


import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, 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 ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
	from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor



class 							A   :
					'''simple docstring'''






					def __init__(self   :						Optional[Any]				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Optional[int]=13				, _UpperCAmelCase   :						List[str]=32				, _UpperCAmelCase   :						Dict=3				, _UpperCAmelCase   :						Dict=4				, _UpperCAmelCase   :						Union[str, Any]=[10, 20, 30, 40]				, _UpperCAmelCase   :						Optional[int]=[2, 2, 3, 2]				, _UpperCAmelCase   :						Optional[Any]=True				, _UpperCAmelCase   :						str=True				, _UpperCAmelCase   :						Tuple=37				, _UpperCAmelCase   :						str="gelu"				, _UpperCAmelCase   :						Dict=10				, _UpperCAmelCase   :						Dict=0.02				, _UpperCAmelCase   :						List[Any]=["stage2", "stage3", "stage4"]				, _UpperCAmelCase   :						List[Any]=[2, 3, 4]				, _UpperCAmelCase   :						Optional[Any]=None				, )						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		parent
							lowercase__					      =		batch_size
							lowercase__					      =		image_size
							lowercase__					      =		num_channels
							lowercase__					      =		num_stages
							lowercase__					      =		hidden_sizes
							lowercase__					      =		depths
							lowercase__					      =		is_training
							lowercase__					      =		use_labels
							lowercase__					      =		intermediate_size
							lowercase__					      =		hidden_act
							lowercase__					      =		num_labels
							lowercase__					      =		initializer_range
							lowercase__					      =		out_features
							lowercase__					      =		out_indices
							lowercase__					      =		scope

					def   lowerCamelCase__					(self   :						Any		)						->       Dict:




							"""simple docstring"""


							lowercase__					      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

							lowercase__					      =		None
							if self.use_labels:
									lowercase__					      =		ids_tensor([self.batch_size]				, self.num_labels		)

							lowercase__					      =		self.get_config()

							return config, pixel_values, labels

					def   lowerCamelCase__					(self   :						int		)						->       Dict:




							"""simple docstring"""


							return ConvNextVaConfig(
							    num_channels=self.num_channels				, hidden_sizes=self.hidden_sizes				, depths=self.depths				, num_stages=self.num_stages				, hidden_act=self.hidden_act				, is_decoder=_UpperCAmelCase				, initializer_range=self.initializer_range				, out_features=self.out_features				, out_indices=self.out_indices				, num_labels=self.num_labels				, )

					def   lowerCamelCase__					(self   :						Union[str, Any]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						Tuple		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		ConvNextVaModel(config=_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		model(_UpperCAmelCase		)
							# expected last hidden states: B, C, H // 32, W // 32
							self.parent.assertEqual(
							    result.last_hidden_state.shape				, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				, )

					def   lowerCamelCase__					(self   :						int				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						Optional[Any]		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		ConvNextVaForImageClassification(_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		model(_UpperCAmelCase				, labels=_UpperCAmelCase		)
							self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_labels)		)

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						Dict		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		ConvNextVaBackbone(config=_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		model(_UpperCAmelCase		)

							# verify hidden states
							self.parent.assertEqual(len(result.feature_maps		)				, len(config.out_features		)		)
							self.parent.assertListEqual(list(result.feature_maps[0].shape		)				, [self.batch_size, self.hidden_sizes[1], 4, 4]		)

							# verify channels
							self.parent.assertEqual(len(model.channels		)				, len(config.out_features		)		)
							self.parent.assertListEqual(model.channels				, config.hidden_sizes[1:]		)

							# verify backbone works with out_features=None
							lowercase__					      =		None
							lowercase__					      =		ConvNextVaBackbone(config=_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		model(_UpperCAmelCase		)

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps		)				, 1		)
							self.parent.assertListEqual(list(result.feature_maps[0].shape		)				, [self.batch_size, self.hidden_sizes[-1], 1, 1]		)

							# verify channels
							self.parent.assertEqual(len(model.channels		)				, 1		)
							self.parent.assertListEqual(model.channels				, [config.hidden_sizes[-1]]		)

					def   lowerCamelCase__					(self   :						List[Any]		)						->       int:




							"""simple docstring"""


							lowercase__					      =		self.prepare_config_and_inputs()
							lowercase__      ,     lowercase__      ,     lowercase__					      =		config_and_inputs
							lowercase__					      =		{"""pixel_values""": pixel_values}
							return config, inputs_dict


					def   lowerCamelCase__					(self   :						Optional[Any]		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		self.prepare_config_and_inputs()
							lowercase__      ,     lowercase__      ,     lowercase__					      =		config_and_inputs
							lowercase__					      =		{"""pixel_values""": pixel_values, """labels""": labels}
							return config, inputs_dict






