






"""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    lowercase__			:
			_UpperCAmelCase		:List[Any]   			= LEDConfig
			_UpperCAmelCase		:Union[str, Any]   			= {}
			_UpperCAmelCase		:int   			= "gelu"


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

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


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

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

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


			def 	UpperCAmelCase__						(      self     :  List[Any]   ,       snake_case__     :  str   ,       snake_case__     :  List[str]					):
										lowerCamelCase_       :							Any									=TFLEDModel(config=snake_case__					).get_decoder()
										lowerCamelCase_       :							List[str]									=inputs_dict["""input_ids"""]

										lowerCamelCase_       :							int									=input_ids[:1, :]
										lowerCamelCase_       :							List[str]									=inputs_dict["""attention_mask"""][:1, :]
										lowerCamelCase_       :							Union[str, Any]									=1

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

										lowerCamelCase_       :							List[Any]									=outputs.to_tuple()

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

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

										lowerCamelCase_       :							Tuple									=model(snake_case__   ,       attention_mask=snake_case__					)[0]
										lowerCamelCase_       :							Tuple									=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
										lowerCamelCase_       :							Dict									=int(ids_tensor((1,)   ,       output_from_past.shape[-1]					)					)
										lowerCamelCase_       :							Tuple									=output_from_no_past[:, -3:, random_slice_idx]
										lowerCamelCase_       :							Tuple									=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  _snake_case				(     lowerCamelCase__	:    Optional[int]				,						lowerCamelCase__	:    Dict				,						lowerCamelCase__	:    List[str]				,						lowerCamelCase__	:    Optional[Any]=None				,						lowerCamelCase__	:    List[str]=None				,						lowerCamelCase__	:    Tuple=None				,						lowerCamelCase__	:    List[str]=None				,						)				->	Union[str, Any]:



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


			def 	UpperCAmelCase__						(      self     :  List[str]					):
										lowerCamelCase_       :							List[Any]									=TFLEDModelTester(self					)
										lowerCamelCase_       :							Any									=ConfigTester(self   ,       config_class=snake_case__					)


			def 	UpperCAmelCase__						(      self     :  str					):
										self.config_tester.run_common_tests()


			def 	UpperCAmelCase__						(      self     :  Union[str, Any]					):
										lowerCamelCase_       :							Optional[Any]									=self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*snake_case__					)


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

										lowerCamelCase_       :							List[Any]									=True
										lowerCamelCase_       :							str									=self.model_tester.seq_length
										lowerCamelCase_       :							List[Any]									=self.model_tester.encoder_seq_length

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

																	if self.is_encoder_decoder:
																								lowerCamelCase_       :							Dict									=model_class(snake_case__					)
																								lowerCamelCase_       :							Any									=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"]
																	lowerCamelCase_       :							List[str]									=True
																	lowerCamelCase_       :							List[str]									=model_class(snake_case__					)
																	lowerCamelCase_       :							Tuple									=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
																	lowerCamelCase_       :							str									=True
																	lowerCamelCase_       :							Union[str, Any]									=True
																	lowerCamelCase_       :							Tuple									=model_class(snake_case__					)
																	lowerCamelCase_       :							Optional[Any]									=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     :  Any					):
										pass


			def 	UpperCAmelCase__						(      self     :  Any					):
										pass


def  _snake_case				(     lowerCamelCase__	:    List[Any]  )				->	List[str]:
							return tf.constant(_UpperCamelCase				,						dtype=tf.intaa  )


A__       :				List[str] 					=					1E-4







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


			def 	UpperCAmelCase__						(      self     :  Any					):
										lowerCamelCase_       :							Optional[Any]									=TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"					).led

