpy(np.log(A__      )      ).to(A__      )

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

						# interpolate sigmas
						A__							:   Any					       =		sigmas.log().lerp(sigmas.roll(1      ).log()   ,	0.5      ).exp()

						A__							:   List[Any]					       =		torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
						A__							:   List[Any]					       =		torch.cat(
						    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )

						if str(A__      ).startswith("""mps"""      ):
							# mps does not support float64
							A__							:   Dict					       =		torch.from_numpy(A__      ).to(A__   ,	dtype=torch.floataa      )
						else:
							A__							:   Union[str, Any]					       =		torch.from_numpy(A__      ).to(A__      )

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

						A__							:   Tuple					       =		torch.cat([timesteps[:1], interleaved_timesteps]      )

						A__							:   Any					       =		None

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






					def   __A      (			self   ,	A__      ):
						# get log sigma
						A__							:   Union[str, Any]					       =		sigma.log()

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

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

						A__							:   Union[str, Any]					       =		self.log_sigmas[low_idx]
						A__							:   Any					       =		self.log_sigmas[high_idx]

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

						# transform interpolation to time range
						A__							:   List[str]					       =		(1 - w) * low_idx + w * high_idx
						A__							:   str					       =		t.view(sigma.shape      )
						return t






					@property
					def   __A      (			self      ):
						return self.sample is None






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__ = True   ,	):
						A__							:   List[str]					       =		self.index_for_timestep(A__      )

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

						if self.state_in_first_order:
							A__							:   int					       =		self.sigmas[step_index]
							A__							:   int					       =		self.sigmas_interpol[step_index + 1]
							A__							:   Optional[int]					       =		self.sigmas[step_index + 1]
						else:
							# 2nd order / KDPM2's method
							A__							:   List[str]					       =		self.sigmas[step_index - 1]
							A__							:   Dict					       =		self.sigmas_interpol[step_index]
							A__							:   Tuple					       =		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
						A__							:   Optional[int]					       =		0
						A__							:   Optional[Any]					       =		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":
							A__							:   Tuple					       =		sigma_hat if self.state_in_first_order else sigma_interpol
							A__							:   List[str]					       =		sample - sigma_input * model_output
						elif self.config.prediction_type == "v_prediction":
							A__							:   Tuple					       =		sigma_hat if self.state_in_first_order else sigma_interpol
							A__							:   Union[str, 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
							A__							:   List[str]					       =		(sample - pred_original_sample) / sigma_hat
							# 3. delta timestep
							A__							:   Optional[int]					       =		sigma_interpol - sigma_hat

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

							# 3. delta timestep
							A__							:   str					       =		sigma_next - sigma_hat

							A__							:   Dict					       =		self.sample
							A__							:   Union[str, Any]					       =		None

						A__							:   int					       =		sample + derivative * dt

						if not return_dict:
							return (prev_sample,)

						return SchedulerOutput(prev_sample=A__      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	):
						# Make sure sigmas and timesteps have the same device and dtype as original_samples
						A__							:   Optional[int]					       =		self.sigmas.to(device=original_samples.device   ,	dtype=original_samples.dtype      )
						if original_samples.device.type == "mps" and torch.is_floating_point(A__      ):
							# mps does not support float64
							A__							:   str					       =		self.timesteps.to(original_samples.device   ,	dtype=torch.floataa      )
							A__							:   List[str]					       =		timesteps.to(original_samples.device   ,	dtype=torch.floataa      )
						else:
							A__							:   List[str]					       =		self.timesteps.to(original_samples.device      )
							A__							:   Tuple					       =		timesteps.to(original_samples.device      )

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

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

						A__							:   Dict					       =		original_samples + noise * sigma
						return noisy_samples






					def __len__(			self      ):
						return self.config.num_train_timesteps






import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


A_	:			Optional[int]  	=      '<<<<<<< This should probably be modified because it mentions: '

A_	:			int  	=      '=======\n>>>>>>>\n'

A_	:			Optional[Any]  	=      [
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

A_	:			List[Any]  	=      [
    # (pattern, replacement)
    # Order is important here for some replacements
    (r'tfds\.core', r'datasets'),
    (r'tf\.io\.gfile\.GFile', r'open'),
    (r'tf\.([\w\d]+)', r'datasets.Value(\'\1\')'),
    (r'tfds\.features\.Text\(\)', r'datasets.Value(\'string\')'),
    (r'tfds\.features\.Text\(', r'datasets.Value(\'string\'),'),
    (r'features\s*=\s*tfds.features.FeaturesDict\(', r'features=datasets.Features('),
    (r'tfds\.features\.FeaturesDict\(', r'dict('),
    (r'The TensorFlow Datasets Authors', r'The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'),
    (r'tfds\.', r'datasets.'),
    (r'dl_manager\.manual_dir', r'self.config.data_dir'),
    (r'self\.builder_config', r'self.config'),
]

def 						UpperCamelCase (lowercase_:			Namespace )					->				str:
	return ConvertCommand(args.tfds_path   ,				args.datasets_directory )



class    _a						(__magic_name__    ):


					'''simple docstring'''






