
"""simple docstring"""





import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


_a	 =							get_logger(__name__)







def 			__a  (	__lowerCamelCase						):
   return TestCommand(
       args.dataset,	args.name,	args.cache_dir,	args.data_dir,	args.all_configs,	args.save_info or args.save_infos,	args.ignore_verifications,	args.force_redownload,	args.clear_cache,	)


class A_	(lowercase__   ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__    :		str						  =	False  # to tell pytest it's not a test class







  @staticmethod
  def  UpperCamelCase__      (	lowercase_						):

     """simple docstring"""
     UpperCAmelCase_      :      List[str]      =					parser.add_parser("test"       ,     help="Test dataset implementation."						)
     test_parser.add_argument("--name"       ,     type=lowercase_       ,     default=lowercase_       ,     help="Dataset processing name"						)
     test_parser.add_argument(
         "--cache_dir"       ,     type=lowercase_       ,     default=lowercase_       ,     help="Cache directory where the datasets are stored."       ,     )
     test_parser.add_argument(
         "--data_dir"       ,     type=lowercase_       ,     default=lowercase_       ,     help="Can be used to specify a manual directory to get the files from."       ,     )
     test_parser.add_argument("--all_configs"       ,     action="store_true"       ,     help="Test all dataset configurations"						)
     test_parser.add_argument(
         "--save_info"       ,     action="store_true"       ,     help="Save the dataset infos in the dataset card (README.md)"						)
     test_parser.add_argument(
         "--ignore_verifications"       ,     action="store_true"       ,     help="Run the test without checksums and splits checks."       ,     )
     test_parser.add_argument("--force_redownload"       ,     action="store_true"       ,     help="Force dataset redownload"						)
     test_parser.add_argument(
         "--clear_cache"       ,     action="store_true"       ,     help="Remove downloaded files and cached datasets after each config test"       ,     )
     # aliases
     test_parser.add_argument("--save_infos"       ,     action="store_true"       ,     help="alias to save_info"						)
     test_parser.add_argument("dataset"       ,     type=lowercase_       ,     help="Name of the dataset to download"						)
     test_parser.set_defaults(func=_test_command_factory						)







  def __init__(	self       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     ):

     """simple docstring"""
     UpperCAmelCase_      :      List[Any]      =					dataset
     UpperCAmelCase_      :      str      =					name
     UpperCAmelCase_      :      List[Any]      =					cache_dir
     UpperCAmelCase_      :      Any      =					data_dir
     UpperCAmelCase_      :      Any      =					all_configs
     UpperCAmelCase_      :      Union[str, Any]      =					save_infos
     UpperCAmelCase_      :      Optional[int]      =					ignore_verifications
     UpperCAmelCase_      :      List[Any]      =					force_redownload
     UpperCAmelCase_      :      Union[str, Any]      =					clear_cache
     if clear_cache and not cache_dir:
        print(
            "When --clear_cache is used, specifying a cache directory is mandatory.\n"
            "The 'download' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n"
            "Please provide a --cache_dir that will be used to test the dataset script."						)
        exit(1						)
     if save_infos:
        UpperCAmelCase_      :      Dict      =					True







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     fl_logger().setLevel(lowercase_						)
     if self._name is not None and self._all_configs:
        print("Both parameters `config` and `all_configs` can't be used at once."						)
        exit(1						)
     UpperCAmelCase_      :      List[Any]      =					self._dataset, self._name
     UpperCAmelCase_      :      List[Any]      =					dataset_module_factory(lowercase_						)
     UpperCAmelCase_      :      Dict      =					import_main_class(module.module_path						)
     UpperCAmelCase_      :      Any      =					len(builder_cls.BUILDER_CONFIGS						) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

     def get_builders() -> Generator[DatasetBuilder, None, None]:
        if self._all_configs and builder_cls.BUILDER_CONFIGS:
           for i, config in enumerate(builder_cls.BUILDER_CONFIGS						):
              if "config_name" in module.builder_kwargs:
                 yield builder_cls(
                     cache_dir=self._cache_dir       ,     data_dir=self._data_dir       ,     **module.builder_kwargs       ,     )
              else:
                 yield builder_cls(
                     config_name=config.name       ,     cache_dir=self._cache_dir       ,     data_dir=self._data_dir       ,     **module.builder_kwargs       ,     )
        else:
           if "config_name" in module.builder_kwargs:
              yield builder_cls(cache_dir=self._cache_dir       ,     data_dir=self._data_dir       ,     **module.builder_kwargs						)
           else:
              yield builder_cls(
                  config_name=lowercase_       ,     cache_dir=self._cache_dir       ,     data_dir=self._data_dir       ,     **module.builder_kwargs       ,     )

