

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


a		=logging.get_logger(__name__)

a		={
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class        A_		(       __A				):
							_UpperCAmelCase    :  Dict						      =      "fnet"
							def __init__(		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Tuple=3_2_0_0_0   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=7_6_8   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=1_2   ,SCREAMING_SNAKE_CASE__		:			str=3_0_7_2   ,SCREAMING_SNAKE_CASE__		:			Optional[int]="gelu_new"   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=0.1   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=5_1_2   ,SCREAMING_SNAKE_CASE__		:			Tuple=4   ,SCREAMING_SNAKE_CASE__		:			Tuple=0.02   ,SCREAMING_SNAKE_CASE__		:			Tuple=1E-12   ,SCREAMING_SNAKE_CASE__		:			List[str]=False   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=5_1_2   ,SCREAMING_SNAKE_CASE__		:			Any=3   ,SCREAMING_SNAKE_CASE__		:			str=1   ,SCREAMING_SNAKE_CASE__		:			List[Any]=2   ,**SCREAMING_SNAKE_CASE__		:			List[str]   ,):
								super().__init__(pad_token_id=_UpperCamelCase   ,bos_token_id=_UpperCamelCase   ,eos_token_id=_UpperCamelCase   ,**_UpperCamelCase)

								__lowerCamelCase				:				Union[str, Any]					       = vocab_size
								__lowerCamelCase				:				str					       = max_position_embeddings
								__lowerCamelCase				:				List[str]					       = hidden_size
								__lowerCamelCase				:				Optional[int]					       = num_hidden_layers
								__lowerCamelCase				:				Any					       = intermediate_size
								__lowerCamelCase				:				str					       = hidden_act
								__lowerCamelCase				:				str					       = hidden_dropout_prob
								__lowerCamelCase				:				str					       = initializer_range
								__lowerCamelCase				:				List[str]					       = type_vocab_size
								__lowerCamelCase				:				List[Any]					       = layer_norm_eps
								__lowerCamelCase				:				Tuple					       = use_tpu_fourier_optimizations
								__lowerCamelCase				:				Any					       = tpu_short_seq_length







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				"philschmid/bart-large-cnn-samsum"
			SCREAMING_SNAKE_CASE     :   Tuple									=				(
			    "This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
			    "and returns a summary of the text."
			)
			SCREAMING_SNAKE_CASE     :   str									=				"summarizer"
			SCREAMING_SNAKE_CASE     :   str									=				AutoTokenizer
			SCREAMING_SNAKE_CASE     :   str									=				AutoModelForSeqaSeqLM

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				int							)      ->Optional[int]:
									return self.pre_processor(_UpperCamelCase							,   return_tensors='''pt'''							,   truncation=_UpperCamelCase							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Optional[int]							)      ->Tuple:
									return self.model.generate(**_UpperCamelCase							)[0]







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Optional[int]							)      ->Any:
									return self.pre_processor.decode(_UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							,   clean_up_tokenization_spaces=_UpperCamelCase							)