






"""simple docstring"""


from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase				:    int     =						logging.get_logger(__name__)

_UpperCamelCase				:    Optional[int]     =						{
    'microsoft/prophetnet-large-uncased': (
        'https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'
    ),
}






class 						snake_case  (  UpperCAmelCase       ):
    __magic_name__         =     '''prophetnet'''
    __magic_name__         =     ['''past_key_values''']
    __magic_name__         =     {
        '''num_attention_heads''': '''num_encoder_attention_heads''',
    }





    def __init__(							self	:					Optional[Any]				,		A	:					Optional[float] = 0.1				,		A	:					Optional[Union[str, Callable]] = "gelu"				,		A	:					Optional[int] = 3_0_5_2_2				,		A	:					Optional[int] = 1_0_2_4				,		A	:					Optional[int] = 4_0_9_6				,		A	:					Optional[int] = 1_2				,		A	:					Optional[int] = 1_6				,		A	:					Optional[int] = 4_0_9_6				,		A	:					Optional[int] = 1_2				,		A	:					Optional[int] = 1_6				,		A	:					Optional[float] = 0.1				,		A	:					Optional[float] = 0.1				,		A	:					Optional[int] = 5_1_2				,		A	:					Optional[float] = 0.02				,		A	:					Optional[bool] = True				,		A	:					Optional[bool] = True				,		A	:					Optional[int] = 0				,		A	:					Optional[int] = 2				,		A	:					Optional[int] = 3_2				,		A	:					Optional[int] = 1_2_8				,		A	:					Optional[bool] = False				,		A	:					Optional[float] = 0.0				,		A	:					Optional[bool] = True				,		A	:					Optional[int] = 0				,		A	:					Optional[int] = 1				,		A	:					Optional[int] = 2				,		**A	:					List[str]				,		):


      '''simple docstring'''




      a     :			Optional[Any]				=   vocab_size
      a     :			Union[str, Any]				=   hidden_size
      a     :			Optional[Any]				=   encoder_ffn_dim
      a     :			Dict				=   num_encoder_layers
      a     :			Tuple				=   num_encoder_attention_heads
      a     :			Tuple				=   decoder_ffn_dim
      a     :			Dict				=   num_decoder_layers
      a     :			Optional[Any]				=   num_decoder_attention_heads
      a     :			Tuple				=   max_position_embeddings
      a     :			int				=   init_std  # Normal(0, this parameter)
      a     :			Optional[Any]				=   activation_function

      # parameters for prophetnet
      a     :			List[Any]				=   ngram
      a     :			List[str]				=   num_buckets
      a     :			Optional[Any]				=   relative_max_distance
      a     :			Optional[int]				=   disable_ngram_loss
      a     :			Optional[Any]				=   eps

      # 3 Types of Dropout
      a     :			Dict				=   attention_dropout
      a     :			Tuple				=   activation_dropout
      a     :			Dict				=   dropout

      a     :			Tuple				=   use_cache

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





    @property
    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      return self.num_encoder_layers + self.num_decoder_layers





    @num_hidden_layers.setter
    def 				lowerCamelCase__      (							self	:					Union[str, Any]				,		A	:					List[Any]			):


      '''simple docstring'''




      raise NotImplementedError(
          'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
          ' `num_decoder_layers`.'			)







"""simple docstring"""




import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil
@pytest.fixture
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					tmp_path / "text.txt"
					A			=					textwrap.dedent(
					    "\\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n\n        Second paragraph:\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n        "      )
					with open(lowercase__			,							"w"			,							encoding="utf-8"      ) as f:
										f.write(lowercase__      )
					return str(lowercase__      )
@pytest.fixture
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					tmp_path / "text_with_image.txt"
					with open(lowercase__			,							"w"			,							encoding="utf-8"      ) as f:
										f.write(lowercase__      )
					return str(lowercase__      )
@pytest.mark.parametrize("keep_linebreaks"			,							[True, False]      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							encoding="utf-8"      ) as f:
										A			=					f.read().splitlines(keepends=lowercase__      )
					A			=					Text(keep_linebreaks=lowercase__			,							encoding="utf-8"      )
					A			=					text._generate_tables([[text_file]]      )
					A			=					pa.concat_tables([table for _, table in generator]      ).to_pydict()["text"]
					assert generated_content == expected_content
@require_pil
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							encoding="utf-8"      ) as f:
										A			=					f.read().splitlines()[0]
					A			=					Text(encoding="utf-8"			,							features=Features({"image": Image()}      )      )
					A			=					text._generate_tables([[text_file_with_image]]      )
					A			=					pa.concat_tables([table for _, table in generator]      )
					assert pa_table.schema.field("image"      ).type == Image()()
					A			=					pa_table.to_pydict()["image"]
					assert generated_content == [{"path": image_file, "bytes": None}]
@pytest.mark.parametrize("sample_by"			,							["line", "paragraph", "document"]      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							encoding="utf-8"      ) as f:
										A			=					f.read()
					if sample_by == "line":
										A			=					expected_content.splitlines()
					elif sample_by == "paragraph":
										A			=					expected_content.split("\n\n"      )
					elif sample_by == "document":
										A			=					[expected_content]
					A			=					Text(sample_by=lowercase__			,							encoding="utf-8"			,							chunksize=100      )
					A			=					text._generate_tables([[text_file]]      )
					A			=					pa.concat_tables([table for _, table in generator]      ).to_pydict()["text"]
					assert generated_content == expected_content





