



"""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        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





					_a   			=							LEDConfig
					_a   			=							{}
					_a   			=							'gelu'



					def __init__(       self							:  List[str],  lowerCamelCase							:  Tuple,  lowerCamelCase							:  int=13,  lowerCamelCase							:  str=7,  lowerCamelCase							:  Dict=True,  lowerCamelCase							:  str=False,  lowerCamelCase							:  List[Any]=99,  lowerCamelCase							:  Optional[Any]=32,  lowerCamelCase							:  Optional[int]=2,  lowerCamelCase							:  Dict=4,  lowerCamelCase							:  Any=37,  lowerCamelCase							:  List[str]=0.1,  lowerCamelCase							:  Optional[int]=0.1,  lowerCamelCase							:  Dict=20,  lowerCamelCase							:  Tuple=2,  lowerCamelCase							:  Union[str, Any]=1,  lowerCamelCase							:  List[Any]=0,  lowerCamelCase							:  Optional[Any]=4,  )->					int:
								lowerCamelCase__		:		Union[str, Any]			=parent
								lowerCamelCase__		:		List[str]			=batch_size
								lowerCamelCase__		:		str			=seq_length
								lowerCamelCase__		:		Dict			=is_training
								lowerCamelCase__		:		Dict			=use_labels
								lowerCamelCase__		:		Union[str, Any]			=vocab_size
								lowerCamelCase__		:		Tuple			=hidden_size
								lowerCamelCase__		:		str			=num_hidden_layers
								lowerCamelCase__		:		Dict			=num_attention_heads
								lowerCamelCase__		:		Optional[int]			=intermediate_size

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



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

								lowerCamelCase__		:		Optional[int]			=ids_tensor([self.batch_size, self.seq_length],  self.vocab_size     )

								lowerCamelCase__		:		Dict			=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__		:		int			=prepare_led_inputs_dict(lowerCamelCase,  lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__		:		Optional[int]			=tf.concat(
								    [tf.zeros_like(lowerCamelCase     )[:, :-1], tf.ones_like(lowerCamelCase     )[:, -1:]],  axis=-1,  )
								lowerCamelCase__		:		str			=global_attention_mask
								return config, inputs_dict




					def 		snake_case       (       self							:  str,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  str     )->					Optional[Any]:
								lowerCamelCase__		:		Union[str, Any]			=TFLEDModel(config=lowerCamelCase     ).get_decoder()
								lowerCamelCase__		:		str			=inputs_dict['''input_ids''']

								lowerCamelCase__		:		str			=input_ids[:1, :]
								lowerCamelCase__		:		str			=inputs_dict['''attention_mask'''][:1, :]
								lowerCamelCase__		:		Optional[int]			=1

								# first forward pass
								lowerCamelCase__		:		Optional[Any]			=model(lowerCamelCase,  attention_mask=lowerCamelCase,  use_cache=lowerCamelCase     )

								lowerCamelCase__	,   lowerCamelCase__		:		Union[str, Any]			=outputs.to_tuple()

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

								# append to next input_ids and
								lowerCamelCase__		:		Optional[Any]			=tf.concat([input_ids, next_tokens],  axis=-1     )
								lowerCamelCase__		:		List[str]			=tf.concat([attention_mask, next_attn_mask],  axis=-1     )

								lowerCamelCase__		:		Union[str, Any]			=model(lowerCamelCase,  attention_mask=lowerCamelCase     )[0]
								lowerCamelCase__		:		Optional[Any]			=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
								lowerCamelCase__		:		Optional[Any]			=int(ids_tensor((1,),  output_from_past.shape[-1]     )     )
								lowerCamelCase__		:		int			=output_from_no_past[:, -3:, random_slice_idx]
								lowerCamelCase__		:		Any			=output_from_past[:, :, random_slice_idx]

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






def      snake_case__   (		__lowerCamelCase				:    str							,		__lowerCamelCase				:    Optional[Any]							,		__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    List[Any]=None							,		__lowerCamelCase				:    Tuple=None							,		__lowerCamelCase				:    Dict=None							,		__lowerCamelCase				:    int=None							,		):
			"""simple docstring"""
			if attention_mask is None:
						lowerCamelCase__		:		List[str]			=tf.cast(tf.math.not_equal(__lowerCamelCase							,		config.pad_token_id	)							,		tf.inta	)
			if decoder_attention_mask is None:
						lowerCamelCase__		:		Tuple			=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__		:		Optional[Any]			=tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
			if decoder_head_mask is None:
						lowerCamelCase__		:		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        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_		,   lowerCAmelCase_		,   unittest.TestCase   ):


					'''simple docstring'''





					_a   			=							(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
					_a   			=							(TFLEDForConditionalGeneration,) if is_tf_available() else ()
					_a   			=							(
					    {
					        'conversational': TFLEDForConditionalGeneration,
					        'feature-extraction': TFLEDModel,
					        'summarization': TFLEDForConditionalGeneration,
					        'text2text-generation': TFLEDForConditionalGeneration,
					        'translation': TFLEDForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					_a   			=							True
					_a   			=							False
					_a   			=							False
					_a   			=							False



					def 		snake_case       (       self							:  Optional[int]     )->					Union[str, Any]:
								lowerCamelCase__		:		int			=TFLEDModelTester(self     )
								lowerCamelCase__		:		Tuple			=ConfigTester(self,  config_class=lowerCamelCase     )



					def 		snake_case       (       self							:  Any     )->					Dict:
								self.config_tester.run_common_tests()



					def 		snake_case       (       self							:  List[str]     )->					Optional[int]:
								lowerCamelCase__		:		Dict			=self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase     )



