



import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


_lowercase						  =		get_logger(__name__)
class     _UpperCAmelCase			:
     UpperCamelCase__		    =				"""dummy_data"""
     UpperCamelCase__		    =				"""datasets"""
     UpperCamelCase__		    =				False




     def __init__(				self ,						a__ ,						a__ ,						a__ ,						a__ = None ,						a__ = False ,						a__ = True ,						a__ = None ,						):
         A__		 =					0
         A__		 =					dataset_name
         A__		 =					cache_dir
         A__		 =					use_local_dummy_data
         A__		 =					config
         # download_callbacks take a single url as input
         A__		 =					download_callbacks or []
         # if False, it doesn't load existing files and it returns the paths of the dummy files relative
         # to the dummy_data zip file root
         A__		 =					load_existing_dummy_data

         # TODO(PVP, QL) might need to make this more general
         A__		 =					str(lowercase__)
         # to be downloaded
         A__		 =					None
         A__		 =					None




     @property
     def 		snake_case_		(				self):





         if self._dummy_file is None:
             A__		 =					self.download_dummy_data()
         return self._dummy_file




     @property
     def 		snake_case_		(				self):





         if self.config is not None:
             # structure is dummy / config_name / version_name
             return os.path.join('''dummy''' ,						self.config.name ,						self.version_name)
         # structure is dummy / version_name
         return os.path.join('''dummy''' ,						self.version_name)




     @property
     def 		snake_case_		(				self):
         return os.path.join(self.dummy_data_folder ,						'''dummy_data.zip''')




     def 		snake_case_		(				self):
         A__		 =					(
             self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
         )

         A__		 =					cached_path(
             lowercase__ ,						cache_dir=self.cache_dir ,						extract_compressed_file=lowercase__ ,						force_extract=lowercase__)

         return os.path.join(lowercase__ ,						self.dummy_file_name)




     @property
     def 		snake_case_		(				self):
         return os.path.join(self.datasets_scripts_dir ,						self.dataset_name ,						self.dummy_zip_file)




     @property
     def 		snake_case_		(				self):





         if self._bucket_url is None:
             A__		 =					hf_github_url(self.dataset_name ,						self.dummy_zip_file.replace(os.sep ,						'''/'''))
         return self._bucket_url




     @property
     def 		snake_case_		(				self):





         if os.path.isdir(self.dummy_file):
             return self.dummy_file
         # else cut off path to file -> example `xsum`.
         return "/".join(self.dummy_file.replace(os.sep ,						'''/''').split('''/''')[:-1])




     def 		snake_case_		(				self ,						a__ ,						*a__):





         if self.load_existing_dummy_data:
             # dummy data is downloaded and tested
             A__		 =					self.dummy_file
         else:
             # dummy data cannot be downloaded and only the path to dummy file is returned
             A__		 =					self.dummy_file_name

         # special case when data_url is a dict
         if isinstance(lowercase__ ,						lowercase__):
             return self.create_dummy_data_dict(lowercase__ ,						lowercase__)
         elif isinstance(lowercase__ ,						(list, tuple)):
             return self.create_dummy_data_list(lowercase__ ,						lowercase__)
         else:
             return self.create_dummy_data_single(lowercase__ ,						lowercase__)




     def 		snake_case_		(				self ,						a__ ,						*a__):
         return self.download_and_extract(lowercase__)




     def 		snake_case_		(				self ,						a__ ,						a__):
         return self.download_and_extract(lowercase__)




     def 		snake_case_		(				self ,						a__ ,						*a__ ,						**a__):
         return path




     def 		snake_case_		(				self):
         return {}




     def 		snake_case_		(				self ,						a__ ,						a__):
         A__		 =					{}
         for key, single_urls in data_url.items():
             for download_callback in self.download_callbacks:
                 if isinstance(lowercase__ ,						lowercase__):
                     for single_url in single_urls:
                         download_callback(lowercase__)
                 else:
                     A__		 =					single_urls
                     download_callback(lowercase__)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
             if isinstance(lowercase__ ,						lowercase__):
                 A__		 =					[os.path.join(lowercase__ ,						urllib.parse.quote_plus(Path(lowercase__).name)) for x in single_urls]
             else:
                 A__		 =					single_urls
                 A__		 =					os.path.join(lowercase__ ,						urllib.parse.quote_plus(Path(lowercase__).name))
             A__		 =					value

