
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Dict							 =       ["""image_processor""", """tokenizer"""]
	_UpperCamelCase     :     Optional[int]							 =       """BridgeTowerImageProcessor"""
	_UpperCamelCase     :     Dict							 =       ("""RobertaTokenizer""", """RobertaTokenizerFast""")





	def __init__(       self		,    snake_case		,    snake_case       ):
		super().__init__(snake_case		,    snake_case       )





	def __call__(       self		,    snake_case		,    snake_case = None		,    snake_case = True		,    snake_case = False		,    snake_case = None		,    snake_case = None		,    snake_case = 0		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = False		,    snake_case = False		,    snake_case = False		,    snake_case = False		,    snake_case = True		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    self.tokenizer(
		    text=snake_case		,    add_special_tokens=snake_case		,    padding=snake_case		,    truncation=snake_case		,    max_length=snake_case		,    stride=snake_case		,    pad_to_multiple_of=snake_case		,    return_token_type_ids=snake_case		,    return_attention_mask=snake_case		,    return_overflowing_tokens=snake_case		,    return_special_tokens_mask=snake_case		,    return_offsets_mapping=snake_case		,    return_length=snake_case		,    verbose=snake_case		,    return_tensors=snake_case		,    **snake_case		,    )
		# add pixel_values + pixel_mask
		lowercase       =    self.image_processor(
		    snake_case		,    return_tensors=snake_case		,    do_normalize=snake_case		,    do_center_crop=snake_case		,    **snake_case       )
		encoding.update(snake_case       )

		return encoding





	def 	SCREAMING_SNAKE_CASE__						(       self		,    *snake_case		,    **snake_case       ):
		return self.tokenizer.batch_decode(*snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    *snake_case		,    **snake_case       ):
		return self.tokenizer.decode(*snake_case		,    **snake_case       )





	@property
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.tokenizer.model_input_names
		lowercase       =    self.image_processor.model_input_names
		return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )



import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 (
	    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
	    MODEL_MAPPING,
	    LevitForImageClassification,
	    LevitForImageClassificationWithTeacher,
	    LevitModel,
	)
	from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import LevitImageProcessor







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.config_class(**self.inputs_dict       )
		self.parent.assertTrue(hasattr(snake_case		,    'hidden_sizes'       )       )
		self.parent.assertTrue(hasattr(snake_case		,    'num_attention_heads'       )       )







class        A_   :




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case=13		,    snake_case=64		,    snake_case=3		,    snake_case=3		,    snake_case=2		,    snake_case=1		,    snake_case=16		,    snake_case=[128, 256, 384]		,    snake_case=[4, 6, 8]		,    snake_case=[2, 3, 4]		,    snake_case=[16, 16, 16]		,    snake_case=0		,    snake_case=[2, 2, 2]		,    snake_case=[2, 2, 2]		,    snake_case=0.02		,    snake_case=True		,    snake_case=True		,    snake_case=2		,    ):
		lowercase       =    parent
		lowercase       =    batch_size
		lowercase       =    image_size
		lowercase       =    num_channels
		lowercase       =    kernel_size
		lowercase       =    stride
		lowercase       =    padding
		lowercase       =    hidden_sizes
		lowercase       =    num_attention_heads
		lowercase       =    depths
		lowercase       =    key_dim
		lowercase       =    drop_path_rate
		lowercase       =    patch_size
		lowercase       =    attention_ratio
		lowercase       =    mlp_ratio
		lowercase       =    initializer_range
		lowercase       =    [
		    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
		    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
		]
		lowercase       =    is_training
		lowercase       =    use_labels
		lowercase       =    num_labels
		lowercase       =    initializer_range





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		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 	SCREAMING_SNAKE_CASE__						(       self       ):
		return LevitConfig(
		    image_size=self.image_size		,    num_channels=self.num_channels		,    kernel_size=self.kernel_size		,    stride=self.stride		,    padding=self.padding		,    patch_size=self.patch_size		,    hidden_sizes=self.hidden_sizes		,    num_attention_heads=self.num_attention_heads		,    depths=self.depths		,    key_dim=self.key_dim		,    drop_path_rate=self.drop_path_rate		,    mlp_ratio=self.mlp_ratio		,    attention_ratio=self.attention_ratio		,    initializer_range=self.initializer_range		,    down_ops=self.down_ops		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    LevitModel(config=snake_case       )
		model.to(snake_case       )
		model.eval()
		lowercase       =    model(snake_case       )
		lowercase       =    (self.image_size, self.image_size)
		lowercase  ,					lowercase       =    image_size[0], image_size[1]
		for _ in range(4       ):
			lowercase       =    floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1       )
			lowercase       =    floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1       )
		self.parent.assertEqual(
		    result.last_hidden_state.shape		,    (self.batch_size, ceil(height / 4       ) * ceil(width / 4       ), self.hidden_sizes[-1])		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    self.num_labels
		lowercase       =    LevitForImageClassification(snake_case       )
		model.to(snake_case       )
		model.eval()
		lowercase       =    model(snake_case		,    labels=snake_case       )
		self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_labels)       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.prepare_config_and_inputs()
		lowercase  ,					lowercase  ,					lowercase       =    config_and_inputs
		lowercase       =    {'pixel_values': pixel_values}
		return config, inputs_dict







@require_torch
class        A_   (			__lowerCamelCase      ,	__lowerCamelCase      ,	unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Tuple							 =       (
	    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
	    if is_torch_available()
	    else ()
	)
	_UpperCamelCase     :     Dict							 =       (
	    {
	        """feature-extraction""": LevitModel,
	        """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
	    }
	    if is_torch_available()
	    else {}
	)

