



"""simple docstring"""




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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block






@dataclass
class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  torch.FloatTensor







class       UpperCAmelCase_    (							_UpperCamelCase			,     _UpperCamelCase		):



      @register_to_config
      def __init__(	self	:      int	,      A	:      int = 6_5_5_3_6	,      A	:      Optional[int] = None	,      A	:      int = 2	,      A	:      int = 2	,      A	:      int = 0	,      A	:      str = "fourier"	,      A	:      bool = True	,      A	:      bool = False	,      A	:      float = 0.0	,      A	:      Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")	,      A	:      Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")	,      A	:      Tuple[str] = "UNetMidBlock1D"	,      A	:      str = None	,      A	:      Tuple[int] = (3_2, 3_2, 6_4)	,      A	:      str = None	,      A	:      int = 8	,      A	:      int = 1	,      A	:      bool = False	,      ):
           super().__init__()
           _UpperCAmelCase     :      List[str]														=       sample_size

           # time
           if time_embedding_type == "fourier":
                _UpperCAmelCase     :      Dict														=       GaussianFourierProjection(
                    embedding_size=8	,      set_W_to_weight=A	,      log=A	,      flip_sin_to_cos=A		)
                _UpperCAmelCase     :      Any														=       2 * block_out_channels[0]
           elif time_embedding_type == "positional":
                _UpperCAmelCase     :      Optional[int]														=       Timesteps(
                    block_out_channels[0]	,      flip_sin_to_cos=A	,      downscale_freq_shift=A		)
                _UpperCAmelCase     :      Optional[int]														=       block_out_channels[0]

           if use_timestep_embedding:
                _UpperCAmelCase     :      Any														=       block_out_channels[0] * 4
                _UpperCAmelCase     :      Dict														=       TimestepEmbedding(
                    in_channels=A	,      time_embed_dim=A	,      act_fn=A	,      out_dim=block_out_channels[0]	,      )

           _UpperCAmelCase     :      List[str]														=       nn.ModuleList([]		)
           _UpperCAmelCase     :      Optional[Any]														=       None
           _UpperCAmelCase     :      int														=       nn.ModuleList([]		)
           _UpperCAmelCase     :      Tuple														=       None

           # down
           _UpperCAmelCase     :      List[Any]														=       in_channels
           for i, down_block_type in enumerate(A		):
                _UpperCAmelCase     :      Union[str, Any]														=       output_channel
                _UpperCAmelCase     :      Dict														=       block_out_channels[i]

                if i == 0:
                     input_channel += extra_in_channels

                _UpperCAmelCase     :      Any														=       i == len(A		) - 1

                _UpperCAmelCase     :      Optional[int]														=       get_down_block(
                    A	,      num_layers=A	,      in_channels=A	,      out_channels=A	,      temb_channels=block_out_channels[0]	,      add_downsample=not is_final_block or downsample_each_block	,      )
                self.down_blocks.append(A		)

           # mid
           _UpperCAmelCase     :      List[str]														=       get_mid_block(
               A	,      in_channels=block_out_channels[-1]	,      mid_channels=block_out_channels[-1]	,      out_channels=block_out_channels[-1]	,      embed_dim=block_out_channels[0]	,      num_layers=A	,      add_downsample=A	,      )

           # up
           _UpperCAmelCase     :      Tuple														=       list(reversed(A		)		)
           _UpperCAmelCase     :      Optional[Any]														=       reversed_block_out_channels[0]
           if out_block_type is None:
                _UpperCAmelCase     :      int														=       out_channels
           else:
                _UpperCAmelCase     :      List[str]														=       block_out_channels[0]

           for i, up_block_type in enumerate(A		):
                _UpperCAmelCase     :      Any														=       output_channel
                _UpperCAmelCase     :      Dict														=       (
                    reversed_block_out_channels[i + 1] if i < len(A		) - 1 else final_upsample_channels
                )

                _UpperCAmelCase     :      List[Any]														=       i == len(A		) - 1

                _UpperCAmelCase     :      str														=       get_up_block(
                    A	,      num_layers=A	,      in_channels=A	,      out_channels=A	,      temb_channels=block_out_channels[0]	,      add_upsample=not is_final_block	,      )
                self.up_blocks.append(A		)
                _UpperCAmelCase     :      Tuple														=       output_channel

           # out
           _UpperCAmelCase     :      Union[str, Any]														=       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4	,      3_2		)
           _UpperCAmelCase     :      List[Any]														=       get_out_block(
               out_block_type=A	,      num_groups_out=A	,      embed_dim=block_out_channels[0]	,      out_channels=A	,      act_fn=A	,      fc_dim=block_out_channels[-1] // 4	,      )



      def 		snake_case_   (	self	:      Any	,      A	:      torch.FloatTensor	,      A	:      Union[torch.Tensor, float, int]	,      A	:      bool = True	,      ):
           _UpperCAmelCase     :      Any														=       timestep
           if not torch.is_tensor(A		):
                _UpperCAmelCase     :      Union[str, Any]														=       torch.tensor([timesteps]	,      dtype=torch.long	,      device=sample.device		)
           elif torch.is_tensor(A		) and len(timesteps.shape		) == 0:
                _UpperCAmelCase     :      Union[str, Any]														=       timesteps[None].to(sample.device		)