         # make sure that values are unique
         if all(isinstance(lowercase__ ,						lowercase__) for i in dummy_data_dict.values()) and len(set(dummy_data_dict.values())) < len(
             dummy_data_dict.values()):
             # append key to value to make its name unique
             A__		 =					{key: value + key for key, value in dummy_data_dict.items()}

         return dummy_data_dict




     def 		snake_case_		(				self ,						a__ ,						a__):
         A__		 =					[]
         # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
         A__		 =					all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}''' ,						lowercase__)) for url in data_url)
         A__		 =					all(
             url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed''') for url in data_url)
         if data_url and (is_tf_records or is_pubmed_records):
             A__		 =					[data_url[0]] * len(lowercase__)
         for single_url in data_url:
             for download_callback in self.download_callbacks:
                 download_callback(lowercase__)
             # we force the name of each key to be the last file / folder name of the url path
             # if the url has arguments, we need to encode them with urllib.parse.quote_plus
             A__		 =					os.path.join(lowercase__ ,						urllib.parse.quote_plus(single_url.split('''/''')[-1]))
             dummy_data_list.append(lowercase__)
         return dummy_data_list




     def 		snake_case_		(				self ,						a__ ,						a__):





         for download_callback in self.download_callbacks:
             download_callback(lowercase__)
         # we force the name of each key to be the last file / folder name of the url path
         # if the url has arguments, we need to encode them with urllib.parse.quote_plus
         A__		 =					os.path.join(lowercase__ ,						urllib.parse.quote_plus(data_url.split('''/''')[-1]))
         if os.path.exists(lowercase__) or not self.load_existing_dummy_data:
             return value
         else:
             # Backward compatibility, maybe deprecate at one point.
             # For many datasets with single url calls to dl_manager.download_and_extract,
             # the dummy_data.zip file is actually the zipped downloaded file
             # while now we expected the dummy_data.zip file to be a directory containing
             # the downloaded file.
             return path_to_dummy_data




     def 		snake_case_		(				self):
         pass




     def 		snake_case_		(				self):
         pass




     def 		snake_case_		(				self ,						a__):





         def _iter_archive_members(a__):
             # this preserves the order of the members inside the ZIP archive
             A__		 =					Path(self.dummy_file).parent
             A__		 =					path.relative_to(lowercase__)
             with ZipFile(self.local_path_to_dummy_data) as zip_file:
                 A__		 =					zip_file.namelist()
             for member in members:
                 if member.startswith(relative_path.as_posix()):
                     yield dummy_parent_path.joinpath(lowercase__)

         A__		 =					Path(lowercase__)
         A__		 =					_iter_archive_members(lowercase__) if self.use_local_dummy_data else path.rglob('''*''')
         for file_path in file_paths:
             if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''')):
                 yield file_path.relative_to(lowercase__).as_posix(), file_path.open('''rb''')




     def 		snake_case_		(				self ,						a__):





         if not isinstance(lowercase__ ,						lowercase__):
             A__		 =					[paths]
         for path in paths:
             if os.path.isfile(lowercase__):
                 if os.path.basename(lowercase__).startswith(('''.''', '''__''')):
                     return
                 yield path
             else:
                 for dirpath, dirnames, filenames in os.walk(lowercase__):
                     if os.path.basename(lowercase__).startswith(('''.''', '''__''')):
                         continue
                     dirnames.sort()
                     for filename in sorted(lowercase__):
                         if filename.startswith(('''.''', '''__''')):
                             continue
                         yield os.path.join(lowercase__ ,						lowercase__)





