


from __future__ import annotations

import unittest

from transformers import RoFormerConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TFRoFormerForCausalLM,
	    TFRoFormerForMaskedLM,
	    TFRoFormerForMultipleChoice,
	    TFRoFormerForQuestionAnswering,
	    TFRoFormerForSequenceClassification,
	    TFRoFormerForTokenClassification,
	    TFRoFormerModel,
	)
	from transformers.models.roformer.modeling_tf_roformer import (
	    TFRoFormerSelfAttention,
	    TFRoFormerSinusoidalPositionalEmbedding,
	)



class 							A   :
					'''simple docstring'''






					def __init__(self   :						Tuple				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						Optional[int]=13				, _UpperCAmelCase   :						Union[str, Any]=7				, _UpperCAmelCase   :						List[Any]=True				, _UpperCAmelCase   :						str=True				, _UpperCAmelCase   :						str=True				, _UpperCAmelCase   :						int=True				, _UpperCAmelCase   :						Tuple=99				, _UpperCAmelCase   :						Union[str, Any]=32				, _UpperCAmelCase   :						List[str]=2				, _UpperCAmelCase   :						Optional[Any]=4				, _UpperCAmelCase   :						Dict=37				, _UpperCAmelCase   :						Optional[int]="gelu"				, _UpperCAmelCase   :						Dict=0.1				, _UpperCAmelCase   :						Optional[Any]=0.1				, _UpperCAmelCase   :						Dict=512				, _UpperCAmelCase   :						List[Any]=16				, _UpperCAmelCase   :						Tuple=2				, _UpperCAmelCase   :						str=0.02				, _UpperCAmelCase   :						Any=3				, _UpperCAmelCase   :						Optional[Any]=4				, _UpperCAmelCase   :						Dict=None				, )						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		parent
							lowercase__					      =		13
							lowercase__					      =		7
							lowercase__					      =		True
							lowercase__					      =		True
							lowercase__					      =		True
							lowercase__					      =		True
							lowercase__					      =		99
							lowercase__					      =		32
							lowercase__					      =		2
							lowercase__					      =		4
							lowercase__					      =		37
							lowercase__					      =		"""gelu"""
							lowercase__					      =		0.1
							lowercase__					      =		0.1
							lowercase__					      =		512
							lowercase__					      =		16
							lowercase__					      =		2
							lowercase__					      =		0.02
							lowercase__					      =		3
							lowercase__					      =		4
							lowercase__					      =		None

					def   lowerCamelCase__					(self   :						Any		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size		)

							lowercase__					      =		None
							if self.use_input_mask:
									lowercase__					      =		random_attention_mask([self.batch_size, self.seq_length]		)

							lowercase__					      =		None
							if self.use_token_type_ids:
									lowercase__					      =		ids_tensor([self.batch_size, self.seq_length]				, self.type_vocab_size		)

							lowercase__					      =		None
							lowercase__					      =		None
							lowercase__					      =		None
							if self.use_labels:
									lowercase__					      =		ids_tensor([self.batch_size]				, self.type_sequence_label_size		)
									lowercase__					      =		ids_tensor([self.batch_size, self.seq_length]				, self.num_labels		)
									lowercase__					      =		ids_tensor([self.batch_size]				, self.num_choices		)

							lowercase__					      =		RoFormerConfig(
							    vocab_size=self.vocab_size				, hidden_size=self.hidden_size				, num_hidden_layers=self.num_hidden_layers				, num_attention_heads=self.num_attention_heads				, intermediate_size=self.intermediate_size				, hidden_act=self.hidden_act				, hidden_dropout_prob=self.hidden_dropout_prob				, attention_probs_dropout_prob=self.attention_probs_dropout_prob				, max_position_embeddings=self.max_position_embeddings				, type_vocab_size=self.type_vocab_size				, initializer_range=self.initializer_range				, return_dict=_UpperCAmelCase				, )

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						str		)						->       int:




							"""simple docstring"""


							lowercase__					      =		TFRoFormerModel(config=_UpperCAmelCase		)
							lowercase__					      =		{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

							lowercase__					      =		[input_ids, input_mask]
							lowercase__					      =		model(_UpperCAmelCase		)

							lowercase__					      =		model(_UpperCAmelCase		)

							self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)		)

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						Any				, _UpperCAmelCase   :						Any				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						Tuple		)						->       int:




							"""simple docstring"""


							lowercase__					      =		True
							lowercase__					      =		TFRoFormerForCausalLM(config=_UpperCAmelCase		)
							lowercase__					      =		{
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}
							lowercase__					      =		model(_UpperCAmelCase		)["""logits"""]
							self.parent.assertListEqual(
							    list(prediction_scores.numpy().shape		)				, [self.batch_size, self.seq_length, self.vocab_size]		)

					def   lowerCamelCase__					(self   :						str				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						Dict				, _UpperCAmelCase   :						str		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		TFRoFormerForMaskedLM(config=_UpperCAmelCase		)
							lowercase__					      =		{
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}
							lowercase__					      =		model(_UpperCAmelCase		)
							self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.seq_length, self.vocab_size)		)

					def   lowerCamelCase__					(self   :						Optional[int]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Dict				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						str		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		self.num_labels
							lowercase__					      =		TFRoFormerForSequenceClassification(config=_UpperCAmelCase		)
							lowercase__					      =		{
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}

							lowercase__					      =		model(_UpperCAmelCase		)
							self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_labels)		)

