"""simple docstring"""
from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__   :      Union[str, Any]							=logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__   :      Optional[int]							={
    """nielsr/canine-s""": 2048,
}

# Unicode defines 1,114,112 total “codepoints”
SCREAMING_SNAKE_CASE__   :      Optional[Any]							=111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
SCREAMING_SNAKE_CASE__   :      Tuple							=0

SCREAMING_SNAKE_CASE__   :      str							=0xe_0_0_0
SCREAMING_SNAKE_CASE__   :      Optional[int]							=0xe_0_0_1
SCREAMING_SNAKE_CASE__   :      Optional[Any]							=0xe_0_0_2
SCREAMING_SNAKE_CASE__   :      Dict							=0xe_0_0_3
SCREAMING_SNAKE_CASE__   :      str							=0xe_0_0_4

# Maps special codepoints to human-readable names.
SCREAMING_SNAKE_CASE__   :      Dict[int, str]							={
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
SCREAMING_SNAKE_CASE__   :      Dict[str, int]							={name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}



class      _UpperCAmelCase						( __SCREAMING_SNAKE_CASE						):






    """simple docstring"""
    __snake_case    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







    def __init__(			self    , _lowercase=chr(A_					)    , _lowercase=chr(A_					)    , _lowercase=chr(A_					)    , _lowercase=chr(A_					)    , _lowercase=chr(A_					)    , _lowercase=chr(A_					)    , _lowercase=False    , _lowercase=2048    , **_lowercase    , )	->	List[str]:
        _lowerCamelCase	: Dict       =       AddedToken(A_    , lstrip=A_    , rstrip=A_					) if isinstance(A_    , A_					) else bos_token
        _lowerCamelCase	: Optional[Any]       =       AddedToken(A_    , lstrip=A_    , rstrip=A_					) if isinstance(A_    , A_					) else eos_token
        _lowerCamelCase	: List[str]       =       AddedToken(A_    , lstrip=A_    , rstrip=A_					) if isinstance(A_    , A_					) else sep_token
        _lowerCamelCase	: Any       =       AddedToken(A_    , lstrip=A_    , rstrip=A_					) if isinstance(A_    , A_					) else cls_token
        _lowerCamelCase	: List[str]       =       AddedToken(A_    , lstrip=A_    , rstrip=A_					) if isinstance(A_    , A_					) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        _lowerCamelCase	: Any       =       AddedToken(A_    , lstrip=A_    , rstrip=A_					) if isinstance(A_    , A_					) else mask_token

        super().__init__(
            bos_token=A_    , eos_token=A_    , sep_token=A_    , cls_token=A_    , pad_token=A_    , mask_token=A_    , add_prefix_space=A_    , model_max_length=A_    , **A_    , )

        # Creates a mapping for looking up the IDs of special symbols.
        _lowerCamelCase	: int       =       {}
        for codepoint, name in SPECIAL_CODEPOINTS.items():
            _lowerCamelCase	: str       =       codepoint

        # Creates a mapping for looking up the string forms of special symbol IDs.
        _lowerCamelCase	: List[Any]       =       {
            codepoint: name for name, codepoint in self._special_codepoints.items()
        }

        _lowerCamelCase	: Any       =       UNICODE_VOCAB_SIZE
        _lowerCamelCase	: int       =       len(self._special_codepoints					)







    @property
    def 				a__					(			self					)	->	int:
        return self._unicode_vocab_size







    def 				a__					(			self    , _lowercase					)	->	Union[str, Any]:
        return list(A_					)







    def 				a__					(			self    , _lowercase					)	->	int:

        try:
            return ord(A_					)
        except TypeError:
            raise ValueError(F'''invalid token: \'{token}\''''					)







    def 				a__					(			self    , _lowercase					)	->	Tuple:

        try:
            if index in SPECIAL_CODEPOINTS:
                return SPECIAL_CODEPOINTS[index]
            return chr(A_					)
        except TypeError:
            raise ValueError(F'''invalid id: {index}'''					)







    def 				a__					(			self    , _lowercase					)	->	List[str]:
        return "".join(A_					)







    def 				a__					(			self    , _lowercase    , _lowercase = None					)	->	str:
        _lowerCamelCase	: Tuple       =       [self.sep_token_id]
        _lowerCamelCase	: Optional[Any]       =       [self.cls_token_id]

        _lowerCamelCase	: Optional[int]       =       cls + token_ids_a + sep
        if token_ids_a is not None:
            result += token_ids_a + sep
        return result







    def 				a__					(			self    , _lowercase    , _lowercase = None    , _lowercase = False					)	->	Union[str, Any]:

        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=A_    , token_ids_a=A_    , already_has_special_tokens=A_					)

        _lowerCamelCase	: List[str]       =       [1] + ([0] * len(A_					)) + [1]
        if token_ids_a is not None:
            result += ([0] * len(A_					)) + [1]
        return result







    def 				a__					(			self    , _lowercase    , _lowercase = None					)	->	str:
        _lowerCamelCase	: Tuple       =       [self.sep_token_id]
        _lowerCamelCase	: Dict       =       [self.cls_token_id]

