





'''simple docstring'''





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  __A:

     def __init__(      self,       A,       A=2,       A=True,       A=False,       A=10,       A=3,       A=32 * 8,       A=32 * 8,       A=4,       A=64,       ):





            """simple docstring"""

            _UpperCamelCase              =						parent
            _UpperCamelCase              =						batch_size
            _UpperCamelCase              =						is_training
            _UpperCamelCase              =						use_auxiliary_loss
            _UpperCamelCase              =						num_queries
            _UpperCamelCase              =						num_channels
            _UpperCamelCase              =						min_size
            _UpperCamelCase              =						max_size
            _UpperCamelCase              =						num_labels
            _UpperCamelCase              =						hidden_dim
            _UpperCamelCase              =						hidden_dim

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]      ).to(
                A      )

            _UpperCamelCase              =						torch.ones([self.batch_size, self.min_size, self.max_size],       device=A      )

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

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MaskaFormerConfig(
                hidden_size=self.hidden_dim,       )
            _UpperCamelCase              =						self.num_queries
            _UpperCamelCase              =						self.num_labels

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

            _UpperCamelCase              =						64
            _UpperCamelCase              =						128
            _UpperCamelCase              =						self.hidden_dim
            _UpperCamelCase              =						self.hidden_dim
            _UpperCamelCase              =						self.hidden_dim
            return config

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						output.encoder_hidden_states
            _UpperCamelCase              =						output.pixel_decoder_hidden_states
            _UpperCamelCase              =						output.transformer_decoder_hidden_states

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

     def 	_UpperCamelCase     (      self,       A,       A,       A,       A=False      ):





            """simple docstring"""

            with torch.no_grad():
                   _UpperCamelCase              =						MaskaFormerModel(config=A      )
                   model.to(A      )
                   model.eval()

                   _UpperCamelCase              =						model(pixel_values=A,       pixel_mask=A      )
                   _UpperCamelCase              =						model(A,       output_hidden_states=A      )

            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(A,       A      )



     def 	_UpperCamelCase     (      self,       A,       A,       A,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						MaskaFormerForUniversalSegmentation(config=A      )
            model.to(A      )
            model.eval()

            def comm_check_on_output(A      ):
                   # 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():
                   _UpperCamelCase              =						model(pixel_values=A,       pixel_mask=A      )
                   _UpperCamelCase              =						model(A      )

                   comm_check_on_output(A      )

                   _UpperCamelCase              =						model(
                       pixel_values=A,       pixel_mask=A,       mask_labels=A,       class_labels=A      )

            comm_check_on_output(A      )

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






@require_torch
class  __A(						__UpperCAmelCase						,			__UpperCAmelCase						,			unittest.TestCase ):
     __A	  =       (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
     __A	  =       {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

     __A	  =       False
     __A	  =       False
     __A	  =       False
     __A	  =       False

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MaskaFormerModelTester(self      )
            _UpperCamelCase              =						ConfigTester(self,       config_class=A,       has_text_modality=A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            self.config_tester.run_common_tests()

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.create_and_check_maskaformer_model(A,       **A,       output_hidden_states=A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*A      )

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





            """simple docstring"""

            pass

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





            """simple docstring"""

            pass

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





            """simple docstring"""

            pass

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





            """simple docstring"""

            pass

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





            """simple docstring"""

            pass

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





            """simple docstring"""

            pass

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						self.model_tester.prepare_config_and_inputs_for_common()

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

                   _UpperCamelCase              =						['''pixel_values''']
                   self.assertListEqual(arg_names[:1],       A      )

     @slow
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
                   _UpperCamelCase              =						MaskaFormerModel.from_pretrained(A      )
                   self.assertIsNotNone(A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

            _UpperCamelCase              =						MaskaFormerForUniversalSegmentation(A      ).to(A      )
            _UpperCamelCase              =						model(**A      )
            self.assertTrue(outputs.loss is not None      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.create_and_check_maskaformer_model(A,       **A,       output_hidden_states=A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						self.model_tester.prepare_config_and_inputs_for_common()

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            if not self.model_tester.is_training:
                   return

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

            _UpperCamelCase              =						model_class(A      )
            model.to(A      )
            model.train()

            _UpperCamelCase              =						model(A,       mask_labels=A,       class_labels=A      ).loss
            loss.backward()



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

            _UpperCamelCase              =						model_class(A      ).to(A      )
            model.train()

            _UpperCamelCase              =						model(A,       mask_labels=A,       class_labels=A      )

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

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

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

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

            outputs.loss.backward(retain_graph=A      )

