






"""simple docstring"""




import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE


class    lowercase_    (_UpperCAmelCase     ):
      A__	:		str       			=				''''''
      A__	:		str       			=				(
          None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
      )
      A__	:		str       			=				None  # compression type in fsspec. ex: "gzip"
      A__	:		str       			=				None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz






      def __init__(    self     ,     a_ = ""     ,     a_ = None     ,     a_ = None     ,     **a_	)	->str:







        '''simple docstring'''


        super().__init__(self     ,     **a_	)
        # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
        _a							=	fsspec.open(
            a_     ,     mode="rb"     ,     protocol=a_     ,     compression=self.compression     ,     client_kwargs={
                "requote_redirect_url": False,  # see https://github.com/huggingface/datasets/pull/5459
                "trust_env": True,  # Enable reading proxy env variables.
                **(target_options or {}).pop("client_kwargs"     ,     {}	),  # To avoid issues if it was already passed.
            }     ,     **(target_options or {})     ,     )
        _a							=	os.path.basename(self.file.path.split("::"	)[0]	)
        _a							=	(
            self.compressed_name[: self.compressed_name.rindex("."	)]
            if "." in self.compressed_name
            else self.compressed_name
        )
        _a							=	None






      @classmethod
      def 			lowerCamelCase__    (    cls     ,     a_	)	->Any:







        '''simple docstring'''


        return super()._strip_protocol(a_	).lstrip("/"	)






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        if self.dir_cache is None:
          _a							=	{**self.file.fs.info(self.file.path	), "name": self.uncompressed_name}
          _a							=	{f["name"]: f}






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







        '''simple docstring'''


        return self.file.open().read()






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_ = "rb"     ,     a_=None     ,     a_=True     ,     a_=None     ,     **a_     ,     )	->Optional[int]:







        '''simple docstring'''


        _a							=	self._strip_protocol(a_	)
        if mode != "rb":
          raise ValueError(f'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\''''	)
        return self.file.open()







class    lowercase_    (_UpperCAmelCase     ):
      A__	:		Tuple       			=				'''bz2'''
      A__	:		Any       			=				'''bz2'''
      A__	:		str       			=				'''.bz2'''







class    lowercase_    (_UpperCAmelCase     ):
      A__	:		Optional[Any]       			=				'''gzip'''
      A__	:		List[str]       			=				'''gzip'''
      A__	:		str       			=				'''.gz'''







class    lowercase_    (_UpperCAmelCase     ):
      A__	:		str       			=				'''lz4'''
      A__	:		List[Any]       			=				'''lz4'''
      A__	:		Optional[Any]       			=				'''.lz4'''







class    lowercase_    (_UpperCAmelCase     ):
      A__	:		Dict       			=				'''xz'''
      A__	:		List[Any]       			=				'''xz'''
      A__	:		int       			=				'''.xz'''







class    lowercase_    (_UpperCAmelCase     ):
      A__	:		Tuple       			=				'''zstd'''
      A__	:		int       			=				'''zstd'''
      A__	:		int       			=				'''.zst'''






      def __init__(    self     ,     a_     ,     a_ = "rb"     ,     a_ = None     ,     a_ = None     ,     a_ = DEFAULT_BLOCK_SIZE     ,     **a_     ,     )	->Dict:







        '''simple docstring'''


        super().__init__(
            fo=a_     ,     mode=a_     ,     target_protocol=a_     ,     target_options=a_     ,     block_size=a_     ,     **a_     ,     )
        # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
        #
        # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
        #     out.close = close
        # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
        #
        # see https://github.com/intake/filesystem_spec/issues/725
        _a							=	self.file.__enter__


        class    lowercase_    :






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







                '''simple docstring'''


                _a							=	file_






              def __enter__(    self	)	->List[str]:







                '''simple docstring'''


                self._file.__enter__()
                return self






              def __exit__(    self     ,     *a_     ,     **a_	)	->Optional[Any]:







                '''simple docstring'''


                self._file.__exit__(*a_     ,     **a_	)






              def __iter__(    self	)	->Optional[Any]:







                '''simple docstring'''


                return iter(self._file	)






              def 			lowerCamelCase__    (    self	)	->Dict:







                '''simple docstring'''


                return next(self._file	)






              def __getattr__(    self     ,     a_	)	->Optional[Any]:







                '''simple docstring'''


                return getattr(self._file     ,     a_	)

        def fixed_enter(*a_     ,     **a_	):
          return WrappedFile(_enter(*a_     ,     **a_	)	)

        _a							=	fixed_enter










"""simple docstring"""




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class    lowercase_    (_UpperCAmelCase     ):
      A__	:		Union[str, Any]       			=				(PNDMScheduler,)
      A__	:		Optional[int]       			=				(('''num_inference_steps''', 50),)






      def 			lowerCamelCase__    (    self     ,     **a_	)	->Optional[Any]:







        '''simple docstring'''


        _a							=	{
            "num_train_timesteps": 1_0_0_0,
            "beta_start": 0.0_001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**a_	)
        return config






      def 			lowerCamelCase__    (    self     ,     a_=0     ,     **a_	)	->Tuple:







        '''simple docstring'''


        _a							=	dict(self.forward_default_kwargs	)
        _a							=	kwargs.pop("num_inference_steps"     ,     a_	)
        _a							=	self.dummy_sample
        _a							=	0.1 * sample
        _a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
          _a							=	self.get_scheduler_config(**a_	)
          _a							=	scheduler_class(**a_	)
          scheduler.set_timesteps(a_	)
          # copy over dummy past residuals
          _a							=	dummy_past_residuals[:]

