






'''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  _snake_case :

					"""simple docstring"""





					_UpperCamelCase					=   LEDConfig
					_UpperCamelCase					=   {}
					_UpperCamelCase					=   "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 ,			)   ->						Union[str, Any]:
										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 			__SCREAMING_SNAKE_CASE			(  self  )   ->						List[Any]:
										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(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__  )
										a_						= tf.concat(
										    [tf.zeros_like(UpperCAmelCase__  )[:, :-1], tf.ones_like(UpperCAmelCase__  )[:, -1:]] ,			axis=-1 ,			)
										a_						= global_attention_mask
										return config, inputs_dict




					def 			__SCREAMING_SNAKE_CASE			(  self ,			UpperCAmelCase__ ,			UpperCAmelCase__  )   ->						List[Any]:
										a_						= TFLEDModel(config=UpperCAmelCase__  ).get_decoder()
										a_						= inputs_dict['input_ids']

										a_						= input_ids[:1, :]
										a_						= inputs_dict['attention_mask'][:1, :]
										a_						= 1

										# first forward pass
										a_						= model(UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__ ,			use_cache=UpperCAmelCase__  )

										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(UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__  )[0]
										a_						= 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
										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(UpperCAmelCase__ ,			UpperCAmelCase__ ,			rtol=1e-3  )



def 				a						( _UpperCAmelCase       ,	_UpperCAmelCase       ,	_UpperCAmelCase       ,	_UpperCAmelCase=None       ,	_UpperCAmelCase=None       ,	_UpperCAmelCase=None       ,	_UpperCAmelCase=None       ,	)					->					Union[str, Any]:






					"""simple docstring"""







					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  _snake_case (					snake_case  ,       snake_case  ,       unittest.TestCase       ):

					"""simple docstring"""





					_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 			__SCREAMING_SNAKE_CASE			(  self  )   ->						str:
										a_						= TFLEDModelTester(self  )
										a_						= ConfigTester(self ,			config_class=UpperCAmelCase__  )




					def 			__SCREAMING_SNAKE_CASE			(  self  )   ->						Optional[Any]:
										self.config_tester.run_common_tests()




					def 			__SCREAMING_SNAKE_CASE			(  self  )   ->						str:
										a_						= self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase__  )




					def 			__SCREAMING_SNAKE_CASE			(  self  )   ->						Optional[Any]:
										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(UpperCAmelCase__  ):
															a_						= 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__  ):
															a_						= [t.numpy() for t in outputs.encoder_attentions]
															a_						= [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:
															a_						= True
															a_						= False
															a_						= False
															a_						= model_class(UpperCAmelCase__  )
															a_						= model(self._prepare_for_class(UpperCAmelCase__ ,			UpperCAmelCase__  )  )
															a_						= len(UpperCAmelCase__  )
															self.assertEqual(config.output_hidden_states ,			UpperCAmelCase__  )
															check_encoder_attentions_output(UpperCAmelCase__  )

															if self.is_encoder_decoder:
																				a_						= model_class(UpperCAmelCase__  )
																				a_						= 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"]
															a_						= True
															a_						= model_class(UpperCAmelCase__  )
															a_						= 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
															a_						= True
															a_						= True
															a_						= model_class(UpperCAmelCase__  )
															a_						= 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 			__SCREAMING_SNAKE_CASE			(  self  )   ->						List[str]:
										pass




					def 			__SCREAMING_SNAKE_CASE			(  self  )   ->						Union[str, Any]:
										pass



def 				a						( _UpperCAmelCase			)					->					str:






					"""simple docstring"""







