

import inspect
import unittest

from transformers import MobileNetVaConfig
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 MobileNetVaForImageClassification, MobileNetVaModel
			from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import MobileNetVaImageProcessor

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Dict   =						self.config_class(**self.inputs_dict			)
												self.parent.assertTrue(hasattr(_a			,				'''tf_padding'''			)			)
												self.parent.assertTrue(hasattr(_a			,				'''depth_multiplier'''			)			)



class 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a=1_3			,				_a=3			,				_a=3_2			,				_a=0.25			,				_a=8			,				_a=True			,				_a=1_0_2_4			,				_a=3_2			,				_a="relu6"			,				_a=0.1			,				_a=0.02			,				_a=True			,				_a=True			,				_a=1_0			,				_a=None			,				)       ->       Any:
												_a      :       List[str]   =						parent
												_a      :       Tuple   =						batch_size
												_a      :       int   =						num_channels
												_a      :       Dict   =						image_size
												_a      :       Any   =						depth_multiplier
												_a      :       Tuple   =						min_depth
												_a      :       List[Any]   =						tf_padding
												_a      :       Optional[int]   =						int(last_hidden_size * depth_multiplier			)
												_a      :       List[str]   =						output_stride
												_a      :       Dict   =						hidden_act
												_a      :       Optional[int]   =						classifier_dropout_prob
												_a      :       str   =						use_labels
												_a      :       Optional[int]   =						is_training
												_a      :       Dict   =						num_labels
												_a      :       Union[str, Any]   =						initializer_range
												_a      :       Optional[Any]   =						scope


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       Optional[Any]   =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

												_a      :       List[Any]   =						None
												_a      :       Any   =						None
												if self.use_labels:
																	_a      :       List[Any]   =						ids_tensor([self.batch_size]			,				self.num_labels			)
																	_a      :       Any   =						ids_tensor([self.batch_size, self.image_size, self.image_size]			,				self.num_labels			)

												_a      :       str   =						self.get_config()

												return config, pixel_values, labels, pixel_labels


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												return MobileNetVaConfig(
												    num_channels=self.num_channels			,				image_size=self.image_size			,				depth_multiplier=self.depth_multiplier			,				min_depth=self.min_depth			,				tf_padding=self.tf_padding			,				hidden_act=self.hidden_act			,				classifier_dropout_prob=self.classifier_dropout_prob			,				initializer_range=self.initializer_range			,				)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			,				_a			)       ->       Dict:
												_a      :       Union[str, Any]   =						MobileNetVaModel(config=_a			)
												model.to(_a			)
												model.eval()
												_a      :       Optional[Any]   =						model(_a			)
												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 							__lowercase							(     self			,				_a			,				_a			,				_a			,				_a			)       ->       Dict:
												_a      :       Any   =						self.num_labels
												_a      :       str   =						MobileNetVaForImageClassification(_a			)
												model.to(_a			)
												model.eval()
												_a      :       int   =						model(_a			,				labels=_a			)
												self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)




							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Dict   =						self.prepare_config_and_inputs()
												_a ,   _a ,   _a ,   _a      :       Tuple   =						config_and_inputs
												_a      :       List[str]   =						{'''pixel_values''': pixel_values}
												return config, inputs_dict



@require_torch
class 							UpperCAmelCase_						(     __lowercase							,			__lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		str             =       (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
							UpperCAmelCase__			:		Union[str, Any]             =       (
							    {"feature-extraction": MobileNetVaModel, "image-classification": MobileNetVaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase__			:		Dict             =       False
							UpperCAmelCase__			:		List[Any]             =       False
							UpperCAmelCase__			:		Tuple             =       False
							UpperCAmelCase__			:		str             =       False


							def 							__lowercase							(     self			)       ->       str:
												_a      :       Optional[Any]   =						MobileNetVaModelTester(self			)
												_a      :       List[str]   =						MobileNetVaConfigTester(self			,				config_class=_a			,				has_text_modality=_a			)


							def 							__lowercase							(     self			)       ->       str:
												self.config_tester.run_common_tests()


							@unittest.skip(reason='''MobileNetV1 does not use inputs_embeds'''			)
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												pass


							@unittest.skip(reason='''MobileNetV1 does not support input and output embeddings'''			)
							def 							__lowercase							(     self			)       ->       int:
												pass


							@unittest.skip(reason='''MobileNetV1 does not output attentions'''			)
							def 							__lowercase							(     self			)       ->       Tuple:
												pass


							def 							__lowercase							(     self			)       ->       List[str]:
												_a ,   _a      :       Dict   =						self.model_tester.prepare_config_and_inputs_for_common()

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

																	_a      :       Any   =						['''pixel_values''']
																	self.assertListEqual(arg_names[:1]			,				_a			)


