"""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   UpperCAmelCase_			:
						lowerCamelCase__	:					str										=						LEDConfig
						lowerCamelCase__	:					Dict										=						{}
						lowerCamelCase__	:					Dict										=						'''gelu'''


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

													lowercase__    :	Tuple     					=			hidden_dropout_prob
													lowercase__    :	int     					=			attention_probs_dropout_prob
													lowercase__    :	Any     					=			max_position_embeddings
													lowercase__    :	int     					=			eos_token_id
													lowercase__    :	Any     					=			pad_token_id
													lowercase__    :	str     					=			bos_token_id
													lowercase__    :	Dict     					=			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
													lowercase__    :	List[str]     					=			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
													lowercase__    :	Optional[Any]     					=			(
													    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
													)


						def 	_UpperCAmelCase (     self							) ->      Tuple:
													lowercase__    :	Optional[int]     					=			ids_tensor([self.batch_size, self.seq_length - 1]	,    self.vocab_size							)
													lowercase__    :	List[Any]     					=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)	,    1							)
													lowercase__    :	List[Any]     					=			tf.concat([input_ids, eos_tensor]	,    axis=1							)

													lowercase__    :	Any     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size							)

													lowercase__    :	str     					=			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	,    )
													lowercase__    :	Dict     					=			prepare_led_inputs_dict(snake_case__	,    snake_case__	,    snake_case__							)
													lowercase__    :	List[str]     					=			tf.concat(
													    [tf.zeros_like(snake_case__							)[:, :-1], tf.ones_like(snake_case__							)[:, -1:]]	,    axis=-1	,    )
													lowercase__    :	int     					=			global_attention_mask
													return config, inputs_dict


						def 	_UpperCAmelCase (     self	,    a	,    a							) ->      Optional[int]:
													lowercase__    :	List[str]     					=			TFLEDModel(config=snake_case__							).get_decoder()
													lowercase__    :	Tuple     					=			inputs_dict['input_ids']

													lowercase__    :	List[Any]     					=			input_ids[:1, :]
													lowercase__    :	List[Any]     					=			inputs_dict['attention_mask'][:1, :]
													lowercase__    :	Union[str, Any]     					=			1

													# first forward pass
													lowercase__    :	str     					=			model(snake_case__	,    attention_mask=snake_case__	,    use_cache=snake_case__							)

													lowercase__					,   lowercase__    :	str     					=			outputs.to_tuple()

													# create hypothetical next token and extent to next_input_ids
													lowercase__    :	Union[str, Any]     					=			ids_tensor((self.batch_size, 3)	,    config.vocab_size							)
													lowercase__    :	List[str]     					=			tf.cast(ids_tensor((self.batch_size, 3)	,    2							)	,    tf.inta							)

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

													lowercase__    :	Union[str, Any]     					=			model(snake_case__	,    attention_mask=snake_case__							)[0]
													lowercase__    :	Any     					=			model(snake_case__	,    attention_mask=snake_case__	,    past_key_values=snake_case__							)[0]

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

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

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





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				List[str]=None     ,				_lowerCAmelCase						:				Any=None     ,				_lowerCAmelCase						:				Union[str, Any]=None     ,				_lowerCAmelCase						:				int=None     ,				):





							'''simple docstring'''

							if attention_mask is None:
														lowercase__    :	Tuple     					=			tf.cast(tf.math.not_equal(lowerCAmelCase__     ,				config.pad_token_id  )     ,				tf.inta  )
							if decoder_attention_mask is None:
														lowercase__    :	str     					=			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:
														lowercase__    :	List[Any]     					=			tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
							if decoder_head_mask is None:
														lowercase__    :	Optional[Any]     					=			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   UpperCAmelCase_			(					__a				,							__a				,							unittest.TestCase):
						lowerCamelCase__	:					str										=						(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
						lowerCamelCase__	:					int										=						(TFLEDForConditionalGeneration,) if is_tf_available() else ()
						lowerCamelCase__	:					List[Any]										=						(
						    {
						        '''conversational''': TFLEDForConditionalGeneration,
						        '''feature-extraction''': TFLEDModel,
						        '''summarization''': TFLEDForConditionalGeneration,
						        '''text2text-generation''': TFLEDForConditionalGeneration,
						        '''translation''': TFLEDForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						lowerCamelCase__	:					Any										=						True
						lowerCamelCase__	:					Dict										=						False
						lowerCamelCase__	:					int										=						False
						lowerCamelCase__	:					Optional[int]										=						False


