






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:   Union[str, Any]		,     __A:   Dict		,     __A:   Optional[Any]=13		,     __A:   Optional[int]=7		,     __A:   str=True		,     __A:   Union[str, Any]=False		,     __A:   List[Any]=99		,     __A:   Tuple=32		,     __A:   str=2		,     __A:   Optional[Any]=4		,     __A:   Optional[int]=37		,     __A:   str=0.1		,     __A:   Tuple=0.1		,     __A:   List[Any]=20		,     __A:   Optional[Any]=2		,     __A:   Union[str, Any]=1		,     __A:   Optional[int]=0		,     __A:   Optional[Any]=4		,     )    ->				List[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       __A (  self:   Dict   )    ->				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(__A		,     __A		,     __A   )
												_A								= tf.concat(
												    [tf.zeros_like(__A   )[:, :-1], tf.ones_like(__A   )[:, -1:]]		,     axis=-1		,     )
												_A								= global_attention_mask
												return config, inputs_dict



							def       __A (  self:   int		,     __A:   Dict		,     __A:   Optional[int]   )    ->				List[Any]:
												_A								= TFLEDModel(config=__A   ).get_decoder()
												_A								= inputs_dict['''input_ids''']

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

												# first forward pass
												_A								= model(__A		,     attention_mask=__A		,     use_cache=__A   )

												_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(__A		,     attention_mask=__A   )[0]
												_A								= model(__A		,     attention_mask=__A		,     past_key_values=__A   )[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(__A		,     __A		,     rtol=1e-3   )




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase=None					,			_lowercase=None					,			_lowercase=None					,			_lowercase=None					,			):

					'''simple docstring'''







					if attention_mask is None:
										_A								= tf.cast(tf.math.not_equal(_lowercase					,			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 						SCREAMING_SNAKE_CASE				( snake_case  ,	snake_case  ,	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       __A (  self:   Any   )    ->				List[Any]:
												_A								= TFLEDModelTester(self   )
												_A								= ConfigTester(self		,     config_class=__A   )

							def       __A (  self:   int   )    ->				Union[str, Any]:
												self.config_tester.run_common_tests()

							def       __A (  self:   List[str]   )    ->				int:
												_A								= self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*__A   )

							def       __A (  self:   Optional[int]   )    ->				Optional[int]:
												_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(__A:   str   ):
																	_A								= outputs.decoder_attentions
																	self.assertEqual(len(__A   )		,     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:   Tuple   ):
																	_A								= [t.numpy() for t in outputs.encoder_attentions]
																	_A								= [t.numpy() for t in outputs.encoder_global_attentions]
																	self.assertEqual(len(__A   )		,     self.model_tester.num_hidden_layers   )
																	self.assertEqual(len(__A   )		,     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(__A   )
																	_A								= model(self._prepare_for_class(__A		,     __A   )   )
																	_A								= len(__A   )
																	self.assertEqual(config.output_hidden_states		,     __A   )
																	check_encoder_attentions_output(__A   )

																	if self.is_encoder_decoder:
																						_A								= model_class(__A   )
																						_A								= model(self._prepare_for_class(__A		,     __A   )   )
																						self.assertEqual(config.output_hidden_states		,     __A   )
																						check_decoder_attentions_output(__A   )

																	# Check that output attentions can also be changed via the config
																	del inputs_dict["output_attentions"]
																	_A								= True
																	_A								= model_class(__A   )
																	_A								= model(self._prepare_for_class(__A		,     __A   )   )
																	self.assertEqual(config.output_hidden_states		,     __A   )
																	check_encoder_attentions_output(__A   )

																	# Check attention is always last and order is fine
																	_A								= True
																	_A								= True
																	_A								= model_class(__A   )
																	_A								= model(self._prepare_for_class(__A		,     __A   )   )

																	self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)		,     len(__A   )   )
																	self.assertEqual(model.config.output_hidden_states		,     __A   )
																	check_encoder_attentions_output(__A   )

