




from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__UpperCamelCase					:       Optional[Any]    					=      logging.get_logger(__name__)

class 		__lowerCAmelCase	(	__magic_name__ ):
							UpperCamelCase__					     =  ['''input_values''', '''padding_mask''']
							def __init__(						self						:Optional[int]		,      __magic_name__						:int = 1		,      __magic_name__						:int = 2_4000		,      __magic_name__						:float = 0.0		,      __magic_name__						:float = None		,      __magic_name__						:float = None		,      **__magic_name__						:str		,      ):







								'''simple docstring'''







								super().__init__(feature_size=__magic_name__		,      sampling_rate=__magic_name__		,      padding_value=__magic_name__		,      **__magic_name__				)
								a										=							chunk_length_s
								a										=							overlap
							@property
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								if self.chunk_length_s is None:
									return None
								else:
									return int(self.chunk_length_s * self.sampling_rate				)
							@property
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								if self.chunk_length_s is None or self.overlap is None:
									return None
								else:
									return max(1		,      int((1.0 - self.overlap) * self.chunk_length				)				)





							def __call__(						self						:Union[str, Any]		,      __magic_name__						:Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		,      __magic_name__						:Optional[Union[bool, str, PaddingStrategy]] = None		,      __magic_name__						:Optional[bool] = False		,      __magic_name__						:Optional[int] = None		,      __magic_name__						:Optional[Union[str, TensorType]] = None		,      __magic_name__						:Optional[int] = None		,      ):







								'''simple docstring'''







								if sampling_rate is not None:
									if sampling_rate != self.sampling_rate:
										raise ValueError(
										    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
										    F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
										    F' {self.sampling_rate} and not {sampling_rate}.'				)
								else:
									logger.warning(
									    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
									    """Failing to do so can result in silent errors that might be hard to debug."""				)

								if padding and truncation:
									raise ValueError("""Both padding and truncation were set. Make sure you only set one."""				)
								elif padding is None:
									# by default let's pad the inputs
									a										=							True

								a										=							bool(
								    isinstance(__magic_name__		,      (list, tuple)				) and (isinstance(raw_audio[0]		,      (np.ndarray, tuple, list)				))				)

								if is_batched:
									a										=							[np.asarray(__magic_name__		,      dtype=np.floataa				).T for audio in raw_audio]
								elif not is_batched and not isinstance(__magic_name__		,      np.ndarray				):
									a										=							np.asarray(__magic_name__		,      dtype=np.floataa				)
								elif isinstance(__magic_name__		,      np.ndarray				) and raw_audio.dtype is np.dtype(np.floataa				):
									a										=							raw_audio.astype(np.floataa				)

								# always return batch
								if not is_batched:
									a										=							[np.asarray(__magic_name__				).T]

								# verify inputs are valid
								for idx, example in enumerate(__magic_name__				):
									if example.ndim > 2:
										raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'				)
									if self.feature_size == 1 and example.ndim != 1:
										raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'				)
									if self.feature_size == 2 and example.shape[-1] != 2:
										raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'				)

								a										=							None
								a										=							BatchFeature({"""input_values""": raw_audio}				)
								if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
									if truncation:
										a										=							min(array.shape[0] for array in raw_audio				)
										a										=							int(np.floor(max_length / self.chunk_stride				)				)
										a										=							(nb_step - 1) * self.chunk_stride + self.chunk_length
									elif padding:
										a										=							max(array.shape[0] for array in raw_audio				)
										a										=							int(np.ceil(max_length / self.chunk_stride				)				)
										a										=							(nb_step - 1) * self.chunk_stride + self.chunk_length
										a										=							"""max_length"""
									else:
										a										=							input_values

        # normal padding on batch
								if padded_inputs is None:
									a										=							self.pad(
									    __magic_name__		,      max_length=__magic_name__		,      truncation=__magic_name__		,      padding=__magic_name__		,      return_attention_mask=__magic_name__		,      )
									if padding:
										a										=							padded_inputs.pop("""attention_mask"""				)

								a										=							[]
								for example in padded_inputs.pop("""input_values"""				):
									if self.feature_size == 1:
										a										=							example[..., None]
									input_values.append(example.T				)

								a										=							input_values
								if return_tensors is not None:
									a										=							padded_inputs.convert_to_tensors(__magic_name__				)

								return padded_inputs







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 		__lowerCAmelCase	:
							UpperCamelCase__					     =  LEDConfig
							UpperCamelCase__					     =  {}
							UpperCamelCase__					     =  '''gelu'''
							def __init__(						self						:Optional[int]		,      __magic_name__						:Dict		,      __magic_name__						:List[str]=13		,      __magic_name__						:Union[str, Any]=7		,      __magic_name__						:str=True		,      __magic_name__						:Union[str, Any]=False		,      __magic_name__						:Union[str, Any]=99		,      __magic_name__						:List[Any]=32		,      __magic_name__						:str=2		,      __magic_name__						:List[str]=4		,      __magic_name__						:str=37		,      __magic_name__						:Any=0.1		,      __magic_name__						:Dict=0.1		,      __magic_name__						:List[str]=20		,      __magic_name__						:Union[str, Any]=2		,      __magic_name__						:List[Any]=1		,      __magic_name__						:Optional[int]=0		,      __magic_name__						:Optional[int]=4		,      ):







