




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

		if is_vision_available():
				from transformers import MaskaFormerImageProcessor

if is_vision_available():
		from PIL import Image


class 							__UpperCamelCase			:





				'''simple docstring'''



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_=2       , UpperCAmelCase_=True       , UpperCAmelCase_=False       , UpperCAmelCase_=10       , UpperCAmelCase_=3       , UpperCAmelCase_=32 * 8       , UpperCAmelCase_=32 * 8       , UpperCAmelCase_=4       , UpperCAmelCase_=64       , ):
											lowerCAmelCase   =		parent
											lowerCAmelCase   =		batch_size
											lowerCAmelCase   =		is_training
											lowerCAmelCase   =		use_auxiliary_loss
											lowerCAmelCase   =		num_queries
											lowerCAmelCase   =		num_channels
											lowerCAmelCase   =		min_size
											lowerCAmelCase   =		max_size
											lowerCAmelCase   =		num_labels
											lowerCAmelCase   =		hidden_dim
											lowerCAmelCase   =		hidden_dim



				def       __snake_case		( self						):
											lowerCAmelCase   =		floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]						).to(
											    UpperCAmelCase_						)

											lowerCAmelCase   =		torch.ones([self.batch_size, self.min_size, self.max_size]       , device=UpperCAmelCase_						)

											lowerCAmelCase   =		(
											    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]       , device=UpperCAmelCase_						) > 0.5
											).float()
											lowerCAmelCase   =		(torch.rand((self.batch_size, self.num_labels)       , device=UpperCAmelCase_						) > 0.5).long()

											lowerCAmelCase   =		self.get_config()
											return config, pixel_values, pixel_mask, mask_labels, class_labels



				def       __snake_case		( self						):
											lowerCAmelCase   =		MaskaFormerConfig(
											    hidden_size=self.hidden_dim       , )
											lowerCAmelCase   =		self.num_queries
											lowerCAmelCase   =		self.num_labels

											lowerCAmelCase   =		[1, 1, 1, 1]
											lowerCAmelCase   =		self.num_channels

											lowerCAmelCase   =		64
											lowerCAmelCase   =		1_28
											lowerCAmelCase   =		self.hidden_dim
											lowerCAmelCase   =		self.hidden_dim
											lowerCAmelCase   =		self.hidden_dim
											return config



				def       __snake_case		( self						):
											lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   =		self.prepare_config_and_inputs()
											lowerCAmelCase   =		{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
											return config, inputs_dict



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_						):
											lowerCAmelCase   =		output.encoder_hidden_states
											lowerCAmelCase   =		output.pixel_decoder_hidden_states
											lowerCAmelCase   =		output.transformer_decoder_hidden_states

											self.parent.assertTrue(len(UpperCAmelCase_						)       , len(config.backbone_config.depths						)						)
											self.parent.assertTrue(len(UpperCAmelCase_						)       , len(config.backbone_config.depths						)						)
											self.parent.assertTrue(len(UpperCAmelCase_						)       , config.decoder_layers						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_=False						):
											with torch.no_grad():
																		lowerCAmelCase   =		MaskaFormerModel(config=UpperCAmelCase_						)
																		model.to(UpperCAmelCase_						)
																		model.eval()

																		lowerCAmelCase   =		model(pixel_values=UpperCAmelCase_       , pixel_mask=UpperCAmelCase_						)
																		lowerCAmelCase   =		model(UpperCAmelCase_       , output_hidden_states=UpperCAmelCase_						)

											self.parent.assertEqual(
											    output.transformer_decoder_last_hidden_state.shape       , (self.batch_size, self.num_queries, self.hidden_dim)       , )
											# let's ensure the other two hidden state exists
											self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None						)
											self.parent.assertTrue(output.encoder_last_hidden_state is not None						)

											if output_hidden_states:
																		self.check_output_hidden_state(UpperCAmelCase_       , UpperCAmelCase_						)






