
'''simple docstring'''
import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


a_											=						"<<<<<<< This should probably be modified because it mentions: "

a_											=						"=======\n>>>>>>>\n"

a_											=						[
    "TextEncoderConfig",
    "ByteTextEncoder",
    "SubwordTextEncoder",
    "encoder_config",
    "maybe_build_from_corpus",
    "manual_dir",
]

a_											=						[
    # (pattern, replacement)
    # Order is important here for some replacements
    (R"tfds\.core", R"datasets"),
    (R"tf\.io\.gfile\.GFile", R"open"),
    (R"tf\.([\w\d]+)", R"datasets.Value('\1')"),
    (R"tfds\.features\.Text\(\)", R"datasets.Value('string')"),
    (R"tfds\.features\.Text\(", R"datasets.Value('string'),"),
    (R"features\s*=\s*tfds.features.FeaturesDict\(", R"features=datasets.Features("),
    (R"tfds\.features\.FeaturesDict\(", R"dict("),
    (R"The TensorFlow Datasets Authors", R"The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"),
    (R"tfds\.", R"datasets."),
    (R"dl_manager\.manual_dir", R"self.config.data_dir"),
    (R"self\.builder_config", R"self.config"),
]

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    return ConvertCommand(args.tfds_path,		args.datasets_directory       )






class    UpperCAmelCase_				(	__UpperCAmelCase					):





 @staticmethod
 def        snake_case__		(    lowercase_):
     snake_case_ :    Optional[int]     			=						parser.add_parser(
         "convert" ,       help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset." ,       )
     train_parser.add_argument(
         "--tfds_path" ,       type=UpperCAmelCase_ ,       required=UpperCAmelCase_ ,       help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert." ,       )
     train_parser.add_argument(
         "--datasets_directory" ,       type=UpperCAmelCase_ ,       required=UpperCAmelCase_ ,       help="Path to the HuggingFace Datasets folder.")
     train_parser.set_defaults(func=UpperCAmelCase_)





 def __init__(    self ,       lowercase_ ,       lowercase_ ,       *lowercase_):
     snake_case_ :    Tuple     			=						get_logger("datasets-cli/converting")

     snake_case_ :    Any     			=						tfds_path
     snake_case_ :    Optional[int]     			=						datasets_directory





 def        snake_case__		(    self):
     if os.path.isdir(self._tfds_path):
         snake_case_ :    List[str]     			=						os.path.abspath(self._tfds_path)
     elif os.path.isfile(self._tfds_path):
         snake_case_ :    Union[str, Any]     			=						os.path.dirname(self._tfds_path)
     else:
         raise ValueError("--tfds_path is neither a directory nor a file. Please check path.")

     snake_case_ :    Any     			=						os.path.abspath(self._datasets_directory)

     self._logger.info(F'Converting datasets from {abs_tfds_path} to {abs_datasets_path}')

     snake_case_ :    List[str]     			=						[]
     snake_case_ :    str     			=						[]
     snake_case_ :    str     			=						{}

     if os.path.isdir(self._tfds_path):
         snake_case_ :    Optional[Any]     			=						os.listdir(UpperCAmelCase_)
     else:
         snake_case_ :    Optional[int]     			=						[os.path.basename(self._tfds_path)]

     for f_name in file_names:
         self._logger.info(F'Looking at file {f_name}')
         snake_case_ :    Any     			=						os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_)
         snake_case_ :    str     			=						os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_)

         if not os.path.isfile(UpperCAmelCase_) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
             self._logger.info("Skipping file")
             continue

         with open(UpperCAmelCase_ ,       encoding="utf-8") as f:
             snake_case_ :    Any     			=						f.readlines()

         snake_case_ :    Dict     			=						[]
         snake_case_ :    str     			=						False
         snake_case_ :    int     			=						False
         snake_case_ :    int     			=						[]
         for line in lines:
             snake_case_ :    List[str]     			=						line

             # Convert imports
             if "import tensorflow.compat.v2 as tf" in out_line:
                 continue
             elif "@tfds.core" in out_line:
                 continue
             elif "builder=self" in out_line:
                 continue
             elif "import tensorflow_datasets.public_api as tfds" in out_line:
                 snake_case_ :    List[str]     			=						"import datasets\n"
             elif "import tensorflow" in out_line:
                 # order is important here
                 snake_case_ :    Optional[int]     			=						""
                 continue
             elif "from absl import logging" in out_line:
                 snake_case_ :    int     			=						"from datasets import logging\n"
             elif "getLogger" in out_line:
                 snake_case_ :    Dict     			=						out_line.replace("getLogger" ,       "get_logger")
             elif any(expression in out_line for expression in TO_HIGHLIGHT):
                 snake_case_ :    List[str]     			=						True
                 snake_case_ :    Optional[int]     			=						list(filter(lambda lowercase_: e in out_line ,       UpperCAmelCase_))
                 out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(UpperCAmelCase_) + "\n")
                 out_lines.append(UpperCAmelCase_)
                 out_lines.append(UpperCAmelCase_)
                 continue
             else:
                 for pattern, replacement in TO_CONVERT:
                     snake_case_ :    Dict     			=						re.sub(UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_)

