


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_ :
						lowerCamelCase_         =     LEDConfig
						lowerCamelCase_         =     {}
						lowerCamelCase_         =     'gelu'




						def __init__(	self						:   Union[str, Any]   ,     __A						:   Optional[Any]   ,     __A						:   str=13   ,     __A						:   Any=7   ,     __A						:   Union[str, Any]=True   ,     __A						:   Dict=False   ,     __A						:   Any=99   ,     __A						:   str=32   ,     __A						:   Union[str, Any]=2   ,     __A						:   Optional[Any]=4   ,     __A						:   List[str]=37   ,     __A						:   Any=0.1   ,     __A						:   Dict=0.1   ,     __A						:   Any=20   ,     __A						:   List[str]=2   ,     __A						:   Any=1   ,     __A						:   int=0   ,     __A						:   Optional[int]=4   ,     )      ->Optional[int]:






									"""simple docstring"""






									a__ :Optional[int]						  =    parent
									a__ :str						  =    batch_size
									a__ :Dict						  =    seq_length
									a__ :Optional[int]						  =    is_training
									a__ :Tuple						  =    use_labels
									a__ :Dict						  =    vocab_size
									a__ :Any						  =    hidden_size
									a__ :Tuple						  =    num_hidden_layers
									a__ :Union[str, Any]						  =    num_attention_heads
									a__ :str						  =    intermediate_size

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




						def  _snake_case					(	self						:   Union[str, Any]						)      ->Tuple:






									"""simple docstring"""






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

									a__ :str						  =    ids_tensor([self.batch_size, self.seq_length]   ,     self.vocab_size						)

									a__ :List[Any]						  =    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__ :Any						  =    prepare_led_inputs_dict(_UpperCAmelCase   ,     _UpperCAmelCase   ,     _UpperCAmelCase						)
									a__ :List[str]						  =    tf.concat(
									    [tf.zeros_like(_UpperCAmelCase						)[:, :-1], tf.ones_like(_UpperCAmelCase						)[:, -1:]]   ,     axis=-1   ,     )
									a__ :Dict						  =    global_attention_mask
									return config, inputs_dict






						def  _snake_case					(	self						:   Optional[int]   ,     __A						:   int   ,     __A						:   Any						)      ->str:






									"""simple docstring"""






									a__ :Dict						  =    TFLEDModel(config=_UpperCAmelCase						).get_decoder()
									a__ :Optional[int]						  =    inputs_dict["input_ids"]

									a__ :Optional[Any]						  =    input_ids[:1, :]
									a__ :Optional[int]						  =    inputs_dict["attention_mask"][:1, :]
									a__ :Tuple						  =    1

									# first forward pass
									a__ :List[str]						  =    model(_UpperCAmelCase   ,     attention_mask=_UpperCAmelCase   ,     use_cache=_UpperCAmelCase						)

									a__  ,  a__ :List[str]						  =    outputs.to_tuple()

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

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

									a__ :Tuple						  =    model(_UpperCAmelCase   ,     attention_mask=_UpperCAmelCase						)[0]
									a__ :Optional[int]						  =    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__ :Optional[int]						  =    int(ids_tensor((1,)   ,     output_from_past.shape[-1]						)						)
									a__ :int						  =    output_from_no_past[:, -3:, random_slice_idx]
									a__ :Tuple						  =    output_from_past[:, :, random_slice_idx]

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






def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   List[str]	,  a	:   Tuple	,  a	:   Union[str, Any]=None	,  a	:   Union[str, Any]=None	,  a	:   Any=None	,  a	:   Union[str, Any]=None	,  )    ->	str:





			"""simple docstring"""
			if attention_mask is None:
						a__ :Optional[int]						  =    tf.cast(tf.math.not_equal(lowerCAmelCase__	,  config.pad_token_id				)	,  tf.inta				)
			if decoder_attention_mask is None:
						a__ :List[Any]						  =    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__ :Tuple						  =    tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
			if decoder_head_mask is None:
						a__ :Optional[int]						  =    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):
						lowerCamelCase_         =     (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
						lowerCamelCase_         =     (TFLEDForConditionalGeneration,) if is_tf_available() else ()
						lowerCamelCase_         =     (
						    {
						        'conversational': TFLEDForConditionalGeneration,
						        'feature-extraction': TFLEDModel,
						        'summarization': TFLEDForConditionalGeneration,
						        'text2text-generation': TFLEDForConditionalGeneration,
						        'translation': TFLEDForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						lowerCamelCase_         =     True
						lowerCamelCase_         =     False
						lowerCamelCase_         =     False
						lowerCamelCase_         =     False




						def  _snake_case					(	self						:   str						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :str						  =    TFLEDModelTester(self						)
									a__ :List[Any]						  =    ConfigTester(self   ,     config_class=_UpperCAmelCase						)




						def  _snake_case					(	self						:   Tuple						)      ->str:






									"""simple docstring"""






									self.config_tester.run_common_tests()




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






									"""simple docstring"""






									a__ :Optional[int]						  =    self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase						)




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






									"""simple docstring"""






									a__  ,  a__ :Optional[int]						  =    self.model_tester.prepare_config_and_inputs_for_common()
									a__ :Dict						  =    tf.zeros_like(inputs_dict["attention_mask"]						)
									a__ :List[Any]						  =    2
									a__ :Optional[Any]						  =    tf.where(
									    tf.range(self.model_tester.seq_length						)[None, :] < num_global_attn_indices   ,     1   ,     inputs_dict["global_attention_mask"]   ,     )

									a__ :int						  =    True
									a__ :Optional[int]						  =    self.model_tester.seq_length
									a__ :List[Any]						  =    self.model_tester.encoder_seq_length

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

												if self.is_encoder_decoder:
															a__ :Optional[int]						  =    model_class(_UpperCAmelCase						)
															a__ :str						  =    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__ :Optional[Any]						  =    True
												a__ :List[str]						  =    model_class(_UpperCAmelCase						)
												a__ :List[Any]						  =    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__ :int						  =    True
												a__ :List[Any]						  =    True
												a__ :Any						  =    model_class(_UpperCAmelCase						)
												a__ :Tuple						  =    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  _snake_case					(	self						:   Dict						)      ->Union[str, Any]:






									"""simple docstring"""






									pass






						def  _snake_case					(	self						:   Optional[int]						)      ->Union[str, Any]:






									"""simple docstring"""






									pass






def 				lowerCamelCase__	(      a	:   int				)    ->	str:





			"""simple docstring"""
			return tf.constant(lowerCAmelCase__	,  dtype=tf.intaa				)


snake_case__			 =						1e-4







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




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






									"""simple docstring"""






									a__ :List[str]						  =    TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"						).led

									# change to intended input here
									a__ :Tuple						  =    _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]						)
									a__ :Union[str, Any]						  =    _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]						)
									a__ :List[Any]						  =    prepare_led_inputs_dict(model.config   ,     _UpperCAmelCase   ,     _UpperCAmelCase						)
									a__ :List[str]						  =    model(**_UpperCAmelCase						)[0]
									a__ :List[str]						  =    (1, 1024, 768)
									self.assertEqual(output.shape   ,     _UpperCAmelCase						)
									# change to expected output here
									a__ :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]   ,     _UpperCAmelCase   ,     atol=1E-3						)






						def  _snake_case					(	self						:   str						)      ->List[Any]:






									"""simple docstring"""






									a__ :Optional[Any]						  =    TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"						)