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				(DDPMScheduler,)




     def 		snake_case_		(				self ,						**a__):
         A__		 =					{
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
             '''variance_type''': '''fixed_small''',
             '''clip_sample''': True,
         }

         config.update(**a__)
         return config




     def 		snake_case_		(				self):
         for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=a__)




     def 		snake_case_		(				self):
         for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] ,						[0.0_0_2, 0.0_2, 0.2, 2]):
             self.check_over_configs(beta_start=a__ ,						beta_end=a__)




     def 		snake_case_		(				self):
         for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=a__)




     def 		snake_case_		(				self):
         for variance in ["fixed_small", "fixed_large", "other"]:
             self.check_over_configs(variance_type=a__)




     def 		snake_case_		(				self):
         for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=a__)




     def 		snake_case_		(				self):
         self.check_over_configs(thresholding=a__)
         for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(
                     thresholding=a__ ,						prediction_type=a__ ,						sample_max_value=a__ ,						)




     def 		snake_case_		(				self):
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(prediction_type=a__)




     def 		snake_case_		(				self):
         for t in [0, 5_0_0, 9_9_9]:
             self.check_over_forward(time_step=a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7) - 0.0_0_9_7_9)) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9) - 0.0_2)) < 1e-5




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					len(a__)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter
         A__		 =					torch.manual_seed(0)

         for t in reversed(range(a__)):
             # 1. predict noise residual
             A__		 =					model(a__ ,						a__)

             # 2. predict previous mean of sample x_t-1
             A__		 =					scheduler.step(a__ ,						a__ ,						a__ ,						generator=a__).prev_sample

             # if t > 0:
             #     noise = self.dummy_sample_deter
             #     variance = scheduler.get_variance(t) ** (0.5) * noise
             #
             # sample = pred_prev_sample + variance
             A__		 =					pred_prev_sample

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 2_5_8.9_6_0_6) < 1e-2
         assert abs(result_mean.item() - 0.3_3_7_2) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config(prediction_type='''v_prediction''')
         A__		 =					scheduler_class(**a__)

         A__		 =					len(a__)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter
         A__		 =					torch.manual_seed(0)

         for t in reversed(range(a__)):
             # 1. predict noise residual
             A__		 =					model(a__ ,						a__)

             # 2. predict previous mean of sample x_t-1
             A__		 =					scheduler.step(a__ ,						a__ ,						a__ ,						generator=a__).prev_sample

             # if t > 0:
             #     noise = self.dummy_sample_deter
             #     variance = scheduler.get_variance(t) ** (0.5) * noise
             #
             # sample = pred_prev_sample + variance
             A__		 =					pred_prev_sample

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 2_0_2.0_2_9_6) < 1e-2
         assert abs(result_mean.item() - 0.2_6_3_1) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[1_0_0, 8_7, 5_0, 1, 0]

         scheduler.set_timesteps(timesteps=a__)

         A__		 =					scheduler.timesteps

         for i, timestep in enumerate(a__):
             if i == len(a__) - 1:
                 A__		 =					-1
             else:
                 A__		 =					timesteps[i + 1]

             A__		 =					scheduler.previous_timestep(a__)
             A__		 =					prev_t.item()

             self.assertEqual(a__ ,						a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[1_0_0, 8_7, 5_0, 5_1, 0]

         with self.assertRaises(a__ ,						msg='''`custom_timesteps` must be in descending order.'''):
             scheduler.set_timesteps(timesteps=a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[1_0_0, 8_7, 5_0, 1, 0]
         A__		 =					len(a__)

         with self.assertRaises(a__ ,						msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
             scheduler.set_timesteps(num_inference_steps=a__ ,						timesteps=a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[scheduler.config.num_train_timesteps]

         with self.assertRaises(
             a__ ,						msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' ,						):
             scheduler.set_timesteps(timesteps=a__)