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

          _a							=	scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

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

          _a							=	scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

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






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        pass






      def 			lowerCamelCase__    (    self     ,     a_=0     ,     **a_	)	->Tuple:







        '''simple docstring'''


        _a							=	dict(self.forward_default_kwargs	)
        _a							=	kwargs.pop("num_inference_steps"     ,     a_	)
        _a							=	self.dummy_sample
        _a							=	0.1 * sample
        _a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)
          scheduler.set_timesteps(a_	)

          # copy over dummy past residuals (must be after setting timesteps)
          _a							=	dummy_past_residuals[:]

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

            # copy over dummy past residual (must be after setting timesteps)
            _a							=	dummy_past_residuals[:]

          _a							=	scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

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

          _a							=	scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

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






      def 			lowerCamelCase__    (    self     ,     **a_	)	->Optional[int]:







        '''simple docstring'''


        _a							=	self.scheduler_classes[0]
        _a							=	self.get_scheduler_config(**a_	)
        _a							=	scheduler_class(**a_	)

        _a							=	1_0
        _a							=	self.dummy_model()
        _a							=	self.dummy_sample_deter
        scheduler.set_timesteps(a_	)

        for i, t in enumerate(scheduler.prk_timesteps	):
          _a							=	model(a_     ,     a_	)
          _a							=	scheduler.step_prk(a_     ,     a_     ,     a_	).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps	):
          _a							=	model(a_     ,     a_	)
          _a							=	scheduler.step_plms(a_     ,     a_     ,     a_	).prev_sample

        return sample






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        _a							=	dict(self.forward_default_kwargs	)

        _a							=	kwargs.pop("num_inference_steps"     ,     a_	)

        for scheduler_class in self.scheduler_classes:
          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)

          _a							=	self.dummy_sample
          _a							=	0.1 * sample

          if num_inference_steps is not None and hasattr(a_     ,     "set_timesteps"	):
            scheduler.set_timesteps(a_	)
          elif num_inference_steps is not None and not hasattr(a_     ,     "set_timesteps"	):
            _a							=	num_inference_steps

          # copy over dummy past residuals (must be done after set_timesteps)
          _a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
          _a							=	dummy_past_residuals[:]

          _a							=	scheduler.step_prk(a_     ,     0     ,     a_     ,     **a_	).prev_sample
          _a							=	scheduler.step_prk(a_     ,     1     ,     a_     ,     **a_	).prev_sample

          self.assertEqual(output_a.shape     ,     sample.shape	)
          self.assertEqual(output_a.shape     ,     output_a.shape	)

          _a							=	scheduler.step_plms(a_     ,     0     ,     a_     ,     **a_	).prev_sample
          _a							=	scheduler.step_plms(a_     ,     1     ,     a_     ,     **a_	).prev_sample

          self.assertEqual(output_a.shape     ,     sample.shape	)
          self.assertEqual(output_a.shape     ,     output_a.shape	)






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        for timesteps in [1_0_0, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=a_	)






      def 			lowerCamelCase__    (    self	)	->Optional[Any]:







        '''simple docstring'''


        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=a_	)

        _a							=	self.scheduler_classes[0]
        _a							=	self.get_scheduler_config(steps_offset=1	)
        _a							=	scheduler_class(**a_	)
        scheduler.set_timesteps(1_0	)
        assert torch.equal(
            scheduler.timesteps     ,     torch.LongTensor(
                [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]	)     ,     )






      def 			lowerCamelCase__    (    self	)	->Tuple:







        '''simple docstring'''


        for beta_start, beta_end in zip([0.0_001, 0.001]     ,     [0.002, 0.02]	):
          self.check_over_configs(beta_start=a_     ,     beta_end=a_	)






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


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






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=a_	)






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        for t in [1, 5, 1_0]:
          self.check_over_forward(time_step=a_	)






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







        '''simple docstring'''


        for t, num_inference_steps in zip([1, 5, 1_0]     ,     [1_0, 5_0, 1_0_0]	):
          self.check_over_forward(num_inference_steps=a_	)






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







        '''simple docstring'''


        _a							=	2_7

        for scheduler_class in self.scheduler_classes:
          _a							=	self.dummy_sample
          _a							=	0.1 * sample

          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)

          scheduler.set_timesteps(a_	)

          # before power of 3 fix, would error on first step, so we only need to do two
          for i, t in enumerate(scheduler.prk_timesteps[:2]	):
            _a							=	scheduler.step_prk(a_     ,     a_     ,     a_	).prev_sample






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        with self.assertRaises(a_	):
          _a							=	self.scheduler_classes[0]
          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)

          scheduler.step_plms(self.dummy_sample     ,     1     ,     self.dummy_sample	).prev_sample






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        _a							=	self.full_loop()
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 198.1_318	) < 1E-2
        assert abs(result_mean.item() - 0.2_580	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->Optional[Any]:







        '''simple docstring'''


        _a							=	self.full_loop(prediction_type="v_prediction"	)
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 67.3_986	) < 1E-2
        assert abs(result_mean.item() - 0.0_878	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a							=	self.full_loop(set_alpha_to_one=a_     ,     beta_start=0.01	)
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 230.0_399	) < 1E-2
        assert abs(result_mean.item() - 0.2_995	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        _a							=	self.full_loop(set_alpha_to_one=a_     ,     beta_start=0.01	)
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 186.9_482	) < 1E-2
        assert abs(result_mean.item() - 0.2_434	) < 1E-3


