
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil

class __snake_case		(      lowerCAmelCase     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      pa.array(TypedSequence([1, 2, 3]      )      )
           self.assertEqual(arr.type		,pa.intaa()      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           with self.assertRaises(snake_case      ):
                  lowercase							:     Dict       	=      pa.array(TypedSequence([1, 2, 3]      )		,type=pa.intaa()      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           with self.assertRaises(snake_case      ):
                  lowercase							:     Tuple       	=      pa.array(TypedSequence([1, 2, 3]		,try_type=Value("""bool"""      )		,type=Value("""int64"""      )      )      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      pa.array(TypedSequence([1, 2, 3]		,type=Value("""int32"""      )      )      )
           self.assertEqual(arr.type		,pa.intaa()      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           with self.assertRaises((TypeError, pa.lib.ArrowInvalid)      ):
                  lowercase							:     str       	=      pa.array(TypedSequence(["""foo""", """bar"""]		,type=Value("""int64"""      )      )      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      pa.array(TypedSequence([1, 2, 3]		,try_type=Value("""int32"""      )      )      )
           self.assertEqual(arr.type		,pa.intaa()      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      pa.array(TypedSequence(["""foo""", """bar"""]		,try_type=Value("""int64"""      )      )      )
           self.assertEqual(arr.type		,pa.string()      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      pa.array(TypedSequence([[[1, 2, 3]]]		,type=ArrayaD((1, 3)		,"""int64"""      )      )      )
           self.assertEqual(arr.type		,ArrayaDExtensionType((1, 3)		,"""int64"""      )      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           with self.assertRaises((TypeError, pa.lib.ArrowInvalid)      ):
                  lowercase							:     Dict       	=      pa.array(TypedSequence(["""foo""", """bar"""]		,type=ArrayaD((1, 3)		,"""int64"""      )      )      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      pa.array(TypedSequence([[[1, 2, 3]]]		,try_type=ArrayaD((1, 3)		,"""int64"""      )      )      )
           self.assertEqual(arr.type		,ArrayaDExtensionType((1, 3)		,"""int64"""      )      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      pa.array(TypedSequence(["""foo""", """bar"""]		,try_type=ArrayaD((1, 3)		,"""int64"""      )      )      )
           self.assertEqual(arr.type		,pa.string()      )
    @require_pil
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           import PIL.Image

           lowercase							:     Dict       	=      PIL.Image.fromarray(np.arange(10		,dtype=np.uinta      ).reshape(2		,5      )      )
           with patch(
               """datasets.arrow_writer.cast_to_python_objects"""		,side_effect=snake_case      ) as mock_cast_to_python_objects:
                  lowercase							:     int       	=      pa.array(TypedSequence([{"""path""": None, """bytes""": b"""image_bytes"""}, pil_image]		,type=Image()      )      )
                  lowercase	,			lowercase							:     Optional[int]       	=      mock_cast_to_python_objects.call_args_list[-1]
                  self.assertIn("""optimize_list_casting"""		,snake_case      )
                  self.assertFalse(kwargs["""optimize_list_casting"""]      )







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     Union[str, Any]       	=      pa.BufferReader(SCREAMING_SNAKE_CASE__				) if isinstance(SCREAMING_SNAKE_CASE__					,	pa.Buffer				) else pa.memory_map(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Any       	=      pa.ipc.open_stream(SCREAMING_SNAKE_CASE__				)
       lowercase							:     pa.Table       	=      f.read_all()
       assert len(pa_table.to_batches()				) == expected_num_chunks
       assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
       del pa_table







