'''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 	__A				:



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



    def  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_	   =       ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size			)

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

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

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

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

         UpperCAmelCase_	   =       self.get_config()

         UpperCAmelCase_	   =       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   :   List[str]			):
         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   :   Tuple       ,      __a   :   Any       ,      __a   :   int       ,      __a   :   Union[str, Any]       ,      __a   :   Any       ,      __a   :   Dict       ,      *__a   :   str			):
         UpperCAmelCase_	   =       CTRLModel(config=__a			)
         model.to(__a			)
         model.eval()

         model(__a       ,      token_type_ids=__a       ,      head_mask=__a			)
         model(__a       ,      token_type_ids=__a			)
         UpperCAmelCase_	   =       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   :   List[Any]       ,      __a   :   Tuple       ,      __a   :   Tuple       ,      __a   :   List[str]       ,      __a   :   Union[str, Any]       ,      __a   :   Tuple       ,      *__a   :   List[str]			):
         UpperCAmelCase_	   =       CTRLLMHeadModel(__a			)
         model.to(__a			)
         model.eval()

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

         (
             (
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			
         )	   =       config_and_inputs

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

         return config, inputs_dict






    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   Union[str, Any]       ,      __a   :   Optional[int]       ,      __a   :   Optional[Any]       ,      __a   :   List[Any]       ,      *__a   :   Dict			):
         UpperCAmelCase_	   =       self.num_labels
         UpperCAmelCase_	   =       CTRLForSequenceClassification(__a			)
         model.to(__a			)
         model.eval()
         UpperCAmelCase_	   =       ids_tensor([self.batch_size]       ,      self.type_sequence_label_size			)
         UpperCAmelCase_	   =       model(__a       ,      token_type_ids=__a       ,      labels=__a			)
         self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)			)




@require_torch
class 	__A				(			UpperCamelCase__		,     UpperCamelCase__		,     UpperCamelCase__		,     unittest.TestCase     ):
    a__	:						List[str]	       =  (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
    a__	:						List[str]	       =  (CTRLLMHeadModel,) if is_torch_available() else ()
    a__	:						str	       =  (
        {
            """feature-extraction""": CTRLModel,
            """text-classification""": CTRLForSequenceClassification,
            """text-generation""": CTRLLMHeadModel,
            """zero-shot""": CTRLForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    a__	:						Dict	       =  True
    a__	:						Optional[Any]	       =  False
    a__	:						List[Any]	       =  False



    def  _lowercase						(self   :   List[str]       ,      __a   :   List[Any]       ,      __a   :   Optional[Any]       ,      __a   :   Optional[Any]       ,      __a   :   str       ,      __a   :   Union[str, 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  _lowercase						(self   :   Optional[Any]			):
         UpperCAmelCase_	   =       CTRLModelTester(self			)
         UpperCAmelCase_	   =       ConfigTester(self       ,      config_class=__a       ,      n_embd=37			)



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



    def  _lowercase						(self   :   Optional[Any]			):
         self.config_tester.run_common_tests()



    def  _lowercase						(self   :   List[Any]			):
         UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_model(*__a			)



    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_	   =       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   :   Union[str, Any]			):
         pass



    @slow
    def  _lowercase						(self   :   Union[str, Any]			):
         for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_	   =       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   :   Optional[int]			):
         pass




@require_torch
class 	__A				(			unittest.TestCase     ):



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






    @slow
    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_	   =       CTRLLMHeadModel.from_pretrained("ctrl"			)
         model.to(__a			)
         UpperCAmelCase_	   =       torch.tensor(
             [[11859, 0, 1611, 8]]       ,      dtype=torch.long       ,      device=__a			)  # Legal the president is
         UpperCAmelCase_	   =       [
             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

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





'''simple docstring'''





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_:					int  ={
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					List[str]  =['MaskFormerFeatureExtractor']
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =['MaskFormerImageProcessor']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					Dict  =[
        'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MaskFormerForInstanceSegmentation',
        'MaskFormerModel',
        'MaskFormerPreTrainedModel',
    ]
    SCREAMING_SNAKE_CASE_:					List[str]  =[
        'MaskFormerSwinBackbone',
        'MaskFormerSwinModel',
        'MaskFormerSwinPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .configuration_maskformer_swin import MaskFormerSwinConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_maskformer import MaskFormerFeatureExtractor
        from .image_processing_maskformer import MaskFormerImageProcessor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .modeling_maskformer_swin import (
            MaskFormerSwinBackbone,
            MaskFormerSwinModel,
            MaskFormerSwinPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_:					Dict  =_LazyModule(__name__, globals()['__file__'], _import_structure)