        _lowerCamelCase	: str       =       len(cls + token_ids_a + sep					) * [0]
        if token_ids_a is not None:
            result += len(token_ids_a + sep					) * [1]
        return result







    def 				a__					(			self    , _lowercase    , _lowercase = None					)	->	int:
        return ()









import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      CTRLForSequenceClassification,
      CTRLLMHeadModel,
      CTRLModel,
  )



class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_=14   ,				A_=7   ,				A_=True   ,				A_=True   ,				A_=True   ,				A_=True   ,				A_=True   ,				A_=99   ,				A_=32   ,				A_=5   ,				A_=4   ,				A_=37   ,				A_="gelu"   ,				A_=0.1   ,				A_=0.1   ,				A_=5_12   ,				A_=16   ,				A_=2   ,				A_=0.02   ,				A_=3   ,				A_=4   ,				A_=None   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			parent
        SCREAMING_SNAKE_CASE__											=			batch_size
        SCREAMING_SNAKE_CASE__											=			seq_length
        SCREAMING_SNAKE_CASE__											=			is_training
        SCREAMING_SNAKE_CASE__											=			use_token_type_ids
        SCREAMING_SNAKE_CASE__											=			use_input_mask
        SCREAMING_SNAKE_CASE__											=			use_labels
        SCREAMING_SNAKE_CASE__											=			use_mc_token_ids
        SCREAMING_SNAKE_CASE__											=			vocab_size
        SCREAMING_SNAKE_CASE__											=			hidden_size
        SCREAMING_SNAKE_CASE__											=			num_hidden_layers
        SCREAMING_SNAKE_CASE__											=			num_attention_heads
        SCREAMING_SNAKE_CASE__											=			intermediate_size
        SCREAMING_SNAKE_CASE__											=			hidden_act
        SCREAMING_SNAKE_CASE__											=			hidden_dropout_prob
        SCREAMING_SNAKE_CASE__											=			attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE__											=			max_position_embeddings
        SCREAMING_SNAKE_CASE__											=			type_vocab_size
        SCREAMING_SNAKE_CASE__											=			type_sequence_label_size
        SCREAMING_SNAKE_CASE__											=			initializer_range
        SCREAMING_SNAKE_CASE__											=			num_labels
        SCREAMING_SNAKE_CASE__											=			num_choices
        SCREAMING_SNAKE_CASE__											=			scope
        SCREAMING_SNAKE_CASE__											=			self.vocab_size - 1



    def  lowercase_					(       self     ):





        '''simple docstring'''

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

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

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

        SCREAMING_SNAKE_CASE__											=			None
        if self.use_mc_token_ids:
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.num_choices]   ,				self.seq_length     )

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

        SCREAMING_SNAKE_CASE__											=			self.get_config()

        SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self     ):





        '''simple docstring'''

        return 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   ,				)



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				*A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CTRLModel(config=A_     )
        model.to(A_     )
        model.eval()

        model(A_   ,				token_type_ids=A_   ,				head_mask=A_     )
        model(A_   ,				token_type_ids=A_     )
        SCREAMING_SNAKE_CASE__											=			model(A_     )
        self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)     )
        self.parent.assertEqual(len(result.past_key_values     )   ,				config.n_layer     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				*A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CTRLLMHeadModel(A_     )
        model.to(A_     )
        model.eval()

        SCREAMING_SNAKE_CASE__											=			model(A_   ,				token_type_ids=A_   ,				labels=A_     )
        self.parent.assertEqual(result.loss.shape   ,				()     )
        self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.prepare_config_and_inputs()

        (
            (
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		
        )											=			config_and_inputs

        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

        return config, inputs_dict




    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				*A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.num_labels
        SCREAMING_SNAKE_CASE__											=			CTRLForSequenceClassification(A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size]   ,				self.type_sequence_label_size     )
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				token_type_ids=A_   ,				labels=A_     )
        self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)     )





@require_torch
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Union[str, Any]				=	(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
    lowerCamelCase__    :		List[Any]				=	(CTRLLMHeadModel,) if is_torch_available() else ()
    lowerCamelCase__    :		Tuple				=	(
        {
            """feature-extraction""": CTRLModel,
            """text-classification""": CTRLForSequenceClassification,
            """text-generation""": CTRLLMHeadModel,
            """zero-shot""": CTRLForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase__    :		List[Any]				=	True
    lowerCamelCase__    :		Optional[int]				=	False
    lowerCamelCase__    :		Any				=	False



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CTRLModelTester(self     )
        SCREAMING_SNAKE_CASE__											=			ConfigTester(self   ,				config_class=A_   ,				n_embd=37     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().tearDown()
        # clean-up as much as possible GPU memory occupied by PyTorch
        gc.collect()
        torch.cuda.empty_cache()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self.config_tester.run_common_tests()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_model(*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(*A_     )



    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__											=			CTRLModel.from_pretrained(A_     )
            self.assertIsNotNone(A_     )




    @unittest.skip('''The model doesn\'t support left padding'''     )  # and it's not used enough to be worth fixing :)
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass





@require_torch
class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().tearDown()
        # clean-up as much as possible GPU memory occupied by PyTorch
        gc.collect()
        torch.cuda.empty_cache()




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CTRLLMHeadModel.from_pretrained('''ctrl'''     )
        model.to(A_     )
        SCREAMING_SNAKE_CASE__											=			torch.tensor(
            [[1_18_59, 0, 16_11, 8]]   ,				dtype=torch.long   ,				device=A_     )  # Legal the president is
        SCREAMING_SNAKE_CASE__											=			[
            1_18_59,
            0,
            16_11,
            8,
            5,
            1_50,
            2_64_49,
            2,
            19,
            3_48,
            4_69,
            3,
            25_95,
            48,
            2_07_40,
            24_65_33,
            24_65_33,
            19,
            30,
            5,
        ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

        SCREAMING_SNAKE_CASE__											=			model.generate(A_   ,				do_sample=A_     )
        self.assertListEqual(output_ids[0].tolist()   ,				A_     )