@pytest.mark.parametrize("""writer_batch_size"""					,	[None, 1, 10]				)
@pytest.mark.parametrize(
    """fields"""					,	[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       lowercase							:     int       	=      pa.BufferOutputStream()
       lowercase							:     Optional[int]       	=      pa.schema(SCREAMING_SNAKE_CASE__				) if fields else None
       with ArrowWriter(stream=SCREAMING_SNAKE_CASE__					,	schema=SCREAMING_SNAKE_CASE__					,	writer_batch_size=SCREAMING_SNAKE_CASE__				) as writer:
              writer.write({"""col_1""": """foo""", """col_2""": 1}				)
              writer.write({"""col_1""": """bar""", """col_2""": 2}				)
              lowercase	,			lowercase							:     Dict       	=      writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              lowercase							:     Optional[int]       	=      {"""col_1""": pa.string(), """col_2""": pa.intaa()}
       assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE__					,	metadata=writer._schema.metadata				)
       _check_output(output.getvalue()					,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)







def 						_snake_case(  ) ->    Dict:
       lowercase							:     Union[str, Any]       	=      pa.BufferOutputStream()
       lowercase							:     List[str]       	=      Features({"""labels""": ClassLabel(names=["""neg""", """pos"""]				)}				)
       with ArrowWriter(stream=SCREAMING_SNAKE_CASE__					,	features=SCREAMING_SNAKE_CASE__				) as writer:
              writer.write({"""labels""": 0}				)
              writer.write({"""labels""": 1}				)
              lowercase	,			lowercase							:     str       	=      writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       assert writer._schema == features.arrow_schema
       assert writer._schema.metadata == features.arrow_schema.metadata
       lowercase							:     List[Any]       	=      pa.BufferReader(output.getvalue()				)
       lowercase							:     Any       	=      pa.ipc.open_stream(SCREAMING_SNAKE_CASE__				)
       lowercase							:     pa.Table       	=      f.read_all()
       lowercase							:     List[str]       	=      pa_table.schema
       assert pa_table.num_rows == 2
       assert schema == features.arrow_schema
       assert schema.metadata == features.arrow_schema.metadata
       assert features == Features.from_arrow_schema(SCREAMING_SNAKE_CASE__				)







@pytest.mark.parametrize("""writer_batch_size"""					,	[None, 1, 10]				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     Dict       	=      pa.BufferOutputStream()
       with ArrowWriter(
           stream=SCREAMING_SNAKE_CASE__					,	writer_batch_size=SCREAMING_SNAKE_CASE__					,	hash_salt="""split_name"""					,	check_duplicates=SCREAMING_SNAKE_CASE__					,	) as writer:
              with pytest.raises(SCREAMING_SNAKE_CASE__				):
                     writer.write({"""col_1""": """foo""", """col_2""": 1}					,	key=[1, 2]				)
                     lowercase	,			lowercase							:     Optional[Any]       	=      writer.finalize()







@pytest.mark.parametrize("""writer_batch_size"""					,	[None, 2, 10]				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Any:
       lowercase							:     List[Any]       	=      pa.BufferOutputStream()
       with ArrowWriter(
           stream=SCREAMING_SNAKE_CASE__					,	writer_batch_size=SCREAMING_SNAKE_CASE__					,	hash_salt="""split_name"""					,	check_duplicates=SCREAMING_SNAKE_CASE__					,	) as writer:
              with pytest.raises(SCREAMING_SNAKE_CASE__				):
                     writer.write({"""col_1""": """foo""", """col_2""": 1}					,	key=10				)
                     writer.write({"""col_1""": """bar""", """col_2""": 2}					,	key=10				)
                     lowercase	,			lowercase							:     Dict       	=      writer.finalize()







@pytest.mark.parametrize("""writer_batch_size"""					,	[None, 2, 10]				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Dict:
       lowercase							:     str       	=      pa.BufferOutputStream()
       with ArrowWriter(
           stream=SCREAMING_SNAKE_CASE__					,	writer_batch_size=SCREAMING_SNAKE_CASE__					,	hash_salt="""split_name"""					,	check_duplicates=SCREAMING_SNAKE_CASE__					,	) as writer:
              writer.write({"""col_1""": """foo""", """col_2""": 1}					,	key=1				)
              writer.write({"""col_1""": """bar""", """col_2""": 2}					,	key=2				)
              lowercase	,			lowercase							:     Optional[int]       	=      writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       _check_output(output.getvalue()					,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)







