



import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY



@is_pipeline_test
@require_torch_or_tf
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		snake_case_			=MODEL_FOR_CAUSAL_LM_MAPPING
		snake_case_			=TF_MODEL_FOR_CAUSAL_LM_MAPPING



		@require_torch
		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  pipeline(task='''text-generation'''	,model='''sshleifer/tiny-ctrl'''	,framework='''pt'''				)
						# Using `do_sample=False` to force deterministic output
						lowerCAmelCase__  :       Optional[int]				=  text_generator('''This is a test'''	,do_sample=__lowerCamelCase				)
						self.assertEqual(
						    __lowerCamelCase	,[
						        {
						            '''generated_text''': (
						                '''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
						                ''' oscope. FiliFili@@'''
						            )
						        }
						    ]	,)

						lowerCAmelCase__  :       Dict				=  text_generator(['''This is a test''', '''This is a second test''']				)
						self.assertEqual(
						    __lowerCamelCase	,[
						        [
						            {
						                '''generated_text''': (
						                    '''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
						                    ''' oscope. FiliFili@@'''
						                )
						            }
						        ],
						        [
						            {
						                '''generated_text''': (
						                    '''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
						                    ''' oscope. oscope. FiliFili@@'''
						                )
						            }
						        ],
						    ]	,)

						lowerCAmelCase__  :       Optional[int]				=  text_generator('''This is a test'''	,do_sample=__lowerCamelCase	,num_return_sequences=2	,return_tensors=__lowerCamelCase				)
						self.assertEqual(
						    __lowerCamelCase	,[
						        {'''generated_token_ids''': ANY(__lowerCamelCase				)},
						        {'''generated_token_ids''': ANY(__lowerCamelCase				)},
						    ]	,)
						lowerCAmelCase__  :       Union[str, Any]				=  text_generator.model.config.eos_token_id
						lowerCAmelCase__  :       str				=  '''<pad>'''
						lowerCAmelCase__  :       Optional[int]				=  text_generator(
						    ['''This is a test''', '''This is a second test''']	,do_sample=__lowerCamelCase	,num_return_sequences=2	,batch_size=2	,return_tensors=__lowerCamelCase	,)
						self.assertEqual(
						    __lowerCamelCase	,[
						        [
						            {'''generated_token_ids''': ANY(__lowerCamelCase				)},
						            {'''generated_token_ids''': ANY(__lowerCamelCase				)},
						        ],
						        [
						            {'''generated_token_ids''': ANY(__lowerCamelCase				)},
						            {'''generated_token_ids''': ANY(__lowerCamelCase				)},
						        ],
						    ]	,)



		@require_tf
		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  pipeline(task='''text-generation'''	,model='''sshleifer/tiny-ctrl'''	,framework='''tf'''				)

						# Using `do_sample=False` to force deterministic output
						lowerCAmelCase__  :       str				=  text_generator('''This is a test'''	,do_sample=__lowerCamelCase				)
						self.assertEqual(
						    __lowerCamelCase	,[
						        {
						            '''generated_text''': (
						                '''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
						                ''' please,'''
						            )
						        }
						    ]	,)

						lowerCAmelCase__  :       List[str]				=  text_generator(['''This is a test''', '''This is a second test''']	,do_sample=__lowerCamelCase				)
						self.assertEqual(
						    __lowerCamelCase	,[
						        [
						            {
						                '''generated_text''': (
						                    '''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
						                    ''' please,'''
						                )
						            }
						        ],
						        [
						            {
						                '''generated_text''': (
						                    '''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
						                    ''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
						                )
						            }
						        ],
						    ]	,)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  TextGenerationPipeline(model=__lowerCamelCase	,tokenizer=__lowerCamelCase				)
						return text_generator, ["This is a test", "Another test"]



