"""simple docstring"""







import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch


_a	:	Any=					random.Random()
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Dict     ,				UpperCAmelCase_       :							List[Any]=1.0     ,				UpperCAmelCase_       :							Dict=None     ,				UpperCAmelCase_       :							Optional[Any]=None    )				->     Optional[int]:

	'''simple docstring'''





	if rng is None:
		__snake_case     :				Optional[Any] 						=					global_rng

	__snake_case     :				int 						=					[]
	for batch_idx in range(shape[0]    ):
		values.append([]    )
		for _ in range(shape[1]    ):
			values[-1].append(rng.random() * scale    )

	return values

@require_torch
class 						UpperCamelCase     (       unittest.TestCase		):



				def __init__(self							: str      ,    _A							: List[str]      ,    _A							: List[Any]=7      ,    _A							: List[str]=4_00      ,    _A							: Optional[int]=20_00      ,    _A							: Dict=1      ,    _A							: Optional[Any]=0.0      ,    _A							: List[str]=2_40_00      ,    _A							: Tuple=True      ,    )       ->      int:
					__snake_case     :				Any 						=					parent
					__snake_case     :				List[Any] 						=					batch_size
					__snake_case     :				Dict 						=					min_seq_length
					__snake_case     :				Optional[int] 						=					max_seq_length
					__snake_case     :				Optional[int] 						=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					__snake_case     :				List[str] 						=					feature_size
					__snake_case     :				Optional[Any] 						=					padding_value
					__snake_case     :				str 						=					sampling_rate
					__snake_case     :				Optional[int] 						=					return_attention_mask



				def    _lowercase	(self							: int)       ->      Tuple:
					return {
					    "feature_size": self.feature_size,
					    "padding_value": self.padding_value,
					    "sampling_rate": self.sampling_rate,
					    "return_attention_mask": self.return_attention_mask,
					}






				def    _lowercase	(self							: Optional[Any]      ,    _A							: str=False      ,    _A							: Union[str, Any]=False)       ->      List[Any]:
					def _flatten(_A							: Dict):
						return list(itertools.chain(*_A))

					if equal_length:
						__snake_case     :				Dict 						=					floats_list((self.batch_size, self.max_seq_length))
					else:
						# make sure that inputs increase in size
						__snake_case     :				int 						=					[
						    _flatten(floats_list((x, self.feature_size)))
						    for x in range(self.min_seq_length      ,    self.max_seq_length      ,    self.seq_length_diff)
						]

					if numpify:
						__snake_case     :				Any 						=					[np.asarray(_A) for x in audio_inputs]

					return audio_inputs







@require_torch
class 						UpperCamelCase     (       lowercase ,      unittest.TestCase		):
				UpperCAmelCase							: int       =						EncodecFeatureExtractor



				def    _lowercase	(self							: Dict)       ->      Any:
					__snake_case     :				Tuple 						=					EnCodecFeatureExtractionTester(self)



				def    _lowercase	(self							: Dict)       ->      Any:
					# Tests that all call wrap to encode_plus and batch_encode_plus
					__snake_case     :				Dict 						=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
					# create three inputs of length 800, 1000, and 1200
					__snake_case     :				Dict 						=					[floats_list((1, x))[0] for x in range(8_00      ,    14_00      ,    2_00)]
					__snake_case     :				Optional[int] 						=					[np.asarray(_A) for audio_input in audio_inputs]

					# Test not batched input
					__snake_case     :				Optional[int] 						=					feat_extract(audio_inputs[0]      ,    return_tensors='np').input_values
					__snake_case     :				Tuple 						=					feat_extract(np_audio_inputs[0]      ,    return_tensors='np').input_values
					self.assertTrue(np.allclose(_A      ,    _A      ,    atol=1E-3))

					# Test batched
					__snake_case     :				Optional[int] 						=					feat_extract(_A      ,    padding=_A      ,    return_tensors='np').input_values
					__snake_case     :				Tuple 						=					feat_extract(_A      ,    padding=_A      ,    return_tensors='np').input_values
					for enc_seq_a, enc_seq_a in zip(_A      ,    _A):
						self.assertTrue(np.allclose(_A      ,    _A      ,    atol=1E-3))



				def    _lowercase	(self							: Dict)       ->      str:
					__snake_case     :				int 						=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
					__snake_case     :				str 						=					np.random.rand(1_00).astype(np.floataa)
					__snake_case     :				Optional[Any] 						=					np_audio_inputs.tolist()

					for inputs in [py_audio_inputs, np_audio_inputs]:
						__snake_case     :				int 						=					feature_extractor.pad([{'input_values': inputs}]      ,    return_tensors='np')
						self.assertTrue(np_processed.input_values.dtype == np.floataa)
						__snake_case     :				Optional[Any] 						=					feature_extractor.pad([{'input_values': inputs}]      ,    return_tensors='pt')
						self.assertTrue(pt_processed.input_values.dtype == torch.floataa)



				def    _lowercase	(self							: Optional[int]      ,    _A							: int)       ->      Dict:
					from datasets import load_dataset

					__snake_case     :				int 						=					load_dataset('hf-internal-testing/librispeech_asr_dummy'      ,    'clean'      ,    split='validation')
					# automatic decoding with librispeech
					__snake_case     :				Union[str, Any] 						=					ds.sort('id').select(range(_A))[:num_samples]['audio']

					return [x["array"] for x in audio_samples]



				def    _lowercase	(self							: Any)       ->      int:
					# fmt: off
					__snake_case     :				str 						=					torch.tensor(
					    [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
					     3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
					     2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
					     4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
					     7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
					     4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03])
					# fmt: on
					__snake_case     :				Optional[Any] 						=					self._load_datasamples(1)
					__snake_case     :				str 						=					EncodecFeatureExtractor()
					__snake_case     :				Union[str, Any] 						=					feature_extractor(_A      ,    return_tensors='pt').input_values
					self.assertEquals(input_values.shape      ,    (1, 1, 9_36_80))
					self.assertTrue(torch.allclose(input_values[0, 0, :30]      ,    _A      ,    atol=1E-6))



				def    _lowercase	(self							: Dict)       ->      Union[str, Any]:
					# fmt: off
					__snake_case     :				Dict 						=					torch.tensor(
					    [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
					     3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
					     2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
					     4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
					     7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
					     4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03])
					# fmt: on
					__snake_case     :				int 						=					self._load_datasamples(1)
					__snake_case     :				Optional[int] 						=					[np.tile(input_audio[0][None]      ,    reps=(2, 1))]
					input_audio[0][1] *= 0.5
					__snake_case     :				str 						=					EncodecFeatureExtractor(feature_size=2)
					__snake_case     :				Optional[Any] 						=					feature_extractor(_A      ,    return_tensors='pt').input_values
					self.assertEquals(input_values.shape      ,    (1, 2, 9_36_80))
					self.assertTrue(torch.allclose(input_values[0, 0, :30]      ,    _A      ,    atol=1E-6))
					self.assertTrue(torch.allclose(input_values[0, 1, :30]      ,    EXPECTED_INPUT_VALUES * 0.5      ,    atol=1E-6))






				def    _lowercase	(self							: str)       ->      str:
					__snake_case     :				Optional[int] 						=					self._load_datasamples(2)
					# would be easier if the stride was like
					__snake_case     :				Union[str, Any] 						=					EncodecFeatureExtractor(feature_size=1      ,    chunk_length_s=1      ,    overlap=0.01)

					# pad and trunc raise an error ?
					with self.assertRaisesRegex(
					    _A      ,    '^Both padding and truncation were set. Make sure you only set one.$'      ,    ):
						__snake_case     :				List[str] 						=					feature_extractor(
						    _A      ,    padding='max_length'      ,    truncation=_A      ,    return_tensors='pt').input_values

					# truncate to chunk
					__snake_case     :				List[str] 						=					feature_extractor(_A      ,    truncation=_A      ,    return_tensors='pt').input_values
					self.assertEquals(truncated_outputs.shape      ,    (2, 1, 7_15_20))  # 2 chunks

					# force truncate to max_length
					__snake_case     :				List[str] 						=					feature_extractor(
					    _A      ,    truncation=_A      ,    max_length=4_80_00      ,    return_tensors='pt').input_values
					self.assertEquals(truncated_outputs.shape      ,    (2, 1, 4_80_00))

