

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

import numpy as np
import torch

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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =42
			lowerCamelCase__           =None

def  lowerCamelCase__ (					_a							,			_a=0.999							,			_a="cosine"							,			):
	if alpha_transform_type == "cosine":

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

	elif alpha_transform_type == "exp":

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

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

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






class _UpperCamelCase							(							__A							,	__A   ):
			'''simple docstring'''







			@register_to_config
			def __init__(			self							: List[Any]      ,      a							: int = 1000      ,      a							: str = "fixed_small_log"      ,      a							: bool = True      ,      a							: Optional[float] = 1.0      ,      a							: str = "epsilon"      ,      a							: str = "squaredcos_cap_v2"      ,      )							->       List[str]:

				"""simple docstring"""

				if beta_schedule != "squaredcos_cap_v2":
					raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       betas_for_alpha_bar(a	)

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

				# standard deviation of the initial noise distribution
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       1.0

				# setable values
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       None
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.from_numpy(np.arange(0      ,      a	)[::-1].copy()	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       variance_type
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: torch.FloatTensor      ,      a							: Optional[int] = None	)							->       torch.FloatTensor:

				"""simple docstring"""

				return sample
			def 							__UpperCamelCase (			self							: Any      ,      a							: int      ,      a							: Union[str, torch.device] = None	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_inference_steps
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       (np.arange(0      ,      a	) * step_ratio).round()[::-1].copy().astype(np.intaa	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.from_numpy(a	).to(a	)
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: Any      ,      a							: int=None      ,      a							: List[str]=None      ,      a							: Any=None	)							->       Dict:

				"""simple docstring"""

				if prev_timestep is None:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       t - 1

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.alphas_cumprod[t]
				SCREAMING_SNAKE_CASE   :      Dict				     =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
				SCREAMING_SNAKE_CASE   :      List[str]				     =       1 - alpha_prod_t
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       1 - alpha_prod_t_prev

				if prev_timestep == t - 1:
					SCREAMING_SNAKE_CASE   :      int				     =       self.betas[t]
				else:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       1 - alpha_prod_t / alpha_prod_t_prev

				# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
				# and sample from it to get previous sample
				# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
				SCREAMING_SNAKE_CASE   :      str				     =       beta_prod_t_prev / beta_prod_t * beta

				if variance_type is None:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.config.variance_type

				# hacks - were probably added for training stability
				if variance_type == "fixed_small_log":
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.log(torch.clamp(a      ,      min=1e-20	)	)
					SCREAMING_SNAKE_CASE   :      str				     =       torch.exp(0.5 * variance	)
				elif variance_type == "learned_range":
					# NOTE difference with DDPM scheduler
					SCREAMING_SNAKE_CASE   :      Any				     =       variance.log()
					SCREAMING_SNAKE_CASE   :      str				     =       beta.log()

					SCREAMING_SNAKE_CASE   :      str				     =       (predicted_variance + 1) / 2
					SCREAMING_SNAKE_CASE   :      List[str]				     =       frac * max_log + (1 - frac) * min_log

				return variance
			def 							__UpperCamelCase (			self							: Any      ,      a							: torch.FloatTensor      ,      a							: int      ,      a							: torch.FloatTensor      ,      a							: Optional[int] = None      ,      a							: Optional[int]=None      ,      a							: bool = True      ,      )							->       Union[UnCLIPSchedulerOutput, Tuple]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
					SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.split(a      ,      sample.shape[1]      ,      dim=1	)
				else:
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       None

				# 1. compute alphas, betas
				if prev_timestep is None:
					SCREAMING_SNAKE_CASE   :      str				     =       t - 1

				SCREAMING_SNAKE_CASE   :      int				     =       self.alphas_cumprod[t]
				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
				SCREAMING_SNAKE_CASE   :      Dict				     =       1 - alpha_prod_t
				SCREAMING_SNAKE_CASE   :      List[str]				     =       1 - alpha_prod_t_prev

				if prev_timestep == t - 1:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       self.betas[t]
					SCREAMING_SNAKE_CASE   :      Dict				     =       self.alphas[t]
				else:
					SCREAMING_SNAKE_CASE   :      Any				     =       1 - alpha_prod_t / alpha_prod_t_prev
					SCREAMING_SNAKE_CASE   :      int				     =       1 - beta

