





import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


lowerCAmelCase_    =    '''http://www.mocksite.com/file1.txt'''
lowerCAmelCase_    =    '''"text": ["foo", "foo"]'''
lowerCAmelCase_    =    '''6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'''



class  __lowerCAmelCase	:
   lowerCamelCase_					:					int	      =				200
   lowerCamelCase_					:					List[str]	      =				{'''Content-Length''': '''100'''}
   lowerCamelCase_					:					str	      =				{}






   def       lowerCamelCase  (self ,							**__magic_name__   )					->       Optional[int]:


       '''simple docstring'''



       return [bytes(__magic_name__ ,							'''utf-8'''   )]






def 					lowerCamelCase_						(    *_UpperCamelCase			,    **_UpperCamelCase			)		->						int:






    """simple docstring"""


    return MockResponse()






@pytest.mark.parametrize('''urls_type'''			,    [str, list, dict]			)
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Tuple:






    """simple docstring"""


    import requests

    monkeypatch.setattr(_UpperCamelCase			,    '''request'''			,    _UpperCamelCase			)

    snake_case_						:   Dict           =  URL
    if issubclass(_UpperCamelCase			,    _UpperCamelCase			):
        snake_case_						:   Union[str, Any]           =  url
    elif issubclass(_UpperCamelCase			,    _UpperCamelCase			):
        snake_case_						:   Tuple           =  [url]
    elif issubclass(_UpperCamelCase			,    _UpperCamelCase			):
        snake_case_						:   Dict           =  {'''train''': url}
    snake_case_						:   Any           =  '''dummy'''
    snake_case_						:   Optional[int]           =  '''downloads'''
    snake_case_						:   Optional[int]           =  tmp_path
    snake_case_						:   int           =  DownloadConfig(
        cache_dir=os.path.join(_UpperCamelCase			,    _UpperCamelCase			)			,    use_etag=_UpperCamelCase			,    )
    snake_case_						:   str           =  DownloadManager(dataset_name=_UpperCamelCase			,    download_config=_UpperCamelCase			)
    snake_case_						:   int           =  dl_manager.download(_UpperCamelCase			)
    snake_case_						:   Tuple           =  urls
    for downloaded_paths in [downloaded_paths]:
        if isinstance(_UpperCamelCase			,    _UpperCamelCase			):
            snake_case_						:   Optional[Any]           =  [downloaded_paths]
            snake_case_						:   List[str]           =  [urls]
        elif isinstance(_UpperCamelCase			,    _UpperCamelCase			):
            assert "train" in downloaded_paths.keys()
            snake_case_						:   Union[str, Any]           =  downloaded_paths.values()
            snake_case_						:   Optional[int]           =  urls.values()
        assert downloaded_paths
        for downloaded_path, input_url in zip(_UpperCamelCase			,    _UpperCamelCase			):
            assert downloaded_path == dl_manager.downloaded_paths[input_url]
            snake_case_						:   List[str]           =  Path(_UpperCamelCase			)
            snake_case_						:   Union[str, Any]           =  downloaded_path.parts
            assert parts[-1] == HASH
            assert parts[-2] == cache_subdir
            assert downloaded_path.exists()
            snake_case_						:   Optional[Any]           =  downloaded_path.read_text()
            assert content == CONTENT
            snake_case_						:   Optional[int]           =  downloaded_path.with_suffix('''.json'''			)
            assert metadata_downloaded_path.exists()
            snake_case_						:   Optional[int]           =  json.loads(metadata_downloaded_path.read_text()			)
            assert metadata_content == {"url": URL, "etag": None}






@pytest.mark.parametrize('''paths_type'''			,    [str, list, dict]			)
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						int:






    """simple docstring"""