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
											lowerCAmelCase   =		MaskaFormerForUniversalSegmentation(config=UpperCAmelCase_						)
											model.to(UpperCAmelCase_						)
											model.eval()

											def comm_check_on_output(UpperCAmelCase_						):
																		# let's still check that all the required stuff is there
																		self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None						)
																		self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None						)
																		self.parent.assertTrue(result.encoder_last_hidden_state is not None						)
																		# okay, now we need to check the logits shape
																		# due to the encoder compression, masks have a //4 spatial size
																		self.parent.assertEqual(
																		    result.masks_queries_logits.shape       , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)       , )
																		# + 1 for null class
																		self.parent.assertEqual(
																		    result.class_queries_logits.shape       , (self.batch_size, self.num_queries, self.num_labels + 1)						)

											with torch.no_grad():
																		lowerCAmelCase   =		model(pixel_values=UpperCAmelCase_       , pixel_mask=UpperCAmelCase_						)
																		lowerCAmelCase   =		model(UpperCAmelCase_						)

																		comm_check_on_output(UpperCAmelCase_						)

																		lowerCAmelCase   =		model(
																		    pixel_values=UpperCAmelCase_       , pixel_mask=UpperCAmelCase_       , mask_labels=UpperCAmelCase_       , class_labels=UpperCAmelCase_						)

											comm_check_on_output(UpperCAmelCase_						)

											self.parent.assertTrue(result.loss is not None						)
											self.parent.assertEqual(result.loss.shape       , torch.Size([1]						)						)




@require_torch
class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Optional[int]										=(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
				__a							: int										={"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

				__a							: Optional[int]										=False
				__a							: Dict										=False
				__a							: List[str]										=False
				__a							: Optional[int]										=False



				def       __snake_case		( self						):
											lowerCAmelCase   =		MaskaFormerModelTester(self						)
											lowerCAmelCase   =		ConfigTester(self       , config_class=UpperCAmelCase_       , has_text_modality=UpperCAmelCase_						)



				def       __snake_case		( self						):
											self.config_tester.run_common_tests()



				def       __snake_case		( self						):
											lowerCAmelCase   ,     lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskaformer_model(UpperCAmelCase_       , **UpperCAmelCase_       , output_hidden_states=UpperCAmelCase_						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*UpperCAmelCase_						)



				@unittest.skip(reason='''Mask2Former does not use inputs_embeds'''						)
				def       __snake_case		( self						):
											pass



				@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''						)
				def       __snake_case		( self						):
											pass



				@unittest.skip(reason='''Mask2Former is not a generative model'''						)
				def       __snake_case		( self						):
											pass



				@unittest.skip(reason='''Mask2Former does not use token embeddings'''						)
				def       __snake_case		( self						):
											pass



				@require_torch_multi_gpu
				@unittest.skip(
				    reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''						)
				def       __snake_case		( self						):
											pass



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



				def       __snake_case		( self						):
											lowerCAmelCase   ,     lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs_for_common()

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

																		lowerCAmelCase   =		['''pixel_values''']
																		self.assertListEqual(arg_names[:1]       , UpperCAmelCase_						)



				@slow
				def       __snake_case		( self						):
											for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																		lowerCAmelCase   =		MaskaFormerModel.from_pretrained(UpperCAmelCase_						)
																		self.assertIsNotNone(UpperCAmelCase_						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		(self.model_tester.min_size,) * 2
											lowerCAmelCase   =		{
											    '''pixel_values''': torch.randn((2, 3, *size)       , device=UpperCAmelCase_						),
											    '''mask_labels''': torch.randn((2, 10, *size)       , device=UpperCAmelCase_						),
											    '''class_labels''': torch.zeros(2       , 10       , device=UpperCAmelCase_						).long(),
											}
											lowerCAmelCase   =		self.model_tester.get_config()

											lowerCAmelCase   =		MaskaFormerForUniversalSegmentation(UpperCAmelCase_						).to(UpperCAmelCase_						)
											lowerCAmelCase   =		model(**UpperCAmelCase_						)
											self.assertTrue(outputs.loss is not None						)



				def       __snake_case		( self						):
											lowerCAmelCase   ,     lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskaformer_model(UpperCAmelCase_       , **UpperCAmelCase_       , output_hidden_states=UpperCAmelCase_						)



				def       __snake_case		( self						):
											lowerCAmelCase   ,     lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		lowerCAmelCase   =		model_class(UpperCAmelCase_						).to(UpperCAmelCase_						)
																		lowerCAmelCase   =		model(**UpperCAmelCase_       , output_attentions=UpperCAmelCase_						)
																		self.assertTrue(outputs.attentions is not None						)



