

"""simple docstring"""





import unittest

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


if is_flax_available():
   import optax
   from flax.training.common_utils import onehot

   from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
   from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @slow
 def 					_lowercase (			self							:			str	):
    __lowercase								=						FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"	)
    __lowercase								=						AutoTokenizer.from_pretrained("google/mt5-small"	)

    __lowercase								=						tokenizer("Hello there",					return_tensors="np"	).input_ids
    __lowercase								=						tokenizer("Hi I am",					return_tensors="np"	).input_ids

    __lowercase								=						shift_tokens_right(UpperCAmelCase__,					model.config.pad_token_id,					model.config.decoder_start_token_id	)

    __lowercase								=						model(UpperCAmelCase__,					decoder_input_ids=UpperCAmelCase__	).logits
    __lowercase								=						optax.softmax_cross_entropy(UpperCAmelCase__,					onehot(UpperCAmelCase__,					logits.shape[-1]	)	).mean()

    __lowercase								=						-(labels.shape[-1] * loss.item())

    __lowercase								=						-84.9_127
    self.assertTrue(abs(mtf_score - EXPECTED_SCORE	) < 1E-4	)







"""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.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	"openai/whisper-base"
 __UpperCAmelCase	:					Union[str, Any]   		=	(
     "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
     "transcribed text."
 )
 __UpperCAmelCase	:					List[str]   		=	"transcriber"
 __UpperCAmelCase	:					Optional[Any]   		=	WhisperProcessor
 __UpperCAmelCase	:					str   		=	WhisperForConditionalGeneration

 __UpperCAmelCase	:					List[str]   		=	["audio"]
 __UpperCAmelCase	:					Tuple   		=	["text"]


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			int	):
    return self.pre_processor(UpperCAmelCase__,					return_tensors="pt"	).input_features


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Optional[Any]	):
    return self.model.generate(inputs=UpperCAmelCase__	)



 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			Optional[int]	):
    return self.pre_processor.batch_decode(UpperCAmelCase__,					skip_special_tokens=UpperCAmelCase__	)[0]




