



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__       =	random.Random()





def 							A     (					_UpperCAmelCase    :       Optional[Any]							,       _UpperCAmelCase    :       Tuple=1.0							,       _UpperCAmelCase    :       Optional[int]=None							,       _UpperCAmelCase    :       int=None   )    ->			Optional[int]:







			'''simple docstring'''
			if rng is None:
						_UpperCAmelCase			=       global_rng

			_UpperCAmelCase			=       []
			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       :  Union[str, Any]			,		A       :  Optional[Any]			,		A       :  Any=7			,		A       :  str=4_00			,		A       :  List[Any]=20_00			,		A       :  List[Any]=1			,		A       :  Any=0.0			,		A       :  Tuple=1_60_00			,		A       :  Optional[int]=True			,		A       :  str=True			,		)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       parent
						_UpperCAmelCase			=       batch_size
						_UpperCAmelCase			=       min_seq_length
						_UpperCAmelCase			=       max_seq_length
						_UpperCAmelCase			=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						_UpperCAmelCase			=       feature_size
						_UpperCAmelCase			=       padding_value
						_UpperCAmelCase			=       sampling_rate
						_UpperCAmelCase			=       return_attention_mask
						_UpperCAmelCase			=       do_normalize



			def    _lowerCamelCase	(						self       :  int)       ->   List[Any]:



						"""simple docstring"""






						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    _lowerCamelCase	(						self       :  str			,		A       :  Optional[Any]=False			,		A       :  Any=False)       ->   Optional[Any]:



						"""simple docstring"""






						def _flatten(A       :  Tuple):
									return list(itertools.chain(*A))

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

						return speech_inputs







class       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					WavaVecaFeatureExtractor



			def    _lowerCamelCase	(						self       :  List[str])       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       WavaVecaFeatureExtractionTester(self)



			def    _lowerCamelCase	(						self       :  str			,		A       :  str)       ->   Optional[int]:



						"""simple docstring"""






						self.assertTrue(np.all(np.mean(A			,		axis=0) < 1E-3))
						self.assertTrue(np.all(np.abs(np.var(A			,		axis=0) - 1) < 1E-3))



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
						# create three inputs of length 800, 1000, and 1200
						_UpperCAmelCase			=       [floats_list((1, x))[0] for x in range(8_00			,		14_00			,		2_00)]
						_UpperCAmelCase			=       [np.asarray(A) for speech_input in speech_inputs]

						# Test not batched input
						_UpperCAmelCase			=       feat_extract(speech_inputs[0]			,		return_tensors='np').input_values
						_UpperCAmelCase			=       feat_extract(np_speech_inputs[0]			,		return_tensors='np').input_values
						self.assertTrue(np.allclose(A			,		A			,		atol=1E-3))

						# Test batched
						_UpperCAmelCase			=       feat_extract(A			,		return_tensors='np').input_values
						_UpperCAmelCase			=       feat_extract(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))

						# Test 2-D numpy arrays are batched.
						_UpperCAmelCase			=       [floats_list((1, x))[0] for x in (8_00, 8_00, 8_00)]
						_UpperCAmelCase			=       np.asarray(A)
						_UpperCAmelCase			=       feat_extract(A			,		return_tensors='np').input_values
						_UpperCAmelCase			=       feat_extract(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    _lowerCamelCase	(						self       :  str)       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
						_UpperCAmelCase			=       [floats_list((1, x))[0] for x in range(8_00			,		14_00			,		2_00)]

						_UpperCAmelCase			=       ['longest', 'max_length', 'do_not_pad']
						_UpperCAmelCase			=       [None, 16_00, None]
						for max_length, padding in zip(A			,		A):
									_UpperCAmelCase			=       feat_extract(A			,		padding=A			,		max_length=A			,		return_tensors='np')
									_UpperCAmelCase			=       processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:8_00])
									self.assertTrue(input_values[0][8_00:].sum() < 1E-6)
									self._check_zero_mean_unit_variance(input_values[1][:10_00])
									self.assertTrue(input_values[0][10_00:].sum() < 1E-6)
									self._check_zero_mean_unit_variance(input_values[2][:12_00])



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



						"""simple docstring"""






						_UpperCAmelCase			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
						_UpperCAmelCase			=       range(8_00			,		14_00			,		2_00)
						_UpperCAmelCase			=       [floats_list((1, x))[0] for x in lengths]