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

											lowerCAmelCase   =		self.all_model_classes[1]
											lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs()

											lowerCAmelCase   =		model_class(UpperCAmelCase_						)
											model.to(UpperCAmelCase_						)
											model.train()

											lowerCAmelCase   =		model(UpperCAmelCase_       , mask_labels=UpperCAmelCase_       , class_labels=UpperCAmelCase_						).loss
											loss.backward()






				def       __snake_case		( self						):
											lowerCAmelCase   =		self.all_model_classes[1]
											lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs()
											lowerCAmelCase   =		True
											lowerCAmelCase   =		True

											lowerCAmelCase   =		model_class(UpperCAmelCase_						).to(UpperCAmelCase_						)
											model.train()

											lowerCAmelCase   =		model(UpperCAmelCase_       , mask_labels=UpperCAmelCase_       , class_labels=UpperCAmelCase_						)

											lowerCAmelCase   =		outputs.encoder_hidden_states[0]
											encoder_hidden_states.retain_grad()

											lowerCAmelCase   =		outputs.pixel_decoder_hidden_states[0]
											pixel_decoder_hidden_states.retain_grad()

											lowerCAmelCase   =		outputs.transformer_decoder_hidden_states[0]
											transformer_decoder_hidden_states.retain_grad()

											lowerCAmelCase   =		outputs.attentions[0]
											attentions.retain_grad()

											outputs.loss.backward(retain_graph=UpperCAmelCase_						)

											self.assertIsNotNone(encoder_hidden_states.grad						)
											self.assertIsNotNone(pixel_decoder_hidden_states.grad						)
											self.assertIsNotNone(transformer_decoder_hidden_states.grad						)
											self.assertIsNotNone(attentions.grad						)




UpperCAmelCase_						=1e-4



def       UpperCAmelCase       (     ):
							lowerCAmelCase   =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							return image


@require_vision
@slow
class 							__UpperCamelCase			( unittest.TestCase  ):





				'''simple docstring'''



				@cached_property
				def       __snake_case		( self						):
											return "facebook/mask2former-swin-small-coco-instance"



				@cached_property
				def       __snake_case		( self						):
											return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints						) if is_vision_available() else None



				def       __snake_case		( self						):
											lowerCAmelCase   =		MaskaFormerModel.from_pretrained(self.model_checkpoints						).to(UpperCAmelCase_						)
											lowerCAmelCase   =		self.default_image_processor
											lowerCAmelCase   =		prepare_img()
											lowerCAmelCase   =		image_processor(UpperCAmelCase_       , return_tensors='''pt'''						).to(UpperCAmelCase_						)
											lowerCAmelCase   =		inputs['''pixel_values'''].shape
											# check size is divisible by 32
											self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0						)
											# check size
											self.assertEqual(UpperCAmelCase_       , (1, 3, 3_84, 3_84)						)

											with torch.no_grad():
																		lowerCAmelCase   =		model(**UpperCAmelCase_						)

											lowerCAmelCase   =		torch.tensor(
											    [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]						).to(UpperCAmelCase_						)
											self.assertTrue(
											    torch.allclose(
											        outputs.encoder_last_hidden_state[0, 0, :3, :3]       , UpperCAmelCase_       , atol=UpperCAmelCase_						)						)

											lowerCAmelCase   =		torch.tensor(
											    [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]						).to(UpperCAmelCase_						)
											self.assertTrue(
											    torch.allclose(
											        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]       , UpperCAmelCase_       , atol=UpperCAmelCase_						)						)

											lowerCAmelCase   =		torch.tensor(
											    [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]						).to(UpperCAmelCase_						)
											self.assertTrue(
											    torch.allclose(
											        outputs.transformer_decoder_last_hidden_state[0, :3, :3]       , UpperCAmelCase_       , atol=UpperCAmelCase_						)						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(UpperCAmelCase_						).eval()
											lowerCAmelCase   =		self.default_image_processor
											lowerCAmelCase   =		prepare_img()
											lowerCAmelCase   =		image_processor(UpperCAmelCase_       , return_tensors='''pt'''						).to(UpperCAmelCase_						)
											lowerCAmelCase   =		inputs['''pixel_values'''].shape
											# check size is divisible by 32
											self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0						)
											# check size
											self.assertEqual(UpperCAmelCase_       , (1, 3, 3_84, 3_84)						)