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						Dict		)						->       int:




							"""simple docstring"""


							lowercase__					      =		self.num_choices
							lowercase__					      =		TFRoFormerForMultipleChoice(config=_UpperCAmelCase		)
							lowercase__					      =		tf.tile(tf.expand_dims(_UpperCAmelCase				, 1		)				, (1, self.num_choices, 1)		)
							lowercase__					      =		tf.tile(tf.expand_dims(_UpperCAmelCase				, 1		)				, (1, self.num_choices, 1)		)
							lowercase__					      =		tf.tile(tf.expand_dims(_UpperCAmelCase				, 1		)				, (1, self.num_choices, 1)		)
							lowercase__					      =		{
							    """input_ids""": multiple_choice_inputs_ids,
							    """attention_mask""": multiple_choice_input_mask,
							    """token_type_ids""": multiple_choice_token_type_ids,
							}
							lowercase__					      =		model(_UpperCAmelCase		)
							self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_choices)		)

					def   lowerCamelCase__					(self   :						Tuple				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						Union[str, Any]		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		self.num_labels
							lowercase__					      =		TFRoFormerForTokenClassification(config=_UpperCAmelCase		)
							lowercase__					      =		{
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}
							lowercase__					      =		model(_UpperCAmelCase		)
							self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.seq_length, self.num_labels)		)

					def   lowerCamelCase__					(self   :						List[str]				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						Any				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						Dict				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						Tuple		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		TFRoFormerForQuestionAnswering(config=_UpperCAmelCase		)
							lowercase__					      =		{
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}

							lowercase__					      =		model(_UpperCAmelCase		)
							self.parent.assertEqual(result.start_logits.shape				, (self.batch_size, self.seq_length)		)
							self.parent.assertEqual(result.end_logits.shape				, (self.batch_size, self.seq_length)		)


					def   lowerCamelCase__					(self   :						Tuple		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		self.prepare_config_and_inputs()
							(
							    (
							    lowercase__
							)      ,     (
							    lowercase__
							)      ,     (
							    lowercase__
							)      ,     (
							    lowercase__
							)      ,     (
							    lowercase__
							)      ,     (
							    lowercase__
							)      ,     (
							    lowercase__
							)      ,     
							)					      =		config_and_inputs
							lowercase__					      =		{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
							return config, inputs_dict






@require_tf
class 							A   (	UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			(
					    (
					        TFRoFormerModel,
					        TFRoFormerForCausalLM,
					        TFRoFormerForMaskedLM,
					        TFRoFormerForQuestionAnswering,
					        TFRoFormerForSequenceClassification,
					        TFRoFormerForTokenClassification,
					        TFRoFormerForMultipleChoice,
					    )
					    if is_tf_available()
					    else ()
					)
					A__      =			(
					    {
					        '''feature-extraction''': TFRoFormerModel,
					        '''fill-mask''': TFRoFormerForMaskedLM,
					        '''question-answering''': TFRoFormerForQuestionAnswering,
					        '''text-classification''': TFRoFormerForSequenceClassification,
					        '''text-generation''': TFRoFormerForCausalLM,
					        '''token-classification''': TFRoFormerForTokenClassification,
					        '''zero-shot''': TFRoFormerForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)

					A__      =			False
					A__      =			False

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						Any				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						List[str]		)						->       Any:




							"""simple docstring"""


							if pipeline_test_casse_name == "TextGenerationPipelineTests":
									return True

							return False

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




							"""simple docstring"""


							lowercase__					      =		TFRoFormerModelTester(self		)
							lowercase__					      =		ConfigTester(self				, config_class=_UpperCAmelCase				, hidden_size=37		)

					def   lowerCamelCase__					(self   :						Dict		)						->       Optional[int]:




							"""simple docstring"""


							self.config_tester.run_common_tests()

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




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Tuple		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase		)

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




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_lm_head(*_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Tuple		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase		)

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




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase		)

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




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Optional[int]		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase		)


					@slow
					def   lowerCamelCase__					(self   :						int		)						->       Optional[int]:




							"""simple docstring"""


							lowercase__					      =		TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""		)
							self.assertIsNotNone(_UpperCAmelCase		)






@require_tf
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					@slow
					def   lowerCamelCase__					(self   :						int		)						->       int:




							"""simple docstring"""


							lowercase__					      =		TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""		)
							lowercase__					      =		tf.constant([[0, 1, 2, 3, 4, 5]]		)
							lowercase__					      =		model(_UpperCAmelCase		)[0]

							# TODO Replace vocab size
							lowercase__					      =		5_0000

							lowercase__					      =		[1, 6, vocab_size]
							self.assertEqual(output.shape				, _UpperCAmelCase		)

							print(output[:, :3, :3]		)