           _UpperCAmelCase     :      Union[str, Any]														=       self.time_proj(A		)
           if self.config.use_timestep_embedding:
                _UpperCAmelCase     :      str														=       self.time_mlp(A		)
           else:
                _UpperCAmelCase     :      Union[str, Any]														=       timestep_embed[..., None]
                _UpperCAmelCase     :      Any														=       timestep_embed.repeat([1, 1, sample.shape[2]]		).to(sample.dtype		)
                _UpperCAmelCase     :      Dict														=       timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])		)

           # 2. down
           _UpperCAmelCase     :      Dict														=       ()
           for downsample_block in self.down_blocks:
                _UpperCAmelCase							,			_UpperCAmelCase     :      Tuple														=       downsample_block(hidden_states=A	,      temb=A		)
                down_block_res_samples += res_samples

           # 3. mid
           if self.mid_block:
                _UpperCAmelCase     :      Tuple														=       self.mid_block(A	,      A		)

           # 4. up
           for i, upsample_block in enumerate(self.up_blocks		):
                _UpperCAmelCase     :      Optional[int]														=       down_block_res_samples[-1:]
                _UpperCAmelCase     :      Any														=       down_block_res_samples[:-1]
                _UpperCAmelCase     :      Union[str, Any]														=       upsample_block(A	,      res_hidden_states_tuple=A	,      temb=A		)

           # 5. post-process
           if self.out_block:
                _UpperCAmelCase     :      Tuple														=       self.out_block(A	,      A		)

           if not return_dict:
                return (sample,)

           return UNetaDOutput(sample=A		)





"""simple docstring"""




import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      List[str]														=       tmp_path / "file.csv"
     _UpperCAmelCase     :      Union[str, Any]														=       textwrap.dedent(
         "\\n        header1,header2\n        1,2\n        10,20\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       tmp_path / "malformed_file.csv"
     _UpperCAmelCase     :      Dict														=       textwrap.dedent(
         "\\n        header1,header2\n        1,2\n        10,20,\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		Dict							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       tmp_path / "csv_with_image.csv"
     _UpperCAmelCase     :      List[str]														=       textwrap.dedent(
         f'\\n        image\n        {image_file}\n        '							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      Dict														=       tmp_path / "csv_with_label.csv"
     _UpperCAmelCase     :      Any														=       textwrap.dedent(
         "\\n        label\n        good\n        bad\n        good\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       tmp_path / "csv_with_int_list.csv"
     _UpperCAmelCase     :      Any														=       textwrap.dedent(
         "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		str		,    SCREAMING_SNAKE_CASE__      :		Dict							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       Csv()
     _UpperCAmelCase     :      Optional[int]														=       csv._generate_tables([[csv_file, malformed_csv_file]]							)
     with pytest.raises(SCREAMING_SNAKE_CASE__		,    match="Error tokenizing data"							):
          for _ in generator:
               pass
     assert any(
         record.levelname == "ERROR"
         and "Failed to read file" in record.message
         and os.path.basename(SCREAMING_SNAKE_CASE__							) in record.message
         for record in caplog.records							)

@require_pil
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Union[str, Any]							)    ->  Union[str, Any]:





     '''simple docstring'''

     with open(SCREAMING_SNAKE_CASE__		,    encoding="utf-8"							) as f:
          _UpperCAmelCase     :      Union[str, Any]														=       f.read().splitlines()[1]
     _UpperCAmelCase     :      Tuple														=       Csv(encoding="utf-8"		,    features=Features({"image": Image()}							)							)
     _UpperCAmelCase     :      Tuple														=       csv._generate_tables([[csv_file_with_image]]							)
     _UpperCAmelCase     :      List[str]														=       pa.concat_tables([table for _, table in generator]							)
     assert pa_table.schema.field("image"							).type == Image()()
     _UpperCAmelCase     :      Optional[Any]														=       pa_table.to_pydict()["image"]
     assert generated_content == [{"path": image_file, "bytes": None}]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  Optional[Any]:





     '''simple docstring'''

     with open(SCREAMING_SNAKE_CASE__		,    encoding="utf-8"							) as f:
          _UpperCAmelCase     :      Tuple														=       f.read().splitlines()[1:]
     _UpperCAmelCase     :      Optional[Any]														=       Csv(encoding="utf-8"		,    features=Features({"label": ClassLabel(names=["good", "bad"]							)}							)							)
     _UpperCAmelCase     :      int														=       csv._generate_tables([[csv_file_with_label]]							)
     _UpperCAmelCase     :      Dict														=       pa.concat_tables([table for _, table in generator]							)
     assert pa_table.schema.field("label"							).type == ClassLabel(names=["good", "bad"]							)()
     _UpperCAmelCase     :      Dict														=       pa_table.to_pydict()["label"]
     assert generated_content == [ClassLabel(names=["good", "bad"]							).straint(SCREAMING_SNAKE_CASE__							) for label in labels]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       Csv(encoding="utf-8"		,    sep=","		,    converters={"int_list": lambda SCREAMING_SNAKE_CASE__							: [int(SCREAMING_SNAKE_CASE__							) for i in x.split()]}							)
     _UpperCAmelCase     :      Dict														=       csv._generate_tables([[csv_file_with_int_list]]							)
     _UpperCAmelCase     :      List[Any]														=       pa.concat_tables([table for _, table in generator]							)
     assert pa.types.is_list(pa_table.schema.field("int_list"							).type							)
     _UpperCAmelCase     :      Dict														=       pa_table.to_pydict()["int_list"]
     assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