											with torch.no_grad():
																		lowerCAmelCase   =		model(**UpperCAmelCase_						)
											# masks_queries_logits
											lowerCAmelCase   =		outputs.masks_queries_logits
											self.assertEqual(
											    masks_queries_logits.shape       , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)						)
											lowerCAmelCase   =		[
											    [-8.7839, -9.0056, -8.8121],
											    [-7.4104, -7.0313, -6.5401],
											    [-6.6105, -6.3427, -6.4675],
											]
											lowerCAmelCase   =		torch.tensor(UpperCAmelCase_						).to(UpperCAmelCase_						)
											self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       , UpperCAmelCase_       , atol=UpperCAmelCase_						)						)
											# class_queries_logits
											lowerCAmelCase   =		outputs.class_queries_logits
											self.assertEqual(class_queries_logits.shape       , (1, model.config.num_queries, model.config.num_labels + 1)						)
											lowerCAmelCase   =		torch.tensor(
											    [
											        [1.8324, -8.0835, -4.1922],
											        [0.8450, -9.0050, -3.6053],
											        [0.3045, -7.7293, -3.0275],
											    ]						).to(UpperCAmelCase_						)
											self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       , UpperCAmelCase_       , atol=UpperCAmelCase_						)						)






				def       __snake_case		( self						):
											lowerCAmelCase   =		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(UpperCAmelCase_						).eval()
											lowerCAmelCase   =		self.default_image_processor

											lowerCAmelCase   =		image_processor(
											    [np.zeros((3, 8_00, 13_33)						), np.zeros((3, 8_00, 13_33)						)]       , segmentation_maps=[np.zeros((3_84, 3_84)						).astype(np.floataa						), np.zeros((3_84, 3_84)						).astype(np.floataa						)]       , return_tensors='''pt'''       , )

											lowerCAmelCase   =		inputs['''pixel_values'''].to(UpperCAmelCase_						)
											lowerCAmelCase   =		[el.to(UpperCAmelCase_						) for el in inputs['''mask_labels''']]
											lowerCAmelCase   =		[el.to(UpperCAmelCase_						) for el in inputs['''class_labels''']]

											with torch.no_grad():
																		lowerCAmelCase   =		model(**UpperCAmelCase_						)

											self.assertTrue(outputs.loss is not None						)









from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   ,     lowerCAmelCase   =		analyze_text(_snake_case							)
							lowerCAmelCase   =		list(''' ''' + ascii_lowercase							)
							# what is our total sum of probabilities.
							lowerCAmelCase   =		sum(single_char_strings.values()							)

							# one length string
							lowerCAmelCase   =		0
							# for each alpha we go in our dict and if it is in it we calculate entropy
							for ch in my_alphas:
														if ch in single_char_strings:
																					lowerCAmelCase   =		single_char_strings[ch]
																					lowerCAmelCase   =		my_str / all_sum
																					my_fir_sum += prob * math.loga(_snake_case							)  # entropy formula.

    # print entropy
							print(F"""{round(-1 * my_fir_sum							):.1f}"""							)

							# two len string
							lowerCAmelCase   =		sum(two_char_strings.values()							)
							lowerCAmelCase   =		0
							# for each alpha (two in size) calculate entropy.
							for cha in my_alphas:
														for cha in my_alphas:
																					lowerCAmelCase   =		cha + cha
																					if sequence in two_char_strings:
																												lowerCAmelCase   =		two_char_strings[sequence]
																												lowerCAmelCase   =		int(_snake_case							) / all_sum
																												my_sec_sum += prob * math.loga(_snake_case							)

    # print second entropy
							print(F"""{round(-1 * my_sec_sum							):.1f}"""							)

							# print the difference between them
							print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)							):.1f}"""							)



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		Counter()  # type: ignore
							lowerCAmelCase   =		Counter()  # type: ignore
							single_char_strings[text[-1]] += 1

							# first case when we have space at start.
							two_char_strings[" " + text[0]] += 1
							for i in range(0						, len(_snake_case							) - 1							):
														single_char_strings[text[i]] += 1
														two_char_strings[text[i : i + 2]] += 1
							return single_char_strings, two_char_strings



def       UpperCAmelCase       (     ):
							import doctest

							doctest.testmod()
							# text = (
							#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
							#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
							#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
							#     "beloved evident through no service elderly is. Blind there if every no so "
							#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
							#     "of message cottage windows do besides against uncivil.  Delightful "
							#     "unreserved impossible few estimating men favourable see entreaties. She "
							#     "propriety immediate was improving. He or entrance humoured likewise "
							#     "moderate. Much nor game son say feel. Fat make met can must form into "
							#     "gate. Me we offending prevailed discovery. "
							# )

							# calculate_prob(text)


if __name__ == "__main__":
		main()



