


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       :
			lowercase						       =			LEDConfig
			lowercase						       =			{}
			lowercase						       =			"""gelu"""




			def __init__(    self				:		Any							,							__magic_name__				:		int							,							__magic_name__				:		int=1_3							,							__magic_name__				:		List[str]=7							,							__magic_name__				:		Optional[int]=True							,							__magic_name__				:		Optional[Any]=False							,							__magic_name__				:		Optional[Any]=9_9							,							__magic_name__				:		List[str]=3_2							,							__magic_name__				:		List[Any]=2							,							__magic_name__				:		Tuple=4							,							__magic_name__				:		List[Any]=3_7							,							__magic_name__				:		Tuple=0.1							,							__magic_name__				:		int=0.1							,							__magic_name__				:		int=2_0							,							__magic_name__				:		int=2							,							__magic_name__				:		Any=1							,							__magic_name__				:		List[str]=0							,							__magic_name__				:		Union[str, Any]=4							,							):







						"""simple docstring"""







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

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




			def 	lowerCamelCase_       (    self				:		Optional[int]    ):







						"""simple docstring"""







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

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

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





			def 	lowerCamelCase_       (    self				:		Dict							,							__magic_name__				:		List[Any]							,							__magic_name__				:		List[Any]    ):







						"""simple docstring"""







						UpperCamelCase								=       TFLEDModel(config=UpperCamelCase__    ).get_decoder()
						UpperCamelCase								=       inputs_dict["""input_ids"""]

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

						# first forward pass
						UpperCamelCase								=       model(UpperCamelCase__							,							attention_mask=UpperCamelCase__							,							use_cache=UpperCamelCase__    )

						UpperCamelCase	,	UpperCamelCase								=       outputs.to_tuple()

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

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

						UpperCamelCase								=       model(UpperCamelCase__							,							attention_mask=UpperCamelCase__    )[0]
						UpperCamelCase								=       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
						UpperCamelCase								=       int(ids_tensor((1,)							,							output_from_past.shape[-1]    )    )
						UpperCamelCase								=       output_from_no_past[:, -3:, random_slice_idx]
						UpperCamelCase								=       output_from_past[:, :, random_slice_idx]

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






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		int							,	SCREAMING_SNAKE_CASE_ :		Dict							,	SCREAMING_SNAKE_CASE_ :		Optional[int]							,	SCREAMING_SNAKE_CASE_ :		Dict=None							,	SCREAMING_SNAKE_CASE_ :		Union[str, Any]=None							,	SCREAMING_SNAKE_CASE_ :		Dict=None							,	SCREAMING_SNAKE_CASE_ :		Tuple=None							,	):





			"""simple docstring"""




			if attention_mask is None:
						UpperCamelCase								=       tf.cast(tf.math.not_equal(UpperCAmelCase__							,	config.pad_token_id						)							,	tf.inta						)
			if decoder_attention_mask is None:
						UpperCamelCase								=       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:
						UpperCamelCase								=       tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
			if decoder_head_mask is None:
						UpperCamelCase								=       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       (							_snake_case   ,			_snake_case   ,			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 	lowerCamelCase_       (    self				:		Optional[int]    ):







						"""simple docstring"""







						UpperCamelCase								=       TFLEDModelTester(self    )
						UpperCamelCase								=       ConfigTester(self							,							config_class=UpperCamelCase__    )




			def 	lowerCamelCase_       (    self				:		str    ):







						"""simple docstring"""







						self.config_tester.run_common_tests()




			def 	lowerCamelCase_       (    self				:		Tuple    ):







						"""simple docstring"""







						UpperCamelCase								=       self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase__    )




			def 	lowerCamelCase_       (    self				:		str    ):







						"""simple docstring"""