@pytest.mark.parametrize("""writer_batch_size"""					,	[None, 1, 10]				)
@pytest.mark.parametrize(
    """fields"""					,	[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Dict:
       lowercase							:     Union[str, Any]       	=      pa.BufferOutputStream()
       lowercase							:     Tuple       	=      pa.schema(SCREAMING_SNAKE_CASE__				) if fields else None
       with ArrowWriter(stream=SCREAMING_SNAKE_CASE__					,	schema=SCREAMING_SNAKE_CASE__					,	writer_batch_size=SCREAMING_SNAKE_CASE__				) as writer:
              writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}				)
              writer.write_batch({"""col_1""": [], """col_2""": []}				)
              lowercase	,			lowercase							:     str       	=      writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              lowercase							:     Union[str, Any]       	=      {"""col_1""": pa.string(), """col_2""": pa.intaa()}
       assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE__					,	metadata=writer._schema.metadata				)
       _check_output(output.getvalue()					,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)







@pytest.mark.parametrize("""writer_batch_size"""					,	[None, 1, 10]				)
@pytest.mark.parametrize(
    """fields"""					,	[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     Tuple       	=      pa.BufferOutputStream()
       lowercase							:     List[str]       	=      pa.schema(SCREAMING_SNAKE_CASE__				) if fields else None
       with ArrowWriter(stream=SCREAMING_SNAKE_CASE__					,	schema=SCREAMING_SNAKE_CASE__					,	writer_batch_size=SCREAMING_SNAKE_CASE__				) as writer:
              writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}				)				)
              lowercase	,			lowercase							:     str       	=      writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              lowercase							:     Union[str, Any]       	=      {"""col_1""": pa.string(), """col_2""": pa.intaa()}
       assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE__					,	metadata=writer._schema.metadata				)
       _check_output(output.getvalue()					,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)







@pytest.mark.parametrize("""writer_batch_size"""					,	[None, 1, 10]				)
@pytest.mark.parametrize(
    """fields"""					,	[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    int:
       lowercase							:     List[str]       	=      pa.BufferOutputStream()
       lowercase							:     List[Any]       	=      pa.schema(SCREAMING_SNAKE_CASE__				) if fields else None
       with ArrowWriter(stream=SCREAMING_SNAKE_CASE__					,	schema=SCREAMING_SNAKE_CASE__					,	writer_batch_size=SCREAMING_SNAKE_CASE__				) as writer:
              writer.write_row(pa.Table.from_pydict({"""col_1""": ["""foo"""], """col_2""": [1]}				)				)
              writer.write_row(pa.Table.from_pydict({"""col_1""": ["""bar"""], """col_2""": [2]}				)				)
              lowercase	,			lowercase							:     Optional[int]       	=      writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              lowercase							:     str       	=      {"""col_1""": pa.string(), """col_2""": pa.intaa()}
       assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE__					,	metadata=writer._schema.metadata				)
       _check_output(output.getvalue()					,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)







def 						_snake_case(  ) ->    int:
       with tempfile.TemporaryDirectory() as tmp_dir:
              lowercase							:     Tuple       	=      {"""col_1""": pa.string(), """col_2""": pa.intaa()}
              lowercase							:     List[Any]       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""test.arrow"""				)
              with ArrowWriter(path=SCREAMING_SNAKE_CASE__					,	schema=pa.schema(SCREAMING_SNAKE_CASE__				)				) as writer:
                     writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}				)
                     lowercase	,			lowercase							:     str       	=      writer.finalize()
              assert num_examples == 2
              assert num_bytes > 0
              assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE__					,	metadata=writer._schema.metadata				)
              _check_output(SCREAMING_SNAKE_CASE__					,	1				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Tuple:
       if pa.types.is_list(SCREAMING_SNAKE_CASE__				):
              return get_base_dtype(arr_type.value_type				)
       else:
              return arr_type







