






"""simple docstring"""

import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
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


lowercase    			=       random.Random()






def _lowerCAmelCase	(  __lowerCamelCase:List[str] ,						__lowerCamelCase:int=1.0 ,						__lowerCamelCase:Tuple=None ,						__lowerCamelCase:List[str]=None   ):

						'''simple docstring'''






						if rng is None:
												__magic_name__            =     global_rng

						__magic_name__            =     []
						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 						A_							(      unittest.TestCase  ):







				def __init__(							self							:						int						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						Any=7						,    __lowerCamelCase							:						Union[str, Any]=4_0_0						,    __lowerCamelCase							:						List[Any]=2_0_0_0						,    __lowerCamelCase							:						List[Any]=1						,    __lowerCamelCase							:						List[str]=0.0						,    __lowerCamelCase							:						Dict=1_6_0_0_0						,    __lowerCamelCase							:						Optional[Any]=True						,    __lowerCamelCase							:						int=8_0						,    __lowerCamelCase							:						str=1_6						,    __lowerCamelCase							:						Optional[int]=6_4						,    __lowerCamelCase							:						Tuple="hann_window"						,    __lowerCamelCase							:						Union[str, Any]=8_0						,    __lowerCamelCase							:						Any=7_6_0_0						,    __lowerCamelCase							:						Dict=1e-10						,    __lowerCamelCase							:						Tuple=True						,    )				->					Optional[int]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     min_seq_length
										__magic_name__            =     max_seq_length
										__magic_name__            =     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										__magic_name__            =     feature_size
										__magic_name__            =     padding_value
										__magic_name__            =     sampling_rate
										__magic_name__            =     do_normalize
										__magic_name__            =     num_mel_bins
										__magic_name__            =     hop_length
										__magic_name__            =     win_length
										__magic_name__            =     win_function
										__magic_name__            =     fmin
										__magic_name__            =     fmax
										__magic_name__            =     mel_floor
										__magic_name__            =     return_attention_mask







				def     _snake_case					(							self							:						Tuple	)				->					Any:
										return {
										    "feature_size": self.feature_size,
										    "padding_value": self.padding_value,
										    "sampling_rate": self.sampling_rate,
										    "do_normalize": self.do_normalize,
										    "num_mel_bins": self.num_mel_bins,
										    "hop_length": self.hop_length,
										    "win_length": self.win_length,
										    "win_function": self.win_function,
										    "fmin": self.fmin,
										    "fmax": self.fmax,
										    "mel_floor": self.mel_floor,
										    "return_attention_mask": self.return_attention_mask,
										}







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

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

										return speech_inputs







				def     _snake_case					(							self							:						List[Any]						,    __lowerCamelCase							:						Tuple=False						,    __lowerCamelCase							:						Any=False	)				->					int:
										if equal_length:
																__magic_name__            =     [floats_list((self.max_seq_length, self.num_mel_bins)	) for _ in range(self.batch_size	)]
										else:
																# make sure that inputs increase in size
																__magic_name__            =     [
																    floats_list((x, self.num_mel_bins)	)
																    for x in range(self.min_seq_length						,    self.max_seq_length						,    self.seq_length_diff	)
																]

										if numpify:
																__magic_name__            =     [np.asarray(__lowerCamelCase	) for x in speech_inputs]

										return speech_inputs





@require_torch
class 						A_							(      snake_case_					,							unittest.TestCase  ):
				UpperCAmelCase__        =				SpeechTaFeatureExtractor







				def     _snake_case					(							self							:						int	)				->					Optional[Any]:
										__magic_name__            =     SpeechTaFeatureExtractionTester(self	)







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						Dict	)				->					Optional[Any]:
										self.assertTrue(np.all(np.mean(__lowerCamelCase						,    axis=0	) < 1e-3	)	)
										self.assertTrue(np.all(np.abs(np.var(__lowerCamelCase						,    axis=0	) - 1	) < 1e-3	)	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Union[str, Any]:
										# Tests that all call wrap to encode_plus and batch_encode_plus
										__magic_name__            =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
										# create three inputs of length 800, 1000, and 1200
										__magic_name__            =     [floats_list((1, x)	)[0] for x in range(8_0_0						,    1_4_0_0						,    2_0_0	)]
										__magic_name__            =     [np.asarray(__lowerCamelCase	) for speech_input in speech_inputs]

