"""simple docstring"""


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      (DPMSolverSinglestepScheduler,)
       SCREAMING_SNAKE_CASE_			=      (("num_inference_steps", 2_5),)







       def 		a_						( self,							**lowerCAmelCase__)      ->      int:
            snake_case_					       =					{
                'num_train_timesteps': 1000,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
                'solver_order': 2,
                'prediction_type': 'epsilon',
                'thresholding': False,
                'sample_max_value': 1.0,
                'algorithm_type': 'dpmsolver++',
                'solver_type': 'midpoint',
                'lambda_min_clipped': -float('inf'),
                'variance_type': None,
            }

            config.update(**lowerCAmelCase__)
            return config







       def 		a_						( self,							lowerCAmelCase__=0,							**lowerCAmelCase__)      ->      List[Any]:
            snake_case_					       =					dict(self.forward_default_kwargs)
            snake_case_					       =					kwargs.pop('num_inference_steps',							lowerCAmelCase__)
            snake_case_					       =					self.dummy_sample
            snake_case_					       =					0.1 * sample
            snake_case_					       =					[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                 snake_case_					       =					self.get_scheduler_config(**lowerCAmelCase__)
                 snake_case_					       =					scheduler_class(**lowerCAmelCase__)
                 scheduler.set_timesteps(lowerCAmelCase__)
                 # copy over dummy past residuals
                 snake_case_					       =					dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(lowerCAmelCase__)
                      snake_case_					       =					scheduler_class.from_pretrained(lowerCAmelCase__)
                      new_scheduler.set_timesteps(lowerCAmelCase__)
                      # copy over dummy past residuals
                      snake_case_					       =					dummy_past_residuals[: new_scheduler.config.solver_order]

                 snake_case_       ,	snake_case_					       =					sample, sample
                 for t in range(lowerCAmelCase__,							time_step + scheduler.config.solver_order + 1):
                      snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample
                      snake_case_					       =					new_scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample

                      assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"







       def 		a_						( self)      ->      Union[str, Any]:
            pass







       def 		a_						( self,							lowerCAmelCase__=0,							**lowerCAmelCase__)      ->      int:
            snake_case_					       =					dict(self.forward_default_kwargs)
            snake_case_					       =					kwargs.pop('num_inference_steps',							lowerCAmelCase__)
            snake_case_					       =					self.dummy_sample
            snake_case_					       =					0.1 * sample
            snake_case_					       =					[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                 snake_case_					       =					self.get_scheduler_config()
                 snake_case_					       =					scheduler_class(**lowerCAmelCase__)
                 scheduler.set_timesteps(lowerCAmelCase__)

                 # copy over dummy past residuals (must be after setting timesteps)
                 snake_case_					       =					dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(lowerCAmelCase__)
                      snake_case_					       =					scheduler_class.from_pretrained(lowerCAmelCase__)
                      # copy over dummy past residuals
                      new_scheduler.set_timesteps(lowerCAmelCase__)

                      # copy over dummy past residual (must be after setting timesteps)
                      snake_case_					       =					dummy_past_residuals[: new_scheduler.config.solver_order]

                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample
                 snake_case_					       =					new_scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__).prev_sample

                 assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"







       def 		a_						( self,							lowerCAmelCase__=None,							**lowerCAmelCase__)      ->      Union[str, Any]:
            if scheduler is None:
                 snake_case_					       =					self.scheduler_classes[0]
                 snake_case_					       =					self.get_scheduler_config(**lowerCAmelCase__)
                 snake_case_					       =					scheduler_class(**lowerCAmelCase__)

            snake_case_					       =					self.scheduler_classes[0]
            snake_case_					       =					self.get_scheduler_config(**lowerCAmelCase__)
            snake_case_					       =					scheduler_class(**lowerCAmelCase__)

            snake_case_					       =					10
            snake_case_					       =					self.dummy_model()
            snake_case_					       =					self.dummy_sample_deter
            scheduler.set_timesteps(lowerCAmelCase__)

            for i, t in enumerate(scheduler.timesteps):
                 snake_case_					       =					model(lowerCAmelCase__,							lowerCAmelCase__)
                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__).prev_sample

            return sample







       def 		a_						( self)      ->      List[Any]:
            snake_case_					       =					DPMSolverSinglestepScheduler(**self.get_scheduler_config())
            snake_case_					       =					50
            snake_case_					       =					self.dummy_model()
            snake_case_					       =					self.dummy_sample_deter
            scheduler.set_timesteps(lowerCAmelCase__)