								'''simple docstring'''







								a										=							parent
								a										=							batch_size
								a										=							seq_length
								a										=							is_training
								a										=							use_labels
								a										=							vocab_size
								a										=							hidden_size
								a										=							num_hidden_layers
								a										=							num_attention_heads
								a										=							intermediate_size

								a										=							hidden_dropout_prob
								a										=							attention_probs_dropout_prob
								a										=							max_position_embeddings
								a										=							eos_token_id
								a										=							pad_token_id
								a										=							bos_token_id
								a										=							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
								a										=							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
								a										=							(
								    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
								)
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								a										=							ids_tensor([self.batch_size, self.seq_length - 1]		,      self.vocab_size				)
								a										=							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)		,      1				)
								a										=							tf.concat([input_ids, eos_tensor]		,      axis=1				)

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

								a										=							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		,      )
								a										=							prepare_led_inputs_dict(__magic_name__		,      __magic_name__		,      __magic_name__				)
								a										=							tf.concat(
								    [tf.zeros_like(__magic_name__				)[:, :-1], tf.ones_like(__magic_name__				)[:, -1:]]		,      axis=-1		,      )
								a										=							global_attention_mask
								return config, inputs_dict





							def       lowerCamelCase__							(						self						:Union[str, Any]		,      __magic_name__						:Optional[int]		,      __magic_name__						:List[Any]				):







								'''simple docstring'''







								a										=							TFLEDModel(config=__magic_name__				).get_decoder()
								a										=							inputs_dict["""input_ids"""]

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

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

								a				, a										=							outputs.to_tuple()

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

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

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

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

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

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







def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			)      ->       List[str]:
	if attention_mask is None:
		a										=							tf.cast(tf.math.not_equal(__lowerCamelCase							,			config.pad_token_id    )							,			tf.inta    )
	if decoder_attention_mask is None:
		a										=							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:
		a										=							tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
	if decoder_head_mask is None:
		a										=							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 		__lowerCAmelCase	(	__magic_name__      ,       __magic_name__      ,       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       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								a										=							TFLEDModelTester(self				)
								a										=							ConfigTester(self		,      config_class=__magic_name__				)
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								self.config_tester.run_common_tests()
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*__magic_name__				)
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								a				, a										=							self.model_tester.prepare_config_and_inputs_for_common()
								a										=							tf.zeros_like(inputs_dict["""attention_mask"""]				)
								a										=							2
								a										=							tf.where(
								    tf.range(self.model_tester.seq_length				)[None, :] < num_global_attn_indices		,      1		,      inputs_dict["""global_attention_mask"""]		,      )

								a										=							True
								a										=							self.model_tester.seq_length
								a										=							self.model_tester.encoder_seq_length

								def check_decoder_attentions_output(__magic_name__						:int				):
									a										=							outputs.decoder_attentions
									self.assertEqual(len(__magic_name__				)		,      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(__magic_name__						:Any				):
									a										=							[t.numpy() for t in outputs.encoder_attentions]
									a										=							[t.numpy() for t in outputs.encoder_global_attentions]
									self.assertEqual(len(__magic_name__				)		,      self.model_tester.num_hidden_layers				)
									self.assertEqual(len(__magic_name__				)		,      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:
									a										=							True
									a										=							False
									a										=							False
									a										=							model_class(__magic_name__				)
									a										=							model(self._prepare_for_class(__magic_name__		,      __magic_name__				)				)
									a										=							len(__magic_name__				)
									self.assertEqual(config.output_hidden_states		,      __magic_name__				)
									check_encoder_attentions_output(__magic_name__				)

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

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

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

									self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)		,      len(__magic_name__				)				)
									self.assertEqual(model.config.output_hidden_states		,      __magic_name__				)
									check_encoder_attentions_output(__magic_name__				)
							@unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing."""				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								pass





							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								pass







def 		__A		(	__lowerCamelCase    )      ->       int:
	return tf.constant(__lowerCamelCase							,			dtype=tf.intaa    )


__UpperCamelCase					:       int    					=      1E-4



@slow
@require_tf
class 		__lowerCAmelCase	(	unittest.TestCase ):
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""				).led

								# change to intended input here
								a										=							_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]				)
								a										=							_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]				)
								a										=							prepare_led_inputs_dict(model.config		,      __magic_name__		,      __magic_name__				)
								a										=							model(**__magic_name__				)[0]
								a										=							(1, 1024, 768)
								self.assertEqual(output.shape		,      __magic_name__				)
								# change to expected output here
								a										=							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]		,      __magic_name__		,      atol=1E-3				)





							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""				)

								# change to intended input here
								a										=							_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]				)
								a										=							_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]				)
								a										=							prepare_led_inputs_dict(model.config		,      __magic_name__		,      __magic_name__				)
								a										=							model(**__magic_name__				)[0]
								a										=							(1, 1024, model.config.vocab_size)
								self.assertEqual(output.shape		,      __magic_name__				)
								# change to expected output here
								a										=							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]		,      __magic_name__		,      atol=1E-3		,      rtol=1E-3				)

