

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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__UpperCAmelCase      = logging.get_logger(__name__)

__UpperCAmelCase      = {
    """gpt2""": """https://huggingface.co/gpt2/resolve/main/config.json""",
    """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/config.json""",
    """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/config.json""",
    """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/config.json""",
    """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/config.json""",
}



class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Optional[Any]						      ="gpt2"
  lowerCamelCase    :			Tuple						      =["past_key_values"]
  lowerCamelCase    :			Tuple						      ={
      "hidden_size": "n_embd",
      "max_position_embeddings": "n_positions",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }







  def __init__(			self :    Dict       ,							lowerCAmelCase :    Optional[Any]=5_02_57       ,							lowerCAmelCase :    Optional[int]=10_24       ,							lowerCAmelCase :    List[str]=7_68       ,							lowerCAmelCase :    List[str]=12       ,							lowerCAmelCase :    int=12       ,							lowerCAmelCase :    str=None       ,							lowerCAmelCase :    Optional[Any]="gelu_new"       ,							lowerCAmelCase :    List[str]=0.1       ,							lowerCAmelCase :    Tuple=0.1       ,							lowerCAmelCase :    int=0.1       ,							lowerCAmelCase :    List[Any]=1e-5       ,							lowerCAmelCase :    Optional[int]=0.02       ,							lowerCAmelCase :    Optional[int]="cls_index"       ,							lowerCAmelCase :    List[str]=True       ,							lowerCAmelCase :    Any=None       ,							lowerCAmelCase :    List[str]=True       ,							lowerCAmelCase :    List[Any]=0.1       ,							lowerCAmelCase :    List[Any]=True       ,							lowerCAmelCase :    Dict=True       ,							lowerCAmelCase :    List[str]=5_02_56       ,							lowerCAmelCase :    int=5_02_56       ,							lowerCAmelCase :    Any=False       ,							lowerCAmelCase :    Optional[Any]=False       ,							**lowerCAmelCase :    Optional[int]       ,							)							->	Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :			Any					    =						vocab_size
     __lowerCAmelCase  :			int					    =						n_positions
     __lowerCAmelCase  :			Optional[int]					    =						n_embd
     __lowerCAmelCase  :			List[str]					    =						n_layer
     __lowerCAmelCase  :			Optional[Any]					    =						n_head
     __lowerCAmelCase  :			List[Any]					    =						n_inner
     __lowerCAmelCase  :			Optional[Any]					    =						activation_function
     __lowerCAmelCase  :			Any					    =						resid_pdrop
     __lowerCAmelCase  :			Any					    =						embd_pdrop
     __lowerCAmelCase  :			Optional[Any]					    =						attn_pdrop
     __lowerCAmelCase  :			List[str]					    =						layer_norm_epsilon
     __lowerCAmelCase  :			Optional[Any]					    =						initializer_range
     __lowerCAmelCase  :			List[Any]					    =						summary_type
     __lowerCAmelCase  :			int					    =						summary_use_proj
     __lowerCAmelCase  :			Union[str, Any]					    =						summary_activation
     __lowerCAmelCase  :			Dict					    =						summary_first_dropout
     __lowerCAmelCase  :			Optional[Any]					    =						summary_proj_to_labels
     __lowerCAmelCase  :			Optional[Any]					    =						scale_attn_weights
     __lowerCAmelCase  :			Tuple					    =						use_cache
     __lowerCAmelCase  :			Any					    =						scale_attn_by_inverse_layer_idx
     __lowerCAmelCase  :			str					    =						reorder_and_upcast_attn

     __lowerCAmelCase  :			int					    =						bos_token_id
     __lowerCAmelCase  :			List[str]					    =						eos_token_id

     super().__init__(bos_token_id=lowerCAmelCase       ,							eos_token_id=lowerCAmelCase       ,							**lowerCAmelCase    )






class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  def __init__(			self :    Tuple       ,							lowerCAmelCase :    PretrainedConfig       ,							lowerCAmelCase :    str = "default"       ,							lowerCAmelCase :    List[PatchingSpec] = None       ,							lowerCAmelCase :    bool = False       ,							)							->	List[Any]:






     """simple docstring"""







     super().__init__(lowerCAmelCase       ,							task=lowerCAmelCase       ,							patching_specs=lowerCAmelCase       ,							use_past=lowerCAmelCase    )
     if not getattr(self._config       ,							"""pad_token_id"""       ,							lowerCAmelCase    ):
        # TODO: how to do that better?
        __lowerCAmelCase  :			List[str]					    =						0







  @property
  def        SCREAMING_SNAKE_CASE    (			self :    Dict    )							->	Mapping[str, Mapping[int, str]]:






     """simple docstring"""







