






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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
							from ...onnx.config import PatchingSpec
							from ...tokenization_utils_base import PreTrainedTokenizerBase


__UpperCamelCase    :						Dict		     =  logging.get_logger(__name__)

__UpperCamelCase    :						Dict		     =  {
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}


class      __SCREAMING_SNAKE_CASE(		a_		):
							_UpperCAmelCase       			=				"longformer"
							def __init__(   self:					Union[str, Any]				,		UpperCamelCase:					Tuple = 5_12				,		UpperCamelCase:					Optional[Any] = 2				,		UpperCamelCase:					List[str] = 1				,		UpperCamelCase:					Optional[Any] = 0				,		UpperCamelCase:					List[Any] = 2				,		UpperCamelCase:					int = 3_05_22				,		UpperCamelCase:					List[str] = 7_68				,		UpperCamelCase:					Dict = 12				,		UpperCamelCase:					Any = 12				,		UpperCamelCase:					Any = 30_72				,		UpperCamelCase:					Optional[Any] = "gelu"				,		UpperCamelCase:					Optional[Any] = 0.1				,		UpperCamelCase:					int = 0.1				,		UpperCamelCase:					str = 5_12				,		UpperCamelCase:					List[Any] = 2				,		UpperCamelCase:					Union[str, Any] = 0.02				,		UpperCamelCase:					Optional[int] = 1e-12				,		UpperCamelCase:					Optional[int] = False				,		**UpperCamelCase:					Optional[int]				,		)						->						List[str]:
											super().__init__(pad_token_id=_a				,		**_a  )

											snake_case__													=   attention_window
											snake_case__													=   sep_token_id
											snake_case__													=   bos_token_id
											snake_case__													=   eos_token_id
											snake_case__													=   vocab_size
											snake_case__													=   hidden_size
											snake_case__													=   num_hidden_layers
											snake_case__													=   num_attention_heads
											snake_case__													=   hidden_act
											snake_case__													=   intermediate_size
											snake_case__													=   hidden_dropout_prob
											snake_case__													=   attention_probs_dropout_prob
											snake_case__													=   max_position_embeddings
											snake_case__													=   type_vocab_size
											snake_case__													=   initializer_range
											snake_case__													=   layer_norm_eps
											snake_case__													=   onnx_export


class      __SCREAMING_SNAKE_CASE(		a_		):
							def __init__(   self:					Optional[int]				,		UpperCamelCase:					str				,		UpperCamelCase:					List[str] = "default"				,		UpperCamelCase:					Optional[Any] = None  )						->						Optional[Any]:
											super().__init__(_a				,		_a				,		_a  )
											snake_case__													=   True
							@property
							def lowerCAmelCase_       (   self:					Any  )						->						Dict:
											if self.task == "multiple-choice":
															snake_case__													=   {0: "batch", 1: "choice", 2: "sequence"}
											else:
															snake_case__													=   {0: "batch", 1: "sequence"}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											        ('global_attention_mask', dynamic_axis),
											    ]  )
							@property
							def lowerCAmelCase_       (   self:					int  )						->						Tuple:
											snake_case__													=   super().outputs
											if self.task == "default":
															snake_case__													=   {0: "batch"}
											return outputs
							@property
							def lowerCAmelCase_       (   self:					List[Any]  )						->						Dict:
											return 1e-4
							@property
							def lowerCAmelCase_       (   self:					Tuple  )						->						int:
											# needs to be >= 14 to support tril operator
											return max(super().default_onnx_opset				,		14  )


							def lowerCAmelCase_       (   self:					Dict				,		UpperCamelCase:					str				,		UpperCamelCase:					Any = -1				,		UpperCamelCase:					Any = -1				,		UpperCamelCase:					Dict = False				,		UpperCamelCase:					str = None				,		)						->						List[str]:
											snake_case__													=   super().generate_dummy_inputs(
											    preprocessor=_a				,		batch_size=_a				,		seq_length=_a				,		is_pair=_a				,		framework=_a  )
											import torch

											# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
											# makes the export fail randomly
											snake_case__													=   torch.zeros_like(inputs['input_ids']  )
											# make every second token global
											snake_case__													=   1

											return inputs






"""simple docstring"""




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


_A			:       int   		=       """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""

class 		a__						(      unittest.TestCase,			a_						):
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							load_tool("text-question-answering"				)
          self.tool.setup()
          lowercase    :				Dict       		=							load_tool("text-question-answering"	,    remote=_a				)
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							self.tool(_a	,    "What did Hugging Face do in April 2021?"				)
          self.assertEqual(_a	,    "launched the BigScience Research Workshop"				)
   def 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							self.remote_tool(_a	,    "What did Hugging Face do in April 2021?"				)
          self.assertEqual(_a	,    "launched the BigScience Research Workshop"				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.tool(text=_a	,    question="What did Hugging Face do in April 2021?"				)
          self.assertEqual(_a	,    "launched the BigScience Research Workshop"				)

   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							self.remote_tool(text=_a	,    question="What did Hugging Face do in April 2021?"				)
          self.assertEqual(_a	,    "launched the BigScience Research Workshop"				)


