





from __future__ import annotations

import unittest

from transformers import CTRLConfig, 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.models.ctrl.modeling_tf_ctrl import (
         TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFCTRLForSequenceClassification,
         TFCTRLLMHeadModel,
         TFCTRLModel,
     )




class 			_lowerCamelCase	(      a      ):



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  )    ->				Any:






        '''simple docstring'''






        __snake_case				:			Optional[int]            =    parent
        __snake_case				:			Union[str, Any]            =    13
        __snake_case				:			List[str]            =    7
        __snake_case				:			Union[str, Any]            =    True
        __snake_case				:			Any            =    True
        __snake_case				:			Any            =    True
        __snake_case				:			List[Any]            =    True
        __snake_case				:			Optional[Any]            =    True
        __snake_case				:			Optional[Any]            =    99
        __snake_case				:			List[str]            =    32
        __snake_case				:			Dict            =    2
        __snake_case				:			Union[str, Any]            =    4
        __snake_case				:			Optional[int]            =    37
        __snake_case				:			Tuple            =    "gelu"
        __snake_case				:			Optional[int]            =    0.1
        __snake_case				:			List[Any]            =    0.1
        __snake_case				:			Optional[int]            =    512
        __snake_case				:			int            =    16
        __snake_case				:			List[str]            =    2
        __snake_case				:			List[Any]            =    0.02
        __snake_case				:			int            =    3
        __snake_case				:			int            =    4
        __snake_case				:			Tuple            =    None
        __snake_case				:			str            =    self.vocab_size - 1







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






        '''simple docstring'''






        __snake_case				:			Optional[Any]            =    ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size				)

        __snake_case				:			Any            =    None
        if self.use_input_mask:
          __snake_case				:			List[Any]            =    random_attention_mask([self.batch_size, self.seq_length]				)

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

        __snake_case				:			List[Any]            =    None
        if self.use_mc_token_ids:
          __snake_case				:			Dict            =    ids_tensor([self.batch_size, self.num_choices]			,  self.seq_length				)

        __snake_case				:			str            =    None
        __snake_case				:			List[Any]            =    None
        __snake_case				:			Union[str, Any]            =    None
        if self.use_labels:
          __snake_case				:			Optional[int]            =    ids_tensor([self.batch_size]			,  self.type_sequence_label_size				)
          __snake_case				:			List[Any]            =    ids_tensor([self.batch_size, self.seq_length]			,  self.num_labels				)
          __snake_case				:			List[Any]            =    ids_tensor([self.batch_size]			,  self.num_choices				)

        __snake_case				:			Tuple            =    CTRLConfig(
            vocab_size=self.vocab_size			,  n_embd=self.hidden_size			,  n_layer=self.num_hidden_layers			,  n_head=self.num_attention_heads			,  n_positions=self.max_position_embeddings			,  pad_token_id=self.pad_token_id			,  )

        __snake_case				:			Union[str, Any]            =    ids_tensor([self.num_hidden_layers, self.num_attention_heads]			,  2				)

        return (
            config,
            input_ids,
            input_mask,
            head_mask,
            token_type_ids,
            mc_token_ids,
            sequence_labels,
            token_labels,
            choice_labels,
        )







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  *UpperCAmelCase				)    ->				Optional[int]:






        '''simple docstring'''






        __snake_case				:			Dict            =    TFCTRLModel(config=UpperCAmelCase				)
        __snake_case				:			Optional[Any]            =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
        __snake_case				:			List[Any]            =    model(UpperCAmelCase				)

        __snake_case				:			Tuple            =    [input_ids, None, input_mask]  # None is the input for 'past'
        __snake_case				:			Optional[int]            =    model(UpperCAmelCase				)

        __snake_case				:			Optional[int]            =    model(UpperCAmelCase				)

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







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  *UpperCAmelCase				)    ->				int:






        '''simple docstring'''






        __snake_case				:			Union[str, Any]            =    TFCTRLLMHeadModel(config=UpperCAmelCase				)
        __snake_case				:			Any            =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
        __snake_case				:			Optional[int]            =    model(UpperCAmelCase				)
        self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.vocab_size)				)