     __lowerCAmelCase  :			List[str]					    =						OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}    )
     if self.use_past:
        self.fill_with_past_key_values_(lowerCAmelCase       ,							direction="""inputs"""    )
        __lowerCAmelCase  :			Any					    =						{0: """batch""", 1: """past_sequence + sequence"""}
     else:
        __lowerCAmelCase  :			str					    =						{0: """batch""", 1: """sequence"""}

     return common_inputs







  @property
  def        SCREAMING_SNAKE_CASE    (			self :    List[str]    )							->	int:






     """simple docstring"""







     return self._config.n_layer







  @property
  def        SCREAMING_SNAKE_CASE    (			self :    Tuple    )							->	int:






     """simple docstring"""







     return self._config.n_head







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[Any]       ,							lowerCAmelCase :    PreTrainedTokenizer       ,							lowerCAmelCase :    int = -1       ,							lowerCAmelCase :    int = -1       ,							lowerCAmelCase :    bool = False       ,							lowerCAmelCase :    Optional[TensorType] = None       ,							)							->	Mapping[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[Any]					    =						super(lowerCAmelCase       ,							self    ).generate_dummy_inputs(
         lowerCAmelCase       ,							batch_size=lowerCAmelCase       ,							seq_length=lowerCAmelCase       ,							is_pair=lowerCAmelCase       ,							framework=lowerCAmelCase    )

     # We need to order the input in the way they appears in the forward()
     __lowerCAmelCase  :			Optional[Any]					    =						OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}    )

     # Need to add the past_keys
     if self.use_past:
        if not is_torch_available():
           raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""    )
        else:
           import torch

           __lowerCAmelCase    ,__lowerCAmelCase  :			List[Any]					    =						common_inputs["""input_ids"""].shape
           # Not using the same length for past_key_values
           __lowerCAmelCase  :			Union[str, Any]					    =						seqlen + 2
           __lowerCAmelCase  :			Union[str, Any]					    =						(
               batch,
               self.num_attention_heads,
               past_key_values_length,
               self._config.hidden_size // self.num_attention_heads,
           )
           __lowerCAmelCase  :			Union[str, Any]					    =						[
               (torch.zeros(lowerCAmelCase    ), torch.zeros(lowerCAmelCase    )) for _ in range(self.num_layers    )
           ]

     __lowerCAmelCase  :			str					    =						common_inputs["""attention_mask"""]
     if self.use_past:
        __lowerCAmelCase  :			Optional[Any]					    =						ordered_inputs["""attention_mask"""].dtype
        __lowerCAmelCase  :			List[Any]					    =						torch.cat(
            [ordered_inputs["""attention_mask"""], torch.ones(lowerCAmelCase       ,							lowerCAmelCase       ,							dtype=lowerCAmelCase    )]       ,							dim=1    )

     return ordered_inputs







  @property
  def        SCREAMING_SNAKE_CASE    (			self :    Union[str, Any]    )							->	int:






     """simple docstring"""







     return 13





import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def 	snake_case_      (__A     :						Any					,   __A     :						Union[str, Any]     )			->	int:
   assert isinstance(__A					,   __A     )
   assert dataset.num_rows == 4
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_1", "col_2", "col_3"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""					,   [False, True]     )
def 	snake_case_      (__A     :						int					,   __A     :						int					,   __A     :						List[str]     )			->	Tuple:
   __lowerCAmelCase  :			Dict					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			int					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      __lowerCAmelCase  :			List[str]					    =						CsvDatasetReader(__A					,   cache_dir=__A					,   keep_in_memory=__A     ).read()
   _check_csv_dataset(__A					,   __A     )





@pytest.mark.parametrize(
    """features"""					,   [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]					,   )
def 	snake_case_      (__A     :						Dict					,   __A     :						List[Any]					,   __A     :						List[str]     )			->	Union[str, Any]:
   __lowerCAmelCase  :			str					    =						tmp_path / """cache"""
   # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
   __lowerCAmelCase  :			Tuple					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			int					    =						features.copy() if features else default_expected_features
   __lowerCAmelCase  :			List[Any]					    =						(
       Features({feature: Value(__A     ) for feature, dtype in features.items()}     ) if features is not None else None
   )
   __lowerCAmelCase  :			Tuple					    =						CsvDatasetReader(__A					,   features=__A					,   cache_dir=__A     ).read()
   _check_csv_dataset(__A					,   __A     )





@pytest.mark.parametrize("""split"""					,   [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def 	snake_case_      (__A     :						str					,   __A     :						Optional[int]					,   __A     :						int     )			->	Union[str, Any]:
   __lowerCAmelCase  :			str					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Tuple					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			Optional[Any]					    =						CsvDatasetReader(__A					,   cache_dir=__A					,   split=__A     ).read()
   _check_csv_dataset(__A					,   __A     )
   assert dataset.split == split if split else "train"





