

"""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__			:						str							=			random.Random()





def 					__UpperCamelCase (     a__:			Optional[Any]			,						a__:			Union[str, Any]=1.0			,						a__:			Any=None			,						a__:			int=None      )					->      Union[str, Any]:




					"""simple docstring"""



					if rng is None:
										__lowercase        =      global_rng

					__lowercase        =      []
					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       _snake_case  (    unittest.TestCase							):







						def __init__(self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=7  ,				UpperCAmelCase_=4_0_0  ,				UpperCAmelCase_=2_0_0_0  ,				UpperCAmelCase_=1  ,				UpperCAmelCase_=0.0  ,				UpperCAmelCase_=2_4_0_0_0  ,				UpperCAmelCase_=True  ,				)      ->    Optional[int]:
											__lowercase        =      parent
											__lowercase        =      batch_size
											__lowercase        =      min_seq_length
											__lowercase        =      max_seq_length
											__lowercase        =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
											__lowercase        =      feature_size
											__lowercase        =      padding_value
											__lowercase        =      sampling_rate
											__lowercase        =      return_attention_mask







						def 							_UpperCamelCase     (self				)      ->    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,
											}







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_=False  ,				UpperCAmelCase_=False				)      ->    Optional[Any]:
											def _flatten(UpperCAmelCase_				):
																return list(itertools.chain(*UpperCAmelCase_				)				)

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

											return audio_inputs



@require_torch
class       _snake_case  (    UpperCamelCase    ,      unittest.TestCase							):
						__snake_case			      =  EncodecFeatureExtractor







						def 							_UpperCamelCase     (self				)      ->    Optional[Any]:
											__lowercase        =      EnCodecFeatureExtractionTester(self				)







						def 							_UpperCamelCase     (self				)      ->    int:
											# Tests that all call wrap to encode_plus and batch_encode_plus
											__lowercase        =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
											# create three inputs of length 800, 1000, and 1200
											__lowercase        =      [floats_list((1, x)				)[0] for x in range(8_0_0  ,				1_4_0_0  ,				2_0_0				)]
											__lowercase        =      [np.asarray(UpperCAmelCase_				) for audio_input in audio_inputs]

											# Test not batched input
											__lowercase        =      feat_extract(audio_inputs[0]  ,				return_tensors='''np'''				).input_values
											__lowercase        =      feat_extract(np_audio_inputs[0]  ,				return_tensors='''np'''				).input_values
											self.assertTrue(np.allclose(UpperCAmelCase_  ,				UpperCAmelCase_  ,				atol=1e-3				)				)

											# Test batched
											__lowercase        =      feat_extract(UpperCAmelCase_  ,				padding=UpperCAmelCase_  ,				return_tensors='''np'''				).input_values
											__lowercase        =      feat_extract(UpperCAmelCase_  ,				padding=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 							_UpperCamelCase     (self				)      ->    Union[str, Any]:
											__lowercase        =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
											__lowercase        =      np.random.rand(1_0_0				).astype(np.floataa				)
											__lowercase        =      np_audio_inputs.tolist()

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







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_				)      ->    List[str]:
											from datasets import load_dataset

											__lowercase        =      load_dataset('''hf-internal-testing/librispeech_asr_dummy'''  ,				'''clean'''  ,				split='''validation'''				)
											# automatic decoding with librispeech
											__lowercase        =      ds.sort('''id'''				).select(range(UpperCAmelCase_				)				)[:num_samples]['''audio''']

