



"""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        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     Any				,		__A:     Optional[int]				,		__A:     Union[str, Any]=14				,		__A:     int=7				,		__A:     str=True				,		__A:     str=True				,		__A:     Dict=True				,		__A:     int=True				,		__A:     List[str]=True				,		__A:     List[str]=99				,		__A:     List[str]=32				,		__A:     Union[str, Any]=5				,		__A:     Any=4				,		__A:     str=37				,		__A:     Optional[Any]="gelu"				,		__A:     Optional[Any]=0.1				,		__A:     str=0.1				,		__A:     Union[str, Any]=512				,		__A:     Any=16				,		__A:     Optional[int]=2				,		__A:     int=0.0_2				,		__A:     Tuple=3				,		__A:     Tuple=4				,		__A:     List[str]=None				,		):

												'''simple docstring'''

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




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

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

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

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

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

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

												a__          =  self.get_config()

												a__          =  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:     Optional[Any]    ):

												'''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:     int				,		__A:     Union[str, Any]				,		__A:     Optional[Any]				,		__A:     Optional[int]				,		__A:     Optional[int]				,		__A:     Dict				,		*__A:     int    ):

												'''simple docstring'''

												a__          =  CTRLModel(config=lowerCamelCase__    )
												model.to(lowerCamelCase__    )
												model.eval()

												model(lowerCamelCase__				,		token_type_ids=lowerCamelCase__				,		head_mask=lowerCamelCase__    )
												model(lowerCamelCase__				,		token_type_ids=lowerCamelCase__    )
												a__          =  model(lowerCamelCase__    )
												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:     Tuple				,		__A:     Optional[int]				,		__A:     List[str]				,		__A:     Optional[Any]				,		__A:     Optional[int]				,		__A:     List[Any]				,		*__A:     Tuple    ):

												'''simple docstring'''

												a__          =  CTRLLMHeadModel(lowerCamelCase__    )
												model.to(lowerCamelCase__    )
												model.eval()

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




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()

												(
												    a__				
												)          =  config_and_inputs

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

												return config, inputs_dict





							def  lowercase		(   self:     List[str]				,		__A:     List[str]				,		__A:     Optional[int]				,		__A:     Union[str, Any]				,		__A:     List[Any]				,		*__A:     List[str]    ):

												'''simple docstring'''

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







@require_torch
class        _SCREAMING_SNAKE_CASE						(  __lowerCAmelCase       , __lowerCAmelCase       , __lowerCAmelCase       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
							_SCREAMING_SNAKE_CASE    =(CTRLLMHeadModel,) if is_torch_available() else ()
							_SCREAMING_SNAKE_CASE    =(
							    {
							        "feature-extraction": CTRLModel,
							        "text-classification": CTRLForSequenceClassification,
							        "text-generation": CTRLLMHeadModel,
							        "zero-shot": CTRLForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							_SCREAMING_SNAKE_CASE    =True
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False




							def  lowercase		(   self:     List[str]				,		__A:     Optional[Any]				,		__A:     Optional[int]				,		__A:     Any				,		__A:     int				,		__A:     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  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  CTRLModelTester(self    )
												a__          =  ConfigTester(self				,		config_class=lowerCamelCase__				,		n_embd=37    )




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												self.config_tester.run_common_tests()




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_ctrl_model(*lowerCamelCase__    )




							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_lm_head_model(*lowerCamelCase__    )




							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''    )
							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												pass




							@slow
							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

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





							@unittest.skip('''The model doesn\'t support left padding'''    )  # and it's not used enough to be worth fixing :)
							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												pass







@require_torch
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def  lowercase		(   self:     str    ):

												'''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:     int    ):

												'''simple docstring'''

												a__          =  CTRLLMHeadModel.from_pretrained('''ctrl'''    )
												model.to(lowerCamelCase__    )
												a__          =  torch.tensor(
												    [[11859, 0, 1611, 8]]				,		dtype=torch.long				,		device=lowerCamelCase__    )  # Legal the president is
												a__          =  [
												    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

												a__          =  model.generate(lowerCamelCase__				,		do_sample=lowerCamelCase__    )
												self.assertListEqual(output_ids[0].tolist()				,		lowerCamelCase__    )










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__a       :	str             = {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	int             = [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

	__a       :	Dict             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