						_UpperCAmelCase			=       ['longest', 'max_length', 'do_not_pad']
						_UpperCAmelCase			=       [None, 16_00, None]

						for max_length, padding in zip(A			,		A):
									_UpperCAmelCase			=       feat_extract(A			,		max_length=A			,		padding=A)
									_UpperCAmelCase			=       processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:8_00])
									self._check_zero_mean_unit_variance(input_values[1][:10_00])
									self._check_zero_mean_unit_variance(input_values[2][:12_00])



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



						"""simple docstring"""






						_UpperCAmelCase			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
						_UpperCAmelCase			=       [floats_list((1, x))[0] for x in range(8_00			,		14_00			,		2_00)]
						_UpperCAmelCase			=       feat_extract(
						    A			,		truncation=A			,		max_length=10_00			,		padding='max_length'			,		return_tensors='np')
						_UpperCAmelCase			=       processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :8_00])
						self._check_zero_mean_unit_variance(input_values[1])
						self._check_zero_mean_unit_variance(input_values[2])



			def    _lowerCamelCase	(						self       :  str)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
						_UpperCAmelCase			=       [floats_list((1, x))[0] for x in range(8_00			,		14_00			,		2_00)]
						_UpperCAmelCase			=       feat_extract(
						    A			,		truncation=A			,		max_length=10_00			,		padding='longest'			,		return_tensors='np')
						_UpperCAmelCase			=       processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :8_00])
						self._check_zero_mean_unit_variance(input_values[1, :10_00])
						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, 10_00))

						_UpperCAmelCase			=       [floats_list((1, x))[0] for x in range(8_00			,		14_00			,		2_00)]
						_UpperCAmelCase			=       feat_extract(
						    A			,		truncation=A			,		max_length=20_00			,		padding='longest'			,		return_tensors='np')
						_UpperCAmelCase			=       processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :8_00])
						self._check_zero_mean_unit_variance(input_values[1, :10_00])
						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, 12_00))



			@require_torch
			def    _lowerCamelCase	(						self       :  Tuple)       ->   int:



						"""simple docstring"""






						import torch

						_UpperCAmelCase			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
						_UpperCAmelCase			=       np.random.rand(1_00).astype(np.floataa)
						_UpperCAmelCase			=       np_speech_inputs.tolist()

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



			@slow
			@require_torch
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Tuple:



						"""simple docstring"""






						for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
									_UpperCAmelCase			=       WavaVecaConfig.from_pretrained(A)
									_UpperCAmelCase			=       WavaVecaFeatureExtractor.from_pretrained(A)

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





import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
							from sagemaker import Session, TrainingJobAnalytics
							from sagemaker.huggingface import HuggingFace







@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''    ,						'''False'''						)						) is not True    ,						reason='''Skipping test because should only be run when releasing minor transformers version'''    ,						)
@pytest.mark.usefixtures('''sm_env'''						)
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]						)
class       __lowerCAmelCase   (      unittest.TestCase						):



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Any:



						"""simple docstring"""






						if self.framework == "pytorch":
									subprocess.run(
									    F"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split()			,		encoding='utf-8'			,		check=A			,		)
						assert hasattr(self			,		'env')



			def    _lowerCamelCase	(						self       :  Any			,		A       :  Tuple=1)       ->   List[str]:



						"""simple docstring"""






						return HuggingFace(
						    entry_point=self.script			,		source_dir=self.env.test_path			,		role=self.env.role			,		image_uri=self.env.image_uri			,		base_job_name=F"{self.env.base_job_name}-single"			,		instance_count=A			,		instance_type=self.instance_type			,		debugger_hook_config=A			,		hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}			,		metric_definitions=self.env.metric_definitions			,		py_version='py36'			,		)



			def    _lowerCamelCase	(						self       :  Dict			,		A       :  int)       ->   str:



						"""simple docstring"""






						TrainingJobAnalytics(A).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv")



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.create_estimator()

						# run training
						estimator.fit()

						# result dataframe
						_UpperCAmelCase			=       TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

						# extract kpis
						_UpperCAmelCase			=       list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'])
						_UpperCAmelCase			=       list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'])
						# get train time from SageMaker job, this includes starting, preprocessing, stopping
						_UpperCAmelCase			=       (
						    Session().describe_training_job(estimator.latest_training_job.name).get('TrainingTimeInSeconds'			,		99_99_99)
						)

						# assert kpis
						assert train_runtime <= self.results["train_runtime"]
						assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy)
						assert all(t <= self.results['eval_loss'] for t in eval_loss)

						# dump tests result into json file to share in PR
						with open(F"{estimator.latest_training_job.name}.json"			,		'w') as outfile:
									json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss}			,		A)