										# Test not batched input
										__magic_name__            =     feat_extract(speech_inputs[0]						,    return_tensors="np"	).input_values
										__magic_name__            =     feat_extract(np_speech_inputs[0]						,    return_tensors="np"	).input_values
										self.assertTrue(np.allclose(__lowerCamelCase						,    __lowerCamelCase						,    atol=1e-3	)	)

										# Test batched
										__magic_name__            =     feat_extract(__lowerCamelCase						,    return_tensors="np"	).input_values
										__magic_name__            =     feat_extract(__lowerCamelCase						,    return_tensors="np"	).input_values
										for enc_seq_a, enc_seq_a in zip(__lowerCamelCase						,    __lowerCamelCase	):
																self.assertTrue(np.allclose(__lowerCamelCase						,    __lowerCamelCase						,    atol=1e-3	)	)







				def     _snake_case					(							self							:						List[Any]	)				->					Dict:
										__magic_name__            =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
										__magic_name__            =     [floats_list((1, x)	)[0] for x in range(8_0_0						,    1_4_0_0						,    2_0_0	)]

										__magic_name__            =     ["longest", "max_length", "do_not_pad"]
										__magic_name__            =     [None, 1_6_0_0, None]
										for max_length, padding in zip(__lowerCamelCase						,    __lowerCamelCase	):
																__magic_name__            =     feat_extract(__lowerCamelCase						,    padding=__lowerCamelCase						,    max_length=__lowerCamelCase						,    return_tensors="np"	)
																__magic_name__            =     processed.input_values

																self._check_zero_mean_unit_variance(input_values[0][:8_0_0]	)
																self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6	)
																self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]	)
																self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6	)
																self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]	)







				def     _snake_case					(							self							:						int	)				->					Optional[int]:
										__magic_name__            =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
										__magic_name__            =     range(8_0_0						,    1_4_0_0						,    2_0_0	)
										__magic_name__            =     [floats_list((1, x)	)[0] for x in lengths]

										__magic_name__            =     ["longest", "max_length", "do_not_pad"]
										__magic_name__            =     [None, 1_6_0_0, None]

										for max_length, padding in zip(__lowerCamelCase						,    __lowerCamelCase	):
																__magic_name__            =     feat_extract(__lowerCamelCase						,    max_length=__lowerCamelCase						,    padding=__lowerCamelCase	)
																__magic_name__            =     processed.input_values

																self._check_zero_mean_unit_variance(input_values[0][:8_0_0]	)
																self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]	)
																self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]	)







				def     _snake_case					(							self							:						Any	)				->					Optional[Any]:
										__magic_name__            =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
										__magic_name__            =     [floats_list((1, x)	)[0] for x in range(8_0_0						,    1_4_0_0						,    2_0_0	)]
										__magic_name__            =     feat_extract(
										    __lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=1_0_0_0						,    padding="max_length"						,    return_tensors="np"	)
										__magic_name__            =     processed.input_values

										self._check_zero_mean_unit_variance(input_values[0, :8_0_0]	)
										self._check_zero_mean_unit_variance(input_values[1]	)
										self._check_zero_mean_unit_variance(input_values[2]	)







				def     _snake_case					(							self							:						List[str]	)				->					Optional[Any]:
										__magic_name__            =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
										__magic_name__            =     [floats_list((1, x)	)[0] for x in range(8_0_0						,    1_4_0_0						,    2_0_0	)]
										__magic_name__            =     feat_extract(
										    __lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=1_0_0_0						,    padding="longest"						,    return_tensors="np"	)
										__magic_name__            =     processed.input_values

										self._check_zero_mean_unit_variance(input_values[0, :8_0_0]	)
										self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]	)
										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, 1_0_0_0)	)