                # Take care of saving utilities (to later move them together with main script)
             if "tensorflow_datasets" in out_line:
                 snake_case_ :    Any     			=						re.match(R"from\stensorflow_datasets.*import\s([^\.\r\n]+)" ,       UpperCAmelCase_)
                 tfds_imports.extend(imp.strip() for imp in match.group(1).split(","))
                 snake_case_ :    Any     			=						"from . import " + match.group(1)

             # Check we have not forget anything
             if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
                 raise ValueError(F'Error converting {out_line.strip()}')

             if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
                 snake_case_ :    List[Any]     			=						True
             out_lines.append(UpperCAmelCase_)

         if is_builder or "wmt" in f_name:
             # We create a new directory for each dataset
             snake_case_ :    Optional[int]     			=						f_name.replace(".py" ,       "")
             snake_case_ :    List[Any]     			=						os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_)
             snake_case_ :    List[Any]     			=						os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_)
             os.makedirs(UpperCAmelCase_ ,       exist_ok=UpperCAmelCase_)
             self._logger.info(F'Adding directory {output_dir}')
             imports_to_builder_map.update({imp: output_dir for imp in tfds_imports})
         else:
             # Utilities will be moved at the end
             utils_files.append(UpperCAmelCase_)

         if needs_manual_update:
             with_manual_update.append(UpperCAmelCase_)

         with open(UpperCAmelCase_ ,       "w" ,       encoding="utf-8") as f:
             f.writelines(UpperCAmelCase_)
         self._logger.info(F'Converted in {output_file}')

     for utils_file in utils_files:
         try:
             snake_case_ :    List[Any]     			=						os.path.basename(UpperCAmelCase_)
             snake_case_ :    Optional[int]     			=						imports_to_builder_map[f_name.replace(".py" ,       "")]
             self._logger.info(F'Moving {dest_folder} to {utils_file}')
             shutil.copy(UpperCAmelCase_ ,       UpperCAmelCase_)
         except KeyError:
             self._logger.error(F'Cannot find destination folder for {utils_file}. Please copy manually.')

     if with_manual_update:
         for file_path in with_manual_update:
             self._logger.warning(
                 F'You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.')






'''simple docstring'''
import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


a_											=						logging.get_logger(__name__)

enable_full_determinism()






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						UNetaDModel
 UpperCAmelCase_		    =						"""sample"""





 @property
 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						4
     snake_case_ :    int     			=						3
     snake_case_ :    Dict     			=						(32, 32)

     snake_case_ :    str     			=						floats_tensor((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    Union[str, Any]     			=						torch.tensor([10]).to(lowercase_)

     return {"sample": noise, "timestep": time_step}





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 def        snake_case__		(    self):
     snake_case_ :    str     			=						{
         "block_out_channels": (32, 64),
         "down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
         "up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
         "attention_head_dim": 3,
         "out_channels": 3,
         "in_channels": 3,
         "layers_per_block": 2,
         "sample_size": 32,
     }
     snake_case_ :    List[Any]     			=						self.dummy_input
     return init_dict, inputs_dict






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						UNetaDModel
 UpperCAmelCase_		    =						"""sample"""





 @property
 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						4
     snake_case_ :    Dict     			=						4
     snake_case_ :    Dict     			=						(32, 32)

     snake_case_ :    Dict     			=						floats_tensor((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    Any     			=						torch.tensor([10]).to(lowercase_)

     return {"sample": noise, "timestep": time_step}





 @property
 def        snake_case__		(    self):
     return (4, 32, 32)





 @property
 def        snake_case__		(    self):
     return (4, 32, 32)





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						{
         "sample_size": 32,
         "in_channels": 4,
         "out_channels": 4,
         "layers_per_block": 2,
         "block_out_channels": (32, 64),
         "attention_head_dim": 32,
         "down_block_types": ("DownBlock2D", "DownBlock2D"),
         "up_block_types": ("UpBlock2D", "UpBlock2D"),
     }
     snake_case_ :    Any     			=						self.dummy_input
     return init_dict, inputs_dict





 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Tuple     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_)

     self.assertIsNotNone(lowercase_)
     self.assertEqual(len(loading_info["missing_keys"]) ,       0)

     model.to(lowercase_)
     snake_case_ :    int     			=						model(**self.dummy_input).sample

     assert image is not None, "Make sure output is not None"





 @unittest.skipIf(torch_device != "cuda" ,       "This test is supposed to run on GPU")
 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Optional[Any]     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_)
     model.to(lowercase_)
     snake_case_ :    str     			=						model(**self.dummy_input).sample

     assert image is not None, "Make sure output is not None"





 @unittest.skipIf(torch_device != "cuda" ,       "This test is supposed to run on GPU")
 def        snake_case__		(    self):
     # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
     snake_case_			, snake_case_ :    Any     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_)
     model_accelerate.to(lowercase_)
     model_accelerate.eval()

     snake_case_ :    Optional[int]     			=						torch.randn(
         1 ,       model_accelerate.config.in_channels ,       model_accelerate.config.sample_size ,       model_accelerate.config.sample_size ,       generator=torch.manual_seed(0) ,       )
     snake_case_ :    List[str]     			=						noise.to(lowercase_)
     snake_case_ :    Any     			=						torch.tensor([10] * noise.shape[0]).to(lowercase_)

     snake_case_ :    Optional[int]     			=						model_accelerate(lowercase_ ,       lowercase_)["sample"]