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       if isinstance(lst[0]					,	SCREAMING_SNAKE_CASE__				):
              change_first_primitive_element_in_list(lst[0]					,	SCREAMING_SNAKE_CASE__				)
       else:
              lowercase							:     Optional[Any]       	=      value







@pytest.mark.parametrize("""optimized_int_type, expected_dtype"""					,	[(None, pa.intaa()), (Value("""int32"""				), pa.intaa())]				)
@pytest.mark.parametrize("""sequence"""					,	[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     List[str]       	=      pa.array(TypedSequence(SCREAMING_SNAKE_CASE__					,	optimized_int_type=SCREAMING_SNAKE_CASE__				)				)
       assert get_base_dtype(arr.type				) == expected_dtype







@pytest.mark.parametrize(
    """col, expected_dtype"""					,	[
        ("""attention_mask""", pa.inta()),
        ("""special_tokens_mask""", pa.inta()),
        ("""token_type_ids""", pa.inta()),
        ("""input_ids""", pa.intaa()),
        ("""other""", pa.intaa()),
    ]					,	)
@pytest.mark.parametrize("""sequence"""					,	[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Dict:
       # in range
       lowercase							:     Dict       	=      pa.array(OptimizedTypedSequence(SCREAMING_SNAKE_CASE__					,	col=SCREAMING_SNAKE_CASE__				)				)
       assert get_base_dtype(arr.type				) == expected_dtype

       # not in range
       if col != "other":
              # avoids errors due to in-place modifications
              lowercase							:     int       	=      copy.deepcopy(SCREAMING_SNAKE_CASE__				)
              lowercase							:     List[Any]       	=      np.iinfo(expected_dtype.to_pandas_dtype()				).max + 1
              change_first_primitive_element_in_list(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
              lowercase							:     int       	=      pa.array(OptimizedTypedSequence(SCREAMING_SNAKE_CASE__					,	col=SCREAMING_SNAKE_CASE__				)				)
              assert get_base_dtype(arr.type				) == pa.intaa()







@pytest.mark.parametrize("""raise_exception"""					,	[False, True]				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       lowercase							:     Optional[Any]       	=      str(tmp_path / """dataset-train.arrow"""				)
       try:
              with ArrowWriter(path=SCREAMING_SNAKE_CASE__				) as writer:
                     if raise_exception:
                            raise pa.lib.ArrowInvalid()
                     else:
                            writer.stream.close()
       except pa.lib.ArrowInvalid:
              pass
       finally:
              assert writer.stream.closed







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       lowercase							:     str       	=      """mock://dataset-train.arrow"""
       with ArrowWriter(path=SCREAMING_SNAKE_CASE__					,	storage_options=mockfs.storage_options				) as writer:
              assert isinstance(writer._fs					,	type(SCREAMING_SNAKE_CASE__				)				)
              assert writer._fs.storage_options == mockfs.storage_options
              writer.write({"""col_1""": """foo""", """col_2""": 1}				)
              writer.write({"""col_1""": """bar""", """col_2""": 2}				)
              lowercase	,			lowercase							:     Optional[int]       	=      writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       assert mockfs.exists(SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  ) ->    Dict:
       lowercase							:     Any       	=      pa.BufferOutputStream()
       with ParquetWriter(stream=SCREAMING_SNAKE_CASE__				) as writer:
              writer.write({"""col_1""": """foo""", """col_2""": 1}				)
              writer.write({"""col_1""": """bar""", """col_2""": 2}				)
              lowercase	,			lowercase							:     List[str]       	=      writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       lowercase							:     Union[str, Any]       	=      pa.BufferReader(output.getvalue()				)
       lowercase							:     pa.Table       	=      pq.read_table(SCREAMING_SNAKE_CASE__				)
       assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}







