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 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					A_					,					)			->					Optional[Any]:






           """simple docstring"""
           _lowerCamelCase           =					parent
           _lowerCamelCase           =					13
           _lowerCamelCase           =					7
           _lowerCamelCase           =					True
           _lowerCamelCase           =					True
           _lowerCamelCase           =					True
           _lowerCamelCase           =					True
           _lowerCamelCase           =					True
           _lowerCamelCase           =					99
           _lowerCamelCase           =					32
           _lowerCamelCase           =					2
           _lowerCamelCase           =					4
           _lowerCamelCase           =					37
           _lowerCamelCase           =					'''gelu'''
           _lowerCamelCase           =					0.1
           _lowerCamelCase           =					0.1
           _lowerCamelCase           =					5_12
           _lowerCamelCase           =					16
           _lowerCamelCase           =					2
           _lowerCamelCase           =					0.02
           _lowerCamelCase           =					3
           _lowerCamelCase           =					4
           _lowerCamelCase           =					None
           _lowerCamelCase           =					self.vocab_size - 1



     def 		UpperCamelCase_    (       self    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					ids_tensor([self.batch_size, self.seq_length]					,					self.vocab_size    )

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

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

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

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

           _lowerCamelCase           =					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					,					)

           _lowerCamelCase           =					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					,					A_					,					A_					,					A_					,					A_					,					A_					,					*A_    )			->					Optional[Any]:






           """simple docstring"""
           _lowerCamelCase           =					TFCTRLModel(config=A_    )
           _lowerCamelCase           =					{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           _lowerCamelCase           =					model(A_    )

           _lowerCamelCase           =					[input_ids, None, input_mask]  # None is the input for 'past'
           _lowerCamelCase           =					model(A_    )

           _lowerCamelCase           =					model(A_    )

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



     def 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_					,					A_					,					A_					,					*A_    )			->					Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase           =					TFCTRLLMHeadModel(config=A_    )
           _lowerCamelCase           =					{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           _lowerCamelCase           =					model(A_    )
           self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.seq_length, self.vocab_size)    )



     def 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_					,					A_					,					A_					,					*A_    )			->					List[str]:






           """simple docstring"""
           _lowerCamelCase           =					self.num_labels
           _lowerCamelCase           =					ids_tensor([self.batch_size]					,					self.type_sequence_label_size    )
           _lowerCamelCase           =					{
               '''input_ids''': input_ids,
               '''token_type_ids''': token_type_ids,
               '''labels''': sequence_labels,
           }
           _lowerCamelCase           =					TFCTRLForSequenceClassification(A_    )
           _lowerCamelCase           =					model(A_    )
           self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.num_labels)    )





     def 		UpperCamelCase_    (       self    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					self.prepare_config_and_inputs()

           (
               (
               _lowerCamelCase
           )     ,       (
               _lowerCamelCase
           )     ,       (
               _lowerCamelCase
           )     ,       (
               _lowerCamelCase
           )     ,       (
               _lowerCamelCase
           )     ,       (
               _lowerCamelCase
           )     ,       (
               _lowerCamelCase
           )     ,       (
               _lowerCamelCase
           )     ,       (
               _lowerCamelCase
           )     ,       
           )           =					config_and_inputs

           _lowerCamelCase           =					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
           return config, inputs_dict





@require_tf
class 			UpperCamelCase					(			__lowercase ,	__lowercase ,	unittest.TestCase			):

     '''simple docstring'''

     A_					    =	(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
     A_					    =	(TFCTRLLMHeadModel,) if is_tf_available() else ()
     A_					    =	(
         {
             'feature-extraction': TFCTRLModel,
             'text-classification': TFCTRLForSequenceClassification,
             'text-generation': TFCTRLLMHeadModel,
             'zero-shot': TFCTRLForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     A_					    =	False
     A_					    =	False



     def 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_					,					A_					,					A_    )			->					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    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					TFCTRLModelTester(self    )
           _lowerCamelCase           =					ConfigTester(self					,					config_class=A_					,					n_embd=37    )



     def 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           self.config_tester.run_common_tests()



     def 		UpperCamelCase_    (       self    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_ctrl_model(*A_    )



     def 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_ctrl_lm_head(*A_    )