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  *UpperCAmelCase				)    ->				str:






        '''simple docstring'''






        __snake_case				:			Tuple            =    self.num_labels
        __snake_case				:			Optional[Any]            =    ids_tensor([self.batch_size]			,  self.type_sequence_label_size				)
        __snake_case				:			str            =    {
            "input_ids": input_ids,
            "token_type_ids": token_type_ids,
            "labels": sequence_labels,
        }
        __snake_case				:			Tuple            =    TFCTRLForSequenceClassification(UpperCAmelCase				)
        __snake_case				:			Any            =    model(UpperCAmelCase				)
        self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)				)







      def   UpperCAmelCase						(	self				)    ->				str:






        '''simple docstring'''






        __snake_case				:			Optional[int]            =    self.prepare_config_and_inputs()

        (
            (
            __snake_case
        )      ,				(
            __snake_case
        )      ,				(
            __snake_case
        )      ,				(
            __snake_case
        )      ,				(
            __snake_case
        )      ,				(
            __snake_case
        )      ,				(
            __snake_case
        )      ,				(
            __snake_case
        )      ,				(
            __snake_case
        )      ,				
        )				:			Optional[Any]            =    config_and_inputs

        __snake_case				:			Dict            =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
        return config, inputs_dict




@require_tf
class 			_lowerCamelCase	(      a	,				a	,				unittest.TestCase      ):



      """simple docstring"""






      UpperCAmelCase_  : List[str]				      =(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
      UpperCAmelCase_  : List[Any]				      =(TFCTRLLMHeadModel,) if is_tf_available() else ()
      UpperCAmelCase_  : List[Any]				      =(
          {
              "feature-extraction": TFCTRLModel,
              "text-classification": TFCTRLForSequenceClassification,
              "text-generation": TFCTRLLMHeadModel,
              "zero-shot": TFCTRLForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      UpperCAmelCase_  : int				      =False
      UpperCAmelCase_  : List[Any]				      =False







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






        '''simple docstring'''






        if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
          # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
          # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
          # config could not be created.
          return True

        return False







      def   UpperCAmelCase						(	self				)    ->				str:






        '''simple docstring'''






        __snake_case				:			Union[str, Any]            =    TFCTRLModelTester(self				)
        __snake_case				:			Tuple            =    ConfigTester(self			,  config_class=UpperCAmelCase			,  n_embd=37				)







      def   UpperCAmelCase						(	self				)    ->				Dict:






        '''simple docstring'''






        self.config_tester.run_common_tests()







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






        '''simple docstring'''






        __snake_case				:			Union[str, Any]            =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_model(*UpperCAmelCase				)







      def   UpperCAmelCase						(	self				)    ->				Tuple:






        '''simple docstring'''






        __snake_case				:			List[str]            =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_lm_head(*UpperCAmelCase				)







      def   UpperCAmelCase						(	self				)    ->				str:






        '''simple docstring'''






        __snake_case				:			List[str]            =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_for_sequence_classification(*UpperCAmelCase				)







      def   UpperCAmelCase						(	self				)    ->				Tuple:






        '''simple docstring'''






        __snake_case      ,				__snake_case				:			Union[str, Any]            =    self.model_tester.prepare_config_and_inputs_for_common()
        __snake_case				:			List[Any]            =    [TFCTRLLMHeadModel]
        __snake_case				:			int            =    [TFCTRLForSequenceClassification]

        for model_class in self.all_model_classes:
          __snake_case				:			Dict            =    model_class(UpperCAmelCase				)
          model.build()  # may be needed for the get_bias() call below
          assert isinstance(model.get_input_embeddings()			,  tf.keras.layers.Layer				)

          if model_class in list_lm_models:
            __snake_case				:			Union[str, Any]            =    model.get_output_embeddings()
            assert isinstance(UpperCAmelCase			,  tf.keras.layers.Layer				)
            __snake_case				:			Tuple            =    model.get_bias()
            assert isinstance(UpperCAmelCase			,  UpperCAmelCase				)
            for k, v in name.items():
              assert isinstance(UpperCAmelCase			,  tf.Variable				)
          elif model_class in list_other_models_with_output_ebd:
            __snake_case				:			Tuple            =    model.get_output_embeddings()
            assert isinstance(UpperCAmelCase			,  tf.keras.layers.Layer				)
            __snake_case				:			int            =    model.get_bias()
            assert name is None
          else:
            __snake_case				:			Union[str, Any]            =    model.get_output_embeddings()
            assert x is None
            __snake_case				:			List[str]            =    model.get_bias()
            assert name is None