     # two models don't need to stay in the device at the same time
     del model_accelerate
     torch.cuda.empty_cache()
     gc.collect()

     snake_case_			, snake_case_ :    Union[str, Any]     			=						UNetaDModel.from_pretrained(
         "fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_ ,       low_cpu_mem_usage=lowercase_)
     model_normal_load.to(lowercase_)
     model_normal_load.eval()
     snake_case_ :    Optional[Any]     			=						model_normal_load(lowercase_ ,       lowercase_)["sample"]

     assert torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-3)





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update")
     model.eval()
     model.to(lowercase_)

     snake_case_ :    List[str]     			=						torch.randn(
         1 ,       model.config.in_channels ,       model.config.sample_size ,       model.config.sample_size ,       generator=torch.manual_seed(0) ,       )
     snake_case_ :    List[str]     			=						noise.to(lowercase_)
     snake_case_ :    str     			=						torch.tensor([10] * noise.shape[0]).to(lowercase_)

     with torch.no_grad():
         snake_case_ :    Tuple     			=						model(lowercase_ ,       lowercase_).sample

     snake_case_ :    Optional[int]     			=						output[0, -1, -3:, -3:].flatten().cpu()
     # fmt: off
     snake_case_ :    Tuple     			=						torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800])
     # fmt: on

     self.assertTrue(torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-3))






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						UNetaDModel
 UpperCAmelCase_		    =						"""sample"""





 @property
 def        snake_case__		(    self ,       lowercase_=(32, 32)):
     snake_case_ :    List[Any]     			=						4
     snake_case_ :    str     			=						3

     snake_case_ :    str     			=						floats_tensor((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    Dict     			=						torch.tensor(batch_size * [10]).to(dtype=torch.intaa ,       device=lowercase_)

     return {"sample": noise, "timestep": time_step}





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						{
         "block_out_channels": [32, 64, 64, 64],
         "in_channels": 3,
         "layers_per_block": 1,
         "out_channels": 3,
         "time_embedding_type": "fourier",
         "norm_eps": 1E-6,
         "mid_block_scale_factor": math.sqrt(2.0),
         "norm_num_groups": None,
         "down_block_types": [
             "SkipDownBlock2D",
             "AttnSkipDownBlock2D",
             "SkipDownBlock2D",
             "SkipDownBlock2D",
         ],
         "up_block_types": [
             "SkipUpBlock2D",
             "SkipUpBlock2D",
             "AttnSkipUpBlock2D",
             "SkipUpBlock2D",
         ],
     }
     snake_case_ :    Optional[Any]     			=						self.dummy_input
     return init_dict, inputs_dict





 @slow
 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Optional[int]     			=						UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256" ,       output_loading_info=lowercase_)
     self.assertIsNotNone(lowercase_)
     self.assertEqual(len(loading_info["missing_keys"]) ,       0)

     model.to(lowercase_)
     snake_case_ :    Dict     			=						self.dummy_input
     snake_case_ :    Tuple     			=						floats_tensor((4, 3) + (2_56, 2_56)).to(lowercase_)
     snake_case_ :    Tuple     			=						noise
     snake_case_ :    Tuple     			=						model(**lowercase_)

     assert image is not None, "Make sure output is not None"





 @slow
 def        snake_case__		(    self):
     snake_case_ :    Dict     			=						UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256")
     model.to(lowercase_)

     snake_case_ :    List[Any]     			=						4
     snake_case_ :    str     			=						3
     snake_case_ :    Dict     			=						(2_56, 2_56)

     snake_case_ :    Tuple     			=						torch.ones((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    List[Any]     			=						torch.tensor(batch_size * [1E-4]).to(lowercase_)

     with torch.no_grad():
         snake_case_ :    int     			=						model(lowercase_ ,       lowercase_).sample

     snake_case_ :    List[str]     			=						output[0, -3:, -3:, -1].flatten().cpu()
     # fmt: off
     snake_case_ :    Union[str, Any]     			=						torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608])
     # fmt: on

     self.assertTrue(torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-2))





 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
     model.to(lowercase_)

     snake_case_ :    Dict     			=						4
     snake_case_ :    str     			=						3
     snake_case_ :    List[Any]     			=						(32, 32)

     snake_case_ :    int     			=						torch.ones((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    List[Any]     			=						torch.tensor(batch_size * [1E-4]).to(lowercase_)

     with torch.no_grad():
         snake_case_ :    Optional[Any]     			=						model(lowercase_ ,       lowercase_).sample

     snake_case_ :    List[Any]     			=						output[0, -3:, -3:, -1].flatten().cpu()
     # fmt: off
     snake_case_ :    Union[str, Any]     			=						torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256])
     # fmt: on

     self.assertTrue(torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-2))





 def        snake_case__		(    self):
     # not required for this model
     pass




