import os
import re
import shutil
from argparse import ArgumentParser, Namespace

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


lowerCAmelCase_:					List[str]     =					"<<<<<<< This should probably be modified because it mentions: "

lowerCAmelCase_:					List[str]     =					"=======\n>>>>>>>\n"

lowerCAmelCase_:					Tuple     =					[
    "TextEncoderConfig",
    "ByteTextEncoder",
    "SubwordTextEncoder",
    "encoder_config",
    "maybe_build_from_corpus",
    "manual_dir",
]

lowerCAmelCase_:					Optional[Any]     =					[
    # (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 					__a   (		A      ):






     '''simple docstring'''



     return ConvertCommand(args.tfds_path    ,				args.datasets_directory      )






class    a__    (   UpperCAmelCase_	):


   @staticmethod
   def 	snake_case__	(     _UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							parser.add_parser(
            "convert",				help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.",				)
        train_parser.add_argument(
            "--tfds_path",				type=_snake_case,				required=_snake_case,				help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.",				)
        train_parser.add_argument(
            "--datasets_directory",				type=_snake_case,				required=_snake_case,				help="Path to the HuggingFace Datasets folder."  )
        train_parser.set_defaults(func=_snake_case  )


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase,				*_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							get_logger("datasets-cli/converting"  )

        lowercase__				 =							tfds_path
        lowercase__				 =							datasets_directory


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        if os.path.isdir(self._tfds_path  ):
             lowercase__				 =							os.path.abspath(self._tfds_path  )
        elif os.path.isfile(self._tfds_path  ):
             lowercase__				 =							os.path.dirname(self._tfds_path  )
        else:
             raise ValueError("--tfds_path is neither a directory nor a file. Please check path."  )

        lowercase__				 =							os.path.abspath(self._datasets_directory  )

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

        lowercase__				 =							[]
        lowercase__				 =							[]
        lowercase__				 =							{}

        if os.path.isdir(self._tfds_path  ):
             lowercase__				 =							os.listdir(_snake_case  )
        else:
             lowercase__				 =							[os.path.basename(self._tfds_path  )]

        for f_name in file_names:
             self._logger.info(F'''Looking at file {f_name}'''  )
             lowercase__				 =							os.path.join(_snake_case,				_snake_case  )
             lowercase__				 =							os.path.join(_snake_case,				_snake_case  )

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

             with open(_snake_case,				encoding="utf-8"  ) as f:
                  lowercase__				 =							f.readlines()

             lowercase__				 =							[]
             lowercase__				 =							False
             lowercase__				 =							False
             lowercase__				 =							[]
             for line in lines:
                  lowercase__				 =							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:
                       lowercase__				 =							"import datasets\n"
                  elif "import tensorflow" in out_line:
                       # order is important here
                       lowercase__				 =							""
                       continue
                  elif "from absl import logging" in out_line:
                       lowercase__				 =							"from datasets import logging\n"
                  elif "getLogger" in out_line:
                       lowercase__				 =							out_line.replace("getLogger",				"get_logger"  )
                  elif any(expression in out_line for expression in TO_HIGHLIGHT  ):
                       lowercase__				 =							True
                       lowercase__				 =							list(filter(lambda _UpperCAmelCase  : e in out_line,				_snake_case  )  )
                       out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(_snake_case  ) + "\n"  )
                       out_lines.append(_snake_case  )
                       out_lines.append(_snake_case  )
                       continue
                  else:
                       for pattern, replacement in TO_CONVERT:
                            lowercase__				 =							re.sub(_snake_case,				_snake_case,				_snake_case  )

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

             if is_builder or "wmt" in f_name:
                  # We create a new directory for each dataset
                  lowercase__				 =							f_name.replace(".py",				""  )
                  lowercase__				 =							os.path.join(_snake_case,				_snake_case  )
                  lowercase__				 =							os.path.join(_snake_case,				_snake_case  )
                  os.makedirs(_snake_case,				exist_ok=_snake_case  )
                  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(_snake_case  )

             if needs_manual_update:
                  with_manual_update.append(_snake_case  )