					# pad to chunk
					__snake_case     :				int 						=					feature_extractor(_A      ,    padding=_A      ,    return_tensors='pt').input_values
					self.assertEquals(padded_outputs.shape      ,    (2, 1, 9_52_80))

					# pad to chunk
					__snake_case     :				List[str] 						=					feature_extractor(_A      ,    return_tensors='pt').input_values
					self.assertEquals(truncated_outputs.shape      ,    (2, 1, 9_52_80))

					# force pad to max length
					__snake_case     :				str 						=					feature_extractor(
					    _A      ,    padding='max_length'      ,    max_length=10_00_00      ,    return_tensors='pt').input_values
					self.assertEquals(truncated_outputs.shape      ,    (2, 1, 10_00_00))

					# force no pad
					with self.assertRaisesRegex(
					    _A      ,    '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'      ,    ):
						__snake_case     :				Tuple 						=					feature_extractor(_A      ,    padding=_A      ,    return_tensors='pt').input_values

					__snake_case     :				Optional[Any] 						=					feature_extractor(input_audio[0]      ,    padding=_A      ,    return_tensors='pt').input_values
					self.assertEquals(truncated_outputs.shape      ,    (1, 1, 9_36_80))

					# no pad if no chunk_length_s
					__snake_case     :				str 						=					None
					with self.assertRaisesRegex(
					    _A      ,    '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'      ,    ):
						__snake_case     :				int 						=					feature_extractor(_A      ,    padding=_A      ,    return_tensors='pt').input_values

					__snake_case     :				Union[str, Any] 						=					feature_extractor(input_audio[0]      ,    padding=_A      ,    return_tensors='pt').input_values
					self.assertEquals(truncated_outputs.shape      ,    (1, 1, 9_36_80))

					# no pad if no overlap
					__snake_case     :				Tuple 						=					2
					__snake_case     :				Union[str, Any] 						=					None
					with self.assertRaisesRegex(
					    _A      ,    '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'      ,    ):
						__snake_case     :				Optional[int] 						=					feature_extractor(_A      ,    padding=_A      ,    return_tensors='pt').input_values

					__snake_case     :				int 						=					feature_extractor(input_audio[0]      ,    padding=_A      ,    return_tensors='pt').input_values
					self.assertEquals(truncated_outputs.shape      ,    (1, 1, 9_36_80))






"""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


_a	:	Union[str, Any]=					"Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"

class 						UpperCamelCase     (       lowercase		):
				UpperCAmelCase							: str       =						"""google/flan-t5-base"""
				UpperCAmelCase							: int       =						(
				    """This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the """
				    """text where to find the answer, and `question`, which is the question, and returns the answer to the question."""
				)
				UpperCAmelCase							: Dict       =						"""text_qa"""
				UpperCAmelCase							: Dict       =						AutoTokenizer
				UpperCAmelCase							: Optional[Any]       =						AutoModelForSeqaSeqLM

				UpperCAmelCase							: List[str]       =						["""text""", """text"""]
				UpperCAmelCase							: Any       =						["""text"""]



				def    _lowercase	(self							: Tuple      ,    _A							: str      ,    _A							: str)       ->      Tuple:
					__snake_case     :				List[Any] 						=					QA_PROMPT.format(text=_A      ,    question=_A)
					return self.pre_processor(_A      ,    return_tensors='pt')



				def    _lowercase	(self							: Tuple      ,    _A							: Optional[Any])       ->      List[Any]:
					__snake_case     :				List[Any] 						=					self.model.generate(**_A)

					__snake_case						,							__snake_case     :				Any 						=					inputs['input_ids'].shape
					__snake_case     :				Any 						=					output_ids.shape[0]

					return output_ids.reshape(_A      ,    out_b // in_b      ,    *output_ids.shape[1:])[0][0]






				def    _lowercase	(self							: Optional[int]      ,    _A							: int)       ->      str:
					return self.pre_processor.decode(_A      ,    skip_special_tokens=_A      ,    clean_up_tokenization_spaces=_A)