    snake_case_						:   List[Any]           =  str(_UpperCamelCase			)
    if issubclass(_UpperCamelCase			,    _UpperCamelCase			):
        snake_case_						:   Any           =  filename
    elif issubclass(_UpperCamelCase			,    _UpperCamelCase			):
        snake_case_						:   Optional[Any]           =  [filename]
    elif issubclass(_UpperCamelCase			,    _UpperCamelCase			):
        snake_case_						:   Any           =  {'''train''': filename}
    snake_case_						:   Optional[Any]           =  '''dummy'''
    snake_case_						:   Any           =  xz_file.parent
    snake_case_						:   Any           =  '''extracted'''
    snake_case_						:   Optional[int]           =  DownloadConfig(
        cache_dir=_UpperCamelCase			,    use_etag=_UpperCamelCase			,    )
    snake_case_						:   Any           =  DownloadManager(dataset_name=_UpperCamelCase			,    download_config=_UpperCamelCase			)
    snake_case_						:   List[str]           =  dl_manager.extract(_UpperCamelCase			)
    snake_case_						:   Optional[int]           =  paths
    for extracted_paths in [extracted_paths]:
        if isinstance(_UpperCamelCase			,    _UpperCamelCase			):
            snake_case_						:   int           =  [extracted_paths]
            snake_case_						:   int           =  [paths]
        elif isinstance(_UpperCamelCase			,    _UpperCamelCase			):
            assert "train" in extracted_paths.keys()
            snake_case_						:   Any           =  extracted_paths.values()
            snake_case_						:   int           =  paths.values()
        assert extracted_paths
        for extracted_path, input_path in zip(_UpperCamelCase			,    _UpperCamelCase			):
            assert extracted_path == dl_manager.extracted_paths[input_path]
            snake_case_						:   Union[str, Any]           =  Path(_UpperCamelCase			)
            snake_case_						:   Dict           =  extracted_path.parts
            assert parts[-1] == hash_url_to_filename(_UpperCamelCase			,    etag=_UpperCamelCase			)
            assert parts[-2] == extracted_subdir
            assert extracted_path.exists()
            snake_case_						:   List[str]           =  extracted_path.read_text()
            snake_case_						:   Dict           =  text_file.read_text()
            assert extracted_file_content == expected_file_content






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Optional[Any]:






    """simple docstring"""


    assert path.endswith('''.jsonl'''			)
    for num_items, line in enumerate(_UpperCamelCase			,    start=1			):
        snake_case_						:   Union[str, Any]           =  json.loads(line.decode('''utf-8'''			)			)
        assert item.keys() == {"col_1", "col_2", "col_3"}
    assert num_items == 4






@pytest.mark.parametrize('''archive_jsonl'''			,    ['''tar_jsonl_path''', '''zip_jsonl_path''']			)
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Optional[int]:






    """simple docstring"""


    snake_case_						:   List[str]           =  request.getfixturevalue(_UpperCamelCase			)
    snake_case_						:   List[Any]           =  DownloadManager()
    for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(_UpperCamelCase			)			,    start=1			):
        _test_jsonl(_UpperCamelCase			,    _UpperCamelCase			)
    assert num_jsonl == 2






@pytest.mark.parametrize('''archive_nested_jsonl'''			,    ['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path''']			)
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Union[str, Any]:






    """simple docstring"""


    snake_case_						:   Any           =  request.getfixturevalue(_UpperCamelCase			)
    snake_case_						:   List[str]           =  DownloadManager()
    for num_tar, (path, file) in enumerate(dl_manager.iter_archive(_UpperCamelCase			)			,    start=1			):
        for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(_UpperCamelCase			)			,    start=1			):
            _test_jsonl(_UpperCamelCase			,    _UpperCamelCase			)
    assert num_tar == 1
    assert num_jsonl == 2






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[str]:






    """simple docstring"""


    snake_case_						:   str           =  DownloadManager()
    for num_file, file in enumerate(dl_manager.iter_files(_UpperCamelCase			)			,    start=1			):
        assert os.path.basename(_UpperCamelCase			) == ("test.txt" if num_file == 1 else "train.txt")
    assert num_file == 2







import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD



class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				True
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   Tuple           =  []
							snake_case_						:   List[str]           =  []

							for i in range(self.num_layers   ):
											snake_case_						:   Tuple           =  self.in_channels if i == 0 else self.out_channels

											snake_case_						:   Dict           =  FlaxResnetBlockaD(
											    in_channels=__magic_name__ ,							out_channels=self.out_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )

											snake_case_						:   str           =  FlaxTransformeraDModel(
											    in_channels=self.out_channels ,							n_heads=self.num_attention_heads ,							d_head=self.out_channels // self.num_attention_heads ,							depth=1 ,							use_linear_projection=self.use_linear_projection ,							only_cross_attention=self.only_cross_attention ,							use_memory_efficient_attention=self.use_memory_efficient_attention ,							dtype=self.dtype ,							)
											attentions.append(__magic_name__   )

							snake_case_						:   Union[str, Any]           =  resnets
							snake_case_						:   Union[str, Any]           =  attentions

							if self.add_downsample:
											snake_case_						:   List[str]           =  FlaxDownsampleaD(self.out_channels ,							dtype=self.dtype   )






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   str           =  ()

							for resnet, attn in zip(self.resnets ,							self.attentions   ):
											snake_case_						:   Optional[Any]           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											snake_case_						:   List[str]           =  attn(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											output_states += (hidden_states,)

							if self.add_downsample:
											snake_case_						:   Union[str, Any]           =  self.downsamplers_a(__magic_name__   )
											output_states += (hidden_states,)

							return hidden_states, output_states




class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				True
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  []

							for i in range(self.num_layers   ):
											snake_case_						:   List[Any]           =  self.in_channels if i == 0 else self.out_channels

											snake_case_						:   Tuple           =  FlaxResnetBlockaD(
											    in_channels=__magic_name__ ,							out_channels=self.out_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )
							snake_case_						:   Dict           =  resnets

							if self.add_downsample:
											snake_case_						:   Optional[int]           =  FlaxDownsampleaD(self.out_channels ,							dtype=self.dtype   )