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

        for utils_file in utils_files:
             try:
                  lowercase__				 =							os.path.basename(_snake_case  )
                  lowercase__				 =							imports_to_builder_map[f_name.replace(".py",				""  )]
                  self._logger.info(F'''Moving {dest_folder} to {utils_file}'''  )
                  shutil.copy(_snake_case,				_snake_case  )
             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 inspect
import unittest

import torch
from parameterized import parameterized

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

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()






class    a__    (   _a					,					unittest.TestCase	):
   snake_case_							=      PriorTransformer
   snake_case_							=      "hidden_states"


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							4
        lowercase__				 =							8
        lowercase__				 =							7

        lowercase__				 =							floats_tensor((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )

        lowercase__				 =							floats_tensor((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )
        lowercase__				 =							floats_tensor((batch_size, num_embeddings, embedding_dim)  ).to(_UpperCAmelCase  )

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }


   def 	snake_case__	(     self,				_UpperCAmelCase=0  ):
        '''simple docstring'''



        torch.manual_seed(_UpperCAmelCase  )
        lowercase__				 =							4
        lowercase__				 =							8
        lowercase__				 =							7

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )
        lowercase__				 =							torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(_UpperCAmelCase  )

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return (4, 8)


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return (4, 8)


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							{
            "num_attention_heads": 2,
            "attention_head_dim": 4,
            "num_layers": 2,
            "embedding_dim": 8,
            "num_embeddings": 7,
            "additional_embeddings": 4,
        }
        lowercase__				 =							self.dummy_input
        return init_dict, inputs_dict


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							PriorTransformer.from_pretrained(
            "hf-internal-testing/prior-dummy",				output_loading_info=_UpperCAmelCase  )
        self.assertIsNotNone(_UpperCAmelCase  )
        self.assertEqual(len(loading_info["missing_keys"]  ),				0  )

        model.to(_UpperCAmelCase  )
        lowercase__				 =							model(**self.dummy_input  )[0]

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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.prepare_init_args_and_inputs_for_common()

        lowercase__				 =							self.model_class(**_UpperCAmelCase  )
        lowercase__				 =							inspect.signature(model.forward  )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowercase__				 =							[*signature.parameters.keys()]

        lowercase__				 =							["hidden_states", "timestep"]
        self.assertListEqual(arg_names[:2],				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"  )
        lowercase__				 =							model.to(_UpperCAmelCase  )

        if hasattr(_UpperCAmelCase,				"set_default_attn_processor"  ):
             model.set_default_attn_processor()

        lowercase__				 =							self.get_dummy_seed_input()

        with torch.no_grad():
             lowercase__				 =							model(**_UpperCAmelCase  )[0]

        lowercase__				 =							output[0, :5].flatten().cpu()
        print(_UpperCAmelCase  )

        # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
        # the expected output slices are not the same for CPU and GPU.
        lowercase__				 =							torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]  )
        self.assertTrue(torch_all_close(_UpperCAmelCase,				_UpperCAmelCase,				rtol=1E-2  )  )






@slow
class    a__    (   unittest.TestCase	):


   def 	snake_case__	(     self,				_UpperCAmelCase=1,				_UpperCAmelCase=768,				_UpperCAmelCase=77,				_UpperCAmelCase=0  ):
        '''simple docstring'''



        torch.manual_seed(_UpperCAmelCase  )
        lowercase__				 =							batch_size
        lowercase__				 =							embedding_dim
        lowercase__				 =							num_embeddings

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )
        lowercase__				 =							torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(_UpperCAmelCase  )

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
           [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
           # fmt: on
       ]  )
   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior",				subfolder="prior"  )
        model.to(_UpperCAmelCase  )
        lowercase__				 =							self.get_dummy_seed_input(seed=_UpperCAmelCase  )

        with torch.no_grad():
             lowercase__				 =							model(**_UpperCAmelCase  )[0]

        assert list(sample.shape  ) == [1, 768]

        lowercase__				 =							sample[0, :8].flatten().cpu()
        print(_UpperCAmelCase  )
        lowercase__				 =							torch.tensor(_UpperCAmelCase  )

        assert torch_all_close(_UpperCAmelCase,				_UpperCAmelCase,				atol=1E-3  )