										__magic_name__            =     [floats_list((1, x)	)[0] for x in range(8_0_0						,    1_4_0_0						,    2_0_0	)]
										__magic_name__            =     feat_extract(
										    __lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=2_0_0_0						,    padding="longest"						,    return_tensors="np"	)
										__magic_name__            =     processed.input_values

										self._check_zero_mean_unit_variance(input_values[0, :8_0_0]	)
										self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]	)
										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, 1_2_0_0)	)







				def     _snake_case					(							self							:						Any	)				->					Tuple:
										__magic_name__            =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
										__magic_name__            =     np.random.rand(1_0_0	).astype(np.floataa	)
										__magic_name__            =     np_speech_inputs.tolist()

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







				def     _snake_case					(							self							:						Tuple	)				->					List[Any]:
										# Tests that all call wrap to encode_plus and batch_encode_plus
										__magic_name__            =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
										# create three inputs of length 800, 1000, and 1200
										__magic_name__            =     [floats_list((1, x)	)[0] for x in range(8_0_0						,    1_4_0_0						,    2_0_0	)]
										__magic_name__            =     [np.asarray(__lowerCamelCase	) for speech_input in speech_inputs]

										# Test feature size
										__magic_name__            =     feature_extractor(audio_target=__lowerCamelCase						,    padding=__lowerCamelCase						,    return_tensors="np"	).input_values
										self.assertTrue(input_values.ndim == 3	)
										self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins	)

										# Test not batched input
										__magic_name__            =     feature_extractor(speech_inputs[0]						,    return_tensors="np"	).input_values
										__magic_name__            =     feature_extractor(np_speech_inputs[0]						,    return_tensors="np"	).input_values
										self.assertTrue(np.allclose(__lowerCamelCase						,    __lowerCamelCase						,    atol=1e-3	)	)

										# Test batched
										__magic_name__            =     feature_extractor(__lowerCamelCase						,    return_tensors="np"	).input_values
										__magic_name__            =     feature_extractor(__lowerCamelCase						,    return_tensors="np"	).input_values
										for enc_seq_a, enc_seq_a in zip(__lowerCamelCase						,    __lowerCamelCase	):
																self.assertTrue(np.allclose(__lowerCamelCase						,    __lowerCamelCase						,    atol=1e-3	)	)

										# Test 2-D numpy arrays are batched.
										__magic_name__            =     [floats_list((1, x)	)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
										__magic_name__            =     np.asarray(__lowerCamelCase	)
										__magic_name__            =     feature_extractor(__lowerCamelCase						,    return_tensors="np"	).input_values
										__magic_name__            =     feature_extractor(__lowerCamelCase						,    return_tensors="np"	).input_values
										for enc_seq_a, enc_seq_a in zip(__lowerCamelCase						,    __lowerCamelCase	):
																self.assertTrue(np.allclose(__lowerCamelCase						,    __lowerCamelCase						,    atol=1e-3	)	)







				def     _snake_case					(							self							:						Any	)				->					List[str]:
										__magic_name__            =     self.feat_extract_tester.prepare_inputs_for_target()
										__magic_name__            =     self.feature_extraction_class(**self.feat_extract_dict	)
										__magic_name__            =     feat_extract.model_input_names[0]

										__magic_name__            =     BatchFeature({input_name: speech_inputs}	)

										self.assertTrue(all(len(__lowerCamelCase	) == len(__lowerCamelCase	) for x, y in zip(__lowerCamelCase						,    processed_features[input_name]	)	)	)

										__magic_name__            =     self.feat_extract_tester.prepare_inputs_for_target(equal_length=__lowerCamelCase	)
										__magic_name__            =     BatchFeature({input_name: speech_inputs}						,    tensor_type="np"	)

										__magic_name__            =     processed_features[input_name]

										if len(batch_features_input.shape	) < 3:
																__magic_name__            =     batch_features_input[:, :, None]

										self.assertTrue(
										    batch_features_input.shape
										    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.num_mel_bins)	)







				@require_torch
				def     _snake_case					(							self							:						List[str]	)				->					Union[str, Any]:
										__magic_name__            =     self.feat_extract_tester.prepare_inputs_for_target(equal_length=__lowerCamelCase	)
										__magic_name__            =     self.feature_extraction_class(**self.feat_extract_dict	)
										__magic_name__            =     feat_extract.model_input_names[0]