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






           """simple docstring"""
           _lowerCamelCase           =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_ctrl_for_sequence_classification(*A_    )



     def 		UpperCamelCase_    (       self    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase     ,       _lowerCamelCase           =					self.model_tester.prepare_config_and_inputs_for_common()
           _lowerCamelCase           =					[TFCTRLLMHeadModel]
           _lowerCamelCase           =					[TFCTRLForSequenceClassification]

           for model_class in self.all_model_classes:
                 _lowerCamelCase           =					model_class(A_    )
                 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:
                       _lowerCamelCase           =					model.get_output_embeddings()
                       assert isinstance(A_					,					tf.keras.layers.Layer    )
                       _lowerCamelCase           =					model.get_bias()
                       assert isinstance(A_					,					A_    )
                       for k, v in name.items():
                             assert isinstance(A_					,					tf.Variable    )
                 elif model_class in list_other_models_with_output_ebd:
                       _lowerCamelCase           =					model.get_output_embeddings()
                       assert isinstance(A_					,					tf.keras.layers.Layer    )
                       _lowerCamelCase           =					model.get_bias()
                       assert name is None
                 else:
                       _lowerCamelCase           =					model.get_output_embeddings()
                       assert x is None
                       _lowerCamelCase           =					model.get_bias()
                       assert name is None





     @slow
     def 		UpperCamelCase_    (       self    )			->					Dict:






           """simple docstring"""
           for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _lowerCamelCase           =					TFCTRLModel.from_pretrained(A_    )
                 self.assertIsNotNone(A_    )





@require_tf
class 			UpperCamelCase					(			unittest.TestCase			):

     '''simple docstring'''

     @slow
     def 		UpperCamelCase_    (       self    )			->					Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase           =					TFCTRLLMHeadModel.from_pretrained('''ctrl'''    )
           _lowerCamelCase           =					tf.convert_to_tensor([[1_18_59, 0, 16_11, 8]]					,					dtype=tf.intaa    )  # Legal the president is
           _lowerCamelCase           =					[
               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

           _lowerCamelCase           =					model.generate(A_					,					do_sample=A_    )
           self.assertListEqual(output_ids[0].numpy().tolist()					,					A_    )
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


snake_case__						      =			logging.get_logger(__name__)


snake_case__						      =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all MVP models at https://huggingface.co/models?filter=mvp
snake_case__						      =			{
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
}

snake_case__						      =			{
    'RUCAIBox/mvp': 1024,
}
@lru_cache()
def    __magic_name__(	)    ->     List[str]:



      '''simple docstring'''

      _lowerCamelCase           =					(
          list(range(ord('''!'''		)			,    ord('''~'''		) + 1		)		) + list(range(ord('''¡'''		)			,    ord('''¬'''		) + 1		)		) + list(range(ord('''®'''		)			,    ord('''ÿ'''		) + 1		)		)
      )
      _lowerCamelCase           =					bs[:]
      _lowerCamelCase           =					0
      for b in range(2**8		):
            if b not in bs:
                  bs.append(__UpperCAmelCase		)
                  cs.append(2**8 + n		)
                  n += 1
      _lowerCamelCase           =					[chr(__UpperCAmelCase		) for n in cs]
      return dict(zip(__UpperCAmelCase			,    __UpperCAmelCase		)		)
def    __magic_name__(	__UpperCAmelCase		)    ->     List[Any]:



      '''simple docstring'''

      _lowerCamelCase           =					set()
      _lowerCamelCase           =					word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)		)
            _lowerCamelCase           =					char
      return pairs




class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     A_					    =	VOCAB_FILES_NAMES
     A_					    =	PRETRAINED_VOCAB_FILES_MAP
     A_					    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_					    =	['input_ids', 'attention_mask']



     def __init__(       self					,					A_					,					A_					,					A_="replace"					,					A_="<s>"					,					A_="</s>"					,					A_="</s>"					,					A_="<s>"					,					A_="<unk>"					,					A_="<pad>"					,					A_="<mask>"					,					A_=False					,					**A_					,					)			->					Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else bos_token
           _lowerCamelCase           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else eos_token
           _lowerCamelCase           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else sep_token
           _lowerCamelCase           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else cls_token
           _lowerCamelCase           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else unk_token
           _lowerCamelCase           =					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           =					AddedToken(A_					,					lstrip=A_					,					rstrip=A_    ) if isinstance(A_					,					A_    ) else mask_token

           super().__init__(
               errors=A_					,					bos_token=A_					,					eos_token=A_					,					unk_token=A_					,					sep_token=A_					,					cls_token=A_					,					pad_token=A_					,					mask_token=A_					,					add_prefix_space=A_					,					**A_					,					)
           with open(A_					,					encoding='''utf-8'''    ) as vocab_handle:
                 _lowerCamelCase           =					json.load(A_    )
           _lowerCamelCase           =					{v: k for k, v in self.encoder.items()}
           _lowerCamelCase           =					errors  # how to handle errors in decoding
           _lowerCamelCase           =					bytes_to_unicode()
           _lowerCamelCase           =					{v: k for k, v in self.byte_encoder.items()}
           with open(A_					,					encoding='''utf-8'''    ) as merges_handle:
                 _lowerCamelCase           =					merges_handle.read().split('''\n'''    )[1:-1]
           _lowerCamelCase           =					[tuple(merge.split()    ) for merge in bpe_merges]
           _lowerCamelCase           =					dict(zip(A_					,					range(len(A_    )    )    )    )
           _lowerCamelCase           =					{}
           _lowerCamelCase           =					add_prefix_space

           # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
           _lowerCamelCase           =					re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''    )



     @property
     def 		UpperCamelCase_    (       self    )			->					Dict:






           """simple docstring"""
           return len(self.encoder    )



     def 		UpperCamelCase_    (       self    )			->					Tuple:






           """simple docstring"""
           return dict(self.encoder					,					**self.added_tokens_encoder    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					str:






           """simple docstring"""
           if token in self.cache:
                 return self.cache[token]
           _lowerCamelCase           =					tuple(A_    )
           _lowerCamelCase           =					get_pairs(A_    )

           if not pairs:
                 return token

           while True:
                 _lowerCamelCase           =					min(A_					,					key=lambda A_    : self.bpe_ranks.get(A_					,					float('''inf'''    )    )    )
                 if bigram not in self.bpe_ranks:
                       break
                 _lowerCamelCase     ,       _lowerCamelCase           =					bigram
                 _lowerCamelCase           =					[]
                 _lowerCamelCase           =					0
                 while i < len(A_    ):
                       try:
                             _lowerCamelCase           =					word.index(A_					,					A_    )
                       except ValueError:
                             new_word.extend(word[i:]    )
                             break
                       else:
                             new_word.extend(word[i:j]    )
                             _lowerCamelCase           =					j

                       if word[i] == first and i < len(A_    ) - 1 and word[i + 1] == second:
                             new_word.append(first + second    )
                             i += 2
                       else:
                             new_word.append(word[i]    )
                             i += 1
                 _lowerCamelCase           =					tuple(A_    )
                 _lowerCamelCase           =					new_word
                 if len(A_    ) == 1:
                       break
                 else:
                       _lowerCamelCase           =					get_pairs(A_    )
           _lowerCamelCase           =					''' '''.join(A_    )
           _lowerCamelCase           =					word
           return word



     def 		UpperCamelCase_    (       self					,					A_    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					[]
           for token in re.findall(self.pat					,					A_    ):
                 _lowerCamelCase           =					''''''.join(
                     self.byte_encoder[b] for b in token.encode('''utf-8'''    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                 bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A_    ).split(''' '''    )    )
           return bpe_tokens



     def 		UpperCamelCase_    (       self					,					A_    )			->					Optional[Any]:






           """simple docstring"""
           return self.encoder.get(A_					,					self.encoder.get(self.unk_token    )    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					Any:






           """simple docstring"""
           return self.decoder.get(A_    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					''''''.join(A_    )
           _lowerCamelCase           =					bytearray([self.byte_decoder[c] for c in text]    ).decode('''utf-8'''					,					errors=self.errors    )
           return text



     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None    )			->					Tuple[str]:






           """simple docstring"""
           if not os.path.isdir(A_    ):
                 logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
                 return
           _lowerCamelCase           =					os.path.join(
               A_					,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
           _lowerCamelCase           =					os.path.join(
               A_					,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']    )

           with open(A_					,					'''w'''					,					encoding='''utf-8'''    ) as f:
                 f.write(json.dumps(self.encoder					,					indent=2					,					sort_keys=A_					,					ensure_ascii=A_    ) + '''\n'''    )

           _lowerCamelCase           =					0
           with open(A_					,					'''w'''					,					encoding='''utf-8'''    ) as writer:
                 writer.write('''#version: 0.2\n'''    )
                 for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,					key=lambda A_    : kv[1]    ):
                       if index != token_index:
                             logger.warning(
                                 F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                                 ''' Please check that the tokenizer is not corrupted!'''    )
                             _lowerCamelCase           =					token_index
                       writer.write(''' '''.join(A_    ) + '''\n'''    )
                       index += 1

           return vocab_file, merge_file



     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None    )			->					List[int]:






           """simple docstring"""
           if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           _lowerCamelCase           =					[self.cls_token_id]
           _lowerCamelCase           =					[self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep



     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None					,					A_ = False    )			->					List[int]:






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

           if token_ids_a is None:
                 return [1] + ([0] * len(A_    )) + [1]
           return [1] + ([0] * len(A_    )) + [1, 1] + ([0] * len(A_    )) + [1]



     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None    )			->					List[int]:






           """simple docstring"""
           _lowerCamelCase           =					[self.sep_token_id]
           _lowerCamelCase           =					[self.cls_token_id]

           if token_ids_a is None:
                 return len(cls + token_ids_a + sep    ) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]





     def 		UpperCamelCase_    (       self					,					A_					,					A_=False					,					**A_    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					kwargs.pop('''add_prefix_space'''					,					self.add_prefix_space    )
           if (is_split_into_words or add_prefix_space) and (len(A_    ) > 0 and not text[0].isspace()):
                 _lowerCamelCase           =					''' ''' + text
           return (text, kwargs)