


"""simple docstring"""







# 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  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  Optional[int]    					=			"philschmid/bart-large-cnn-samsum"
	snake_case__				:  str    					=			(
	    "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."
	)
	snake_case__				:  Union[str, Any]    					=			"summarizer"
	snake_case__				:  Optional[Any]    					=			AutoTokenizer
	snake_case__				:  Optional[int]    					=			AutoModelForSeqaSeqLM

	snake_case__				:  Any    					=			["text"]
	snake_case__				:  Tuple    					=			["text"]




	def   UpperCAmelCase_      ( self			:				str			,			UpperCAmelCase__			:				Optional[Any]   )       ->	int:
						return self.pre_processor(UpperCAmelCase__			,			return_tensors="pt"			,			truncation=UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				str			,			UpperCAmelCase__			:				int   )       ->	Any:
						return self.model.generate(**UpperCAmelCase__   )[0]




	def   UpperCAmelCase_      ( self			:				Any			,			UpperCAmelCase__			:				List[Any]   )       ->	List[Any]:
						return self.pre_processor.decode(UpperCAmelCase__			,			skip_special_tokens=UpperCAmelCase__			,			clean_up_tokenization_spaces=UpperCAmelCase__   )


'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import numpy as np
					import tensorflow as tf

					from transformers import TFCamembertModel


@require_tf
@require_sentencepiece
@require_tokenizers
class 						__UpperCAmelCase      (					unittest.TestCase						):


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		TFCamembertModel.from_pretrained('jplu/tf-camembert-base'  )

								_snake_case   						=		tf.convert_to_tensor(
								    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]    ,   dtype=tf.intaa    ,   )  # J'aime le camembert !"

								_snake_case   						=		model(lowerCAmelCase_  )['last_hidden_state']
								_snake_case   						=		tf.TensorShape((1, 10, 7_68)  )
								self.assertEqual(output.shape    ,   lowerCAmelCase_  )
								# compare the actual values for a slice.
								_snake_case   						=		tf.convert_to_tensor(
								    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,   dtype=tf.floataa    ,   )
								# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
								# camembert.eval()
								# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,   expected_slice.numpy()    ,   atol=1E-4  )  )

