
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   :
				_lowercase     						=LEDConfig
				_lowercase     						={}
				_lowercase     						='''gelu'''




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=13							,							_UpperCamelCase=7							,							_UpperCamelCase=True							,							_UpperCamelCase=False							,							_UpperCamelCase=99							,							_UpperCamelCase=32							,							_UpperCamelCase=2							,							_UpperCamelCase=4							,							_UpperCamelCase=37							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=20							,							_UpperCamelCase=2							,							_UpperCamelCase=1							,							_UpperCamelCase=0							,							_UpperCamelCase=4							,							)							-> Any:
							lowerCAmelCase_         =      parent
							lowerCAmelCase_         =      batch_size
							lowerCAmelCase_         =      seq_length
							lowerCAmelCase_         =      is_training
							lowerCAmelCase_         =      use_labels
							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      hidden_size
							lowerCAmelCase_         =      num_hidden_layers
							lowerCAmelCase_         =      num_attention_heads
							lowerCAmelCase_         =      intermediate_size

							lowerCAmelCase_         =      hidden_dropout_prob
							lowerCAmelCase_         =      attention_probs_dropout_prob
							lowerCAmelCase_         =      max_position_embeddings
							lowerCAmelCase_         =      eos_token_id
							lowerCAmelCase_         =      pad_token_id
							lowerCAmelCase_         =      bos_token_id
							lowerCAmelCase_         =      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
							lowerCAmelCase_         =      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
							lowerCAmelCase_         =      (
							    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
							)




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      ids_tensor([self.batch_size, self.seq_length - 1]							,							self.vocab_size	)
							lowerCAmelCase_         =      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)							,							1	)
							lowerCAmelCase_         =      tf.concat([input_ids, eos_tensor]							,							axis=1	)

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

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




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase	)							-> int:
							lowerCAmelCase_         =      TFLEDModel(config=_UpperCamelCase	).get_decoder()
							lowerCAmelCase_         =      inputs_dict["input_ids"]

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

							# first forward pass
							lowerCAmelCase_         =      model(_UpperCamelCase							,							attention_mask=_UpperCamelCase							,							use_cache=_UpperCamelCase	)

							lowerCAmelCase_      ,    lowerCAmelCase_         =      outputs.to_tuple()

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

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

							lowerCAmelCase_         =      model(_UpperCamelCase							,							attention_mask=_UpperCamelCase	)[0]
							lowerCAmelCase_         =      model(_UpperCamelCase							,							attention_mask=_UpperCamelCase							,							past_key_values=_UpperCamelCase	)[0]

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

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

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






def   lowerCamelCase__							(							__lowerCAmelCase    :    Union[str, Any]       ,				__lowerCAmelCase    :    Dict       ,				__lowerCAmelCase    :    Optional[Any]       ,				__lowerCAmelCase    :    Any=None       ,				__lowerCAmelCase    :    Any=None       ,				__lowerCAmelCase    :    Optional[int]=None       ,				__lowerCAmelCase    :    str=None       ,				):




			"""simple docstring"""


			if attention_mask is None:
						lowerCAmelCase_         =      tf.cast(tf.math.not_equal(__lowerCAmelCase       ,				config.pad_token_id							)       ,				tf.inta							)
			if decoder_attention_mask is None:
						lowerCAmelCase_         =      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:
						lowerCAmelCase_         =      tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
			if decoder_head_mask is None:
						lowerCAmelCase_         =      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   (      __a   , __a   , unittest.TestCase			):
				_lowercase     						=(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
				_lowercase     						=(TFLEDForConditionalGeneration,) if is_tf_available() else ()
				_lowercase     						=(
				    {
				        '''conversational''': TFLEDForConditionalGeneration,
				        '''feature-extraction''': TFLEDModel,
				        '''summarization''': TFLEDForConditionalGeneration,
				        '''text2text-generation''': TFLEDForConditionalGeneration,
				        '''translation''': TFLEDForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				_lowercase     						=True
				_lowercase     						=False
				_lowercase     						=False
				_lowercase     						=False




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      TFLEDModelTester(self	)
							lowerCAmelCase_         =      ConfigTester(self							,							config_class=_UpperCamelCase	)




				def 	__a		(   self	)							-> List[Any]:
							self.config_tester.run_common_tests()




				def 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase	)




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

							lowerCAmelCase_         =      True
							lowerCAmelCase_         =      self.model_tester.seq_length
							lowerCAmelCase_         =      self.model_tester.encoder_seq_length

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

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

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

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

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




				@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."	)
				def 	__a		(   self	)							-> Dict:
							pass




				def 	__a		(   self	)							-> List[Any]:
							# TODO: Head-masking not yet implement
							pass






def   lowerCamelCase__							(							__lowerCAmelCase    :    Union[str, Any]							):




			"""simple docstring"""


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


_A  =	1e-4





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




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	).led

							# change to intended input here
							lowerCAmelCase_         =      _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]	)
							lowerCAmelCase_         =      _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]	)
							lowerCAmelCase_         =      prepare_led_inputs_dict(model.config							,							_UpperCamelCase							,							_UpperCamelCase	)
							lowerCAmelCase_         =      model(**_UpperCamelCase	)[0]
							lowerCAmelCase_         =      (1, 1_024, 768)
							self.assertEqual(output.shape							,							_UpperCamelCase	)
							# change to expected output here
							lowerCAmelCase_         =      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]							,							_UpperCamelCase							,							atol=1e-3	)




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	)

							# change to intended input here
							lowerCAmelCase_         =      _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]	)
							lowerCAmelCase_         =      _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]	)
							lowerCAmelCase_         =      prepare_led_inputs_dict(model.config							,							_UpperCamelCase							,							_UpperCamelCase	)
							lowerCAmelCase_         =      model(**_UpperCamelCase	)[0]
							lowerCAmelCase_         =      (1, 1_024, model.config.vocab_size)
							self.assertEqual(output.shape							,							_UpperCamelCase	)
							# change to expected output here
							lowerCAmelCase_         =      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]							,							_UpperCamelCase							,							atol=1e-3							,							rtol=1e-3	)





from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


_A  =	datasets.utils.logging.get_logger(__name__)





class       _lowerCAmelCase   (      folder_based_builder.FolderBasedBuilderConfig			):
				_lowercase     						=None
				_lowercase     						=None





class       _lowerCAmelCase   (      folder_based_builder.FolderBasedBuilder			):
				_lowercase     						=datasets.Audio()
				_lowercase     						='''audio'''
				_lowercase     						=AudioFolderConfig
				_lowercase     						=42  # definition at the bottom of the script
				_lowercase     						=AudioClassification(audio_column='''audio'''   , label_column='''label'''			)


_A  =	[
    ".aiff",
    ".au",
    ".avr",
    ".caf",
    ".flac",
    ".htk",
    ".svx",
    ".mat4",
    ".mat5",
    ".mpc2k",
    ".ogg",
    ".paf",
    ".pvf",
    ".raw",
    ".rf64",
    ".sd2",
    ".sds",
    ".ircam",
    ".voc",
    ".w64",
    ".wav",
    ".nist",
    ".wavex",
    ".wve",
    ".xi",
    ".mp3",
    ".opus",
]
_A  =	AUDIO_EXTENSIONS