            # make sure that the first t is uneven
            for i, t in enumerate(scheduler.timesteps[3:]):
                 snake_case_					       =					model(lowerCAmelCase__,							lowerCAmelCase__)
                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__).prev_sample

            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.2574) < 1e-3







       def 		a_						( self)      ->      Dict:
            for timesteps in [25, 50, 100, 999, 1000]:
                 self.check_over_configs(num_train_timesteps=lowerCAmelCase__)







       def 		a_						( self)      ->      Optional[Any]:
            # make sure that iterating over schedulers with same config names gives same results
            # for defaults
            snake_case_					       =					DPMSolverSinglestepScheduler(**self.get_scheduler_config())
            snake_case_					       =					self.full_loop(scheduler=lowerCAmelCase__)
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.2791) < 1e-3

            snake_case_					       =					DEISMultistepScheduler.from_config(scheduler.config)
            snake_case_					       =					DPMSolverMultistepScheduler.from_config(scheduler.config)
            snake_case_					       =					UniPCMultistepScheduler.from_config(scheduler.config)
            snake_case_					       =					DPMSolverSinglestepScheduler.from_config(scheduler.config)

            snake_case_					       =					self.full_loop(scheduler=lowerCAmelCase__)
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.2791) < 1e-3







       def 		a_						( self)      ->      str:
            self.check_over_configs(thresholding=lowerCAmelCase__)
            for order in [1, 2, 3]:
                 for solver_type in ["midpoint", "heun"]:
                      for threshold in [0.5, 1.0, 2.0]:
                           for prediction_type in ["epsilon", "sample"]:
                                self.check_over_configs(
                                    thresholding=lowerCAmelCase__,							prediction_type=lowerCAmelCase__,							sample_max_value=lowerCAmelCase__,							algorithm_type='dpmsolver++',							solver_order=lowerCAmelCase__,							solver_type=lowerCAmelCase__,							)







       def 		a_						( self)      ->      Tuple:
            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=lowerCAmelCase__)







       def 		a_						( self)      ->      Optional[int]:
            for algorithm_type in ["dpmsolver", "dpmsolver++"]:
                 for solver_type in ["midpoint", "heun"]:
                      for order in [1, 2, 3]:
                           for prediction_type in ["epsilon", "sample"]:
                                self.check_over_configs(
                                    solver_order=lowerCAmelCase__,							solver_type=lowerCAmelCase__,							prediction_type=lowerCAmelCase__,							algorithm_type=lowerCAmelCase__,							)
                                snake_case_					       =					self.full_loop(
                                    solver_order=lowerCAmelCase__,							solver_type=lowerCAmelCase__,							prediction_type=lowerCAmelCase__,							algorithm_type=lowerCAmelCase__,							)
                                assert not torch.isnan(lowerCAmelCase__).any(), "Samples have nan numbers"







       def 		a_						( self)      ->      Optional[Any]:
            self.check_over_configs(lower_order_final=lowerCAmelCase__)
            self.check_over_configs(lower_order_final=lowerCAmelCase__)







       def 		a_						( self)      ->      Any:
            self.check_over_configs(lambda_min_clipped=-float('inf'))
            self.check_over_configs(lambda_min_clipped=-5.1)







       def 		a_						( self)      ->      Any:
            self.check_over_configs(variance_type=lowerCAmelCase__)
            self.check_over_configs(variance_type='learned_range')







       def 		a_						( self)      ->      List[Any]:
            for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                 self.check_over_forward(num_inference_steps=lowerCAmelCase__,							time_step=0)







       def 		a_						( self)      ->      int:
            snake_case_					       =					self.full_loop()
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.2791) < 1e-3







       def 		a_						( self)      ->      Dict:
            snake_case_					       =					self.full_loop(use_karras_sigmas=lowerCAmelCase__)
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.2248) < 1e-3







       def 		a_						( self)      ->      Union[str, Any]:
            snake_case_					       =					self.full_loop(prediction_type='v_prediction')
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.1453) < 1e-3







       def 		a_						( self)      ->      Optional[Any]:
            snake_case_					       =					self.full_loop(prediction_type='v_prediction',							use_karras_sigmas=lowerCAmelCase__)
            snake_case_					       =					torch.mean(torch.abs(lowerCAmelCase__))