      @slow
      def   UpperCAmelCase						(	self				)    ->				List[str]:






        '''simple docstring'''






        for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __snake_case				:			Dict            =    TFCTRLModel.from_pretrained(UpperCAmelCase				)
          self.assertIsNotNone(UpperCAmelCase				)




@require_tf
class 			_lowerCamelCase	(      unittest.TestCase      ):



      """simple docstring"""






      @slow
      def   UpperCAmelCase						(	self				)    ->				Optional[Any]:






        '''simple docstring'''






        __snake_case				:			List[Any]            =    TFCTRLLMHeadModel.from_pretrained("ctrl"				)
        __snake_case				:			Dict            =    tf.convert_to_tensor([[11859, 0, 1611, 8]]			,  dtype=tf.intaa				)  # Legal the president is
        __snake_case				:			str            =    [
            11859,
            0,
            1611,
            8,
            5,
            150,
            26449,
            2,
            19,
            348,
            469,
            3,
            2595,
            48,
            20740,
            246533,
            246533,
            19,
            30,
            5,
        ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

        __snake_case				:			List[str]            =    model.generate(UpperCAmelCase			,  do_sample=UpperCAmelCase				)
        self.assertListEqual(output_ids[0].numpy().tolist()			,  UpperCAmelCase				)








_UpperCamelCase    =	[chr(i) for i in range(32, 126)]
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(reversed(range(len(alphabets))))
_UpperCamelCase    =	[]
_UpperCamelCase    =	_UpperCamelCase    =	_UpperCamelCase    =	0







def  lowerCAmelCase__(		)	->      int:
  global gear_one_pos
  global gear_two_pos
  global gear_three_pos
  __snake_case				:			int            =    gear_one[0]
  gear_one.append(lowercase			)
  del gear_one[0]
  gear_one_pos += 1
  if gear_one_pos % int(len(lowercase			)			) == 0:
    __snake_case				:			List[str]            =    gear_two[0]
    gear_two.append(lowercase			)
    del gear_two[0]
    gear_two_pos += 1
    if gear_two_pos % int(len(lowercase			)			) == 0:
      __snake_case				:			Any            =    gear_three[0]
      gear_three.append(lowercase			)
      del gear_three[0]
      gear_three_pos += 1







def  lowerCAmelCase__(		lowercase    :      Tuple			)	->      Union[str, Any]:
  __snake_case				:			Union[str, Any]            =    alphabets.index(lowercase			)
  __snake_case				:			Tuple            =    gear_one[target]
  __snake_case				:			Union[str, Any]            =    gear_two[target]
  __snake_case				:			int            =    gear_three[target]
  __snake_case				:			Optional[int]            =    reflector[target]
  __snake_case				:			Union[str, Any]            =    gear_three.index(lowercase			)
  __snake_case				:			Optional[Any]            =    gear_two.index(lowercase			)
  __snake_case				:			int            =    gear_one.index(lowercase			)
  code.append(alphabets[target]			)
  rotator()


if __name__ == "__main__":
     _UpperCamelCase    =	list(input('''Type your message:\n'''))
     while True:
          try:
               _UpperCamelCase    =	int(input('''Please set token:(must be only digits)\n'''))
               break
          except Exception as error:
               print(error)
     for _ in range(token):
          rotator()
     for j in decode:
          engine(j)
     print('''\n''' + ''''''.join(code))
     print(
         F'''\nYour Token is {token} please write it down.\nIf you want to decode '''
         '''this message again you should input same digits as token!'''
     )

