



'''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      _lowerCAmelCase							:
	__SCREAMING_SNAKE_CASE       :		Tuple		      = LEDConfig
	__SCREAMING_SNAKE_CASE       :		Any		      = {}
	__SCREAMING_SNAKE_CASE       :		str		      = 'gelu'


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

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


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

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

				A_     :						int     =					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_     :						str     =					prepare_led_inputs_dict(lowercase	,				lowercase	,				lowercase							)
				A_     :						Optional[Any]     =					tf.concat(
				    [tf.zeros_like(lowercase							)[:, :-1], tf.ones_like(lowercase							)[:, -1:]]	,				axis=-1	,				)
				A_     :						Optional[int]     =					global_attention_mask
				return config, inputs_dict


	def    _a     (self	,				lowercase	,				lowercase							):
				A_     :						Optional[int]     =					TFLEDModel(config=lowercase							).get_decoder()
				A_     :						List[str]     =					inputs_dict["""input_ids"""]

				A_     :						List[Any]     =					input_ids[:1, :]
				A_     :						Tuple     =					inputs_dict["""attention_mask"""][:1, :]
				A_     :						List[Any]     =					1

				# first forward pass
				A_     :						List[Any]     =					model(lowercase	,				attention_mask=lowercase	,				use_cache=lowercase							)

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

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

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

				A_     :						List[Any]     =					model(lowercase	,				attention_mask=lowercase							)[0]
				A_     :						Optional[int]     =					model(lowercase	,				attention_mask=lowercase	,				past_key_values=lowercase							)[0]

				self.parent.assertEqual(next_tokens.shape[1]	,				output_from_past.shape[1]							)

				# select random slice
				A_     :						Dict     =					int(ids_tensor((1,)	,				output_from_past.shape[-1]							)							)
				A_     :						Optional[Any]     =					output_from_no_past[:, -3:, random_slice_idx]
				A_     :						List[str]     =					output_from_past[:, :, random_slice_idx]

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






def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__=None						,				lowerCamelCase__=None						,				lowerCamelCase__=None						,				lowerCamelCase__=None						,				):
			'''simple docstring'''





			if attention_mask is None:
						A_     :						int     =					tf.cast(tf.math.not_equal(lowerCamelCase__						,				config.pad_token_id   )						,				tf.inta   )
			if decoder_attention_mask is None:
						A_     :						Dict     =					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_     :						List[str]     =					tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
			if decoder_head_mask is None:
						A_     :						List[str]     =					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							(      __UpperCAmelCase				,      __UpperCAmelCase				,      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		Dict		      = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
	__SCREAMING_SNAKE_CASE       :		Optional[Any]		      = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = (
	    {
	        'conversational': TFLEDForConditionalGeneration,
	        'feature-extraction': TFLEDModel,
	        'summarization': TFLEDForConditionalGeneration,
	        'text2text-generation': TFLEDForConditionalGeneration,
	        'translation': TFLEDForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = True
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = False
	__SCREAMING_SNAKE_CASE       :		Tuple		      = False
	__SCREAMING_SNAKE_CASE       :		Any		      = False


	def    _a     (self							):
				A_     :						List[Any]     =					TFLEDModelTester(self							)
				A_     :						List[str]     =					ConfigTester(self	,				config_class=lowercase							)


	def    _a     (self							):
				self.config_tester.run_common_tests()


	def    _a     (self							):
				A_     :						Dict     =					self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*lowercase							)


	def    _a     (self							):
				A_     :						Dict     =					self.model_tester.prepare_config_and_inputs_for_common()
				A_     :						Optional[int]     =					tf.zeros_like(inputs_dict["""attention_mask"""]							)
				A_     :						str     =					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_     :						Any     =					self.model_tester.seq_length
				A_     :						List[str]     =					self.model_tester.encoder_seq_length