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  '''Hello I believe in'''
						lowerCAmelCase__  :       str				=  pipeline('''text-generation'''	,model='''hf-internal-testing/tiny-random-gpt2'''				)
						lowerCAmelCase__  :       Dict				=  text_generator(__lowerCamelCase				)
						self.assertEqual(
						    __lowerCamelCase	,[{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}]	,)

						lowerCAmelCase__  :       Dict				=  text_generator(__lowerCamelCase	,stop_sequence=''' fe'''				)
						self.assertEqual(__lowerCamelCase	,[{'''generated_text''': '''Hello I believe in fe'''}]				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  text_generator.model
						lowerCAmelCase__  :       Union[str, Any]				=  text_generator.tokenizer

						lowerCAmelCase__  :       Optional[int]				=  text_generator('''This is a test'''				)
						self.assertEqual(__lowerCamelCase	,[{'''generated_text''': ANY(__lowerCamelCase				)}]				)
						self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test'''				)				)

						lowerCAmelCase__  :       List[Any]				=  text_generator('''This is a test'''	,return_full_text=__lowerCamelCase				)
						self.assertEqual(__lowerCamelCase	,[{'''generated_text''': ANY(__lowerCamelCase				)}]				)
						self.assertNotIn('''This is a test'''	,outputs[0]['''generated_text''']				)

						lowerCAmelCase__  :       str				=  pipeline(task='''text-generation'''	,model=__lowerCamelCase	,tokenizer=__lowerCamelCase	,return_full_text=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  text_generator('''This is a test'''				)
						self.assertEqual(__lowerCamelCase	,[{'''generated_text''': ANY(__lowerCamelCase				)}]				)
						self.assertNotIn('''This is a test'''	,outputs[0]['''generated_text''']				)

						lowerCAmelCase__  :       List[Any]				=  text_generator('''This is a test'''	,return_full_text=__lowerCamelCase				)
						self.assertEqual(__lowerCamelCase	,[{'''generated_text''': ANY(__lowerCamelCase				)}]				)
						self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test'''				)				)

						lowerCAmelCase__  :       List[str]				=  text_generator(['''This is great !''', '''Something else''']	,num_return_sequences=2	,do_sample=__lowerCamelCase				)
						self.assertEqual(
						    __lowerCamelCase	,[
						        [{'''generated_text''': ANY(__lowerCamelCase				)}, {'''generated_text''': ANY(__lowerCamelCase				)}],
						        [{'''generated_text''': ANY(__lowerCamelCase				)}, {'''generated_text''': ANY(__lowerCamelCase				)}],
						    ]	,)

						if text_generator.tokenizer.pad_token is not None:
										lowerCAmelCase__  :       List[str]				=  text_generator(
										    ['''This is great !''', '''Something else''']	,num_return_sequences=2	,batch_size=2	,do_sample=__lowerCamelCase				)
										self.assertEqual(
										    __lowerCamelCase	,[
										        [{'''generated_text''': ANY(__lowerCamelCase				)}, {'''generated_text''': ANY(__lowerCamelCase				)}],
										        [{'''generated_text''': ANY(__lowerCamelCase				)}, {'''generated_text''': ANY(__lowerCamelCase				)}],
										    ]	,)

						with self.assertRaises(__lowerCamelCase				):
										lowerCAmelCase__  :       List[str]				=  text_generator('''test'''	,return_full_text=__lowerCamelCase	,return_text=__lowerCamelCase				)
						with self.assertRaises(__lowerCamelCase				):
										lowerCAmelCase__  :       List[Any]				=  text_generator('''test'''	,return_full_text=__lowerCamelCase	,return_tensors=__lowerCamelCase				)
						with self.assertRaises(__lowerCamelCase				):
										lowerCAmelCase__  :       Union[str, Any]				=  text_generator('''test'''	,return_text=__lowerCamelCase	,return_tensors=__lowerCamelCase				)