							@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''   )
							def       __A (  self:   Optional[int]   )    ->				List[str]:
												pass



							def       __A (  self:   Dict   )    ->				Optional[Any]:
												# TODO: Head-masking not yet implement
												pass




def      __A					(     _lowercase ):

					'''simple docstring'''







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


__A      =				1e-4





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





							"""simple docstring"""

							def       __A (  self:   Optional[int]   )    ->				Any:
												_A								= TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''   ).led

												# change to intended input here
												_A								= _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
												_A								= _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
												_A								= prepare_led_inputs_dict(model.config		,     __A		,     __A   )
												_A								= model(**__A   )[0]
												_A								= (1, 10_24, 7_68)
												self.assertEqual(output.shape		,     __A   )
												# change to expected output here
												_A								= 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]		,     __A		,     atol=1e-3   )



							def       __A (  self:   Any   )    ->				Union[str, Any]:
												_A								= TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''   )

												# change to intended input here
												_A								= _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
												_A								= _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
												_A								= prepare_led_inputs_dict(model.config		,     __A		,     __A   )
												_A								= model(**__A   )[0]
												_A								= (1, 10_24, model.config.vocab_size)
												self.assertEqual(output.shape		,     __A   )
												# change to expected output here
												_A								= 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]		,     __A		,     atol=1e-3		,     rtol=1e-3   )











import itertools
import string
from collections.abc import Generator, Iterable




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= iter(_lowercase )
					while True:
										_A								= tuple(itertools.islice(_lowercase					,			_lowercase ) )
										if not chunk:
															return
										yield chunk




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
					_A								= ''''''

					if len(_lowercase ) < 2:
										return dirty

					for i in range(len(_lowercase ) - 1 ):
										clean += dirty[i]

										if dirty[i] == dirty[i + 1]:
															clean += "X"

					clean += dirty[-1]

					if len(_lowercase ) & 1:
										clean += "X"

					return clean




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
					# we're using a list instead of a '2d' array because it makes the math
					# for setting up the table and doing the actual encoding/decoding simpler
					_A								= []

					# copy key chars into the table if they are in `alphabet` ignoring duplicates
					for char in key.upper():
										if char not in table and char in alphabet:
															table.append(_lowercase )

    # fill the rest of the table in with the remaining alphabet chars
					for char in alphabet:
										if char not in table:
															table.append(_lowercase )

					return table




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= generate_table(_lowercase )
					_A								= prepare_input(_lowercase )
					_A								= ''''''

					# https://en.wikipedia.org/wiki/Playfair_cipher#Description
					for chara, chara in chunker(_lowercase					,			2 ):
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )

										if rowa == rowa:
															ciphertext += table[rowa * 5 + (cola + 1) % 5]
															ciphertext += table[rowa * 5 + (cola + 1) % 5]
										elif cola == cola:
															ciphertext += table[((rowa + 1) % 5) * 5 + cola]
															ciphertext += table[((rowa + 1) % 5) * 5 + cola]
										else:  # rectangle
															ciphertext += table[rowa * 5 + cola]
															ciphertext += table[rowa * 5 + cola]

					return ciphertext




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= generate_table(_lowercase )
					_A								= ''''''

					# https://en.wikipedia.org/wiki/Playfair_cipher#Description
					for chara, chara in chunker(_lowercase					,			2 ):
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )

										if rowa == rowa:
															plaintext += table[rowa * 5 + (cola - 1) % 5]
															plaintext += table[rowa * 5 + (cola - 1) % 5]
										elif cola == cola:
															plaintext += table[((rowa - 1) % 5) * 5 + cola]
															plaintext += table[((rowa - 1) % 5) * 5 + cola]
										else:  # rectangle
															plaintext += table[rowa * 5 + cola]
															plaintext += table[rowa * 5 + cola]

					return plaintext