				def check_decoder_attentions_output(lowercase							):
							A_     :						Tuple     =					outputs.decoder_attentions
							self.assertEqual(len(lowercase							)	,				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(lowercase							):
							A_     :						Optional[Any]     =					[t.numpy() for t in outputs.encoder_attentions]
							A_     :						Tuple     =					[t.numpy() for t in outputs.encoder_global_attentions]
							self.assertEqual(len(lowercase							)	,				self.model_tester.num_hidden_layers							)
							self.assertEqual(len(lowercase							)	,				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[str]     =					True
							A_     :						Union[str, Any]     =					False
							A_     :						Union[str, Any]     =					False
							A_     :						Tuple     =					model_class(lowercase							)
							A_     :						Any     =					model(self._prepare_for_class(lowercase	,				lowercase							)							)
							A_     :						int     =					len(lowercase							)
							self.assertEqual(config.output_hidden_states	,				lowercase							)
							check_encoder_attentions_output(lowercase							)

							if self.is_encoder_decoder:
										A_     :						Union[str, Any]     =					model_class(lowercase							)
										A_     :						Union[str, Any]     =					model(self._prepare_for_class(lowercase	,				lowercase							)							)
										self.assertEqual(config.output_hidden_states	,				lowercase							)
										check_decoder_attentions_output(lowercase							)

							# Check that output attentions can also be changed via the config
							del inputs_dict["output_attentions"]
							A_     :						List[Any]     =					True
							A_     :						Tuple     =					model_class(lowercase							)
							A_     :						int     =					model(self._prepare_for_class(lowercase	,				lowercase							)							)
							self.assertEqual(config.output_hidden_states	,				lowercase							)
							check_encoder_attentions_output(lowercase							)

							# Check attention is always last and order is fine
							A_     :						List[str]     =					True
							A_     :						List[str]     =					True
							A_     :						str     =					model_class(lowercase							)
							A_     :						int     =					model(self._prepare_for_class(lowercase	,				lowercase							)							)

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


	@unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing."""							)
	def    _a     (self							):
				pass


	def    _a     (self							):
				# TODO: Head-masking not yet implement
				pass






def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





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


lowerCamelCase							:Optional[int]       =						1E-4







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


	def    _a     (self							):
				A_     :						Union[str, Any]     =					TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""							).led

				# change to intended input here
				A_     :						Optional[int]     =					_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]							)
				A_     :						Tuple     =					_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]							)
				A_     :						List[str]     =					prepare_led_inputs_dict(model.config	,				lowercase	,				lowercase							)
				A_     :						Optional[int]     =					model(**lowercase							)[0]
				A_     :						Union[str, Any]     =					(1, 1024, 768)
				self.assertEqual(output.shape	,				lowercase							)
				# change to expected output here
				A_     :						int     =					tf.convert_to_tensor(
				    [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]]	,				)
				tf.debugging.assert_near(output[:, :3, :3]	,				lowercase	,				atol=1E-3							)


	def    _a     (self							):
				A_     :						Tuple     =					TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""							)

				# change to intended input here
				A_     :						Optional[int]     =					_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_     :						Any     =					prepare_led_inputs_dict(model.config	,				lowercase	,				lowercase							)
				A_     :						List[Any]     =					model(**lowercase							)[0]
				A_     :						Optional[int]     =					(1, 1024, model.config.vocab_size)
				self.assertEqual(output.shape	,				lowercase							)
				# change to expected output here
				A_     :						Optional[Any]     =					tf.convert_to_tensor(
				    [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]]	,				)
				tf.debugging.assert_near(output[:, :3, :3]	,				lowercase	,				atol=1E-3	,				rtol=1E-3							)




'''simple docstring'''




lowerCamelCase							:Any       = {
    '''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
    '''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
    '''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
    '''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
    '''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
    '''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
    ''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
    '''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
    '''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowerCamelCase							:Any       = {value: key for key, value in MORSE_CODE_DICT.items()}



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return " ".join(MORSE_CODE_DICT[char] for char in message.upper()   )



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return "".join(REVERSE_DICT[char] for char in message.split()   )



def      a      (			):
			'''simple docstring'''





			A_     :						List[str]     =					"""Morse code here!"""
			print(lowerCamelCase__   )
			A_     :						str     =					encrypt(lowerCamelCase__   )
			print(lowerCamelCase__   )
			A_     :						Dict     =					decrypt(lowerCamelCase__   )
			print(lowerCamelCase__   )


if __name__ == "__main__":
						main()