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







						def 							_UpperCamelCase     (self				)      ->    Tuple:
											# fmt: off
											__lowercase        =      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
											__lowercase        =      self._load_datasamples(1				)
											__lowercase        =      EncodecFeatureExtractor()
											__lowercase        =      feature_extractor(UpperCAmelCase_  ,				return_tensors='''pt'''				).input_values
											self.assertEquals(input_values.shape  ,				(1, 1, 9_3_6_8_0)				)
											self.assertTrue(torch.allclose(input_values[0, 0, :3_0]  ,				UpperCAmelCase_  ,				atol=1e-6				)				)







						def 							_UpperCamelCase     (self				)      ->    Union[str, Any]:
											# fmt: off
											__lowercase        =      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
											__lowercase        =      self._load_datasamples(1				)
											__lowercase        =      [np.tile(input_audio[0][None]  ,				reps=(2, 1)				)]
											input_audio[0][1] *= 0.5
											__lowercase        =      EncodecFeatureExtractor(feature_size=2				)
											__lowercase        =      feature_extractor(UpperCAmelCase_  ,				return_tensors='''pt'''				).input_values
											self.assertEquals(input_values.shape  ,				(1, 2, 9_3_6_8_0)				)
											self.assertTrue(torch.allclose(input_values[0, 0, :3_0]  ,				UpperCAmelCase_  ,				atol=1e-6				)				)
											self.assertTrue(torch.allclose(input_values[0, 1, :3_0]  ,				EXPECTED_INPUT_VALUES * 0.5  ,				atol=1e-6				)				)







						def 							_UpperCamelCase     (self				)      ->    str:
											__lowercase        =      self._load_datasamples(2				)
											# would be easier if the stride was like
											__lowercase        =      EncodecFeatureExtractor(feature_size=1  ,				chunk_length_s=1  ,				overlap=0.0_1				)

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

											# truncate to chunk
											__lowercase        =      feature_extractor(UpperCAmelCase_  ,				truncation=UpperCAmelCase_  ,				return_tensors='''pt'''				).input_values
											self.assertEquals(truncated_outputs.shape  ,				(2, 1, 7_1_5_2_0)				)  # 2 chunks

											# force truncate to max_length
											__lowercase        =      feature_extractor(
											    UpperCAmelCase_  ,				truncation=UpperCAmelCase_  ,				max_length=4_8_0_0_0  ,				return_tensors='''pt'''				).input_values
											self.assertEquals(truncated_outputs.shape  ,				(2, 1, 4_8_0_0_0)				)

											# pad to chunk
											__lowercase        =      feature_extractor(UpperCAmelCase_  ,				padding=UpperCAmelCase_  ,				return_tensors='''pt'''				).input_values
											self.assertEquals(padded_outputs.shape  ,				(2, 1, 9_5_2_8_0)				)

											# pad to chunk
											__lowercase        =      feature_extractor(UpperCAmelCase_  ,				return_tensors='''pt'''				).input_values
											self.assertEquals(truncated_outputs.shape  ,				(2, 1, 9_5_2_8_0)				)

											# force pad to max length
											__lowercase        =      feature_extractor(
											    UpperCAmelCase_  ,				padding='''max_length'''  ,				max_length=1_0_0_0_0_0  ,				return_tensors='''pt'''				).input_values
											self.assertEquals(truncated_outputs.shape  ,				(2, 1, 1_0_0_0_0_0)				)

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

											__lowercase        =      feature_extractor(input_audio[0]  ,				padding=UpperCAmelCase_  ,				return_tensors='''pt'''				).input_values
											self.assertEquals(truncated_outputs.shape  ,				(1, 1, 9_3_6_8_0)				)

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

											__lowercase        =      feature_extractor(input_audio[0]  ,				padding=UpperCAmelCase_  ,				return_tensors='''pt'''				).input_values
											self.assertEquals(truncated_outputs.shape  ,				(1, 1, 9_3_6_8_0)				)

											# no pad if no overlap
											__lowercase        =      2
											__lowercase        =      None
											with self.assertRaisesRegex(
											    UpperCAmelCase_  ,				'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''  ,				):
																__lowercase        =      feature_extractor(UpperCAmelCase_  ,				padding=UpperCAmelCase_  ,				return_tensors='''pt'''				).input_values

