
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 	_lowerCAmelCase					:




				def __init__(					self				,				_UpperCamelCase				,				_UpperCamelCase=2				,				_UpperCamelCase=True				,				_UpperCamelCase=False				,				_UpperCamelCase=10				,				_UpperCamelCase=3				,				_UpperCamelCase=32 * 8				,				_UpperCamelCase=32 * 8				,				_UpperCamelCase=4				,				_UpperCamelCase=64				,				)				->      Any:
							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      __a						(					self     )				->      Optional[Any]:
							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      __a						(					self     )				->      Optional[int]:
							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_						=	128
							lowerCAmelCase_						=	self.hidden_dim
							lowerCAmelCase_						=	self.hidden_dim
							lowerCAmelCase_						=	self.hidden_dim
							return config




				def      __a						(					self     )				->      Tuple:
							lowerCAmelCase_						=	self.prepare_config_and_inputs()
							lowerCAmelCase_						=	{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
							return config, inputs_dict




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      List[str]:
							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      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase=False     )				->      Optional[int]:
							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      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      List[Any]:
							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 	_lowerCAmelCase					(	_UpperCAmelCase					,      _UpperCAmelCase					,      unittest.TestCase       ):
				_lowercase					=(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
				_lowercase					={"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

				_lowercase					=False
				_lowercase					=False
				_lowercase					=False
				_lowercase					=False




				def      __a						(					self     )				->      Tuple:
							lowerCAmelCase_						=	MaskaFormerModelTester(self     )
							lowerCAmelCase_						=	ConfigTester(self				,				config_class=_UpperCAmelCase				,				has_text_modality=_UpperCAmelCase     )




				def      __a						(					self     )				->      Tuple:
							self.config_tester.run_common_tests()




				def      __a						(					self     )				->      str:
							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      __a						(					self     )				->      Dict:
							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      __a						(					self     )				->      Tuple:
							pass




				@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"     )
				def      __a						(					self     )				->      Dict:
							pass




				@unittest.skip(reason="Mask2Former is not a generative model"     )
				def      __a						(					self     )				->      Any:
							pass




				@unittest.skip(reason="Mask2Former does not use token embeddings"     )
				def      __a						(					self     )				->      List[str]:
							pass




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




				@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."     )
				def      __a						(					self     )				->      Tuple:
							pass




				def      __a						(					self     )				->      Optional[int]:
							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      __a						(					self     )				->      int:
							for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
										lowerCAmelCase_						=	MaskaFormerModel.from_pretrained(_UpperCAmelCase     )
										self.assertIsNotNone(_UpperCAmelCase     )




				def      __a						(					self     )				->      List[Any]:
							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      __a						(					self     )				->      Dict:
							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      __a						(					self     )				->      Optional[Any]:
							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      __a						(					self     )				->      Tuple:
							if not self.model_tester.is_training:
										return

							lowerCAmelCase_						=	self.all_model_classes[1]
							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      __a						(					self     )				->      Tuple:
							lowerCAmelCase_						=	self.all_model_classes[1]
							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     )


_A   =     1e-4






def    lowerCamelCase__				(				):




			"""simple docstring"""


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





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




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




				@cached_property
				def      __a						(					self     )				->      Tuple:
							return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints     ) if is_vision_available() else None




				def      __a						(					self     )				->      Any:
							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, 384, 384)     )

							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      __a						(					self     )				->      Tuple:
							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, 384, 384)     )

							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      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints     ).to(_UpperCAmelCase     ).eval()
							lowerCAmelCase_						=	self.default_image_processor

							lowerCAmelCase_						=	image_processor(
							    [np.zeros((3, 800, 1_333)     ), np.zeros((3, 800, 1_333)     )]				,				segmentation_maps=[np.zeros((384, 384)     ).astype(np.floataa     ), np.zeros((384, 384)     ).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     )




"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__A			   = logging.get_logger(__name__)

__A			   = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A			   = {
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__A			   = {
    "yjernite/retribert-base-uncased": 5_1_2,
}


__A			   = {
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = VOCAB_FILES_NAMES
    _UpperCAmelCase	:str						    = PRETRAINED_VOCAB_FILES_MAP
    _UpperCAmelCase	:Optional[int]						    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _UpperCAmelCase	:Optional[int]						    = PRETRAINED_INIT_CONFIGURATION
    _UpperCAmelCase	:str						    = RetriBertTokenizer
    _UpperCAmelCase	:List[str]						    = ["input_ids", "attention_mask"]






    def __init__(   self    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase="[UNK]"    ,			_UpperCAmelCase="[SEP]"    ,			_UpperCAmelCase="[PAD]"    ,			_UpperCAmelCase="[CLS]"    ,			_UpperCAmelCase="[MASK]"    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=None    ,			**_UpperCAmelCase    ,			):
       super().__init__(
           _UpperCAmelCase    ,			tokenizer_file=_UpperCAmelCase    ,			do_lower_case=_UpperCAmelCase    ,			unk_token=_UpperCAmelCase    ,			sep_token=_UpperCAmelCase    ,			pad_token=_UpperCAmelCase    ,			cls_token=_UpperCAmelCase    ,			mask_token=_UpperCAmelCase    ,			tokenize_chinese_chars=_UpperCAmelCase    ,			strip_accents=_UpperCAmelCase    ,			**_UpperCAmelCase    ,			)

       lowercase__:   Optional[Any]					=      json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
       if (
           normalizer_state.get('''lowercase'''    ,			_UpperCAmelCase							) != do_lower_case
           or normalizer_state.get('''strip_accents'''    ,			_UpperCAmelCase							) != strip_accents
           or normalizer_state.get('''handle_chinese_chars'''    ,			_UpperCAmelCase							) != tokenize_chinese_chars
       ):
          lowercase__:   str					=      getattr(_UpperCAmelCase    ,			normalizer_state.pop('''type'''							)							)
          lowercase__:   Optional[Any]					=      do_lower_case
          lowercase__:   Tuple					=      strip_accents
          lowercase__:   str					=      tokenize_chinese_chars
          lowercase__:   Union[str, Any]					=      normalizer_class(**_UpperCAmelCase							)

       lowercase__:   List[str]					=      do_lower_case






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=None							):
       lowercase__:   Union[str, Any]					=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
          output += token_ids_a + [self.sep_token_id]

       return output






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       lowercase__:   Optional[int]					=      [self.sep_token_id]
       lowercase__:   Union[str, Any]					=      [self.cls_token_id]
       if token_ids_a is None:
          return len(cls + token_ids_a + sep							) * [0]
       return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       lowercase__:   List[str]					=      self._tokenizer.model.save(_UpperCAmelCase    ,			name=_UpperCAmelCase							)
       return tuple(_UpperCAmelCase							)