									# change to intended input here
									a__ :Tuple						  =    _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]						)
									a__ :Optional[Any]						  =    _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]						)
									a__ :str						  =    prepare_led_inputs_dict(model.config   ,     _UpperCAmelCase   ,     _UpperCAmelCase						)
									a__ :List[str]						  =    model(**_UpperCAmelCase						)[0]
									a__ :List[Any]						  =    (1, 1024, model.config.vocab_size)
									self.assertEqual(output.shape   ,     _UpperCAmelCase						)
									# change to expected output here
									a__ :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]   ,     _UpperCAmelCase   ,     atol=1E-3   ,     rtol=1E-3						)






import numpy as np


def 				lowerCamelCase__	(      a	:   np.ndarray	,  a	:   np.ndarray	,  a	:   float = 1e-12	,  a	:   int = 100	,  )    ->	tuple[float, np.ndarray]:





			"""simple docstring"""
			assert np.shape(a				)[0] == np.shape(a				)[1]
			# Ensure proper dimensionality.
			assert np.shape(a				)[0] == np.shape(a				)[0]
			# Ensure inputs are either both complex or both real
			assert np.iscomplexobj(a				) == np.iscomplexobj(a				)
			a__ :List[str]						  =    np.iscomplexobj(a				)
			if is_complex:
						# Ensure complex input_matrix is Hermitian
						assert np.array_equal(a	,  input_matrix.conj().T				)

			# Set convergence to False. Will define convergence when we exceed max_iterations
			# or when we have small changes from one iteration to next.

			a__ :List[str]						  =    False
			a__ :str						  =    0
			a__ :Dict						  =    0
			a__ :Tuple						  =    1e12

			while not convergence:
						# Multiple matrix by the vector.
						a__ :Tuple						  =    np.dot(a	,  a				)
						# Normalize the resulting output vector.
						a__ :List[Any]						  =    w / np.linalg.norm(a				)
						# Find rayleigh quotient
						# (faster than usual b/c we know vector is normalized already)
						a__ :List[str]						  =    vector.conj().T if is_complex else vector.T
						a__ :Any						  =    np.dot(a	,  np.dot(a	,  a				)				)

						# Check convergence.
						a__ :Optional[Any]						  =    np.abs(lambda_ - lambda_previous				) / lambda_
						iterations += 1

						if error <= error_tol or iterations >= max_iterations:
									a__ :List[str]						  =    True

						a__ :Optional[int]						  =    lambda_

			if is_complex:
						a__ :Union[str, Any]						  =    np.real(lambda_				)

			return lambda_, vector


def 				lowerCamelCase__	(      )    ->	None:





			"""simple docstring"""
			a__ :List[str]						  =    np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]				)
			a__ :Dict						  =    np.array([41, 4, 20]				)
			a__ :Union[str, Any]						  =    real_input_matrix.astype(np.complexaaa				)
			a__ :Optional[Any]						  =    np.triu(1j * complex_input_matrix	,  1				)
			complex_input_matrix += imag_matrix
			complex_input_matrix += -1 * imag_matrix.T
			a__ :Union[str, Any]						  =    np.array([41, 4, 20]				).astype(np.complexaaa				)

			for problem_type in ["real", "complex"]:
						if problem_type == "real":
									a__ :Union[str, Any]						  =    real_input_matrix
									a__ :int						  =    real_vector
						elif problem_type == "complex":
									a__ :Optional[Any]						  =    complex_input_matrix
									a__ :List[Any]						  =    complex_vector

						# Our implementation.
						a__  ,  a__ :Any						  =    power_iteration(a	,  a				)

						# Numpy implementation.

						# Get eigenvalues and eigenvectors using built-in numpy
						# eigh (eigh used for symmetric or hermetian matrices).
						a__  ,  a__ :int						  =    np.linalg.eigh(a				)
						# Last eigenvalue is the maximum one.
						a__ :int						  =    eigen_values[-1]
						# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
						a__ :Optional[Any]						  =    eigen_vectors[:, -1]

						# Check our implementation and numpy gives close answers.
						assert np.abs(eigen_value - eigen_value_max				) <= 1e-6
						# Take absolute values element wise of each eigenvector.
						# as they are only unique to a minus sign.
						assert np.linalg.norm(np.abs(a				) - np.abs(a				)				) <= 1e-6


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		test_power_iteration()


