



import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
							import jax.numpy as jnp

							from transformers import FlaxXLMRobertaModel







@require_sentencepiece
@require_tokenizers
@require_flax
class 			__lowerCAmelCase    (			unittest.TestCase						):



			@slow
			def     _lowerCamelCase       (						self   :  Optional[Any])   ->    Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base')
						_UpperCAmelCase		       =   AutoTokenizer.from_pretrained('xlm-roberta-base')
						_UpperCAmelCase		       =   'The dog is cute and lives in the garden house'
						_UpperCAmelCase		       =   jnp.array([tokenizer.encode(_snake_case)])

						_UpperCAmelCase		       =   (1, 12, 7_68)  # batch_size, sequence_length, embedding_vector_dim
						_UpperCAmelCase		       =   jnp.array(
						    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]])

						_UpperCAmelCase		       =   model(_snake_case)['last_hidden_state']
						self.assertEqual(output.shape ,		_snake_case)
						# compare the actual values for a slice of last dim
						self.assertTrue(jnp.allclose(output[:, :, -1] ,		_snake_case ,		atol=1E-3))





# 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__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""philschmid/bart-large-cnn-samsum"""
     _SCREAMING_SNAKE_CASE						=	(
         """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						=	"""summarizer"""
     _SCREAMING_SNAKE_CASE						=	AutoTokenizer
     _SCREAMING_SNAKE_CASE						=	AutoModelForSeqaSeqLM

     _SCREAMING_SNAKE_CASE						=	["""text"""]
     _SCREAMING_SNAKE_CASE						=	["""text"""]






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  str					)   ->Any:
            return self.pre_processor(_snake_case, return_tensors='pt', truncation=_snake_case					)






     def    lowercase_   (       self  :  int, _snake_case  :  List[Any]					)   ->Any:
            return self.model.generate(**_snake_case					)[0]






     def    lowercase_   (       self  :  int, _snake_case  :  int					)   ->str:
            return self.pre_processor.decode(_snake_case, skip_special_tokens=_snake_case, clean_up_tokenization_spaces=_snake_case					)