@require_pil
@pytest.mark.parametrize("""embed_local_files"""					,	[False, True]				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       import PIL.Image

       lowercase							:     str       	=      str(tmp_path / """test_image_rgb.jpg"""				)
       PIL.Image.fromarray(np.zeros((5, 5)					,	dtype=np.uinta				)				).save(SCREAMING_SNAKE_CASE__					,	format="""png"""				)
       lowercase							:     Tuple       	=      pa.BufferOutputStream()
       with ParquetWriter(
           stream=SCREAMING_SNAKE_CASE__					,	features=Features({"""image""": Image()}				)					,	embed_local_files=SCREAMING_SNAKE_CASE__				) as writer:
              writer.write({"""image""": image_path}				)
              writer.finalize()
       lowercase							:     int       	=      pa.BufferReader(output.getvalue()				)
       lowercase							:     pa.Table       	=      pq.read_table(SCREAMING_SNAKE_CASE__				)
       lowercase							:     int       	=      pa_table.to_pydict()
       if embed_local_files:
              assert isinstance(out["""image"""][0]["""path"""]					,	SCREAMING_SNAKE_CASE__				)
              with open(SCREAMING_SNAKE_CASE__					,	"""rb"""				) as f:
                     assert out["image"][0]["bytes"] == f.read()
       else:
              assert out["image"][0]["path"] == image_path
              assert out["image"][0]["bytes"] is None







def 						_snake_case(  ) ->    str:
       lowercase							:     List[str]       	=      pa.schema([pa.field("""col_1"""					,	pa.string()					,	nullable=SCREAMING_SNAKE_CASE__				)]				)
       lowercase							:     Optional[int]       	=      pa.BufferOutputStream()
       with ArrowWriter(stream=SCREAMING_SNAKE_CASE__				) as writer:
              writer._build_writer(inferred_schema=SCREAMING_SNAKE_CASE__				)
              assert writer._schema == pa.schema([pa.field("""col_1"""					,	pa.string()				)]				)


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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=0.999					,	SCREAMING_SNAKE_CASE__="cosine"					,	) ->    List[Any]:
       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

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

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

       lowercase							:     int       	=      []
       for i in range(SCREAMING_SNAKE_CASE__				):
              lowercase							:     Dict       	=      i / num_diffusion_timesteps
              lowercase							:     Optional[int]       	=      (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE__				) / alpha_bar_fn(SCREAMING_SNAKE_CASE__				)					,	SCREAMING_SNAKE_CASE__				)				)
       return torch.tensor(SCREAMING_SNAKE_CASE__					,	dtype=torch.floataa				)




class __snake_case		(      lowerCAmelCase					,	lowerCAmelCase     ):
    _a		:						Tuple=				[e.name for e in KarrasDiffusionSchedulers]
    _a		:						int=				2
    @register_to_config
    def __init__(  self		,snake_case = 1000		,snake_case = 0.00_085		,snake_case = 0.012		,snake_case = "linear"		,snake_case = None		,snake_case = "epsilon"		,snake_case = False		,snake_case = False		,snake_case = 1.0		,snake_case = "linspace"		,snake_case = 0		,):



           '''simple docstring'''







           if trained_betas is not None:
                  lowercase							:     List[str]       	=      torch.tensor(snake_case		,dtype=torch.floataa      )
           elif beta_schedule == "linear":
                  lowercase							:     Optional[Any]       	=      torch.linspace(snake_case		,snake_case		,snake_case		,dtype=torch.floataa      )
           elif beta_schedule == "scaled_linear":
                  # this schedule is very specific to the latent diffusion model.
                  lowercase							:     int       	=      (
                      torch.linspace(beta_start**0.5		,beta_end**0.5		,snake_case		,dtype=torch.floataa      ) ** 2
                  )
           elif beta_schedule == "squaredcos_cap_v2":
                  # Glide cosine schedule
                  lowercase							:     Union[str, Any]       	=      betas_for_alpha_bar(snake_case		,alpha_transform_type="""cosine"""      )
           elif beta_schedule == "exp":
                  lowercase							:     int       	=      betas_for_alpha_bar(snake_case		,alpha_transform_type="""exp"""      )
           else:
                  raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"      )