							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       str   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_a			)


							def 							__lowercase							(     self			)       ->       List[Any]:
												def check_hidden_states_output(_a			,				_a			,				_a			):
																	_a      :       Union[str, Any]   =						model_class(_a			)
																	model.to(_a			)
																	model.eval()

																	with torch.no_grad():
																						_a      :       int   =						model(**self._prepare_for_class(_a			,				_a			)			)

																	_a      :       Union[str, Any]   =						outputs.hidden_states

																	_a      :       int   =						2_6
																	self.assertEqual(len(_a			)			,				_a			)

												_a ,   _a      :       Dict   =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_a      :       int   =						True
																	check_hidden_states_output(_a			,				_a			,				_a			)

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

																	check_hidden_states_output(_a			,				_a			,				_a			)


							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       Dict   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_a			)




							@slow
							def 							__lowercase							(     self			)       ->       str:
												for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_a      :       Tuple   =						MobileNetVaModel.from_pretrained(_a			)
																	self.assertIsNotNone(_a			)



def   __UpperCAmelCase					(      )     ->	str:

					"""simple docstring"""

					_a      :       Optional[int]   =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
					return image







@require_torch
@require_vision
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							@cached_property
							def 							__lowercase							(     self			)       ->       List[Any]:
												return (
												    MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224'''			) if is_vision_available() else None
												)




							@slow
							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       List[Any]   =						MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224'''			).to(_a			)

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

												# forward pass
												with torch.no_grad():
																	_a      :       Any   =						model(**_a			)

												# verify the logits
												_a      :       int   =						torch.Size((1, 1_0_0_1)			)
												self.assertEqual(outputs.logits.shape			,				_a			)

												_a      :       List[Any]   =						torch.tensor([-4.1739, -1.1233, 3.1205]			).to(_a			)

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






import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[Any]             =       MgpstrTokenizer
							UpperCAmelCase__			:		int             =       False
							UpperCAmelCase__			:		Union[str, Any]             =       {}
							UpperCAmelCase__			:		List[Any]             =       False


							def 							__lowercase							(     self			)       ->       Any:
												super().setUp()

												# fmt: off
												_a      :       Tuple   =						['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
												# fmt: on
												_a      :       Optional[int]   =						dict(zip(_a			,				range(len(_a			)			)			)			)

												_a      :       Any   =						os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''vocab_file''']			)
												with open(self.vocab_file			,				'''w'''			,				encoding='''utf-8'''			) as fp:
																	fp.write(json.dumps(_a			) + '''\n'''			)


							def 							__lowercase							(     self			,				**_a			)       ->       Dict:
												return MgpstrTokenizer.from_pretrained(self.tmpdirname			,				**_a			)


							def 							__lowercase							(     self			,				_a			)       ->       Tuple:
												_a      :       List[str]   =						'''tester'''
												_a      :       Optional[Any]   =						'''tester'''
												return input_text, output_text


							@unittest.skip('''MGP-STR always lower cases letters.'''			)
							def 							__lowercase							(     self			)       ->       Any:
												pass


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Union[str, Any]   =						self.get_tokenizers(do_lower_case=_a			)
												for tokenizer in tokenizers:
																	with self.subTest(F"""{tokenizer.__class__.__name__}"""			):
																						_a      :       int   =						'''[SPECIAL_TOKEN]'''

																						tokenizer.add_special_tokens({'''cls_token''': special_token}			)
																						_a      :       Tuple   =						tokenizer.encode([special_token]			,				add_special_tokens=_a			)
																						self.assertEqual(len(_a			)			,				1			)

																						_a      :       Tuple   =						tokenizer.decode(_a			,				skip_special_tokens=_a			)
																						self.assertTrue(special_token not in decoded			)


							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       List[Any]   =						self.get_tokenizers()
												for tokenizer in tokenizers:
																	with self.subTest(F"""{tokenizer.__class__.__name__}"""			):
																						_a ,   _a      :       int   =						self.get_input_output_texts(_a			)

																						_a      :       List[str]   =						tokenizer.tokenize(_a			)
																						_a      :       Optional[int]   =						tokenizer.convert_tokens_to_ids(_a			)
																						_a      :       Tuple   =						tokenizer.encode(_a			,				add_special_tokens=_a			)
																						self.assertListEqual(_a			,				_a			)

																						_a      :       Optional[int]   =						tokenizer.convert_ids_to_tokens(_a			)
																						self.assertNotEqual(len(_a			)			,				0			)
																						_a      :       int   =						tokenizer.decode(_a			)
																						self.assertIsInstance(_a			,				_a			)

																						self.assertEqual(text_a.replace(''' '''			,				''''''			)			,				_a			)


							@unittest.skip('''MGP-STR tokenizer only handles one sequence.'''			)
							def 							__lowercase							(     self			)       ->       List[str]:
												pass




							@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''			)
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												pass