										__magic_name__            =     BatchFeature({input_name: speech_inputs}						,    tensor_type="pt"	)

										__magic_name__            =     processed_features[input_name]

										if len(batch_features_input.shape	) < 3:
																__magic_name__            =     batch_features_input[:, :, None]

										self.assertTrue(
										    batch_features_input.shape
										    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.num_mel_bins)	)







				@require_torch
				def     _snake_case					(							self							:						Dict	)				->					Tuple:
										__magic_name__            =     self.feature_extraction_class(**self.feat_extract_dict	)
										__magic_name__            =     self.feat_extract_tester.prepare_inputs_for_target()
										__magic_name__            =     feat_extract.model_input_names[0]

										__magic_name__            =     BatchFeature({input_name: speech_inputs}	)

										__magic_name__            =     feat_extract.num_mel_bins  # hack!

										__magic_name__            =     feat_extract.pad(__lowerCamelCase						,    padding="longest"						,    return_tensors="np"	)[input_name]
										__magic_name__            =     feat_extract.pad(__lowerCamelCase						,    padding="longest"						,    return_tensors="pt"	)[input_name]

										self.assertTrue(abs(input_np.astype(np.floataa	).sum() - input_pt.numpy().astype(np.floataa	).sum()	) < 1e-2	)







				def     _snake_case					(							self							:						List[Any]	)				->					str:
										__magic_name__            =     self.feat_extract_dict
										__magic_name__            =     True
										__magic_name__            =     self.feature_extraction_class(**__lowerCamelCase	)
										__magic_name__            =     self.feat_extract_tester.prepare_inputs_for_target()
										__magic_name__            =     [len(__lowerCamelCase	) for x in speech_inputs]
										__magic_name__            =     feat_extract.model_input_names[0]

										__magic_name__            =     BatchFeature({input_name: speech_inputs}	)

										__magic_name__            =     feat_extract.num_mel_bins  # hack!

										__magic_name__            =     feat_extract.pad(__lowerCamelCase						,    padding="longest"						,    return_tensors="np"	)
										self.assertIn("attention_mask"						,    __lowerCamelCase	)
										self.assertListEqual(list(processed.attention_mask.shape	)						,    list(processed[input_name].shape[:2]	)	)
										self.assertListEqual(processed.attention_mask.sum(-1	).tolist()						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Any	)				->					Optional[int]:
										__magic_name__            =     self.feat_extract_dict
										__magic_name__            =     True
										__magic_name__            =     self.feature_extraction_class(**__lowerCamelCase	)
										__magic_name__            =     self.feat_extract_tester.prepare_inputs_for_target()
										__magic_name__            =     [len(__lowerCamelCase	) for x in speech_inputs]
										__magic_name__            =     feat_extract.model_input_names[0]

										__magic_name__            =     BatchFeature({input_name: speech_inputs}	)
										__magic_name__            =     min(__lowerCamelCase	)

										__magic_name__            =     feat_extract.num_mel_bins  # hack!

										__magic_name__            =     feat_extract.pad(
										    __lowerCamelCase						,    padding="max_length"						,    max_length=__lowerCamelCase						,    truncation=__lowerCamelCase						,    return_tensors="np"	)
										self.assertIn("attention_mask"						,    __lowerCamelCase	)
										self.assertListEqual(
										    list(processed_pad.attention_mask.shape	)						,    [processed_pad[input_name].shape[0], max_length]	)
										self.assertListEqual(
										    processed_pad.attention_mask[:, :max_length].sum(-1	).tolist()						,    [max_length for x in speech_inputs]	)







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						str	)				->					List[Any]:
										from datasets import load_dataset

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

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







				def     _snake_case					(							self							:						Any	)				->					Dict:
										# fmt: off
										__magic_name__            =     torch.tensor(
										    [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
										     3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
										     2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
										     4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
										     7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
										     4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03]	)
										# fmt: on

										__magic_name__            =     self._load_datasamples(1	)
										__magic_name__            =     SpeechTaFeatureExtractor()
										__magic_name__            =     feature_extractor(__lowerCamelCase						,    return_tensors="pt"	).input_values
										self.assertEquals(input_values.shape						,    (1, 9_3_6_8_0)	)
										self.assertTrue(torch.allclose(input_values[0, :3_0]						,    __lowerCamelCase						,    atol=1e-6	)	)







				def     _snake_case					(							self							:						List[str]	)				->					List[Any]:
										# fmt: off
										__magic_name__            =     torch.tensor(
										    [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
										     -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
										     -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
										     -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]	)
										# fmt: on