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  ()

							for resnet in self.resnets:
											snake_case_						:   List[Any]           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											output_states += (hidden_states,)

							if self.add_downsample:
											snake_case_						:   str           =  self.downsamplers_a(__magic_name__   )
											output_states += (hidden_states,)

							return hidden_states, output_states




class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				True
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  []
							snake_case_						:   Optional[Any]           =  []

							for i in range(self.num_layers   ):
											snake_case_						:   List[Any]           =  self.in_channels if (i == self.num_layers - 1) else self.out_channels
											snake_case_						:   Optional[int]           =  self.prev_output_channel if i == 0 else self.out_channels

											snake_case_						:   Dict           =  FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels ,							out_channels=self.out_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )

											snake_case_						:   List[str]           =  FlaxTransformeraDModel(
											    in_channels=self.out_channels ,							n_heads=self.num_attention_heads ,							d_head=self.out_channels // self.num_attention_heads ,							depth=1 ,							use_linear_projection=self.use_linear_projection ,							only_cross_attention=self.only_cross_attention ,							use_memory_efficient_attention=self.use_memory_efficient_attention ,							dtype=self.dtype ,							)
											attentions.append(__magic_name__   )

							snake_case_						:   List[Any]           =  resnets
							snake_case_						:   Tuple           =  attentions

							if self.add_upsample:
											snake_case_						:   List[Any]           =  FlaxUpsampleaD(self.out_channels ,							dtype=self.dtype   )






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       Union[str, Any]:


							'''simple docstring'''



							for resnet, attn in zip(self.resnets ,							self.attentions   ):
											# pop res hidden states
											snake_case_						:   Dict           =  res_hidden_states_tuple[-1]
											snake_case_						:   List[Any]           =  res_hidden_states_tuple[:-1]
											snake_case_						:   Optional[int]           =  jnp.concatenate((hidden_states, res_hidden_states) ,							axis=-1   )

											snake_case_						:   Tuple           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											snake_case_						:   Tuple           =  attn(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )

							if self.add_upsample:
											snake_case_						:   Optional[Any]           =  self.upsamplers_a(__magic_name__   )

							return hidden_states




class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				True
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  []

							for i in range(self.num_layers   ):
											snake_case_						:   Tuple           =  self.in_channels if (i == self.num_layers - 1) else self.out_channels
											snake_case_						:   Optional[Any]           =  self.prev_output_channel if i == 0 else self.out_channels

											snake_case_						:   int           =  FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels ,							out_channels=self.out_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )

							snake_case_						:   Tuple           =  resnets

							if self.add_upsample:
											snake_case_						:   List[str]           =  FlaxUpsampleaD(self.out_channels ,							dtype=self.dtype   )






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       List[Any]:


							'''simple docstring'''



							for resnet in self.resnets:
											# pop res hidden states
											snake_case_						:   Tuple           =  res_hidden_states_tuple[-1]
											snake_case_						:   List[Any]           =  res_hidden_states_tuple[:-1]
											snake_case_						:   Dict           =  jnp.concatenate((hidden_states, res_hidden_states) ,							axis=-1   )

											snake_case_						:   Optional[Any]           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )

							if self.add_upsample:
											snake_case_						:   Optional[int]           =  self.upsamplers_a(__magic_name__   )

							return hidden_states




class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Dict           =  [
							    FlaxResnetBlockaD(
							        in_channels=self.in_channels ,							out_channels=self.in_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
							]

							snake_case_						:   int           =  []

							for _ in range(self.num_layers   ):
											snake_case_						:   str           =  FlaxTransformeraDModel(
											    in_channels=self.in_channels ,							n_heads=self.num_attention_heads ,							d_head=self.in_channels // self.num_attention_heads ,							depth=1 ,							use_linear_projection=self.use_linear_projection ,							use_memory_efficient_attention=self.use_memory_efficient_attention ,							dtype=self.dtype ,							)
											attentions.append(__magic_name__   )

											snake_case_						:   Dict           =  FlaxResnetBlockaD(
											    in_channels=self.in_channels ,							out_channels=self.in_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )

							snake_case_						:   Optional[Any]           =  resnets
							snake_case_						:   Optional[int]           =  attentions






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  self.resnets[0](__magic_name__ ,							__magic_name__   )
							for attn, resnet in zip(self.attentions ,							self.resnets[1:]   ):
											snake_case_						:   Tuple           =  attn(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											snake_case_						:   Union[str, Any]           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )

							return hidden_states