					@staticmethod
					def   __A      (			A__      ):
						A__							:   Any					       =		parser.add_parser(
						    """convert"""   ,	help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."""   ,	)
						train_parser.add_argument(
						    """--tfds_path"""   ,	type=A__   ,	required=A__   ,	help="""Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."""   ,	)
						train_parser.add_argument(
						    """--datasets_directory"""   ,	type=A__   ,	required=A__   ,	help="""Path to the HuggingFace Datasets folder."""      )
						train_parser.set_defaults(func=A__      )






					def __init__(			self   ,	A__   ,	A__   ,	*A__      ):
						A__							:   Optional[Any]					       =		get_logger("""datasets-cli/converting"""      )

						A__							:   Optional[Any]					       =		tfds_path
						A__							:   Dict					       =		datasets_directory






					def   __A      (			self      ):
						if os.path.isdir(self._tfds_path      ):
							A__							:   Any					       =		os.path.abspath(self._tfds_path      )
						elif os.path.isfile(self._tfds_path      ):
							A__							:   int					       =		os.path.dirname(self._tfds_path      )
						else:
							raise ValueError("""--tfds_path is neither a directory nor a file. Please check path."""      )

						A__							:   Optional[int]					       =		os.path.abspath(self._datasets_directory      )

						self._logger.info(F"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}"""      )

						A__							:   Optional[Any]					       =		[]
						A__							:   List[Any]					       =		[]
						A__							:   List[Any]					       =		{}

						if os.path.isdir(self._tfds_path      ):
							A__							:   List[str]					       =		os.listdir(A__      )
						else:
							A__							:   Optional[Any]					       =		[os.path.basename(self._tfds_path      )]

						for f_name in file_names:
							self._logger.info(F"""Looking at file {f_name}"""      )
							A__							:   Any					       =		os.path.join(A__   ,	A__      )
							A__							:   Optional[Any]					       =		os.path.join(A__   ,	A__      )

							if not os.path.isfile(A__      ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
								self._logger.info("""Skipping file"""      )
								continue

							with open(A__   ,	encoding="""utf-8"""      ) as f:
								A__							:   Dict					       =		f.readlines()

							A__							:   Tuple					       =		[]
							A__							:   str					       =		False
							A__							:   Tuple					       =		False
							A__							:   Optional[Any]					       =		[]
							for line in lines:
								A__							:   Optional[int]					       =		line

								# Convert imports
								if "import tensorflow.compat.v2 as tf" in out_line:
									continue
								elif "@tfds.core" in out_line:
									continue
								elif "builder=self" in out_line:
									continue
								elif "import tensorflow_datasets.public_api as tfds" in out_line:
									A__							:   Dict					       =		"""import datasets\n"""
								elif "import tensorflow" in out_line:
									# order is important here
									A__							:   Any					       =		""""""
									continue
								elif "from absl import logging" in out_line:
									A__							:   int					       =		"""from datasets import logging\n"""
								elif "getLogger" in out_line:
									A__							:   List[str]					       =		out_line.replace("""getLogger"""   ,	"""get_logger"""      )
								elif any(expression in out_line for expression in TO_HIGHLIGHT      ):
									A__							:   Optional[Any]					       =		True
									A__							:   int					       =		list(filter(lambda A__      : e in out_line   ,	A__      )      )
									out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(A__      ) + """\n"""      )
									out_lines.append(A__      )
									out_lines.append(A__      )
									continue
								else:
									for pattern, replacement in TO_CONVERT:
										A__							:   List[str]					       =		re.sub(A__   ,	A__   ,	A__      )

                # Take care of saving utilities (to later move them together with main script)
								if "tensorflow_datasets" in out_line:
									A__							:   Dict					       =		re.match(r"""from\stensorflow_datasets.*import\s([^\.\r\n]+)"""   ,	A__      )
									tfds_imports.extend(imp.strip() for imp in match.group(1      ).split(""","""      )      )
									A__							:   Tuple					       =		"""from . import """ + match.group(1      )

								# Check we have not forget anything
								if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
									raise ValueError(F"""Error converting {out_line.strip()}"""      )

								if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
									A__							:   List[str]					       =		True
								out_lines.append(A__      )

							if is_builder or "wmt" in f_name:
								# We create a new directory for each dataset
								A__							:   Tuple					       =		f_name.replace(""".py"""   ,	""""""      )
								A__							:   Optional[int]					       =		os.path.join(A__   ,	A__      )
								A__							:   Optional[Any]					       =		os.path.join(A__   ,	A__      )
								os.makedirs(A__   ,	exist_ok=A__      )
								self._logger.info(F"""Adding directory {output_dir}"""      )
								imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}      )
							else:
								# Utilities will be moved at the end
								utils_files.append(A__      )

							if needs_manual_update:
								with_manual_update.append(A__      )

							with open(A__   ,	"""w"""   ,	encoding="""utf-8"""      ) as f:
								f.writelines(A__      )
							self._logger.info(F"""Converted in {output_file}"""      )

						for utils_file in utils_files:
							try:
								A__							:   int					       =		os.path.basename(A__      )
								A__							:   List[str]					       =		imports_to_builder_map[f_name.replace(""".py"""   ,	""""""      )]
								self._logger.info(F"""Moving {dest_folder} to {utils_file}"""      )
								shutil.copy(A__   ,	A__      )
							except KeyError:
								self._logger.error(F"""Cannot find destination folder for {utils_file}. Please copy manually."""      )

						if with_manual_update:
							for file_path in with_manual_update:
								self._logger.warning(
								    F"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'."""      )