	_UpperCamelCase     :     Dict							 =       False
	_UpperCamelCase     :     List[str]							 =       False
	_UpperCamelCase     :     List[str]							 =       False
	_UpperCamelCase     :     str							 =       False
	_UpperCamelCase     :     List[str]							 =       False





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    LevitModelTester(self       )
		lowercase       =    ConfigTester(self		,    config_class=snake_case		,    has_text_modality=snake_case		,    hidden_size=37       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		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 	SCREAMING_SNAKE_CASE__						(       self       ):
		return





	@unittest.skip(reason='Levit does not use inputs_embeds'       )
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		pass





	@unittest.skip(reason='Levit does not support input and output embeddings'       )
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		pass





	@unittest.skip(reason='Levit does not output attentions'       )
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		pass





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			lowercase       =    model_class(snake_case       )
			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]		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		def check_hidden_states_output(snake_case		,    snake_case		,    snake_case       ):
			lowercase       =    model_class(snake_case       )
			model.to(snake_case       )
			model.eval()

			with torch.no_grad():
				lowercase       =    model(**self._prepare_for_class(snake_case		,    snake_case       )       )

			lowercase       =    outputs.hidden_states

			lowercase       =    len(self.model_tester.depths       ) + 1
			self.assertEqual(len(snake_case       )		,    snake_case       )

			lowercase       =    (self.model_tester.image_size, self.model_tester.image_size)
			lowercase  ,					lowercase       =    image_size[0], image_size[1]
			for _ in range(4       ):
				lowercase       =    floor(
				    (
				        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
				        / self.model_tester.stride
				    )
				    + 1       )
				lowercase       =    floor(
				    (
				        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
				        / self.model_tester.stride
				    )
				    + 1       )
			# verify the first hidden states (first block)
			self.assertListEqual(
			    list(hidden_states[0].shape[-2:]       )		,    [
			        height * width,
			        self.model_tester.hidden_sizes[0],
			    ]		,    )

		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(snake_case		,    snake_case		,    snake_case       )

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

			check_hidden_states_output(snake_case		,    snake_case		,    snake_case       )





	@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		pass





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case=False       ):
		lowercase       =    super()._prepare_for_class(snake_case		,    snake_case		,    return_labels=snake_case       )

		if return_labels:
			if model_class.__name__ == "LevitForImageClassificationWithTeacher":
				del inputs_dict["labels"]

		return inputs_dict





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		if not self.model_tester.is_training:
			return

		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()
		lowercase       =    True

		for model_class in self.all_model_classes:
			# LevitForImageClassificationWithTeacher supports inference-only
			if (
			    model_class in get_values(snake_case       )
			    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
			):
				continue
			lowercase       =    model_class(snake_case       )
			model.to(snake_case       )
			model.train()
			lowercase       =    self._prepare_for_class(snake_case		,    snake_case		,    return_labels=snake_case       )
			lowercase       =    model(**snake_case       ).loss
			loss.backward()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()
		if not self.model_tester.is_training:
			return

		lowercase       =    False
		lowercase       =    True

		for model_class in self.all_model_classes:
			if model_class in get_values(snake_case       ) or not model_class.supports_gradient_checkpointing:
				continue
			# LevitForImageClassificationWithTeacher supports inference-only
			if model_class.__name__ == "LevitForImageClassificationWithTeacher":
				continue
			lowercase       =    model_class(snake_case       )
			model.gradient_checkpointing_enable()
			model.to(snake_case       )
			model.train()
			lowercase       =    self._prepare_for_class(snake_case		,    snake_case		,    return_labels=snake_case       )
			lowercase       =    model(**snake_case       ).loss
			loss.backward()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()

		lowercase       =    [
		    {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
		    {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
		    {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
		]

		for model_class in self.all_model_classes:
			if (
			    model_class
			    not in [
			        *get_values(snake_case       ),
			    ]
			    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
			):
				continue

			for problem_type in problem_types:
				with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}'''       ):
					lowercase       =    problem_type['title']
					lowercase       =    problem_type['num_labels']

					lowercase       =    model_class(snake_case       )
					model.to(snake_case       )
					model.train()

					lowercase       =    self._prepare_for_class(snake_case		,    snake_case		,    return_labels=snake_case       )

					if problem_type["num_labels"] > 1:
						lowercase       =    inputs['labels'].unsqueeze(1       ).repeat(1		,    problem_type['num_labels']       )

					lowercase       =    inputs['labels'].to(problem_type['dtype']       )

					# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
					# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
					# they have the same size." which is a symptom something in wrong for the regression problem.
					# See https://github.com/huggingface/transformers/issues/11780
					with warnings.catch_warnings(record=snake_case       ) as warning_list:
						lowercase       =    model(**snake_case       ).loss
					for w in warning_list:
						if "Using a target size that is different to the input size" in str(w.message       ):
							raise ValueError(
							    F'''Something is going wrong in the regression problem: intercepted {w.message}'''       )

					loss.backward()





	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowercase       =    LevitModel.from_pretrained(snake_case       )
			self.assertIsNotNone(snake_case       )






def 							UpperCAmelCase_		(							):
	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 	SCREAMING_SNAKE_CASE__						(       self       ):
		return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )





	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(
		    snake_case       )

		lowercase       =    self.default_image_processor
		lowercase       =    prepare_img()
		lowercase       =    image_processor(images=snake_case		,    return_tensors='pt'       ).to(snake_case       )

		# forward pass
		with torch.no_grad():
			lowercase       =    model(**snake_case       )

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

		lowercase       =    torch.tensor([1.0_448, -0.3_745, -1.8_317]       ).to(snake_case       )

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