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				if self.config.prediction_type == "epsilon":
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif self.config.prediction_type == "sample":
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       model_output
				else:
					raise ValueError(
					    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
					    " for the UnCLIPScheduler."	)

				# 3. Clip "predicted x_0"
				if self.config.clip_sample:
					SCREAMING_SNAKE_CASE   :      Any				     =       torch.clamp(
					    a      ,      -self.config.clip_sample_range      ,      self.config.clip_sample_range	)

				# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				SCREAMING_SNAKE_CASE   :      Any				     =       (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
				SCREAMING_SNAKE_CASE   :      Tuple				     =       alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

				# 5. Compute predicted previous sample µ_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       0
				if t > 0:
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       randn_tensor(
					    model_output.shape      ,      dtype=model_output.dtype      ,      generator=a      ,      device=model_output.device	)

					SCREAMING_SNAKE_CASE   :      List[Any]				     =       self._get_variance(
					    a      ,      predicted_variance=a      ,      prev_timestep=a      ,      )

					if self.variance_type == "fixed_small_log":
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       variance
					elif self.variance_type == "learned_range":
						SCREAMING_SNAKE_CASE   :      str				     =       (0.5 * variance).exp()
					else:
						raise ValueError(
						    F"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
						    " for the UnCLIPScheduler."	)

					SCREAMING_SNAKE_CASE   :      Any				     =       variance * variance_noise

				SCREAMING_SNAKE_CASE   :      Any				     =       pred_prev_sample + variance

				if not return_dict:
					return (pred_prev_sample,)

				return UnCLIPSchedulerOutput(prev_sample=a      ,      pred_original_sample=a	)






			def 							__UpperCamelCase (			self							: Dict      ,      a							: torch.FloatTensor      ,      a							: torch.FloatTensor      ,      a							: torch.IntTensor      ,      )							->       torch.FloatTensor:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.alphas_cumprod.to(device=original_samples.device      ,      dtype=original_samples.dtype	)
				SCREAMING_SNAKE_CASE   :      str				     =       timesteps.to(original_samples.device	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       alphas_cumprod[timesteps] ** 0.5
				SCREAMING_SNAKE_CASE   :      int				     =       sqrt_alpha_prod.flatten()
				while len(sqrt_alpha_prod.shape	) < len(original_samples.shape	):
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       sqrt_alpha_prod.unsqueeze(-1	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       (1 - alphas_cumprod[timesteps]) ** 0.5
				SCREAMING_SNAKE_CASE   :      List[str]				     =       sqrt_one_minus_alpha_prod.flatten()
				while len(sqrt_one_minus_alpha_prod.shape	) < len(original_samples.shape	):
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       sqrt_one_minus_alpha_prod.unsqueeze(-1	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples


import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


a_						     =    re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
a_						     =    10
a_						     =    256

def  lowerCamelCase__ (					_a):
	if len(_a) < MIN_NUM_TOKENS:
		return None
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       MinHash(num_perm=_a)
	for token in set(_a):
		min_hash.update(token.encode())
	return min_hash

def  lowerCamelCase__ (					_a):
	return {t for t in NON_ALPHA.split(_a) if len(t.strip()) > 0}






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Optional[Any]      ,      *,
			    a							: float = 0.85      ,      )							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       duplication_jaccard_threshold
				SCREAMING_SNAKE_CASE   :      Any				     =       NUM_PERM
				SCREAMING_SNAKE_CASE   :      int				     =       MinHashLSH(threshold=self._duplication_jaccard_threshold      ,      num_perm=self._num_perm	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       defaultdict(a	)
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Tuple      ,      a							: MinHash	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self._index.query(a	)
				if code_key in self._index.keys:
					print(F"Duplicate key {code_key}"	)
					return

				self._index.insert(a      ,      a	)
				if len(a	) > 0:
					for base_duplicate in close_duplicates:
						if base_duplicate in self._duplicate_clusters:
							self._duplicate_clusters[base_duplicate].add(a	)
							break
					else:
						self._duplicate_clusters[close_duplicates[0]].add(a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       List[List[Dict]]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       []
				for base, duplicates in self._duplicate_clusters.items():
					SCREAMING_SNAKE_CASE   :      int				     =       [base] + list(a	)
					# reformat the cluster to be a list of dict
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
					duplicate_clusters.append(a	)
				return duplicate_clusters