     for j, builder in enumerate(get_builders()						):
        print(F"""Testing builder '{builder.config.name}' ({j + 1}/{n_builders})"""						)
        UpperCAmelCase_      :      int      =					os.path.exists(
            os.path.join(builder.get_imported_module_dir()       ,     datasets.config.DATASETDICT_INFOS_FILENAME						)						)  # record checksums only if we need to update a (deprecated) dataset_infos.json
        builder.download_and_prepare(
            download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
            if not self._force_redownload
            else DownloadMode.FORCE_REDOWNLOAD       ,     verification_mode=VerificationMode.NO_CHECKS
            if self._ignore_verifications
            else VerificationMode.ALL_CHECKS       ,     try_from_hf_gcs=lowercase_       ,     )
        builder.as_dataset()
        if self._save_infos:
           builder._save_infos()

        # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
        # The dataset_infos are saved in the YAML part of the README.md

        # Let's move it to the original directory of the dataset script, to allow the user to
        # upload them on S3 at the same time afterwards.
        if self._save_infos:
           UpperCAmelCase_      :      Tuple      =					os.path.join(builder_cls.get_imported_module_dir()       ,     "README.md"						)
           UpperCAmelCase_      :      int      =					Path(lowercase_						).name + ".py"
           UpperCAmelCase_      :      str      =					os.path.join(lowercase_       ,     lowercase_						)
           if os.path.isfile(lowercase_						):
              UpperCAmelCase_      :      int      =					os.path.dirname(lowercase_						)
           elif os.path.isfile(lowercase_						):
              UpperCAmelCase_      :      int      =					path
           elif os.path.isdir(lowercase_						):  # for local directories containing only data files
              UpperCAmelCase_      :      Union[str, Any]      =					path
           else:  # in case of a remote dataset
              UpperCAmelCase_      :      Any      =					None
              print(F"""Dataset card saved at {dataset_readme_path}"""						)

           # Move dataset_info back to the user
           if dataset_dir is not None:
              UpperCAmelCase_      :      str      =					os.path.join(lowercase_       ,     "README.md"						)
              copyfile(lowercase_       ,     lowercase_						)
              print(F"""Dataset card saved at {user_dataset_readme_path}"""						)

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
        if self._clear_cache:
           if os.path.isdir(builder._cache_dir						):
              logger.warning(F"""Clearing cache at {builder._cache_dir}"""						)
              rmtree(builder._cache_dir						)
           UpperCAmelCase_      :      List[Any]      =					os.path.join(self._cache_dir       ,     datasets.config.DOWNLOADED_DATASETS_DIR						)
           if os.path.isdir(lowercase_						):
              logger.warning(F"""Clearing cache at {download_dir}"""						)
              rmtree(lowercase_						)

     print("Test successful."						)




"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_a	 =							logging.get_logger(__name__)

_a	 =							{
    'google/bigbird-pegasus-large-arxiv': (
        'https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'
    ),
    'google/bigbird-pegasus-large-pubmed': (
        'https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'
    ),
    'google/bigbird-pegasus-large-bigpatent': (
        'https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}


class A_			(lowercase__   ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__    :		Optional[int]						  =	"""bigbird_pegasus"""
  SCREAMING_SNAKE_CASE__    :		Any						  =	["""past_key_values"""]
  SCREAMING_SNAKE_CASE__    :		Tuple						  =	{
      """num_attention_heads""": """encoder_attention_heads""",
      """hidden_size""": """d_model""",
      """attention_probs_dropout_prob""": """attention_dropout""",
  }







  def __init__(	self       ,     lowercase_=9_6103       ,     lowercase_=4096       ,     lowercase_=16       ,     lowercase_=4096       ,     lowercase_=16       ,     lowercase_=16       ,     lowercase_=4096       ,     lowercase_=16       ,     lowercase_=0.0       ,     lowercase_=0.0       ,     lowercase_=True       ,     lowercase_=True       ,     lowercase_="gelu_new"       ,     lowercase_=1024       ,     lowercase_=0.1       ,     lowercase_=0.0       ,     lowercase_=0.0       ,     lowercase_=0.02       ,     lowercase_=2       ,     lowercase_=0.0       ,     lowercase_=True       ,     lowercase_=0       ,     lowercase_=2       ,     lowercase_=1       ,     lowercase_="block_sparse"       ,     lowercase_=64       ,     lowercase_=3       ,     lowercase_=False       ,     **lowercase_       ,     ):