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

           #  set all values
           self.set_timesteps(snake_case		,snake_case		,snake_case      )
           lowercase							:     Tuple       	=      use_karras_sigmas
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           if schedule_timesteps is None:
                  lowercase							:     Union[str, Any]       	=      self.timesteps

           lowercase							:     Dict       	=      (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:
                  lowercase							:     Dict       	=      1 if len(snake_case      ) > 1 else 0
           else:
                  lowercase							:     Union[str, Any]       	=      timestep.cpu().item() if torch.is_tensor(snake_case      ) else timestep
                  lowercase							:     str       	=      self._index_counter[timestep_int]

           return indices[pos].item()
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.config.timestep_spacing in ["linspace", "trailing"]:
                  return self.sigmas.max()

           return (self.sigmas.max() ** 2 + 1) ** 0.5
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.index_for_timestep(snake_case      )

           lowercase							:     Dict       	=      self.sigmas[step_index]
           lowercase							:     List[str]       	=      sample / ((sigma**2 + 1) ** 0.5)
           return sample
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     Any       	=      num_inference_steps

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

           lowercase							:     Optional[int]       	=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
           lowercase							:     Dict       	=      np.log(snake_case      )
           lowercase							:     Union[str, Any]       	=      np.interp(snake_case		,np.arange(0		,len(snake_case      )      )		,snake_case      )

           if self.config.use_karras_sigmas:
                  lowercase							:     List[Any]       	=      self._convert_to_karras(in_sigmas=snake_case		,num_inference_steps=self.num_inference_steps      )
                  lowercase							:     Tuple       	=      np.array([self._sigma_to_t(snake_case		,snake_case      ) for sigma in sigmas]      )

           lowercase							:     Any       	=      np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
           lowercase							:     List[Any]       	=      torch.from_numpy(snake_case      ).to(device=snake_case      )
           lowercase							:     List[Any]       	=      torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2      ), sigmas[-1:]]      )

           lowercase							:     Dict       	=      torch.from_numpy(snake_case      )
           lowercase							:     List[Any]       	=      torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2      )]      )

           if str(snake_case      ).startswith("""mps"""      ):
                  # mps does not support float64
                  lowercase							:     Any       	=      timesteps.to(snake_case		,dtype=torch.floataa      )
           else:
                  lowercase							:     str       	=      timesteps.to(device=snake_case      )

           # empty dt and derivative
           lowercase							:     Union[str, Any]       	=      None
           lowercase							:     Any       	=      None

           # for exp beta schedules, such as the one for `pipeline_shap_e.py`
           # we need an index counter
           lowercase							:     str       	=      defaultdict(snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      np.log(snake_case      )

           # get distribution
           lowercase							:     Union[str, Any]       	=      log_sigma - log_sigmas[:, np.newaxis]

           # get sigmas range
           lowercase							:     Optional[int]       	=      np.cumsum((dists >= 0)		,axis=0      ).argmax(axis=0      ).clip(max=log_sigmas.shape[0] - 2      )
           lowercase							:     Any       	=      low_idx + 1

           lowercase							:     str       	=      log_sigmas[low_idx]
           lowercase							:     Dict       	=      log_sigmas[high_idx]

           # interpolate sigmas
           lowercase							:     int       	=      (low - log_sigma) / (low - high)
           lowercase							:     Dict       	=      np.clip(snake_case		,0		,1      )

