





import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_UpperCAmelCase							:			str        =							random.Random()


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None					)     ->	List[Any]:
    if rng is None:
        lowerCamelCase__       : Any   					=						global_rng

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

    return values

class   lowerCAmelCase				(   unittest.TestCase     ):
 def __init__( self    : Optional[Any]     ,	UpperCAmelCase    : Optional[int]     ,	UpperCAmelCase    : Optional[Any]=7     ,	UpperCAmelCase    : Optional[Any]=400     ,	UpperCAmelCase    : List[Any]=2000     ,	UpperCAmelCase    : Any=1     ,	UpperCAmelCase    : Tuple=0.0     ,	UpperCAmelCase    : List[Any]=16000     ,	UpperCAmelCase    : Optional[Any]=True     ,	UpperCAmelCase    : Union[str, Any]=True     ,	)     ->      int:
     lowerCamelCase__       : Union[str, Any]   					=						parent
     lowerCamelCase__       : List[str]   					=						batch_size
     lowerCamelCase__       : Optional[Any]   					=						min_seq_length
     lowerCamelCase__       : int   					=						max_seq_length
     lowerCamelCase__       : List[Any]   					=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     lowerCamelCase__       : List[Any]   					=						feature_size
     lowerCamelCase__       : List[str]   					=						padding_value
     lowerCamelCase__       : List[str]   					=						sampling_rate
     lowerCamelCase__       : Optional[int]   					=						return_attention_mask
     lowerCamelCase__       : List[Any]   					=						do_normalize
 def 	A_      ( self    : Optional[int]      )     ->      List[Any]:
     return {
         "feature_size": self.feature_size,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }







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

     if equal_length:
         lowerCamelCase__       : Optional[Any]   					=						floats_list((self.batch_size, self.max_seq_length)      )
     else:
         # make sure that inputs increase in size
         lowerCamelCase__       : List[Any]   					=						[
             _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:
         lowerCamelCase__       : Optional[Any]   					=						[np.asarray(UpperCAmelCase      ) for x in speech_inputs]

     return speech_inputs


class   lowerCAmelCase				(   __UpperCamelCase,    unittest.TestCase     ):
 UpperCAmelCase__						=						WavaVecaFeatureExtractor
 def 	A_      ( self    : str      )     ->      Dict:
     lowerCamelCase__       : Dict   					=						WavaVecaFeatureExtractionTester(self      )
 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : Optional[Any]      )     ->      Optional[int]:
     self.assertTrue(np.all(np.mean(UpperCAmelCase     ,	axis=0      ) < 1e-3      )      )
     self.assertTrue(np.all(np.abs(np.var(UpperCAmelCase     ,	axis=0      ) - 1      ) < 1e-3      )      )
 def 	A_      ( self    : List[str]      )     ->      Tuple:
     # Tests that all call wrap to encode_plus and batch_encode_plus
     lowerCamelCase__       : Any   					=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
     # create three inputs of length 800, 1000, and 1200
     lowerCamelCase__       : Any   					=						[floats_list((1, x)      )[0] for x in range(800     ,	1400     ,	200      )]
     lowerCamelCase__       : Optional[Any]   					=						[np.asarray(UpperCAmelCase      ) for speech_input in speech_inputs]

     # Test not batched input
     lowerCamelCase__       : Union[str, Any]   					=						feat_extract(speech_inputs[0]     ,	return_tensors='np'      ).input_values
     lowerCamelCase__       : Tuple   					=						feat_extract(np_speech_inputs[0]     ,	return_tensors='np'      ).input_values
     self.assertTrue(np.allclose(UpperCAmelCase     ,	UpperCAmelCase     ,	atol=1e-3      )      )

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

     # Test 2-D numpy arrays are batched.
     lowerCamelCase__       : int   					=						[floats_list((1, x)      )[0] for x in (800, 800, 800)]
     lowerCamelCase__       : Optional[int]   					=						np.asarray(UpperCAmelCase      )
     lowerCamelCase__       : Optional[int]   					=						feat_extract(UpperCAmelCase     ,	return_tensors='np'      ).input_values
     lowerCamelCase__       : str   					=						feat_extract(UpperCAmelCase     ,	return_tensors='np'      ).input_values
     for enc_seq_a, enc_seq_a in zip(UpperCAmelCase     ,	UpperCAmelCase      ):
         self.assertTrue(np.allclose(UpperCAmelCase     ,	UpperCAmelCase     ,	atol=1e-3      )      )
 def 	A_      ( self    : Tuple      )     ->      Any:
     lowerCamelCase__       : Union[str, Any]   					=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
     lowerCamelCase__       : List[Any]   					=						[floats_list((1, x)      )[0] for x in range(800     ,	1400     ,	200      )]

     lowerCamelCase__       : Dict   					=						['longest', 'max_length', 'do_not_pad']
     lowerCamelCase__       : Optional[Any]   					=						[None, 1600, None]
     for max_length, padding in zip(UpperCAmelCase     ,	UpperCAmelCase      ):
         lowerCamelCase__       : List[Any]   					=						feat_extract(UpperCAmelCase     ,	padding=UpperCAmelCase     ,	max_length=UpperCAmelCase     ,	return_tensors='np'      )
         lowerCamelCase__       : str   					=						processed.input_values