     """simple docstring"""
     UpperCAmelCase_      :      Union[str, Any]      =					vocab_size
     UpperCAmelCase_      :      Optional[Any]      =					max_position_embeddings
     UpperCAmelCase_      :      List[str]      =					d_model
     UpperCAmelCase_      :      str      =					encoder_ffn_dim
     UpperCAmelCase_      :      Optional[Any]      =					encoder_layers
     UpperCAmelCase_      :      Optional[Any]      =					encoder_attention_heads
     UpperCAmelCase_      :      str      =					decoder_ffn_dim
     UpperCAmelCase_      :      Optional[Any]      =					decoder_layers
     UpperCAmelCase_      :      Optional[int]      =					decoder_attention_heads
     UpperCAmelCase_      :      Dict      =					dropout
     UpperCAmelCase_      :      Optional[int]      =					attention_dropout
     UpperCAmelCase_      :      Any      =					activation_dropout
     UpperCAmelCase_      :      str      =					activation_function
     UpperCAmelCase_      :      Tuple      =					init_std
     UpperCAmelCase_      :      Optional[int]      =					encoder_layerdrop
     UpperCAmelCase_      :      Union[str, Any]      =					decoder_layerdrop
     UpperCAmelCase_      :      int      =					classifier_dropout
     UpperCAmelCase_      :      Union[str, Any]      =					use_cache
     UpperCAmelCase_      :      List[str]      =					encoder_layers
     UpperCAmelCase_      :      str      =					scale_embedding  # scale factor will be sqrt(d_model) if True

     # extra config
     UpperCAmelCase_      :      List[Any]      =					attention_type
     UpperCAmelCase_      :      Any      =					block_size
     UpperCAmelCase_      :      str      =					num_random_blocks
     UpperCAmelCase_      :      Optional[Any]      =					use_bias

     super().__init__(
         pad_token_id=lowercase_       ,     bos_token_id=lowercase_       ,     eos_token_id=lowercase_       ,     is_encoder_decoder=lowercase_       ,     decoder_start_token_id=lowercase_       ,     **lowercase_       ,     )




class A_			(lowercase__   ):




  '''simple docstring'''







  @property
  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     if self.task in ["default", "seq2seq-lm"]:
        UpperCAmelCase_      :      List[str]      =					OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
            ]						)

        if self.use_past:
           UpperCAmelCase_      :      List[str]      =					{0: "batch"}
           UpperCAmelCase_      :      Any      =					{0: "batch", 1: "past_decoder_sequence + sequence"}
        else:
           UpperCAmelCase_      :      List[str]      =					{0: "batch", 1: "decoder_sequence"}
           UpperCAmelCase_      :      Optional[Any]      =					{0: "batch", 1: "decoder_sequence"}

        if self.use_past:
           self.fill_with_past_key_values_(lowercase_       ,     direction="inputs"						)
     elif self.task == "causal-lm":
        # TODO: figure this case out.
        UpperCAmelCase_      :      str      =					OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
            ]						)
        if self.use_past:
           UpperCAmelCase_  ,     UpperCAmelCase_      :      Dict      =					self.num_layers
           for i in range(lowercase_						):
              UpperCAmelCase_      :      Tuple      =					{0: "batch", 2: "past_sequence + sequence"}
              UpperCAmelCase_      :      List[str]      =					{0: "batch", 2: "past_sequence + sequence"}
     else:
        UpperCAmelCase_      :      Optional[Any]      =					OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
            ]						)

     return common_inputs







  @property
  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     if self.task in ["default", "seq2seq-lm"]:
        UpperCAmelCase_      :      Union[str, Any]      =					super().outputs
     else:
        UpperCAmelCase_      :      Optional[Any]      =					super(lowercase_       ,     self						).outputs
        if self.use_past:
           UpperCAmelCase_  ,     UpperCAmelCase_      :      str      =					self.num_layers
           for i in range(lowercase_						):
              UpperCAmelCase_      :      Any      =					{0: "batch", 2: "past_sequence + sequence"}
              UpperCAmelCase_      :      int      =					{0: "batch", 2: "past_sequence + sequence"}
     return common_outputs







  def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):

     """simple docstring"""
     UpperCAmelCase_      :      str      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)

     # Generate decoder inputs
     UpperCAmelCase_      :      Union[str, Any]      =					seq_length if not self.use_past else 1
     UpperCAmelCase_      :      str      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
     UpperCAmelCase_      :      List[Any]      =					{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
     UpperCAmelCase_      :      Optional[Any]      =					dict(**lowercase_       ,     **lowercase_						)

     if self.use_past:
        if not is_torch_available():
           raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
        else:
           import torch
        UpperCAmelCase_  ,     UpperCAmelCase_      :      Dict      =					common_inputs["input_ids"].shape
        UpperCAmelCase_      :      Union[str, Any]      =					common_inputs["decoder_input_ids"].shape[1]
        UpperCAmelCase_  ,     UpperCAmelCase_      :      Tuple      =					self.num_attention_heads
        UpperCAmelCase_      :      Tuple      =					(
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        UpperCAmelCase_      :      Union[str, Any]      =					decoder_seq_length + 3
        UpperCAmelCase_      :      List[Any]      =					(
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        UpperCAmelCase_      :      List[str]      =					torch.cat(
            [common_inputs["decoder_attention_mask"], torch.ones(lowercase_       ,     lowercase_						)]       ,     dim=1						)