						# Empty prompt is slighly special
						# it requires BOS token to exist.
						# Special case for Pegasus which will always append EOS so will
						# work even without BOS.
						if (
						    text_generator.tokenizer.bos_token_id is not None
						    or "Pegasus" in tokenizer.__class__.__name__
						    or "Git" in model.__class__.__name__
						):
										lowerCAmelCase__  :       int				=  text_generator(''''''				)
										self.assertEqual(__lowerCamelCase	,[{'''generated_text''': ANY(__lowerCamelCase				)}]				)
						else:
										with self.assertRaises((ValueError, AssertionError)				):
														lowerCAmelCase__  :       List[Any]				=  text_generator(''''''				)

						if text_generator.framework == "tf":
										# TF generation does not support max_new_tokens, and it's impossible
										# to control long generation with only max_length without
										# fancy calculation, dismissing tests for now.
										return
						# We don't care about infinite range models.
						# They already work.
						# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
						lowerCAmelCase__  :       Tuple				=  ['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
						if (
						    tokenizer.model_max_length < 1_00_00
						    and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
						):
										# Handling of large generations
										with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)				):
														text_generator('''This is a test''' * 5_00	,max_new_tokens=20				)

										lowerCAmelCase__  :       List[str]				=  text_generator('''This is a test''' * 5_00	,handle_long_generation='''hole'''	,max_new_tokens=20				)
										# Hole strategy cannot work
										with self.assertRaises(__lowerCamelCase				):
														text_generator(
														    '''This is a test''' * 5_00	,handle_long_generation='''hole'''	,max_new_tokens=tokenizer.model_max_length + 10	,)



		@require_torch
		@require_accelerate
		@require_torch_gpu
		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						import torch

						# Classic `model_kwargs`
						lowerCAmelCase__  :       List[str]				=  pipeline(
						    model='''hf-internal-testing/tiny-random-bloom'''	,model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa}	,)
						self.assertEqual(pipe.model.device	,torch.device(0				)				)
						self.assertEqual(pipe.model.lm_head.weight.dtype	,torch.bfloataa				)
						lowerCAmelCase__  :       Any				=  pipe('''This is a test'''				)
						self.assertEqual(
						    __lowerCamelCase	,[
						        {
						            '''generated_text''': (
						                '''This is a test test test test test test test test test test test test test test test test'''
						                ''' test'''
						            )
						        }
						    ]	,)

						# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
						lowerCAmelCase__  :       Union[str, Any]				=  pipeline(model='''hf-internal-testing/tiny-random-bloom'''	,device_map='''auto'''	,torch_dtype=torch.bfloataa				)
						self.assertEqual(pipe.model.device	,torch.device(0				)				)
						self.assertEqual(pipe.model.lm_head.weight.dtype	,torch.bfloataa				)
						lowerCAmelCase__  :       List[Any]				=  pipe('''This is a test'''				)
						self.assertEqual(
						    __lowerCamelCase	,[
						        {
						            '''generated_text''': (
						                '''This is a test test test test test test test test test test test test test test test test'''
						                ''' test'''
						            )
						        }
						    ]	,)

						# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
						lowerCAmelCase__  :       Optional[Any]				=  pipeline(model='''hf-internal-testing/tiny-random-bloom'''	,device_map='''auto'''				)
						self.assertEqual(pipe.model.device	,torch.device(0				)				)
						self.assertEqual(pipe.model.lm_head.weight.dtype	,torch.floataa				)
						lowerCAmelCase__  :       Tuple				=  pipe('''This is a test'''				)
						self.assertEqual(
						    __lowerCamelCase	,[
						        {
						            '''generated_text''': (
						                '''This is a test test test test test test test test test test test test test test test test'''
						                ''' test'''
						            )
						        }
						    ]	,)



		@require_torch
		@require_torch_gpu
		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						import torch

						lowerCAmelCase__  :       Any				=  pipeline(model='''hf-internal-testing/tiny-random-bloom'''	,device=0	,torch_dtype=torch.floataa				)
						pipe('''This is a test'''				)



		@require_torch
		@require_accelerate
		@require_torch_gpu
		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						import torch

						lowerCAmelCase__  :       Any				=  pipeline(model='''hf-internal-testing/tiny-random-bloom'''	,device_map='''auto'''	,torch_dtype=torch.floataa				)
						pipe('''This is a test'''	,do_sample=__lowerCamelCase	,top_p=0.5				)