										__magic_name__            =     self._load_datasamples(1	)
										__magic_name__            =     SpeechTaFeatureExtractor()
										__magic_name__            =     feature_extractor(audio_target=__lowerCamelCase						,    return_tensors="pt"	).input_values
										self.assertEquals(input_values.shape						,    (1, 3_6_6, 8_0)	)
										self.assertTrue(torch.allclose(input_values[0, 0, :3_0]						,    __lowerCamelCase						,    atol=1e-4	)	)











"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFLEDForConditionalGeneration, TFLEDModel

@require_tf
class 						A_							:
				UpperCAmelCase__        =				LEDConfig
				UpperCAmelCase__        =				{}
				UpperCAmelCase__        =				'''gelu'''







				def __init__(							self							:						Optional[Any]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						List[Any]=1_3						,    __lowerCamelCase							:						str=7						,    __lowerCamelCase							:						Any=True						,    __lowerCamelCase							:						int=False						,    __lowerCamelCase							:						List[Any]=9_9						,    __lowerCamelCase							:						Any=3_2						,    __lowerCamelCase							:						Optional[Any]=2						,    __lowerCamelCase							:						Dict=4						,    __lowerCamelCase							:						int=3_7						,    __lowerCamelCase							:						Tuple=0.1						,    __lowerCamelCase							:						Union[str, Any]=0.1						,    __lowerCamelCase							:						Optional[Any]=2_0						,    __lowerCamelCase							:						List[str]=2						,    __lowerCamelCase							:						str=1						,    __lowerCamelCase							:						List[Any]=0						,    __lowerCamelCase							:						Dict=4						,    )				->					Optional[Any]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     seq_length
										__magic_name__            =     is_training
										__magic_name__            =     use_labels
										__magic_name__            =     vocab_size
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     intermediate_size

										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     max_position_embeddings
										__magic_name__            =     eos_token_id
										__magic_name__            =     pad_token_id
										__magic_name__            =     bos_token_id
										__magic_name__            =     attention_window

										# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
										# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
										# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
										# because its local attention only attends to `self.attention_window` and one before and one after
										__magic_name__            =     self.attention_window + 2

										# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
										# the `test_attention_outputs` and `test_hidden_states_output` tests
										__magic_name__            =     (
										    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
										)







				def     _snake_case					(							self							:						Dict	)				->					Optional[Any]:
										__magic_name__            =     ids_tensor([self.batch_size, self.seq_length - 1]						,    self.vocab_size	)
										__magic_name__            =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)						,    1	)
										__magic_name__            =     tf.concat([input_ids, eos_tensor]						,    axis=1	)

										__magic_name__            =     ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size	)

										__magic_name__            =     self.config_cls(
										    vocab_size=self.vocab_size						,    d_model=self.hidden_size						,    encoder_layers=self.num_hidden_layers						,    decoder_layers=self.num_hidden_layers						,    encoder_attention_heads=self.num_attention_heads						,    decoder_attention_heads=self.num_attention_heads						,    encoder_ffn_dim=self.intermediate_size						,    decoder_ffn_dim=self.intermediate_size						,    dropout=self.hidden_dropout_prob						,    attention_dropout=self.attention_probs_dropout_prob						,    max_position_embeddings=self.max_position_embeddings						,    eos_token_ids=[2]						,    bos_token_id=self.bos_token_id						,    pad_token_id=self.pad_token_id						,    decoder_start_token_id=self.pad_token_id						,    attention_window=self.attention_window						,    **self.config_updates						,    )
										__magic_name__            =     prepare_led_inputs_dict(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     tf.concat(
										    [tf.zeros_like(__lowerCamelCase	)[:, :-1], tf.ones_like(__lowerCamelCase	)[:, -1:]]						,    axis=-1						,    )
										__magic_name__            =     global_attention_mask
										return config, inputs_dict







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Any	)				->					Union[str, Any]:
										__magic_name__            =     TFLEDModel(config=__lowerCamelCase	).get_decoder()
										__magic_name__            =     inputs_dict["input_ids"]