            assert abs(result_mean.item() - 0.0649) < 1e-3







       def 		a_						( self)      ->      Optional[int]:
            snake_case_					       =					self.scheduler_classes[0]
            snake_case_					       =					self.get_scheduler_config(thresholding=lowerCAmelCase__,							dynamic_thresholding_ratio=0)
            snake_case_					       =					scheduler_class(**lowerCAmelCase__)

            snake_case_					       =					10
            snake_case_					       =					self.dummy_model()
            snake_case_					       =					self.dummy_sample_deter.half()
            scheduler.set_timesteps(lowerCAmelCase__)

            for i, t in enumerate(scheduler.timesteps):
                 snake_case_					       =					model(lowerCAmelCase__,							lowerCAmelCase__)
                 snake_case_					       =					scheduler.step(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__).prev_sample

            assert sample.dtype == torch.floataa






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


_lowercase: Optional[int]      			=		re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_lowercase: Dict      			=		10
_lowercase: Optional[Any]      			=		256






def     a(      A       :    List[str]       )		->       Optional[MinHash]:
					"""simple docstring"""





					if len(A       ) < MIN_NUM_TOKENS:
										return None
					a					     =   MinHash(num_perm=A       )
					for token in set(A       ):
										min_hash.update(token.encode()       )
					return min_hash






def     a(      A       :    str       )		->       Set[str]:
					"""simple docstring"""





					return {t for t in NON_ALPHA.split(A       ) if len(t.strip()       ) > 0}



class 	_lowercase							:





	"""simple docstring"""




	def __init__(self		,       *,
	    lowerCamelCase_ = 0.85		,       ):
						"""simple docstring"""

						a					     =   duplication_jaccard_threshold
						a					     =   NUM_PERM
						a					     =   MinHashLSH(threshold=self._duplication_jaccard_threshold		,       num_perm=self._num_perm		)

						a					     =   defaultdict(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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

						self._index.insert(lowerCamelCase_		,       lowerCamelCase_		)
						if len(lowerCamelCase_		) > 0:
											for base_duplicate in close_duplicates:
																if base_duplicate in self._duplicate_clusters:
																					self._duplicate_clusters[base_duplicate].add(lowerCamelCase_		)
																					break
											else:
																self._duplicate_clusters[close_duplicates[0]].add(lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

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






def     a(      A       :    Any       )		->       List[Any]:
					"""simple docstring"""





					a     ,				a					     =   element
					a					     =   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     a(      A       :    Type[Dataset]       )		->       List[Any]:
					"""simple docstring"""





					with mp.Pool() as pool:
										for data in pool.imap_unordered(
										    _compute_min_hash ,     ThreadedIterator(A ,     max_queue_size=1_0000       ) ,     chunksize=100 ,     ):
															if data is not None:
																				yield data






def     a(      A       :    Type[Dataset] ,     A       :    float       )		->       Dict:
					"""simple docstring"""





					a					     =   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     a(      A       :    str ,     A       :    str       )		->       float:
					"""simple docstring"""





					a					     =   get_tokens(A       )
					a					     =   get_tokens(A       )
					return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


_lowercase: int      			=		None






def     a(      A       :    str ,     A       :    Tuple       )		->       int:
					"""simple docstring"""





					a					     =   []
					for elementa in cluster:
										a					     =   _shared_dataset[elementa["base_index"]]["content"]
										for elementa in extremes:
															a					     =   _shared_dataset[elementa["base_index"]]["content"]
															if jaccard_similarity(A ,     A       ) >= jaccard_threshold:
																				elementa["copies"] += 1
																				break
										else:
															a					     =   1
															extremes.append(A       )
					return extremes






def     a(      A       :    str ,     A       :    List[str] ,     A       :    int       )		->       Tuple:
					"""simple docstring"""





					global _shared_dataset
					a					     =   dataset
					a					     =   []
					a					     =   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     a(      A       :    Type[Dataset] ,     A       :    float = 0.85       )		->       Tuple[Type[Dataset], List[List[Dict]]]:
					"""simple docstring"""





					a					     =   make_duplicate_clusters(A ,     A       )
					a					     =   {x["base_index"] for cluster in duplicate_clusters for x in cluster}
					a					     =   {}
					a					     =   find_extremes(A ,     A ,     A       )
					for extremes in extremes_clusters:
										for element in extremes:
															a					     =   element
					a					     =   duplicate_indices - set(extreme_dict.keys()       )
					a					     =   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:
															a					     =   element["base_index"] in extreme_dict
															if element["is_extreme"]:
																				a					     =   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