							# TODO Replace values below with what was printed above.
							lowercase__					      =		tf.constant(
							    [
							        [
							            [-0.12_053_341, -1.0_264_901, 0.29_221_946],
							            [-1.5_133_783, 0.197_433, 0.15_190_607],
							            [-5.0_135_403, -3.900_256, -0.84_038_764],
							        ]
							    ]		)
							tf.debugging.assert_near(output[:, :3, :3]				, _UpperCAmelCase				, atol=1E-4		)






@require_tf
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					A__      =			1e-4

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




							"""simple docstring"""


							lowercase__					      =		tf.constant([[4, 10]]		)
							lowercase__					      =		TFRoFormerSinusoidalPositionalEmbedding(num_positions=6				, embedding_dim=6		)

							lowercase__					      =		emba(input_ids.shape		)
							lowercase__					      =		tf.constant(
							    [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]		)

							tf.debugging.assert_near(_UpperCAmelCase				, _UpperCAmelCase				, atol=self.tolerance		)


					def   lowerCamelCase__					(self   :						Tuple		)						->       Optional[int]:




							"""simple docstring"""


							lowercase__					      =		tf.constant(
							    [
							        [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
							        [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
							        [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
							    ]		)
							lowercase__					      =		TFRoFormerSinusoidalPositionalEmbedding(num_positions=512				, embedding_dim=512		)
							emba([2, 16, 512]		)
							lowercase__					      =		emba.weight[:3, :5]

							tf.debugging.assert_near(_UpperCAmelCase				, _UpperCAmelCase				, atol=self.tolerance		)






@require_tf
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					A__      =			1e-4

					def   lowerCamelCase__					(self   :						Optional[int]		)						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		tf.reshape(tf.range(2 * 12 * 16 * 64				, dtype=tf.floataa		)				, shape=(2, 12, 16, 64)		) / 100

							lowercase__					      =		-tf.reshape(tf.range(2 * 12 * 16 * 64				, dtype=tf.floataa		)				, shape=(2, 12, 16, 64)		) / 100

							lowercase__					      =		TFRoFormerSinusoidalPositionalEmbedding(num_positions=32				, embedding_dim=64		)
							lowercase__					      =		embed_positions([2, 16, 768]		)[None, None, :, :]

							lowercase__      ,     lowercase__					      =		TFRoFormerSelfAttention.apply_rotary_position_embeddings(
							    _UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase		)

							lowercase__					      =		tf.constant(
							    [
							        [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
							        [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
							        [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
							        [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
							        [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
							        [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
							    ]		)
							lowercase__					      =		tf.constant(
							    [
							        [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
							        [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
							        [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
							        [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
							        [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
							        [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
							    ]		)

							tf.debugging.assert_near(query_layer[0, 0, :6, :8]				, _UpperCAmelCase				, atol=self.tolerance		)
							tf.debugging.assert_near(key_layer[0, 0, :6, :8]				, _UpperCAmelCase				, atol=self.tolerance		)





A	:		Tuple    =       'ABCDEFGHIJKLMNOPQRSTUVWXYZ'






def 			UpperCamelCase (     )       ->  None:
		"""simple docstring"""




		lowercase__					      =		input("""Enter message: """					)
		lowercase__					      =		input("""Enter key [alphanumeric]: """					)
		lowercase__					      =		input("""Encrypt/Decrypt [e/d]: """					)

		if mode.lower().startswith("""e"""					):
				lowercase__					      =		"""encrypt"""
				lowercase__					      =		encrypt_message(__magic_name__   ,   __magic_name__					)
		elif mode.lower().startswith("""d"""					):
				lowercase__					      =		"""decrypt"""
				lowercase__					      =		decrypt_message(__magic_name__   ,   __magic_name__					)

		print(f'''\n{mode.title()}ed message:'''					)
		print(__magic_name__					)






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str					)       ->  str:
		"""simple docstring"""




		return translate_message(__magic_name__   ,   __magic_name__   ,   """encrypt"""					)






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str					)       ->  str:
		"""simple docstring"""




		return translate_message(__magic_name__   ,   __magic_name__   ,   """decrypt"""					)






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str   ,   __magic_name__					:							str					)       ->  str:
		"""simple docstring"""




		lowercase__					      =		[]
		lowercase__					      =		0
		lowercase__					      =		key.upper()

		for symbol in message:
				lowercase__					      =		LETTERS.find(symbol.upper()					)
				if num != -1:
						if mode == "encrypt":
								num += LETTERS.find(key[key_index]					)
						elif mode == "decrypt":
								num -= LETTERS.find(key[key_index]					)

						num %= len(__magic_name__					)

						if symbol.isupper():
								translated.append(LETTERS[num]					)
						elif symbol.islower():
								translated.append(LETTERS[num].lower()					)

						key_index += 1
						if key_index == len(__magic_name__					):
								lowercase__					      =		0
				else:
						translated.append(__magic_name__					)
		return "".join(__magic_name__					)


if __name__ == "__main__":
	main()

