
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 					lowerCamelCase		:





     def __init__(self				:   str    ,   _A				:   str    ,   _A				:   Tuple=1_4    ,   _A				:   str=7    ,   _A				:   List[str]=True    ,   _A				:   Any=True    ,   _A				:   Optional[int]=True    ,   _A				:   Any=True    ,   _A				:   List[str]=True    ,   _A				:   Any=9_9    ,   _A				:   List[str]=3_2    ,   _A				:   Any=5    ,   _A				:   int=4    ,   _A				:   Optional[int]=3_7    ,   _A				:   List[str]="gelu"    ,   _A				:   Optional[int]=0.1    ,   _A				:   Union[str, Any]=0.1    ,   _A				:   Union[str, Any]=5_1_2    ,   _A				:   Optional[Any]=1_6    ,   _A				:   Optional[Any]=2    ,   _A				:   Any=0.02    ,   _A				:   Any=3    ,   _A				:   Optional[Any]=4    ,   _A				:   Optional[Any]=None    ,   )						->						List[str]:
         snake_case								= parent
         snake_case								= batch_size
         snake_case								= seq_length
         snake_case								= is_training
         snake_case								= use_token_type_ids
         snake_case								= use_input_mask
         snake_case								= use_labels
         snake_case								= use_mc_token_ids
         snake_case								= vocab_size
         snake_case								= hidden_size
         snake_case								= num_hidden_layers
         snake_case								= num_attention_heads
         snake_case								= intermediate_size
         snake_case								= hidden_act
         snake_case								= hidden_dropout_prob
         snake_case								= attention_probs_dropout_prob
         snake_case								= max_position_embeddings
         snake_case								= type_vocab_size
         snake_case								= type_sequence_label_size
         snake_case								= initializer_range
         snake_case								= num_labels
         snake_case								= num_choices
         snake_case								= scope
         snake_case								= self.vocab_size - 1





     def 		UpperCAmelCase(self				:   Dict       )						->						Dict:
         snake_case								= ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size       )

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

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

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

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

         snake_case								= self.get_config()

         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 		UpperCAmelCase(self				:   List[str]       )						->						Dict:
         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 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   Tuple    ,   _A				:   Union[str, Any]    ,   _A				:   Optional[int]    ,   _A				:   Optional[Any]    ,   _A				:   int    ,   *_A				:   List[Any]       )						->						Any:
         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       )
         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 		UpperCAmelCase(self				:   List[Any]    ,   _A				:   Tuple    ,   _A				:   Optional[Any]    ,   _A				:   Tuple    ,   _A				:   str    ,   _A				:   Optional[Any]    ,   *_A				:   List[str]       )						->						Tuple:
         snake_case								= CTRLLMHeadModel(_A       )
         model.to(_A       )
         model.eval()

         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 		UpperCAmelCase(self				:   Dict       )						->						Any:
         snake_case								= self.prepare_config_and_inputs()

         (
             (
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							
         )								= config_and_inputs

         snake_case								= {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

         return config, inputs_dict





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   str    ,   _A				:   int    ,   _A				:   str    ,   _A				:   List[Any]    ,   *_A				:   Any       )						->						Any:
         snake_case								= self.num_labels
         snake_case								= CTRLForSequenceClassification(_A       )
         model.to(_A       )
         model.eval()
         snake_case								= ids_tensor([self.batch_size]    ,   self.type_sequence_label_size       )
         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 					lowerCamelCase		(  A_							,			A_							,			A_							,			unittest.TestCase  ):
     UpperCAmelCase__    :							Union[str, Any]									=		(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
     UpperCAmelCase__    :							Optional[Any]									=		(CTRLLMHeadModel,) if is_torch_available() else ()
     UpperCAmelCase__    :							str									=		(
         {
             "feature-extraction": CTRLModel,
             "text-classification": CTRLForSequenceClassification,
             "text-generation": CTRLLMHeadModel,
             "zero-shot": CTRLForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     UpperCAmelCase__    :							Tuple									=		True
     UpperCAmelCase__    :							List[Any]									=		False
     UpperCAmelCase__    :							Tuple									=		False





     def 		UpperCAmelCase(self				:   int    ,   _A				:   str    ,   _A				:   Union[str, Any]    ,   _A				:   Dict    ,   _A				:   Optional[Any]    ,   _A				:   str       )						->						List[Any]:
         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				:   Any       )						->						List[Any]:
         snake_case								= CTRLModelTester(self       )
         snake_case								= ConfigTester(self    ,   config_class=_A    ,   n_embd=3_7       )





     def 		UpperCAmelCase(self				:   Tuple       )						->						int:
         super().tearDown()
         # clean-up as much as possible GPU memory occupied by PyTorch
         gc.collect()
         torch.cuda.empty_cache()





     def 		UpperCAmelCase(self				:   Any       )						->						int:
         self.config_tester.run_common_tests()