										__magic_name__            =     input_ids[:1, :]
										__magic_name__            =     inputs_dict["attention_mask"][:1, :]
										__magic_name__            =     1

										# first forward pass
										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase						,    use_cache=__lowerCamelCase	)

										__magic_name__      ,    __magic_name__            =     outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										__magic_name__            =     ids_tensor((self.batch_size, 3)						,    config.vocab_size	)
										__magic_name__            =     tf.cast(ids_tensor((self.batch_size, 3)						,    2	)						,    tf.inta	)

										# append to next input_ids and
										__magic_name__            =     tf.concat([input_ids, next_tokens]						,    axis=-1	)
										__magic_name__            =     tf.concat([attention_mask, next_attn_mask]						,    axis=-1	)

										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase	)[0]
										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase						,    past_key_values=__lowerCamelCase	)[0]

										self.parent.assertEqual(next_tokens.shape[1]						,    output_from_past.shape[1]	)

										# select random slice
										__magic_name__            =     int(ids_tensor((1,)						,    output_from_past.shape[-1]	)	)
										__magic_name__            =     output_from_no_past[:, -3:, random_slice_idx]
										__magic_name__            =     output_from_past[:, :, random_slice_idx]

										# test that outputs are equal for slice
										tf.debugging.assert_near(__lowerCamelCase						,    __lowerCamelCase						,    rtol=1e-3	)






def _lowerCAmelCase	(  __lowerCamelCase:str ,						__lowerCamelCase:str ,						__lowerCamelCase:List[Any] ,						__lowerCamelCase:Any=None ,						__lowerCamelCase:Dict=None ,						__lowerCamelCase:List[Any]=None ,						__lowerCamelCase:Union[str, Any]=None ,						):

						'''simple docstring'''






						if attention_mask is None:
												__magic_name__            =     tf.cast(tf.math.not_equal(__lowerCamelCase ,						config.pad_token_id   ) ,						tf.inta   )
						if decoder_attention_mask is None:
												__magic_name__            =     tf.concat(
												    [
												        tf.ones(decoder_input_ids[:, :1].shape ,						dtype=tf.inta   ),
												        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,						config.pad_token_id   ) ,						tf.inta   ),
												    ] ,						axis=-1 ,						)
						if head_mask is None:
												__magic_name__            =     tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
						if decoder_head_mask is None:
												__magic_name__            =     tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
						return {
						    "input_ids": input_ids,
						    "attention_mask": attention_mask,
						    "decoder_input_ids": decoder_input_ids,
						    "decoder_attention_mask": decoder_attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						}

@require_tf
class 						A_							(      snake_case_					,							snake_case_					,							unittest.TestCase  ):
				UpperCAmelCase__        =				(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
				UpperCAmelCase__        =				(TFLEDForConditionalGeneration,) if is_tf_available() else ()
				UpperCAmelCase__        =				(
				    {
				        '''conversational''': TFLEDForConditionalGeneration,
				        '''feature-extraction''': TFLEDModel,
				        '''summarization''': TFLEDForConditionalGeneration,
				        '''text2text-generation''': TFLEDForConditionalGeneration,
				        '''translation''': TFLEDForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				UpperCAmelCase__        =				True
				UpperCAmelCase__        =				False
				UpperCAmelCase__        =				False
				UpperCAmelCase__        =				False







				def     _snake_case					(							self							:						int	)				->					Optional[int]:
										__magic_name__            =     TFLEDModelTester(self	)
										__magic_name__            =     ConfigTester(self						,    config_class=__lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[Any]	)				->					Dict:
										self.config_tester.run_common_tests()







				def     _snake_case					(							self							:						Optional[int]	)				->					List[Any]:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase	)







				def     _snake_case					(							self							:						List[str]	)				->					str:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()
										__magic_name__            =     tf.zeros_like(inputs_dict["attention_mask"]	)
										__magic_name__            =     2
										__magic_name__            =     tf.where(
										    tf.range(self.model_tester.seq_length	)[None, :] < num_global_attn_indices						,    1						,    inputs_dict["global_attention_mask"]						,    )