											__lowercase        =      feature_extractor(input_audio[0]  ,				padding=UpperCAmelCase_  ,				return_tensors='''pt'''				).input_values
											self.assertEquals(truncated_outputs.shape  ,				(1, 1, 9_3_6_8_0)				)





"""simple docstring"""

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a__			:						Dict							=			logging.get_logger(__name__)

a__			:						Any							=			{
    """google/long-t5-local-base""": """https://huggingface.co/google/long-t5-local-base/blob/main/config.json""",
    """google/long-t5-local-large""": """https://huggingface.co/google/long-t5-local-large/blob/main/config.json""",
    """google/long-t5-tglobal-base""": """https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json""",
    """google/long-t5-tglobal-large""": """https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json""",
}


class       _snake_case  (    UpperCamelCase							):
						__snake_case			      =  'longt5'
						__snake_case			      =  ['past_key_values']
						__snake_case			      =  {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}







						def __init__(self  ,				UpperCAmelCase_=3_2_1_2_8  ,				UpperCAmelCase_=5_1_2  ,				UpperCAmelCase_=6_4  ,				UpperCAmelCase_=2_0_4_8  ,				UpperCAmelCase_=6  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=8  ,				UpperCAmelCase_=1_2_7  ,				UpperCAmelCase_=1_6  ,				UpperCAmelCase_=3_2  ,				UpperCAmelCase_=1_2_8  ,				UpperCAmelCase_=0.1  ,				UpperCAmelCase_=1e-6  ,				UpperCAmelCase_=1.0  ,				UpperCAmelCase_="relu"  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_="local"  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=0  ,				UpperCAmelCase_=1  ,				**UpperCAmelCase_  ,				)      ->    int:
											__lowercase        =      vocab_size
											__lowercase        =      d_model
											__lowercase        =      d_kv
											__lowercase        =      d_ff
											__lowercase        =      num_layers
											# default = symmetry
											__lowercase        =      num_decoder_layers if num_decoder_layers is not None else self.num_layers
											__lowercase        =      num_heads
											__lowercase        =      local_radius
											__lowercase        =      global_block_size
											__lowercase        =      relative_attention_num_buckets
											__lowercase        =      relative_attention_max_distance
											__lowercase        =      dropout_rate
											__lowercase        =      layer_norm_epsilon
											__lowercase        =      initializer_factor
											__lowercase        =      feed_forward_proj
											__lowercase        =      encoder_attention_type
											__lowercase        =      use_cache

											__lowercase        =      self.feed_forward_proj.split('''-'''				)
											__lowercase        =      act_info[-1]
											__lowercase        =      act_info[0] == '''gated'''

											if len(UpperCAmelCase_				) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_				) > 2:
																raise ValueError(
																    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
																    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
																    '''\'gated-gelu\' or \'relu\''''				)

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
																__lowercase        =      '''gelu_new'''

											super().__init__(
											    pad_token_id=UpperCAmelCase_  ,				eos_token_id=UpperCAmelCase_  ,				is_encoder_decoder=UpperCAmelCase_  ,				**UpperCAmelCase_  ,				)



class       _snake_case  (    UpperCamelCase							):







						@property
						def 							_UpperCamelCase     (self				)      ->    Mapping[str, Mapping[int, str]]:
											__lowercase        =      {
											    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
											    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
											}
											if self.use_past:
																__lowercase        =      '''past_encoder_sequence + sequence'''
																__lowercase        =      {0: '''batch'''}
																__lowercase        =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
											else:
																__lowercase        =      {0: '''batch''', 1: '''decoder_sequence'''}
																__lowercase        =      {0: '''batch''', 1: '''decoder_sequence'''}

											if self.use_past:
																self.fill_with_past_key_values_(UpperCAmelCase_  ,				direction='''inputs'''				)

											return common_inputs







						@property
						def 							_UpperCamelCase     (self				)      ->    int:
											return 1_3