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Optional[int]:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_model(*_A       )





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





     @slow
     def 		UpperCAmelCase(self				:   Any       )						->						List[str]:
         for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             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 		UpperCAmelCase(self				:   List[Any]       )						->						Any:
         pass







@require_torch
class 					lowerCamelCase		(  unittest.TestCase  ):





     def 		UpperCAmelCase(self				:   List[Any]       )						->						Optional[Any]:
         super().tearDown()
         # clean-up as much as possible GPU memory occupied by PyTorch
         gc.collect()
         torch.cuda.empty_cache()





     @slow
     def 		UpperCAmelCase(self				:   int       )						->						List[Any]:
         snake_case								= CTRLLMHeadModel.from_pretrained("ctrl"       )
         model.to(_A       )
         snake_case								= torch.tensor(
             [[1_1_8_5_9, 0, 1_6_1_1, 8]]    ,   dtype=torch.long    ,   device=_A       )  # Legal the president is
         snake_case								= [
             1_1_8_5_9,
             0,
             1_6_1_1,
             8,
             5,
             1_5_0,
             2_6_4_4_9,
             2,
             1_9,
             3_4_8,
             4_6_9,
             3,
             2_5_9_5,
             4_8,
             2_0_7_4_0,
             2_4_6_5_3_3,
             2_4_6_5_3_3,
             1_9,
             3_0,
             5,
         ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

         snake_case								= model.generate(_A    ,   do_sample=_A       )
         self.assertListEqual(output_ids[0].tolist()    ,   _A       )



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


_A   =			logging.get_logger(__name__)

_A   =			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_A   =			{
    "vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
    "tokenizer_file": {
        "mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
    },
}

_A   =			{"mobilebert-uncased": 5_12}


_A   =			{}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Tuple									=		VOCAB_FILES_NAMES
     UpperCAmelCase__    :							Any									=		PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__    :							Tuple									=		PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase__    :							Tuple									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__    :							Optional[Any]									=		MobileBertTokenizer





     def __init__(self				:   Any    ,   _A				:   str=None    ,   _A				:   str=None    ,   _A				:   Union[str, Any]=True    ,   _A				:   Optional[Any]="[UNK]"    ,   _A				:   int="[SEP]"    ,   _A				:   Dict="[PAD]"    ,   _A				:   int="[CLS]"    ,   _A				:   Union[str, Any]="[MASK]"    ,   _A				:   Any=True    ,   _A				:   Dict=None    ,   **_A				:   List[str]    ,   )						->						List[str]:
         super().__init__(
             _A    ,   tokenizer_file=_A    ,   do_lower_case=_A    ,   unk_token=_A    ,   sep_token=_A    ,   pad_token=_A    ,   cls_token=_A    ,   mask_token=_A    ,   tokenize_chinese_chars=_A    ,   strip_accents=_A    ,   **_A    ,   )

         snake_case								= json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
         if (
             normalizer_state.get("lowercase"    ,   _A       ) != do_lower_case
             or normalizer_state.get("strip_accents"    ,   _A       ) != strip_accents
             or normalizer_state.get("handle_chinese_chars"    ,   _A       ) != tokenize_chinese_chars
         ):
             snake_case								= getattr(_A    ,   normalizer_state.pop("type"       )       )
             snake_case								= do_lower_case
             snake_case								= strip_accents
             snake_case								= tokenize_chinese_chars
             snake_case								= normalizer_class(**_A       )

         snake_case								= do_lower_case





     def 		UpperCAmelCase(self				:   List[str]    ,   _A				:   Union[str, Any]    ,   _A				:   Dict=None       )						->						Optional[Any]:
         snake_case								= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
             output += token_ids_a + [self.sep_token_id]

         return output





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   List[int]    ,   _A				:   Optional[List[int]] = None       )						->						List[int]:
         snake_case								= [self.sep_token_id]
         snake_case								= [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       ) * [0] + len(token_ids_a + sep       ) * [1]





     def 		UpperCAmelCase(self				:   int    ,   _A				:   str    ,   _A				:   Optional[str] = None       )						->						Tuple[str]:
         snake_case								= self._tokenizer.model.save(_A    ,   name=_A       )
         return tuple(_A       )