           # transform interpolation to time range
           lowercase							:     Optional[Any]       	=      (1 - w) * low_idx + w * high_idx
           lowercase							:     Tuple       	=      t.reshape(sigma.shape      )
           return t
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     float       	=      in_sigmas[-1].item()
           lowercase							:     float       	=      in_sigmas[0].item()

           lowercase							:     Dict       	=      7.0  # 7.0 is the value used in the paper
           lowercase							:     Optional[int]       	=      np.linspace(0		,1		,snake_case      )
           lowercase							:     int       	=      sigma_min ** (1 / rho)
           lowercase							:     Any       	=      sigma_max ** (1 / rho)
           lowercase							:     Tuple       	=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self.dt is None
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case = True		,):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      self.index_for_timestep(snake_case      )

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

           if self.state_in_first_order:
                  lowercase							:     str       	=      self.sigmas[step_index]
                  lowercase							:     Optional[int]       	=      self.sigmas[step_index + 1]
           else:
                  # 2nd order / Heun's method
                  lowercase							:     Dict       	=      self.sigmas[step_index - 1]
                  lowercase							:     Optional[Any]       	=      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
           lowercase							:     Union[str, Any]       	=      0
           lowercase							:     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":
                  lowercase							:     Any       	=      sigma_hat if self.state_in_first_order else sigma_next
                  lowercase							:     int       	=      sample - sigma_input * model_output
           elif self.config.prediction_type == "v_prediction":
                  lowercase							:     Union[str, Any]       	=      sigma_hat if self.state_in_first_order else sigma_next
                  lowercase							:     Optional[Any]       	=      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                      sample / (sigma_input**2 + 1)
                  )
           elif self.config.prediction_type == "sample":
                  lowercase							:     Optional[Any]       	=      model_output
           else:
                  raise ValueError(
                      f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"      )

           if self.config.clip_sample:
                  lowercase							:     str       	=      pred_original_sample.clamp(
                      -self.config.clip_sample_range		,self.config.clip_sample_range      )

           if self.state_in_first_order:
                  # 2. Convert to an ODE derivative for 1st order
                  lowercase							:     Optional[int]       	=      (sample - pred_original_sample) / sigma_hat
                  # 3. delta timestep
                  lowercase							:     Union[str, Any]       	=      sigma_next - sigma_hat

                  # store for 2nd order step
                  lowercase							:     Optional[int]       	=      derivative
                  lowercase							:     Union[str, Any]       	=      dt
                  lowercase							:     Union[str, Any]       	=      sample
           else:
                  # 2. 2nd order / Heun's method
                  lowercase							:     Tuple       	=      (sample - pred_original_sample) / sigma_next
                  lowercase							:     Dict       	=      (self.prev_derivative + derivative) / 2

                  # 3. take prev timestep & sample
                  lowercase							:     Tuple       	=      self.dt
                  lowercase							:     Optional[Any]       	=      self.sample

                  # free dt and derivative
                  # Note, this puts the scheduler in "first order mode"
                  lowercase							:     List[str]       	=      None
                  lowercase							:     Tuple       	=      None
                  lowercase							:     Dict       	=      None

           lowercase							:     List[Any]       	=      sample + derivative * dt

           if not return_dict:
                  return (prev_sample,)

           return SchedulerOutput(prev_sample=snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.sigmas.to(device=original_samples.device		,dtype=original_samples.dtype      )
           if original_samples.device.type == "mps" and torch.is_floating_point(snake_case      ):
                  # mps does not support float64
                  lowercase							:     List[Any]       	=      self.timesteps.to(original_samples.device		,dtype=torch.floataa      )
                  lowercase							:     List[str]       	=      timesteps.to(original_samples.device		,dtype=torch.floataa      )
           else:
                  lowercase							:     List[str]       	=      self.timesteps.to(original_samples.device      )
                  lowercase							:     Tuple       	=      timesteps.to(original_samples.device      )

           lowercase							:     Any       	=      [self.index_for_timestep(snake_case		,snake_case      ) for t in timesteps]

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

           lowercase							:     Optional[int]       	=      original_samples + noise * sigma
           return noisy_samples
    def __len__(  self      ):



           '''simple docstring'''







           return self.config.num_train_timesteps