@require_torch
class 							A   (	UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			(
					    (
					        ConvNextVaModel,
					        ConvNextVaForImageClassification,
					        ConvNextVaBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					A__      =			(
					    {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
					    if is_torch_available()
					    else {}
					)

					A__      =			False
					A__      =			False
					A__      =			False
					A__      =			False
					A__      =			False

					def   lowerCamelCase__					(self   :						int		)						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		ConvNextVaModelTester(self		)
							lowercase__					      =		ConfigTester(self				, config_class=_UpperCAmelCase				, has_text_modality=_UpperCAmelCase				, hidden_size=37		)

					def   lowerCamelCase__					(self   :						int		)						->       Tuple:




							"""simple docstring"""


							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()

					def   lowerCamelCase__					(self   :						Tuple		)						->       int:




							"""simple docstring"""


							return

					@unittest.skip(reason="""ConvNextV2 does not use inputs_embeds"""		)
					def   lowerCamelCase__					(self   :						List[str]		)						->       Optional[int]:




							"""simple docstring"""


							pass

					@unittest.skip(reason="""ConvNextV2 does not support input and output embeddings"""		)
					def   lowerCamelCase__					(self   :						Tuple		)						->       Any:




							"""simple docstring"""


							pass

					@unittest.skip(reason="""ConvNextV2 does not use feedforward chunking"""		)
					def   lowerCamelCase__					(self   :						int		)						->       str:




							"""simple docstring"""


							pass

					def   lowerCamelCase__					(self   :						Tuple		)						->       Any:




							"""simple docstring"""


							if not self.model_tester.is_training:
									return

							for model_class in self.all_model_classes:
									lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_with_labels()
									lowercase__					      =		True

									if model_class.__name__ in [
									    *get_values(_UpperCAmelCase		),
									    *get_values(_UpperCAmelCase		),
									]:
											continue

									lowercase__					      =		model_class(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.train()
									lowercase__					      =		self._prepare_for_class(_UpperCAmelCase				, _UpperCAmelCase				, return_labels=_UpperCAmelCase		)
									lowercase__					      =		model(**_UpperCAmelCase		).loss
									loss.backward()

					def   lowerCamelCase__					(self   :						Optional[Any]		)						->       int:




							"""simple docstring"""


							if not self.model_tester.is_training:
									return

							for model_class in self.all_model_classes:
									lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_with_labels()
									lowercase__					      =		False
									lowercase__					      =		True

									if (
									    model_class.__name__
									    in [*get_values(_UpperCAmelCase		), *get_values(_UpperCAmelCase		)]
									    or not model_class.supports_gradient_checkpointing
									):
											continue

									lowercase__					      =		model_class(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.gradient_checkpointing_enable()
									model.train()
									lowercase__					      =		self._prepare_for_class(_UpperCAmelCase				, _UpperCAmelCase				, return_labels=_UpperCAmelCase		)
									lowercase__					      =		model(**_UpperCAmelCase		).loss
									loss.backward()

					def   lowerCamelCase__					(self   :						List[str]		)						->       Optional[int]:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowercase__					      =		model_class(_UpperCAmelCase		)
									lowercase__					      =		inspect.signature(model.forward		)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									lowercase__					      =		[*signature.parameters.keys()]

									lowercase__					      =		["""pixel_values"""]
									self.assertListEqual(arg_names[:1]				, _UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						int		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Optional[int]		)						->       str:




							"""simple docstring"""


							def check_hidden_states_output(_UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						str		):
									lowercase__					      =		model_class(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()

									with torch.no_grad():
											lowercase__					      =		model(**self._prepare_for_class(_UpperCAmelCase				, _UpperCAmelCase		)		)

									lowercase__					      =		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									lowercase__					      =		self.model_tester.num_stages
									self.assertEqual(len(_UpperCAmelCase		)				, expected_num_stages + 1		)

									# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]		)				, [self.model_tester.image_size // 4, self.model_tester.image_size // 4]				, )

							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowercase__					      =		True
									check_hidden_states_output(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase		)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									lowercase__					      =		True

									check_hidden_states_output(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						List[Any]		)						->       str:




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase		)


					@slow
					def   lowerCamelCase__					(self   :						int		)						->       Any:




							"""simple docstring"""


							for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase__					      =		ConvNextVaModel.from_pretrained(_UpperCAmelCase		)
									self.assertIsNotNone(_UpperCAmelCase		)






def 			UpperCamelCase (     )       ->  int:
		"""simple docstring"""




		lowercase__					      =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
		return image







@require_torch
@require_vision
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






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




							"""simple docstring"""


							return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224"""		) if is_vision_available() else None


					@slow
					def   lowerCamelCase__					(self   :						Any		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224"""		).to(_UpperCAmelCase		)

							lowercase__					      =		self.default_image_processor
							lowercase__					      =		prepare_img()
							lowercase__					      =		preprocessor(images=_UpperCAmelCase				, return_tensors="""pt"""		).to(_UpperCAmelCase		)