@pytest.mark.parametrize("""path_type"""					,   [str, list]     )
def 	snake_case_      (__A     :						Union[str, Any]					,   __A     :						Any					,   __A     :						List[str]     )			->	Optional[int]:
   if issubclass(__A					,   __A     ):
      __lowerCAmelCase  :			Dict					    =						csv_path
   elif issubclass(__A					,   __A     ):
      __lowerCAmelCase  :			Tuple					    =						[csv_path]
   __lowerCAmelCase  :			Dict					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Dict					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			List[str]					    =						CsvDatasetReader(__A					,   cache_dir=__A     ).read()
   _check_csv_dataset(__A					,   __A     )





def 	snake_case_      (__A     :						int					,   __A     :						Optional[int]					,   __A     :						Any=("train",)     )			->	Optional[int]:
   assert isinstance(__A					,   __A     )
   for split in splits:
      __lowerCAmelCase  :			int					    =						dataset_dict[split]
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
         assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""					,   [False, True]     )
def 	snake_case_      (__A     :						Optional[Any]					,   __A     :						List[str]					,   __A     :						Any     )			->	List[str]:
   __lowerCAmelCase  :			int					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Optional[Any]					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      __lowerCAmelCase  :			Tuple					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A					,   keep_in_memory=__A     ).read()
   _check_csv_datasetdict(__A					,   __A     )





@pytest.mark.parametrize(
    """features"""					,   [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]					,   )
def 	snake_case_      (__A     :						List[str]					,   __A     :						List[Any]					,   __A     :						Optional[int]     )			->	Optional[int]:
   __lowerCAmelCase  :			Optional[int]					    =						tmp_path / """cache"""
   # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
   __lowerCAmelCase  :			Union[str, Any]					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			Optional[int]					    =						features.copy() if features else default_expected_features
   __lowerCAmelCase  :			Optional[Any]					    =						(
       Features({feature: Value(__A     ) for feature, dtype in features.items()}     ) if features is not None else None
   )
   __lowerCAmelCase  :			Union[str, Any]					    =						CsvDatasetReader({"""train""": csv_path}					,   features=__A					,   cache_dir=__A     ).read()
   _check_csv_datasetdict(__A					,   __A     )





@pytest.mark.parametrize("""split"""					,   [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def 	snake_case_      (__A     :						Optional[Any]					,   __A     :						Optional[Any]					,   __A     :						Union[str, Any]     )			->	Optional[int]:
   if split:
      __lowerCAmelCase  :			Any					    =						{split: csv_path}
   else:
      __lowerCAmelCase  :			Dict					    =						{"""train""": csv_path, """test""": csv_path}
   __lowerCAmelCase  :			List[str]					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			int					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			int					    =						CsvDatasetReader(__A					,   cache_dir=__A     ).read()
   _check_csv_datasetdict(__A					,   __A					,   splits=list(path.keys()     )     )
   assert all(dataset[split].split == split for split in path.keys()     )





def 	snake_case_      (__A     :						List[str]     )			->	Optional[Any]:
   with open(__A					,   encoding="""utf-8"""     ) as csvfile:
      yield from csv.reader(__A     )





def 	snake_case_      (__A     :						Optional[int]					,   __A     :						Optional[int]     )			->	Union[str, Any]:
   __lowerCAmelCase  :			Optional[Any]					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Tuple					    =						os.path.join(__A					,   """tmp.csv"""     )
   __lowerCAmelCase  :			Tuple					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A     ).read()
   CsvDatasetWriter(dataset["""train"""]					,   __A					,   num_proc=1     ).write()

   __lowerCAmelCase  :			Dict					    =						iter_csv_file(__A     )
   __lowerCAmelCase  :			Union[str, Any]					    =						iter_csv_file(__A     )

   for rowa, rowa in zip(__A					,   __A     ):
      assert rowa == rowa





def 	snake_case_      (__A     :						int					,   __A     :						List[str]     )			->	str:
   __lowerCAmelCase  :			Any					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			List[str]					    =						os.path.join(__A					,   """tmp.csv"""     )
   __lowerCAmelCase  :			Dict					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A     ).read()
   CsvDatasetWriter(dataset["""train"""]					,   __A					,   num_proc=2     ).write()

   __lowerCAmelCase  :			Union[str, Any]					    =						iter_csv_file(__A     )
   __lowerCAmelCase  :			Dict					    =						iter_csv_file(__A     )

   for rowa, rowa in zip(__A					,   __A     ):
      assert rowa == rowa





def 	snake_case_      (__A     :						int					,   __A     :						Optional[Any]     )			->	List[str]:
   __lowerCAmelCase  :			Any					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			List[Any]					    =						os.path.join(__A					,   """tmp.csv"""     )
   __lowerCAmelCase  :			List[str]					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A     ).read()
   with pytest.raises(__A     ):
      CsvDatasetWriter(dataset["""train"""]					,   __A					,   num_proc=0     )