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


__lowerCAmelCase				=1e-4





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

					"""simple docstring"""





					def 			__SCREAMING_SNAKE_CASE			(  self  )   ->						int:
										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 ,			UpperCAmelCase__ ,			UpperCAmelCase__  )
										a_						= model(**UpperCAmelCase__  )[0]
										a_						= (1, 1024, 768)
										self.assertEqual(output.shape ,			UpperCAmelCase__  )
										# change to expected output here
										a_						= tf.convert_to_tensor(
										    [[2.3_0_5_0, 2.8_2_7_9, 0.6_5_3_1], [-1.8_4_5_7, -0.1_4_5_5, -3.5_6_6_1], [-1.0_1_8_6, 0.4_5_8_6, -2.2_0_4_3]] ,			)
										tf.debugging.assert_near(output[:, :3, :3] ,			UpperCAmelCase__ ,			atol=1e-3  )




					def 			__SCREAMING_SNAKE_CASE			(  self  )   ->						Optional[Any]:
										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 ,			UpperCAmelCase__ ,			UpperCAmelCase__  )
										a_						= model(**UpperCAmelCase__  )[0]
										a_						= (1, 1024, model.config.vocab_size)
										self.assertEqual(output.shape ,			UpperCAmelCase__  )
										# change to expected output here
										a_						= tf.convert_to_tensor(
										    [[3_3.6_5_0_7, 6.4_5_7_2, 1_6.8_0_8_9], [5.8_7_3_9, -2.4_2_3_8, 1_1.2_9_0_2], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]] ,			)
										tf.debugging.assert_near(output[:, :3, :3] ,			UpperCAmelCase__ ,			atol=1e-3 ,			rtol=1e-3  )







def 						__A						(      ) ->			list[list[int]]:
	return [list(range(1000 - i				,     -1000 - i				,     -1       )       ) for i in range(1000       )]


__UpperCamelCase   :			str          = generate_large_matrix()
__UpperCamelCase   :			Tuple          = (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)





def 						__A						(      __lowerCamelCase       ) ->			None:
	assert all(row == sorted(__lowerCamelCase				,     reverse=__lowerCamelCase       ) for row in grid       )
	assert all(list(__lowerCamelCase       ) == sorted(__lowerCamelCase				,     reverse=__lowerCamelCase       ) for col in zip(*__lowerCamelCase       )       )





def 						__A						(      __lowerCamelCase       ) ->			int:
	a									=				0
	a									=				len(__lowerCamelCase       ) - 1

	# Edge cases such as no values or all numbers are negative.
	if not array or array[0] < 0:
		return 0

	while right + 1 > left:
		a									=				(left + right) // 2
		a									=				array[mid]

		# Num must be negative and the index must be greater than or equal to 0.
		if num < 0 and array[mid - 1] >= 0:
			return mid

		if num >= 0:
			a									=				mid + 1
		else:
			a									=				mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
	return len(__lowerCamelCase       )





def 						__A						(      __lowerCamelCase       ) ->			int:
	a									=				0
	a									=				len(grid[0]       )

	for i in range(len(__lowerCamelCase       )       ):
		a									=				find_negative_index(grid[i][:bound]       )
		total += bound
	return (len(__lowerCamelCase       ) * len(grid[0]       )) - total





def 						__A						(      __lowerCamelCase       ) ->			int:
	return len([number for row in grid for number in row if number < 0]       )





def 						__A						(      __lowerCamelCase       ) ->			int:
	a									=				0
	for row in grid:
		for i, number in enumerate(__lowerCamelCase       ):
			if number < 0:
				total += len(__lowerCamelCase       ) - i
				break
	return total





def 						__A						(      ) ->			None:
	from timeit import timeit

	print("""Running benchmarks"""       )
	a									=				(
	    """from __main__ import count_negatives_binary_search, """
	    """count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
	)
	for func in (
	    "count_negatives_binary_search",  # took 0.7727 seconds
	    "count_negatives_brute_force_with_break",  # took 4.6505 seconds
	    "count_negatives_brute_force",  # took 12.8160 seconds
	):
		a									=				timeit(f'{func}(grid=grid)'				,     setup=__lowerCamelCase				,     number=500       )
		print(f'{func}() took {time:0.4f} seconds'       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()