							# forward pass
							with torch.no_grad():
									lowercase__					      =		model(**_UpperCAmelCase		)

							# verify the logits
							lowercase__					      =		torch.Size((1, 1000)		)
							self.assertEqual(outputs.logits.shape				, _UpperCAmelCase		)

							lowercase__					      =		torch.tensor([0.9_996, 0.1_966, -0.4_386]		).to(_UpperCAmelCase		)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]				, _UpperCAmelCase				, atol=1E-4		)		)





import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 							A   (	UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			OpenAIGPTTokenizer
					A__      =			OpenAIGPTTokenizerFast
					A__      =			True
					A__      =			False

					def   lowerCamelCase__					(self   :						List[str]		)						->       int:




							"""simple docstring"""


							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							lowercase__					      =		[
							    """l""",
							    """o""",
							    """w""",
							    """e""",
							    """r""",
							    """s""",
							    """t""",
							    """i""",
							    """d""",
							    """n""",
							    """w</w>""",
							    """r</w>""",
							    """t</w>""",
							    """lo""",
							    """low""",
							    """er</w>""",
							    """low</w>""",
							    """lowest</w>""",
							    """newer</w>""",
							    """wider</w>""",
							    """<unk>""",
							]
							lowercase__					      =		dict(zip(_UpperCAmelCase				, range(len(_UpperCAmelCase		)		)		)		)
							lowercase__					      =		["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

							lowercase__					      =		os.path.join(self.tmpdirname				, VOCAB_FILES_NAMES["""vocab_file"""]		)
							lowercase__					      =		os.path.join(self.tmpdirname				, VOCAB_FILES_NAMES["""merges_file"""]		)
							with open(self.vocab_file				, """w"""		) as fp:
									fp.write(json.dumps(_UpperCAmelCase		)		)
							with open(self.merges_file				, """w"""		) as fp:
									fp.write("""\n""".join(_UpperCAmelCase		)		)

					def   lowerCamelCase__					(self   :						Dict				, _UpperCAmelCase   :						List[Any]		)						->       Any:




							"""simple docstring"""


							return "lower newer", "lower newer"

					def   lowerCamelCase__					(self   :						Dict		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		OpenAIGPTTokenizer(self.vocab_file				, self.merges_file		)

							lowercase__					      =		"""lower"""
							lowercase__					      =		["""low""", """er</w>"""]
							lowercase__					      =		tokenizer.tokenize(_UpperCAmelCase		)
							self.assertListEqual(_UpperCAmelCase				, _UpperCAmelCase		)

							lowercase__					      =		tokens + ["""<unk>"""]
							lowercase__					      =		[14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)				, _UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Optional[int]				, _UpperCAmelCase   :						Union[str, Any]=15		)						->       Optional[int]:




							"""simple docstring"""


							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
											lowercase__					      =		self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase				, **_UpperCAmelCase		)

											# Simple input
											lowercase__					      =		"""This is a simple input"""
											lowercase__					      =		["""This is a simple input 1""", """This is a simple input 2"""]
											lowercase__					      =		("""This is a simple input""", """This is a pair""")
											lowercase__					      =		[
											    ("""This is a simple input 1""", """This is a simple input 2"""),
											    ("""This is a simple pair 1""", """This is a simple pair 2"""),
											]

											# Simple input tests
											self.assertRaises(_UpperCAmelCase				, tokenizer_r.encode				, _UpperCAmelCase				, max_length=_UpperCAmelCase				, padding="""max_length"""		)

											# Simple input
											self.assertRaises(_UpperCAmelCase				, tokenizer_r.encode_plus				, _UpperCAmelCase				, max_length=_UpperCAmelCase				, padding="""max_length"""		)

											# Simple input
											self.assertRaises(
											    _UpperCAmelCase				, tokenizer_r.batch_encode_plus				, _UpperCAmelCase				, max_length=_UpperCAmelCase				, padding="""max_length"""				, )

											# Pair input
											self.assertRaises(_UpperCAmelCase				, tokenizer_r.encode				, _UpperCAmelCase				, max_length=_UpperCAmelCase				, padding="""max_length"""		)

											# Pair input
											self.assertRaises(_UpperCAmelCase				, tokenizer_r.encode_plus				, _UpperCAmelCase				, max_length=_UpperCAmelCase				, padding="""max_length"""		)

											# Pair input
											self.assertRaises(
											    _UpperCAmelCase				, tokenizer_r.batch_encode_plus				, _UpperCAmelCase				, max_length=_UpperCAmelCase				, padding="""max_length"""				, )


					def   lowerCamelCase__					(self   :						Tuple		)						->       Any:




							"""simple docstring"""


							pass






@require_ftfy
@require_spacy
@require_tokenizers
class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					pass