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  '''Hello world'''
						lowerCAmelCase__  :       Optional[int]				=  pipeline('''text-generation'''	,model='''hf-internal-testing/tiny-random-gpt2'''				)
						if text_generator.model.framework == "tf":
										lowerCAmelCase__  :       Tuple				=  logging.get_logger('''transformers.generation.tf_utils'''				)
						else:
										lowerCAmelCase__  :       str				=  logging.get_logger('''transformers.generation.utils'''				)
						lowerCAmelCase__  :       Tuple				=  '''Both `max_new_tokens`'''  # The beggining of the message to be checked in this test

						# Both are set by the user -> log warning
						with CaptureLogger(__lowerCamelCase				) as cl:
										lowerCAmelCase__  :       Union[str, Any]				=  text_generator(__lowerCamelCase	,max_length=10	,max_new_tokens=1				)
						self.assertIn(__lowerCamelCase	,cl.out				)

						# The user only sets one -> no warning
						with CaptureLogger(__lowerCamelCase				) as cl:
										lowerCAmelCase__  :       Dict				=  text_generator(__lowerCamelCase	,max_new_tokens=1				)
						self.assertNotIn(__lowerCamelCase	,cl.out				)

						with CaptureLogger(__lowerCamelCase				) as cl:
										lowerCAmelCase__  :       Tuple				=  text_generator(__lowerCamelCase	,max_length=10				)
						self.assertNotIn(__lowerCamelCase	,cl.out				)






import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
							from transformers.models.mobilevitva.modeling_mobilevitva import (
							    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
							    make_divisible,
							)


if is_vision_available():
							from PIL import Image

							from transformers import MobileViTImageProcessor



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  self.config_class(**self.inputs_dict				)
						self.parent.assertTrue(hasattr(__lowerCamelCase	,'''width_multiplier'''				)				)



class  lowerCamelCase__    :




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase=13	,__lowerCamelCase=64	,__lowerCamelCase=2	,__lowerCamelCase=3	,__lowerCamelCase="swish"	,__lowerCamelCase=3	,__lowerCamelCase=32	,__lowerCamelCase=0.1	,__lowerCamelCase=0.02	,__lowerCamelCase=True	,__lowerCamelCase=True	,__lowerCamelCase=10	,__lowerCamelCase=None	,__lowerCamelCase=0.25	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  parent
						lowerCAmelCase__  :       List[str]				=  batch_size
						lowerCAmelCase__  :       Dict				=  image_size
						lowerCAmelCase__  :       Tuple				=  patch_size
						lowerCAmelCase__  :       List[Any]				=  num_channels
						lowerCAmelCase__  :       Tuple				=  make_divisible(5_12 * width_multiplier	,divisor=8				)
						lowerCAmelCase__  :       Dict				=  hidden_act
						lowerCAmelCase__  :       Optional[int]				=  conv_kernel_size
						lowerCAmelCase__  :       int				=  output_stride
						lowerCAmelCase__  :       List[Any]				=  classifier_dropout_prob
						lowerCAmelCase__  :       Dict				=  use_labels
						lowerCAmelCase__  :       Tuple				=  is_training
						lowerCAmelCase__  :       str				=  num_labels
						lowerCAmelCase__  :       Optional[Any]				=  initializer_range
						lowerCAmelCase__  :       Tuple				=  scope
						lowerCAmelCase__  :       Tuple				=  width_multiplier
						lowerCAmelCase__  :       Optional[int]				=  ffn_dropout
						lowerCAmelCase__  :       Dict				=  attn_dropout