										# change to intended input here
										lowerCamelCase_       :							List[str]									=_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]					)
										lowerCamelCase_       :							List[Any]									=_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]					)
										lowerCamelCase_       :							int									=prepare_led_inputs_dict(model.config   ,       snake_case__   ,       snake_case__					)
										lowerCamelCase_       :							List[Any]									=model(**snake_case__					)[0]
										lowerCamelCase_       :							Tuple									=(1, 1024, 768)
										self.assertEqual(output.shape   ,       snake_case__					)
										# change to expected output here
										lowerCamelCase_       :							Optional[int]									=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     :  Union[str, Any]					):
										lowerCamelCase_       :							Union[str, Any]									=TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"					)

										# change to intended input here
										lowerCamelCase_       :							Optional[Any]									=_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]					)
										lowerCamelCase_       :							int									=_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]					)
										lowerCamelCase_       :							Any									=prepare_led_inputs_dict(model.config   ,       snake_case__   ,       snake_case__					)
										lowerCamelCase_       :							Any									=model(**snake_case__					)[0]
										lowerCamelCase_       :							str									=(1, 1024, model.config.vocab_size)
										self.assertEqual(output.shape   ,       snake_case__					)
										# change to expected output here
										lowerCamelCase_       :							Dict									=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"""






import numpy as np

UpperCAmelCase    :     Optional[Any]   						=		[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]





class      lowerCamelCase__   :







					"""simple docstring"""


					def __init__(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					np.array(UpperCamelCase  )






					def lowerCamelCase__							(       self    : int ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase							,__UpperCAmelCase   :						Optional[Any]      							=					np.where(letter == self.SQUARE  )
												__UpperCAmelCase   :						Optional[Any]      							=					np.concatenate([indexa + 1, indexa + 1]  )
												return indexes






					def lowerCamelCase__							(       self    : Any ,       UpperCamelCase    : int ,       UpperCamelCase    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					self.SQUARE[indexa - 1, indexa - 1]
												return letter






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					message.lower()
												__UpperCAmelCase   :						List[Any]      							=					message.replace(""" """ ,       """"""  )
												__UpperCAmelCase   :						List[Any]      							=					message.replace("""j""" ,       """i"""  )

												__UpperCAmelCase   :						Optional[int]      							=					np.empty((2, len(UpperCamelCase  ))  )
												for letter_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						List[Any]      							=					self.letter_to_numbers(message[letter_index]  )

																			__UpperCAmelCase   :						str      							=					numbers[0]
																			__UpperCAmelCase   :						int      							=					numbers[1]

												__UpperCAmelCase   :						Union[str, Any]      							=					first_step.reshape(2 * len(UpperCamelCase  )  )
												__UpperCAmelCase   :						Optional[Any]      							=					""""""
												for numbers_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Any      							=					int(second_step[numbers_index * 2]  )
																			__UpperCAmelCase   :						Any      							=					int(second_step[(numbers_index * 2) + 1]  )
																			__UpperCAmelCase   :						str      							=					self.numbers_to_letter(UpperCamelCase ,       UpperCamelCase  )
																			__UpperCAmelCase   :						Optional[Any]      							=					encoded_message + letter

												return encoded_message






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					message.lower()
												message.replace(""" """ ,       """"""  )
												__UpperCAmelCase   :						int      							=					np.empty(2 * len(UpperCamelCase  )  )
												for letter_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Any      							=					self.letter_to_numbers(message[letter_index]  )
																			__UpperCAmelCase   :						Any      							=					numbers[0]
																			__UpperCAmelCase   :						Dict      							=					numbers[1]

												__UpperCAmelCase   :						str      							=					first_step.reshape((2, len(UpperCamelCase  ))  )
												__UpperCAmelCase   :						Union[str, Any]      							=					""""""
												for numbers_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Optional[int]      							=					int(second_step[0, numbers_index]  )
																			__UpperCAmelCase   :						Tuple      							=					int(second_step[1, numbers_index]  )
																			__UpperCAmelCase   :						Tuple      							=					self.numbers_to_letter(UpperCamelCase ,       UpperCamelCase  )
																			__UpperCAmelCase   :						Any      							=					decoded_message + letter

												return decoded_message