        UpperCAmelCase_      :      Dict      =					[]
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        UpperCAmelCase_  ,     UpperCAmelCase_      :      Union[str, Any]      =					self.num_layers
        UpperCAmelCase_      :      List[str]      =					min(lowercase_       ,     lowercase_						)
        UpperCAmelCase_      :      Optional[int]      =					max(lowercase_       ,     lowercase_						) - min_num_layers
        UpperCAmelCase_      :      List[Any]      =					"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

        for _ in range(lowercase_						):
           common_inputs["past_key_values"].append(
               (
                   torch.zeros(lowercase_						),
                   torch.zeros(lowercase_						),
                   torch.zeros(lowercase_						),
                   torch.zeros(lowercase_						),
               )						)
        # TODO: test this.
        UpperCAmelCase_      :      Optional[int]      =					encoder_shape if remaining_side_name == "encoder" else decoder_shape
        for _ in range(lowercase_       ,     lowercase_						):
           common_inputs["past_key_values"].append((torch.zeros(lowercase_						), torch.zeros(lowercase_						))						)
     return common_inputs







  def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):

     """simple docstring"""
     UpperCAmelCase_      :      Optional[Any]      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)

     if self.use_past:
        if not is_torch_available():
           raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
        else:
           import torch
        UpperCAmelCase_  ,     UpperCAmelCase_      :      Tuple      =					common_inputs["input_ids"].shape
        # Not using the same length for past_key_values
        UpperCAmelCase_      :      str      =					seqlen + 2
        UpperCAmelCase_  ,     UpperCAmelCase_      :      List[Any]      =					self.num_layers
        UpperCAmelCase_  ,     UpperCAmelCase_      :      Dict      =					self.num_attention_heads
        UpperCAmelCase_      :      Optional[int]      =					(
            batch,
            num_encoder_attention_heads,
            past_key_values_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )

        UpperCAmelCase_      :      str      =					common_inputs["attention_mask"].dtype
        UpperCAmelCase_      :      Union[str, Any]      =					torch.cat(
            [common_inputs["attention_mask"], torch.ones(lowercase_       ,     lowercase_       ,     dtype=lowercase_						)]       ,     dim=1						)
        UpperCAmelCase_      :      List[str]      =					[
            (torch.zeros(lowercase_						), torch.zeros(lowercase_						)) for _ in range(lowercase_						)
        ]
     return common_inputs







  def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):

     """simple docstring"""
     # Copied from OnnxConfig.generate_dummy_inputs
     # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     UpperCAmelCase_      :      Any      =					compute_effective_axis_dimension(
         lowercase_       ,     fixed_dimension=OnnxConfig.default_fixed_batch       ,     num_token_to_add=0						)

     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     UpperCAmelCase_      :      List[str]      =					tokenizer.num_special_tokens_to_add(lowercase_						)
     UpperCAmelCase_      :      Union[str, Any]      =					compute_effective_axis_dimension(
         lowercase_       ,     fixed_dimension=OnnxConfig.default_fixed_sequence       ,     num_token_to_add=lowercase_						)

     # Generate dummy inputs according to compute batch and sequence
     UpperCAmelCase_      :      str      =					[" ".join([tokenizer.unk_token]						) * seq_length] * batch_size
     UpperCAmelCase_      :      int      =					dict(tokenizer(lowercase_       ,     return_tensors=lowercase_						)						)
     return common_inputs







  def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):

     """simple docstring"""
     if self.task in ["default", "seq2seq-lm"]:
        UpperCAmelCase_      :      Dict      =					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            lowercase_       ,     batch_size=lowercase_       ,     seq_length=lowercase_       ,     is_pair=lowercase_       ,     framework=lowercase_						)

     elif self.task == "causal-lm":
        UpperCAmelCase_      :      List[str]      =					self._generate_dummy_inputs_for_causal_lm(
            lowercase_       ,     batch_size=lowercase_       ,     seq_length=lowercase_       ,     is_pair=lowercase_       ,     framework=lowercase_						)
     else:
        UpperCAmelCase_      :      int      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_       ,     batch_size=lowercase_       ,     seq_length=lowercase_       ,     is_pair=lowercase_       ,     framework=lowercase_						)

     return common_inputs







  def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						):

     """simple docstring"""
     if self.task in ["default", "seq2seq-lm"]:
        UpperCAmelCase_      :      Union[str, Any]      =					super()._flatten_past_key_values_(lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
     else:
        UpperCAmelCase_      :      Dict      =					super(lowercase_       ,     self						)._flatten_past_key_values_(
            lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)