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

						UpperCamelCase								=       True
						UpperCamelCase								=       self.model_tester.seq_length
						UpperCamelCase								=       self.model_tester.encoder_seq_length

						def check_decoder_attentions_output(__magic_name__				:		Optional[int]    ):
									UpperCamelCase								=       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(__magic_name__				:		Tuple    ):
									UpperCamelCase								=       [t.numpy() for t in outputs.encoder_attentions]
									UpperCamelCase								=       [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:
									UpperCamelCase								=       True
									UpperCamelCase								=       False
									UpperCamelCase								=       False
									UpperCamelCase								=       model_class(UpperCamelCase__    )
									UpperCamelCase								=       model(self._prepare_for_class(UpperCamelCase__							,							UpperCamelCase__    )    )
									UpperCamelCase								=       len(UpperCamelCase__    )
									self.assertEqual(config.output_hidden_states							,							UpperCamelCase__    )
									check_encoder_attentions_output(UpperCamelCase__    )

									if self.is_encoder_decoder:
												UpperCamelCase								=       model_class(UpperCamelCase__    )
												UpperCamelCase								=       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"]
									UpperCamelCase								=       True
									UpperCamelCase								=       model_class(UpperCamelCase__    )
									UpperCamelCase								=       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
									UpperCamelCase								=       True
									UpperCamelCase								=       True
									UpperCamelCase								=       model_class(UpperCamelCase__    )
									UpperCamelCase								=       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 	lowerCamelCase_       (    self				:		Optional[int]    ):







						"""simple docstring"""







						pass





			def 	lowerCamelCase_       (    self				:		Dict    ):







						"""simple docstring"""







						pass






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		List[Any]						):





			"""simple docstring"""




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


__snake_case      = 1e-4




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




			def 	lowerCamelCase_       (    self				:		Optional[int]    ):







						"""simple docstring"""







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

						# change to intended input here
						UpperCamelCase								=       _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]]    )
						UpperCamelCase								=       _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]]    )
						UpperCamelCase								=       prepare_led_inputs_dict(model.config							,							UpperCamelCase__							,							UpperCamelCase__    )
						UpperCamelCase								=       model(**UpperCamelCase__    )[0]
						UpperCamelCase								=       (1, 1_0_2_4, 7_6_8)
						self.assertEqual(output.shape							,							UpperCamelCase__    )
						# change to expected output here
						UpperCamelCase								=       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]							,							UpperCamelCase__							,							atol=1e-3    )





			def 	lowerCamelCase_       (    self				:		Optional[Any]    ):







						"""simple docstring"""







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

						# change to intended input here
						UpperCamelCase								=       _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]]    )
						UpperCamelCase								=       _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]]    )
						UpperCamelCase								=       prepare_led_inputs_dict(model.config							,							UpperCamelCase__							,							UpperCamelCase__    )
						UpperCamelCase								=       model(**UpperCamelCase__    )[0]
						UpperCamelCase								=       (1, 1_0_2_4, model.config.vocab_size)
						self.assertEqual(output.shape							,							UpperCamelCase__    )
						# change to expected output here
						UpperCamelCase								=       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]							,							UpperCamelCase__							,							atol=1e-3							,							rtol=1e-3    )







'''simple docstring'''







import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""",							[None, 4_00 * 2**20, 6_00 * 2**20]					)
@pytest.mark.parametrize("""input_in_memory_max_size""",							["""default""", 0, 1_00 * 2**20, 9_00 * 2**20]					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Dict:
       if input_in_memory_max_size != "default":
              monkeypatch.setattr(datasets.config,							"""IN_MEMORY_MAX_SIZE""",							UpperCAmelCase__					)
       A_						       =   datasets.config.IN_MEMORY_MAX_SIZE
       if input_in_memory_max_size == "default":
              assert in_memory_max_size == 0
       else:
              assert in_memory_max_size == input_in_memory_max_size
       if dataset_size and in_memory_max_size:
              A_						       =   dataset_size < in_memory_max_size
       else:
              A_						       =   False
       A_						       =   is_small_dataset(UpperCAmelCase__					)
       assert result == expected