			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: List[str]	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.get_duplicate_clusters()
				with open(a      ,      "w"	) as f:
					json.dump(a      ,      a	)

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       element
	SCREAMING_SNAKE_CASE   :      Any				     =       get_min_hash([t for t in NON_ALPHA.split(data["content"]) if len(t.strip()) > 0])
	if min_hash is not None:
		return (index, data["repo_name"], data["path"]), min_hash

def  lowerCamelCase__ (					_a):
	with mp.Pool() as pool:
		for data in pool.imap_unordered(
		    _compute_min_hash							,			ThreadedIterator(_a							,			max_queue_size=10000)							,			chunksize=100							,			):
			if data is not None:
				yield data

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       DuplicationIndex(duplication_jaccard_threshold=_a)

	for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_a))							,			max_queue_size=100)):
		di.add(_a							,			_a)

	# Returns a List[Cluster] where Cluster is List[str] with the filenames.
	return di.get_duplicate_clusters()

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       get_tokens(_a)
	SCREAMING_SNAKE_CASE   :      List[str]				     =       get_tokens(_a)
	return len(tokensa & tokensa) / len(tokensa | tokensa)


a_						     =    None

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       []
	for elementa in cluster:
		SCREAMING_SNAKE_CASE   :      int				     =       _shared_dataset[elementa["base_index"]]["content"]
		for elementa in extremes:
			SCREAMING_SNAKE_CASE   :      int				     =       _shared_dataset[elementa["base_index"]]["content"]
			if jaccard_similarity(_a							,			_a) >= jaccard_threshold:
				elementa["copies"] += 1
				break
		else:
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       1
			extremes.append(_a)
	return extremes

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	global _shared_dataset
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       dataset
	SCREAMING_SNAKE_CASE   :      str				     =       []
	SCREAMING_SNAKE_CASE   :      Dict				     =       partial(_find_cluster_extremes_shared							,			jaccard_threshold=_a)
	with mp.Pool() as pool:
		for extremes in tqdm(
		    pool.imap_unordered(
		        _a							,			_a							,			)							,			total=len(_a)							,			):
			extremes_list.append(_a)
	return extremes_list


def  lowerCamelCase__ (					_a							,			_a = 0.85):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       make_duplicate_clusters(_a							,			_a)
	SCREAMING_SNAKE_CASE   :      Dict				     =       {x["base_index"] for cluster in duplicate_clusters for x in cluster}
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {}
	SCREAMING_SNAKE_CASE   :      List[str]				     =       find_extremes(_a							,			_a							,			_a)
	for extremes in extremes_clusters:
		for element in extremes:
			SCREAMING_SNAKE_CASE   :      str				     =       element
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       duplicate_indices - set(extreme_dict.keys())
	SCREAMING_SNAKE_CASE   :      str				     =       dataset.filter(lambda _a							,			_a: idx not in remove_indices							,			with_indices=_a)

	# update duplicate_clusters
	for cluster in duplicate_clusters:
		for element in cluster:
			SCREAMING_SNAKE_CASE   :      Dict				     =       element["base_index"] in extreme_dict
			if element["is_extreme"]:
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       extreme_dict[element["base_index"]]["copies"]

	print(f"Original dataset size: {len(_a)}")
	print(f"Number of duplicate clusters: {len(_a)}")
	print(f"Files in duplicate cluster: {len(_a)}")
	print(f"Unique files in duplicate cluster: {len(_a)}")
	print(f"Filtered dataset size: {len(_a)}")

	return ds_filter, duplicate_clusters