										__magic_name__            =     True
										__magic_name__            =     self.model_tester.seq_length
										__magic_name__            =     self.model_tester.encoder_seq_length

										def check_decoder_attentions_output(__lowerCamelCase							:						int	):
																__magic_name__            =     outputs.decoder_attentions
																self.assertEqual(len(__lowerCamelCase	)						,    self.model_tester.num_hidden_layers	)
																self.assertListEqual(
																    list(decoder_attentions[0].shape[-3:]	)						,    [self.model_tester.num_attention_heads, seq_length, seq_length]						,    )

										def check_encoder_attentions_output(__lowerCamelCase							:						Any	):
																__magic_name__            =     [t.numpy() for t in outputs.encoder_attentions]
																__magic_name__            =     [t.numpy() for t in outputs.encoder_global_attentions]
																self.assertEqual(len(__lowerCamelCase	)						,    self.model_tester.num_hidden_layers	)
																self.assertEqual(len(__lowerCamelCase	)						,    self.model_tester.num_hidden_layers	)
																self.assertListEqual(
																    list(attentions[0].shape[-3:]	)						,    [self.model_tester.num_attention_heads, seq_length, seq_length]						,    )
																self.assertListEqual(
																    list(global_attentions[0].shape[-3:]	)						,    [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]						,    )

										for model_class in self.all_model_classes:
																__magic_name__            =     True
																__magic_name__            =     False
																__magic_name__            =     False
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     model(self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)	)
																__magic_name__            =     len(__lowerCamelCase	)
																self.assertEqual(config.output_hidden_states						,    __lowerCamelCase	)
																check_encoder_attentions_output(__lowerCamelCase	)

																if self.is_encoder_decoder:
																						__magic_name__            =     model_class(__lowerCamelCase	)
																						__magic_name__            =     model(self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)	)
																						self.assertEqual(config.output_hidden_states						,    __lowerCamelCase	)
																						check_decoder_attentions_output(__lowerCamelCase	)

																# Check that output attentions can also be changed via the config
																del inputs_dict["output_attentions"]
																__magic_name__            =     True
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     model(self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)	)
																self.assertEqual(config.output_hidden_states						,    __lowerCamelCase	)
																check_encoder_attentions_output(__lowerCamelCase	)

																# Check attention is always last and order is fine
																__magic_name__            =     True
																__magic_name__            =     True
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     model(self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)	)

																self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)						,    len(__lowerCamelCase	)	)
																self.assertEqual(model.config.output_hidden_states						,    __lowerCamelCase	)
																check_encoder_attentions_output(__lowerCamelCase	)







				@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."	)
				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[str]:
										pass







				def     _snake_case					(							self							:						int	)				->					str:
										# TODO: Head-masking not yet implement
										pass






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






						return tf.constant(__lowerCamelCase ,						dtype=tf.intaa   )


lowercase    			=       1e-4


@slow
@require_tf
class 						A_							(      unittest.TestCase  ):







				def     _snake_case					(							self							:						Optional[Any]	)				->					List[str]:
										__magic_name__            =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	).led

										# change to intended input here
										__magic_name__            =     _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     prepare_led_inputs_dict(model.config						,    __lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     model(**__lowerCamelCase	)[0]
										__magic_name__            =     (1, 1_0_2_4, 7_6_8)
										self.assertEqual(output.shape						,    __lowerCamelCase	)
										# change to expected output here
										__magic_name__            =     tf.convert_to_tensor(
										    [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]						,    )
										tf.debugging.assert_near(output[:, :3, :3]						,    __lowerCamelCase						,    atol=1e-3	)







				def     _snake_case					(							self							:						Any	)				->					Dict:
										__magic_name__            =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	)

										# change to intended input here
										__magic_name__            =     _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     prepare_led_inputs_dict(model.config						,    __lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     model(**__lowerCamelCase	)[0]
										__magic_name__            =     (1, 1_0_2_4, model.config.vocab_size)
										self.assertEqual(output.shape						,    __lowerCamelCase	)
										# change to expected output here
										__magic_name__            =     tf.convert_to_tensor(
										    [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]						,    )
										tf.debugging.assert_near(output[:, :3, :3]						,    __lowerCamelCase						,    atol=1e-3						,    rtol=1e-3	)



