





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_a     :    Dict         =						{
    'configuration_poolformer': [
        'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'PoolFormerConfig',
        'PoolFormerOnnxConfig',
    ]
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a     :    Dict         =						['PoolFormerFeatureExtractor']
			_a     :    Dict         =						['PoolFormerImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a     :    str         =						[
			    'POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'PoolFormerForImageClassification',
			    'PoolFormerModel',
			    'PoolFormerPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_poolformer import (
			    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    PoolFormerConfig,
			    PoolFormerOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_poolformer import PoolFormerFeatureExtractor
						from .image_processing_poolformer import PoolFormerImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_poolformer import (
						    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PoolFormerForImageClassification,
						    PoolFormerModel,
						    PoolFormerPreTrainedModel,
						)


else:
			import sys

			_a     :    str         =						_LazyModule(__name__, globals()['__file__'], _import_structure)





'''simple docstring'''





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			:



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_=14  ,UpperCAmelCase_=7  ,UpperCAmelCase_=True  ,UpperCAmelCase_=True  ,UpperCAmelCase_=True  ,UpperCAmelCase_=True  ,UpperCAmelCase_=True  ,UpperCAmelCase_=99  ,UpperCAmelCase_=32  ,UpperCAmelCase_=5  ,UpperCAmelCase_=4  ,UpperCAmelCase_=37  ,UpperCAmelCase_="gelu"  ,UpperCAmelCase_=0.1  ,UpperCAmelCase_=0.1  ,UpperCAmelCase_=512  ,UpperCAmelCase_=16  ,UpperCAmelCase_=2  ,UpperCAmelCase_=0.02  ,UpperCAmelCase_=3  ,UpperCAmelCase_=4  ,UpperCAmelCase_=None  ,)	->				Dict:
           lowercase__									=    parent
           lowercase__									=    batch_size
           lowercase__									=    seq_length
           lowercase__									=    is_training
           lowercase__									=    use_token_type_ids
           lowercase__									=    use_input_mask
           lowercase__									=    use_labels
           lowercase__									=    use_mc_token_ids
           lowercase__									=    vocab_size
           lowercase__									=    hidden_size
           lowercase__									=    num_hidden_layers
           lowercase__									=    num_attention_heads
           lowercase__									=    intermediate_size
           lowercase__									=    hidden_act
           lowercase__									=    hidden_dropout_prob
           lowercase__									=    attention_probs_dropout_prob
           lowercase__									=    max_position_embeddings
           lowercase__									=    type_vocab_size
           lowercase__									=    type_sequence_label_size
           lowercase__									=    initializer_range
           lowercase__									=    num_labels
           lowercase__									=    num_choices
           lowercase__									=    scope
           lowercase__									=    self.vocab_size - 1



       def 				_a   (					self   )	->				Any:
           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
           if self.use_mc_token_ids:
               lowercase__									=    ids_tensor([self.batch_size, self.num_choices]  ,self.seq_length   )

           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__									=    self.get_config()

           lowercase__									=    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 				_a   (					self   )	->				Optional[int]:
           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 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,*UpperCAmelCase_   )	->				List[Any]:
           lowercase__									=    CTRLModel(config=UpperCAmelCase_   )
           model.to(UpperCAmelCase_   )
           model.eval()

           model(UpperCAmelCase_  ,token_type_ids=UpperCAmelCase_  ,head_mask=UpperCAmelCase_   )
           model(UpperCAmelCase_  ,token_type_ids=UpperCAmelCase_   )
           lowercase__									=    model(UpperCAmelCase_   )
           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 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,*UpperCAmelCase_   )	->				Optional[int]:
           lowercase__									=    CTRLLMHeadModel(UpperCAmelCase_   )
           model.to(UpperCAmelCase_   )
           model.eval()

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



       def 				_a   (					self   )	->				Union[str, Any]:
           lowercase__									=    self.prepare_config_and_inputs()

           (
               (
               lowercase__
           )							,							(
               lowercase__
           )							,							(
               lowercase__
           )							,							(
               lowercase__
           )							,							(
               lowercase__
           )							,							(
               lowercase__
           )							,							(
               lowercase__
           )							,							(
               lowercase__
           )							,							(
               lowercase__
           )							,							
           )									=    config_and_inputs

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

           return config, inputs_dict







       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,*UpperCAmelCase_   )	->				Optional[int]:
           lowercase__									=    self.num_labels
           lowercase__									=    CTRLForSequenceClassification(UpperCAmelCase_   )
           model.to(UpperCAmelCase_   )
           model.eval()
           lowercase__									=    ids_tensor([self.batch_size]  ,self.type_sequence_label_size   )
           lowercase__									=    model(UpperCAmelCase_  ,token_type_ids=UpperCAmelCase_  ,labels=UpperCAmelCase_   )
           self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)   )





@require_torch
class 					snake_case			(UpperCamelCase		,   UpperCamelCase		,   UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:Union[str, Any]     =       (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
       lowerCAmelCase__	:Dict     =       (CTRLLMHeadModel,) if is_torch_available() else ()
       lowerCAmelCase__	:int     =       (
           {
               "feature-extraction": CTRLModel,
               "text-classification": CTRLForSequenceClassification,
               "text-generation": CTRLLMHeadModel,
               "zero-shot": CTRLForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       lowerCAmelCase__	:str     =       True
       lowerCAmelCase__	:Tuple     =       False
       lowerCAmelCase__	:List[Any]     =       False



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Tuple:
           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 				_a   (					self   )	->				str:
           lowercase__									=    CTRLModelTester(self   )
           lowercase__									=    ConfigTester(self  ,config_class=UpperCAmelCase_  ,n_embd=37   )



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



       def 				_a   (					self   )	->				Tuple:
           self.config_tester.run_common_tests()



       def 				_a   (					self   )	->				Any:
           lowercase__									=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_ctrl_model(*UpperCAmelCase_   )



       def 				_a   (					self   )	->				Optional[Any]:
           lowercase__									=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_lm_head_model(*UpperCAmelCase_   )



       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."   )
       def 				_a   (					self   )	->				Tuple:
           pass



       @slow
       def 				_a   (					self   )	->				List[str]:
           for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__									=    CTRLModel.from_pretrained(UpperCAmelCase_   )
               self.assertIsNotNone(UpperCAmelCase_   )







       @unittest.skip("The model doesn't support left padding"   )  # and it's not used enough to be worth fixing :)
       def 				_a   (					self   )	->				Tuple:
           pass





@require_torch
class 					snake_case			(unittest.TestCase ):



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







       @slow
       def 				_a   (					self   )	->				int:
           lowercase__									=    CTRLLMHeadModel.from_pretrained("ctrl"   )
           model.to(UpperCAmelCase_   )
           lowercase__									=    torch.tensor(
               [[11_859, 0, 1_611, 8]]  ,dtype=torch.long  ,device=UpperCAmelCase_   )  # Legal the president is
           lowercase__									=    [
               11_859,
               0,
               1_611,
               8,
               5,
               150,
               26_449,
               2,
               19,
               348,
               469,
               3,
               2_595,
               48,
               20_740,
               246_533,
               246_533,
               19,
               30,
               5,
           ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

           lowercase__									=    model.generate(UpperCAmelCase_  ,do_sample=UpperCAmelCase_   )
           self.assertListEqual(output_ids[0].tolist()  ,UpperCAmelCase_   )