		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

						lowerCAmelCase__  :       Union[str, Any]				=  None
						lowerCAmelCase__  :       Dict				=  None
						if self.use_labels:
										lowerCAmelCase__  :       Optional[Any]				=  ids_tensor([self.batch_size]	,self.num_labels				)
										lowerCAmelCase__  :       Optional[Any]				=  ids_tensor([self.batch_size, self.image_size, self.image_size]	,self.num_labels				)

						lowerCAmelCase__  :       Tuple				=  self.get_config()

						return config, pixel_values, labels, pixel_labels



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						return MobileViTVaConfig(
						    image_size=self.image_size	,patch_size=self.patch_size	,num_channels=self.num_channels	,hidden_act=self.hidden_act	,conv_kernel_size=self.conv_kernel_size	,output_stride=self.output_stride	,classifier_dropout_prob=self.classifier_dropout_prob	,initializer_range=self.initializer_range	,width_multiplier=self.width_multiplier	,ffn_dropout=self.ffn_dropout_prob	,attn_dropout=self.attn_dropout_prob	,)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  MobileViTVaModel(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Dict				=  model(__lowerCamelCase				)
						self.parent.assertEqual(
						    result.last_hidden_state.shape	,(
						        self.batch_size,
						        self.last_hidden_size,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )	,)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  self.num_labels
						lowerCAmelCase__  :       Union[str, Any]				=  MobileViTVaForImageClassification(__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Dict				=  model(__lowerCamelCase	,labels=__lowerCamelCase				)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  self.num_labels
						lowerCAmelCase__  :       Optional[int]				=  MobileViTVaForSemanticSegmentation(__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Optional[Any]				=  model(__lowerCamelCase				)
						self.parent.assertEqual(
						    result.logits.shape	,(
						        self.batch_size,
						        self.num_labels,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )	,)
						lowerCAmelCase__  :       List[str]				=  model(__lowerCamelCase	,labels=__lowerCamelCase				)
						self.parent.assertEqual(
						    result.logits.shape	,(
						        self.batch_size,
						        self.num_labels,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )	,)



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  self.prepare_config_and_inputs()
						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Optional[int]				=  config_and_inputs
						lowerCAmelCase__  :       Dict				=  {'''pixel_values''': pixel_values}
						return config, inputs_dict



@require_torch
class  lowerCamelCase__    (							lowerCamelCase__   ,       lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=(
		    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)

		snake_case_			=(
		    {
		        """feature-extraction""": MobileViTVaModel,
		        """image-classification""": MobileViTVaForImageClassification,
		        """image-segmentation""": MobileViTVaForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		snake_case_			=False
		snake_case_			=False
		snake_case_			=False
		snake_case_			=False



		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  MobileViTVaModelTester(self				)
						lowerCAmelCase__  :       Optional[int]				=  MobileViTVaConfigTester(self	,config_class=__lowerCamelCase	,has_text_modality=__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						self.config_tester.run_common_tests()



		@unittest.skip(reason='''MobileViTV2 does not use inputs_embeds'''				)
		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						pass



		@unittest.skip(reason='''MobileViTV2 does not support input and output embeddings'''				)
		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						pass



		@unittest.skip(reason='''MobileViTV2 does not output attentions'''				)
		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						pass



		@require_torch_multi_gpu
		@unittest.skip(reason='''Got `CUDA error: misaligned address` for tests after this one being run.'''				)
		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						pass



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

						"""simple docstring"""






						pass



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__  :       List[Any]				=  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowerCAmelCase__  :       Union[str, Any]				=  model_class(__lowerCamelCase				)
										lowerCAmelCase__  :       List[Any]				=  inspect.signature(model.forward				)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowerCAmelCase__  :       str				=  [*signature.parameters.keys()]

										lowerCAmelCase__  :       Union[str, Any]				=  ['''pixel_values''']
										self.assertListEqual(arg_names[:1]	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						def check_hidden_states_output(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				):
										lowerCAmelCase__  :       Union[str, Any]				=  model_class(__lowerCamelCase				)
										model.to(__lowerCamelCase				)
										model.eval()

										with torch.no_grad():
														lowerCAmelCase__  :       Optional[Any]				=  model(**self._prepare_for_class(__lowerCamelCase	,__lowerCamelCase				)				)

										lowerCAmelCase__  :       Dict				=  outputs.hidden_states

										lowerCAmelCase__  :       Any				=  5
										self.assertEqual(len(__lowerCamelCase				)	,__lowerCamelCase				)

										# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
										# with the width and height being successively divided by 2.
										lowerCAmelCase__  :       Optional[Any]				=  2
										for i in range(len(__lowerCamelCase				)				):
														self.assertListEqual(
														    list(hidden_states[i].shape[-2:]				)	,[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]	,)
														divisor *= 2

										self.assertEqual(self.model_tester.output_stride	,divisor // 2				)

						lowerCAmelCase__		,	lowerCAmelCase__  :       Union[str, Any]				=  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowerCAmelCase__  :       Any				=  True
										check_hidden_states_output(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										lowerCAmelCase__  :       Tuple				=  True

										check_hidden_states_output(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase				)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__  :       Optional[int]				=  MobileViTVaModel.from_pretrained(__lowerCamelCase				)
										self.assertIsNotNone(__lowerCamelCase				)






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Dict				=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
				return image



@require_torch
@require_vision
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		@cached_property
		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						return (
						    MobileViTImageProcessor.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''				)
						    if is_vision_available()
						    else None
						)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''				).to(
						    __lowerCamelCase				)

						lowerCAmelCase__  :       str				=  self.default_image_processor
						lowerCAmelCase__  :       Union[str, Any]				=  prepare_img()
						lowerCAmelCase__  :       int				=  image_processor(images=__lowerCamelCase	,return_tensors='''pt'''				).to(__lowerCamelCase				)

						# forward pass
						with torch.no_grad():
										lowerCAmelCase__  :       Union[str, Any]				=  model(**__lowerCamelCase				)

						# verify the logits
						lowerCAmelCase__  :       str				=  torch.Size((1, 10_00)				)
						self.assertEqual(outputs.logits.shape	,__lowerCamelCase				)

						lowerCAmelCase__  :       Dict				=  torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]				).to(__lowerCamelCase				)

						self.assertTrue(torch.allclose(outputs.logits[0, :3]	,__lowerCamelCase	,atol=1e-4				)				)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''				)
						lowerCAmelCase__  :       Optional[Any]				=  model.to(__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''				)

						lowerCAmelCase__  :       Optional[Any]				=  prepare_img()
						lowerCAmelCase__  :       Union[str, Any]				=  image_processor(images=__lowerCamelCase	,return_tensors='''pt'''				).to(__lowerCamelCase				)

						# forward pass
						with torch.no_grad():
										lowerCAmelCase__  :       List[Any]				=  model(**__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  outputs.logits

						# verify the logits
						lowerCAmelCase__  :       Optional[int]				=  torch.Size((1, 21, 32, 32)				)
						self.assertEqual(logits.shape	,__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  torch.tensor(
						    [
						        [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
						        [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
						        [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
						    ]	,device=__lowerCamelCase	,)

						self.assertTrue(torch.allclose(logits[0, :3, :3, :3]	,__lowerCamelCase	,atol=1e-4				)				)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''				)
						lowerCAmelCase__  :       Dict				=  model.to(__lowerCamelCase				)

						lowerCAmelCase__  :       Tuple				=  MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''				)

						lowerCAmelCase__  :       int				=  prepare_img()
						lowerCAmelCase__  :       Any				=  image_processor(images=__lowerCamelCase	,return_tensors='''pt'''				).to(__lowerCamelCase				)

						# forward pass
						with torch.no_grad():
										lowerCAmelCase__  :       List[Any]				=  model(**__lowerCamelCase				)

						lowerCAmelCase__  :       Optional[Any]				=  outputs.logits.detach().cpu()

						lowerCAmelCase__  :       Optional[Any]				=  image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase	,target_sizes=[(50, 60)]				)
						lowerCAmelCase__  :       Tuple				=  torch.Size((50, 60)				)
						self.assertEqual(segmentation[0].shape	,__lowerCamelCase				)

						lowerCAmelCase__  :       Optional[Any]				=  image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  torch.Size((32, 32)				)
						self.assertEqual(segmentation[0].shape	,__lowerCamelCase				)