					def 		snake_case       (       self							:  int     )->					Optional[Any]:
								lowerCamelCase__	,   lowerCamelCase__		:		List[Any]			=self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase__		:		Dict			=tf.zeros_like(inputs_dict['''attention_mask''']     )
								lowerCamelCase__		:		Optional[int]			=2
								lowerCamelCase__		:		Optional[int]			=tf.where(
								    tf.range(self.model_tester.seq_length     )[None, :] < num_global_attn_indices,  1,  inputs_dict['''global_attention_mask'''],  )

								lowerCamelCase__		:		str			=True
								lowerCamelCase__		:		int			=self.model_tester.seq_length
								lowerCamelCase__		:		Tuple			=self.model_tester.encoder_seq_length

								def check_decoder_attentions_output(lowerCamelCase							:  Tuple     ):
											lowerCamelCase__		:		int			=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							:  Dict     ):
											lowerCamelCase__		:		int			=[t.numpy() for t in outputs.encoder_attentions]
											lowerCamelCase__		:		Union[str, Any]			=[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:
											lowerCamelCase__		:		Union[str, Any]			=True
											lowerCamelCase__		:		Dict			=False
											lowerCamelCase__		:		Dict			=False
											lowerCamelCase__		:		List[str]			=model_class(lowerCamelCase     )
											lowerCamelCase__		:		Optional[Any]			=model(self._prepare_for_class(lowerCamelCase,  lowerCamelCase     )     )
											lowerCamelCase__		:		Union[str, Any]			=len(lowerCamelCase     )
											self.assertEqual(config.output_hidden_states,  lowerCamelCase     )
											check_encoder_attentions_output(lowerCamelCase     )

											if self.is_encoder_decoder:
														lowerCamelCase__		:		Any			=model_class(lowerCamelCase     )
														lowerCamelCase__		:		str			=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"]
											lowerCamelCase__		:		Optional[Any]			=True
											lowerCamelCase__		:		Optional[int]			=model_class(lowerCamelCase     )
											lowerCamelCase__		:		List[Any]			=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
											lowerCamelCase__		:		List[Any]			=True
											lowerCamelCase__		:		Tuple			=True
											lowerCamelCase__		:		Optional[Any]			=model_class(lowerCamelCase     )
											lowerCamelCase__		:		List[Any]			=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							:  Any     )->					str:
								pass




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






def      snake_case__   (		__lowerCamelCase				:    int	):
			"""simple docstring"""
			return tf.constant(__lowerCamelCase							,		dtype=tf.intaa	)


_lowercase : Optional[Any]      						=						1E-4

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


					'''simple docstring'''





					def 		snake_case       (       self							:  List[str]     )->					int:
								lowerCamelCase__		:		Tuple			=TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''     ).led

								# change to intended input here
								lowerCamelCase__		:		Tuple			=_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]     )
								lowerCamelCase__		:		Optional[Any]			=_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]     )
								lowerCamelCase__		:		Optional[int]			=prepare_led_inputs_dict(model.config,  lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=model(**lowerCamelCase     )[0]
								lowerCamelCase__		:		List[Any]			=(1, 1024, 768)
								self.assertEqual(output.shape,  lowerCamelCase     )
								# change to expected output here
								lowerCamelCase__		:		List[Any]			=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],  lowerCamelCase,  atol=1E-3     )




					def 		snake_case       (       self							:  Dict     )->					Union[str, Any]:
								lowerCamelCase__		:		List[str]			=TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''     )

								# change to intended input here
								lowerCamelCase__		:		str			=_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]     )
								lowerCamelCase__		:		Tuple			=_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]     )
								lowerCamelCase__		:		Dict			=prepare_led_inputs_dict(model.config,  lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__		:		Optional[int]			=model(**lowerCamelCase     )[0]
								lowerCamelCase__		:		List[Any]			=(1, 1024, model.config.vocab_size)
								self.assertEqual(output.shape,  lowerCamelCase     )
								# change to expected output here
								lowerCamelCase__		:		Tuple			=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],  lowerCamelCase,  atol=1E-3,  rtol=1E-3     )









"""simple docstring"""






class        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





					def __init__(       self							:  Dict,  lowerCamelCase							:  list     )->					None:
								lowerCamelCase__		:		Tuple			=set_counts
								lowerCamelCase__		:		Dict			=max(lowerCamelCase     )
								lowerCamelCase__		:		Union[str, Any]			=len(lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=[1] * num_sets
								lowerCamelCase__		:		str			=list(range(lowerCamelCase     )     )



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  int,  lowerCamelCase							:  int     )->					bool:
								lowerCamelCase__		:		List[Any]			=self.get_parent(lowerCamelCase     )
								lowerCamelCase__		:		str			=self.get_parent(lowerCamelCase     )

								if src_parent == dst_parent:
											return False

								if self.ranks[dst_parent] >= self.ranks[src_parent]:
											self.set_counts[dst_parent] += self.set_counts[src_parent]
											lowerCamelCase__		:		Tuple			=0
											lowerCamelCase__		:		int			=dst_parent
											if self.ranks[dst_parent] == self.ranks[src_parent]:
														self.ranks[dst_parent] += 1
											lowerCamelCase__		:		str			=self.set_counts[dst_parent]
								else:
											self.set_counts[src_parent] += self.set_counts[dst_parent]
											lowerCamelCase__		:		List[Any]			=0
											lowerCamelCase__		:		List[str]			=src_parent
											lowerCamelCase__		:		Dict			=self.set_counts[src_parent]

								lowerCamelCase__		:		str			=max(self.max_set,  lowerCamelCase     )
								return True




					def 		snake_case       (       self							:  Union[str, Any],  lowerCamelCase							:  int     )->					int:
								if self.parents[disj_set] == disj_set:
											return disj_set
								lowerCamelCase__		:		Tuple			=self.get_parent(self.parents[disj_set]     )
								return self.parents[disj_set]