         self._check_zero_mean_unit_variance(input_values[0][:800]      )
         self.assertTrue(input_values[0][800:].sum() < 1e-6      )
         self._check_zero_mean_unit_variance(input_values[1][:1000]      )
         self.assertTrue(input_values[0][1000:].sum() < 1e-6      )
         self._check_zero_mean_unit_variance(input_values[2][:1200]      )
 def 	A_      ( self    : int      )     ->      int:
     lowerCamelCase__       : Dict   					=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
     lowerCamelCase__       : List[str]   					=						range(800     ,	1400     ,	200      )
     lowerCamelCase__       : str   					=						[floats_list((1, x)      )[0] for x in lengths]

     lowerCamelCase__       : Tuple   					=						['longest', 'max_length', 'do_not_pad']
     lowerCamelCase__       : Union[str, Any]   					=						[None, 1600, None]

     for max_length, padding in zip(UpperCAmelCase     ,	UpperCAmelCase      ):
         lowerCamelCase__       : int   					=						feat_extract(UpperCAmelCase     ,	max_length=UpperCAmelCase     ,	padding=UpperCAmelCase      )
         lowerCamelCase__       : Union[str, Any]   					=						processed.input_values

         self._check_zero_mean_unit_variance(input_values[0][:800]      )
         self._check_zero_mean_unit_variance(input_values[1][:1000]      )
         self._check_zero_mean_unit_variance(input_values[2][:1200]      )
 def 	A_      ( self    : List[Any]      )     ->      Dict:
     lowerCamelCase__       : Any   					=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
     lowerCamelCase__       : Dict   					=						[floats_list((1, x)      )[0] for x in range(800     ,	1400     ,	200      )]
     lowerCamelCase__       : Dict   					=						feat_extract(
         UpperCAmelCase     ,	truncation=UpperCAmelCase     ,	max_length=1000     ,	padding='max_length'     ,	return_tensors='np'      )
     lowerCamelCase__       : Union[str, Any]   					=						processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :800]      )
     self._check_zero_mean_unit_variance(input_values[1]      )
     self._check_zero_mean_unit_variance(input_values[2]      )
 def 	A_      ( self    : str      )     ->      Optional[Any]:
     lowerCamelCase__       : Tuple   					=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
     lowerCamelCase__       : List[str]   					=						[floats_list((1, x)      )[0] for x in range(800     ,	1400     ,	200      )]
     lowerCamelCase__       : Tuple   					=						feat_extract(
         UpperCAmelCase     ,	truncation=UpperCAmelCase     ,	max_length=1000     ,	padding='longest'     ,	return_tensors='np'      )
     lowerCamelCase__       : Optional[Any]   					=						processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :800]      )
     self._check_zero_mean_unit_variance(input_values[1, :1000]      )
     self._check_zero_mean_unit_variance(input_values[2]      )

     # make sure that if max_length < longest -> then pad to max_length
     self.assertTrue(input_values.shape == (3, 1000)      )

     lowerCamelCase__       : Optional[Any]   					=						[floats_list((1, x)      )[0] for x in range(800     ,	1400     ,	200      )]
     lowerCamelCase__       : str   					=						feat_extract(
         UpperCAmelCase     ,	truncation=UpperCAmelCase     ,	max_length=2000     ,	padding='longest'     ,	return_tensors='np'      )
     lowerCamelCase__       : str   					=						processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :800]      )
     self._check_zero_mean_unit_variance(input_values[1, :1000]      )
     self._check_zero_mean_unit_variance(input_values[2]      )

     # make sure that if max_length > longest -> then pad to longest
     self.assertTrue(input_values.shape == (3, 1200)      )
 @require_torch
 def 	A_      ( self    : Optional[int]      )     ->      Dict:
     import torch

     lowerCamelCase__       : List[str]   					=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
     lowerCamelCase__       : Dict   					=						np.random.rand(100      ).astype(np.floataa      )
     lowerCamelCase__       : str   					=						np_speech_inputs.tolist()

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







 @slow
 @require_torch
 def 	A_      ( self    : Optional[int]      )     ->      Optional[Any]:
     # this test makes sure that models that are using
     # group norm don't have their feature extractor return the
     # attention_mask
     for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
         lowerCamelCase__       : Any   					=						WavaVecaConfig.from_pretrained(UpperCAmelCase      )
         lowerCamelCase__       : List[Any]   					=						WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase      )

         # only "layer" feature extraction norm should make use of
         # attention_mask
         self.assertEqual(feat_extract.return_attention_mask     ,	config.feat_extract_norm == 'layer'      )












# 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   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						"""philschmid/bart-large-cnn-samsum"""
 UpperCAmelCase__						=						(
     """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."""
 )
 UpperCAmelCase__						=						"""summarizer"""
 UpperCAmelCase__						=						AutoTokenizer
 UpperCAmelCase__						=						AutoModelForSeqaSeqLM

 UpperCAmelCase__						=						["""text"""]
 UpperCAmelCase__						=						["""text"""]
 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : List[Any]      )     ->      List[str]:
     return self.pre_processor(UpperCAmelCase     ,	return_tensors='pt'     ,	truncation=UpperCAmelCase      )
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : Dict      )     ->      str:
     return self.model.generate(**UpperCAmelCase      )[0]







 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : List[Any]      )     ->      Union[str, Any]:
     return self.pre_processor.decode(UpperCAmelCase     ,	skip_special_tokens=UpperCAmelCase     ,	clean_up_tokenization_spaces=UpperCAmelCase      )





