
from typing import TYPE_CHECKING

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


UpperCamelCase_ 						=						{'''configuration_yolos''': ['''YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YolosConfig''', '''YolosOnnxConfig''']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase_ 						=						['''YolosFeatureExtractor''']
					UpperCamelCase_ 						=						['''YolosImageProcessor''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase_ 						=						[
					    '''YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''YolosForObjectDetection''',
					    '''YolosModel''',
					    '''YolosPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_yolos import YolosFeatureExtractor
										from .image_processing_yolos import YolosImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_yolos import (
										    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    YolosForObjectDetection,
										    YolosModel,
										    YolosPreTrainedModel,
										)


else:
					import sys

					UpperCamelCase_ 						=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)













"""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 			__UpperCamelCase							:






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






							def        __a  (  self )      ->					int:
								a :						Optional[Any]      =							ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size )

								a :						str      =							None
								if self.use_input_mask:
									a :						Dict      =							random_attention_mask([self.batch_size, self.seq_length] )

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

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

								a :						List[Any]      =							None
								a :						Optional[int]      =							None
								a :						str      =							None
								if self.use_labels:
									a :						List[str]      =							ids_tensor([self.batch_size] ,   self.type_sequence_label_size )
									a :						List[str]      =							ids_tensor([self.batch_size, self.seq_length] ,   self.num_labels )
									a :						Any      =							ids_tensor([self.batch_size] ,   self.num_choices )

								a :						Tuple      =							self.get_config()

								a :						int      =							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 )      ->					Tuple:
								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 ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   *lowerCAmelCase__ )      ->					str:
								a :						Union[str, Any]      =							CTRLModel(config=lowerCAmelCase__ )
								model.to(lowerCAmelCase__ )
								model.eval()

								model(lowerCAmelCase__ ,   token_type_ids=lowerCAmelCase__ ,   head_mask=lowerCAmelCase__ )
								model(lowerCAmelCase__ ,   token_type_ids=lowerCAmelCase__ )
								a :						List[Any]      =							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        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   *lowerCAmelCase__ )      ->					Optional[int]:
								a :						Tuple      =							CTRLLMHeadModel(lowerCAmelCase__ )
								model.to(lowerCAmelCase__ )
								model.eval()

								a :						List[str]      =							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        __a  (  self )      ->					Optional[int]:
								a :						Tuple      =							self.prepare_config_and_inputs()

								(
								    (
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			
								) :						Optional[Any]      =							config_and_inputs

								a :						List[Any]      =							{"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

								return config, inputs_dict






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


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






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Optional[int]:
								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:
								a :						str      =							CTRLModelTester(self )
								a :						Dict      =							ConfigTester(self ,   config_class=lowerCAmelCase__ ,   n_embd=37 )






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






							def        __a  (  self )      ->					Optional[Any]:
								self.config_tester.run_common_tests()






							def        __a  (  self )      ->					Union[str, Any]:
								a :						Optional[int]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_ctrl_model(*lowerCAmelCase__ )






							def        __a  (  self )      ->					int:
								a :						List[str]      =							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        __a  (  self )      ->					Tuple:
								pass






							@slow
							def        __a  (  self )      ->					Optional[Any]:
								for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a :						Union[str, Any]      =							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        __a  (  self )      ->					Tuple:
								pass


@require_torch
class 			__UpperCamelCase							(		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 )      ->					Dict:
								a :						Dict      =							CTRLLMHeadModel.from_pretrained("ctrl" )
								model.to(lowerCAmelCase__ )
								a :						List[str]      =							torch.tensor(
								    [[1_1859, 0, 1611, 8]] ,   dtype=torch.long ,   device=lowerCAmelCase__ )  # Legal the president is
								a :						Any      =							[
								    1_1859,
								    0,
								    1611,
								    8,
								    5,
								    150,
								    2_6449,
								    2,
								    19,
								    348,
								    469,
								    3,
								    2595,
								    48,
								    2_0740,
								    24_6533,
								    24_6533,
								    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 :						str      =							model.generate(lowerCAmelCase__ ,   do_sample=lowerCAmelCase__ )
								self.assertListEqual(output_ids[0].tolist() ,   lowerCAmelCase__ )


