


"""simple docstring"""







from typing import List, Union

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


lowerCamelCase_  :  Optional[Any]     		=    logging.get_logger(__name__)

lowerCamelCase_  :  Any     		=    {
    """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/config.json""",
    # See all LED models at https://huggingface.co/models?filter=led
}






class  __A							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 __lowerCAmelCase					   =    "led"
 __lowerCAmelCase					   =    {
     "num_attention_heads": "encoder_attention_heads",
     "hidden_size": "d_model",
     "attention_probs_dropout_prob": "attention_dropout",
     "initializer_range": "init_std",
 }


 def __init__(						self					,       __A=5_0265					,       __A=1_6384					,       __A=1024					,       __A=12					,       __A=4096					,       __A=16					,       __A=12					,       __A=4096					,       __A=16					,       __A=0.0					,       __A=0.0					,       __A=True					,       __A=True					,       __A="gelu"					,       __A=1024					,       __A=0.1					,       __A=0.0					,       __A=0.0					,       __A=0.02					,       __A=2					,       __A=0.0					,       __A=1					,       __A=0					,       __A=2					,       __A = 512					,       **__A					,       )   ->	Optional[Any]:
     a											=vocab_size
     a											=max_encoder_position_embeddings
     a											=max_decoder_position_embeddings
     a											=d_model
     a											=encoder_ffn_dim
     a											=encoder_layers
     a											=encoder_attention_heads
     a											=decoder_ffn_dim
     a											=decoder_layers
     a											=decoder_attention_heads
     a											=dropout
     a											=attention_dropout
     a											=activation_dropout
     a											=activation_function
     a											=init_std
     a											=encoder_layerdrop
     a											=decoder_layerdrop
     a											=classifier_dropout
     a											=use_cache
     a											=encoder_layers
     a											=attention_window

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



"""simple docstring"""







import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin






class  __A							(			unittest.TestCase,       _SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	str:
     a											=load_tool('''document-question-answering'''		)
     self.tool.setup()
     a											=load_tool('''document-question-answering'''					,       remote=__A		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     a											=load_dataset('''hf-internal-testing/example-documents'''					,       split='''test'''		)
     a											=dataset[0]['''image''']

     a											=self.tool(__A					,       '''When is the coffee break?'''		)
     self.assertEqual(__A					,       '''11-14 to 11:39 a.m.'''		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[int]:
     a											=load_dataset('''hf-internal-testing/example-documents'''					,       split='''test'''		)
     a											=dataset[0]['''image''']

     a											=self.remote_tool(__A					,       '''When is the coffee break?'''		)
     self.assertEqual(__A					,       '''11-14 to 11:39 a.m.'''		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Union[str, Any]:
     a											=load_dataset('''hf-internal-testing/example-documents'''					,       split='''test'''		)
     a											=dataset[0]['''image''']

     self.tool(document=__A					,       question='''When is the coffee break?'''		)





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[int]:
     a											=load_dataset('''hf-internal-testing/example-documents'''					,       split='''test'''		)
     a											=dataset[0]['''image''']

     a											=self.remote_tool(document=__A					,       question='''When is the coffee break?'''		)
     self.assertEqual(__A					,       '''11-14 to 11:39 a.m.'''		)