						def 	_UpperCAmelCase (     self							) ->      List[Any]:
													lowercase__    :	Optional[Any]     					=			TFLEDModelTester(self							)
													lowercase__    :	Any     					=			ConfigTester(self	,    config_class=snake_case__							)


						def 	_UpperCAmelCase (     self							) ->      Tuple:
													self.config_tester.run_common_tests()


						def 	_UpperCAmelCase (     self							) ->      str:
													lowercase__    :	int     					=			self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.check_decoder_model_past_large_inputs(*snake_case__							)


						def 	_UpperCAmelCase (     self							) ->      Tuple:
													lowercase__					,   lowercase__    :	Dict     					=			self.model_tester.prepare_config_and_inputs_for_common()
													lowercase__    :	str     					=			tf.zeros_like(inputs_dict['attention_mask']							)
													lowercase__    :	List[str]     					=			2
													lowercase__    :	str     					=			tf.where(
													    tf.range(self.model_tester.seq_length							)[None, :] < num_global_attn_indices	,    1	,    inputs_dict['global_attention_mask']	,    )

													lowercase__    :	Union[str, Any]     					=			True
													lowercase__    :	Union[str, Any]     					=			self.model_tester.seq_length
													lowercase__    :	Dict     					=			self.model_tester.encoder_seq_length

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

																				if self.is_encoder_decoder:
																											lowercase__    :	int     					=			model_class(snake_case__							)
																											lowercase__    :	List[str]     					=			model(self._prepare_for_class(snake_case__	,    snake_case__							)							)
																											self.assertEqual(config.output_hidden_states	,    snake_case__							)
																											check_decoder_attentions_output(snake_case__							)

																				# Check that output attentions can also be changed via the config
																				del inputs_dict["output_attentions"]
																				lowercase__    :	Dict     					=			True
																				lowercase__    :	Tuple     					=			model_class(snake_case__							)
																				lowercase__    :	Any     					=			model(self._prepare_for_class(snake_case__	,    snake_case__							)							)
																				self.assertEqual(config.output_hidden_states	,    snake_case__							)
																				check_encoder_attentions_output(snake_case__							)

																				# Check attention is always last and order is fine
																				lowercase__    :	List[Any]     					=			True
																				lowercase__    :	Union[str, Any]     					=			True
																				lowercase__    :	Optional[Any]     					=			model_class(snake_case__							)
																				lowercase__    :	int     					=			model(self._prepare_for_class(snake_case__	,    snake_case__							)							)

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


						@unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'							)
						def 	_UpperCAmelCase (     self							) ->      List[str]:
													pass


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





def        a_  (   _lowerCAmelCase						:				Dict  ):





							'''simple docstring'''

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


_UpperCamelCase     :		Union[str, Any]											=					1e-4






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


						def 	_UpperCAmelCase (     self							) ->      Dict:
													lowercase__    :	List[Any]     					=			TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'							).led

													# change to intended input here
													lowercase__    :	str     					=			_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]]							)
													lowercase__    :	str     					=			_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]]							)
													lowercase__    :	List[Any]     					=			prepare_led_inputs_dict(model.config	,    snake_case__	,    snake_case__							)
													lowercase__    :	Any     					=			model(**snake_case__							)[0]
													lowercase__    :	List[Any]     					=			(1, 1_0_2_4, 7_6_8)
													self.assertEqual(output.shape	,    snake_case__							)
													# change to expected output here
													lowercase__    :	Dict     					=			tf.convert_to_tensor(
													    [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]]	,    )
													tf.debugging.assert_near(output[:, :3, :3]	,    snake_case__	,    atol=1e-3							)


						def 	_UpperCAmelCase (     self							) ->      str:
													lowercase__    :	List[Any]     					=			TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'							)

													# change to intended input here
													lowercase__    :	Dict     					=			_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]]							)
													lowercase__    :	str     					=			_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]]							)
													lowercase__    :	Dict     					=			prepare_led_inputs_dict(model.config	,    snake_case__	,    snake_case__							)
													lowercase__    :	Dict     					=			model(**snake_case__							)[0]
													lowercase__    :	Union[str, Any]     					=			(1, 1_0_2_4, model.config.vocab_size)
													self.assertEqual(output.shape	,    snake_case__							)
													# change to expected output here
													lowercase__    :	str     					=			tf.convert_to_tensor(
													    [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]]	,    )
													tf.debugging.assert_near(output[:, :3, :3]	,    snake_case__	,    atol=1e-3	,    rtol=1e-3							)


"""simple docstring"""





from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments