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






lowercase    : Tuple    	=  1e-4


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

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

     @cached_property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return "facebook/mask2former-swin-small-coco-instance"

     @cached_property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints      ) if is_vision_available() else None

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MaskaFormerModel.from_pretrained(self.model_checkpoints      ).to(A      )
            _UpperCamelCase              =						self.default_image_processor
            _UpperCamelCase              =						prepare_img()
            _UpperCamelCase              =						image_processor(A,       return_tensors='''pt'''      ).to(A      )
            _UpperCamelCase              =						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(A,       (1, 3, 384, 384)      )

            with torch.no_grad():
                   _UpperCamelCase              =						model(**A      )

            _UpperCamelCase              =						torch.tensor(
                [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]      ).to(A      )
            self.assertTrue(
                torch.allclose(
                    outputs.encoder_last_hidden_state[0, 0, :3, :3],       A,       atol=A      )      )

            _UpperCamelCase              =						torch.tensor(
                [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]      ).to(A      )
            self.assertTrue(
                torch.allclose(
                    outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3],       A,       atol=A      )      )

            _UpperCamelCase              =						torch.tensor(
                [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]      ).to(A      )
            self.assertTrue(
                torch.allclose(
                    outputs.transformer_decoder_last_hidden_state[0, :3, :3],       A,       atol=A      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(A      ).eval()
            _UpperCamelCase              =						self.default_image_processor
            _UpperCamelCase              =						prepare_img()
            _UpperCamelCase              =						image_processor(A,       return_tensors='''pt'''      ).to(A      )
            _UpperCamelCase              =						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(A,       (1, 3, 384, 384)      )

            with torch.no_grad():
                   _UpperCamelCase              =						model(**A      )
            # masks_queries_logits
            _UpperCamelCase              =						outputs.masks_queries_logits
            self.assertEqual(
                masks_queries_logits.shape,       (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)      )
            _UpperCamelCase              =						[
                [-8.7_839, -9.0_056, -8.8_121],
                [-7.4_104, -7.0_313, -6.5_401],
                [-6.6_105, -6.3_427, -6.4_675],
            ]
            _UpperCamelCase              =						torch.tensor(A      ).to(A      )
            self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3],       A,       atol=A      )      )
            # class_queries_logits
            _UpperCamelCase              =						outputs.class_queries_logits
            self.assertEqual(class_queries_logits.shape,       (1, model.config.num_queries, model.config.num_labels + 1)      )
            _UpperCamelCase              =						torch.tensor(
                [
                    [1.8_324, -8.0_835, -4.1_922],
                    [0.8_450, -9.0_050, -3.6_053],
                    [0.3_045, -7.7_293, -3.0_275],
                ]      ).to(A      )
            self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3],       A,       atol=A      )      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(A      ).eval()
            _UpperCamelCase              =						self.default_image_processor

            _UpperCamelCase              =						image_processor(
                [np.zeros((3, 800, 1333)      ), np.zeros((3, 800, 1333)      )],       segmentation_maps=[np.zeros((384, 384)      ).astype(np.floataa      ), np.zeros((384, 384)      ).astype(np.floataa      )],       return_tensors='''pt''',       )

            _UpperCamelCase              =						inputs['''pixel_values'''].to(A      )
            _UpperCamelCase              =						[el.to(A      ) for el in inputs['''mask_labels''']]
            _UpperCamelCase              =						[el.to(A      ) for el in inputs['''class_labels''']]

            with torch.no_grad():
                   _UpperCamelCase              =						model(**A      )

            self.assertTrue(outputs.loss is not None      )













import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowercase    : Optional[Any]    	=  logging.getLogger(__name__)

class  __A(						__UpperCAmelCase ):

     def __init__(      self,       A=-1      ):





            """simple docstring"""

            _UpperCamelCase              =						label_idx

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            if isinstance(A,       A      ):
                   _UpperCamelCase              =						mode.value
            _UpperCamelCase              =						os.path.join(A,       F'''{mode}.txt'''      )
            _UpperCamelCase              =						1
            _UpperCamelCase              =						[]
            with open(A,       encoding='''utf-8'''      ) as f:
                   _UpperCamelCase              =						[]
                   _UpperCamelCase              =						[]
                   for line in f:
                          if line.startswith('''-DOCSTART-'''      ) or line == "" or line == "\n":
                                 if words:
                                        examples.append(InputExample(guid=F'''{mode}-{guid_index}''',       words=A,       labels=A      )      )
                                        guid_index += 1
                                        _UpperCamelCase              =						[]
                                        _UpperCamelCase              =						[]
                          else:
                                 _UpperCamelCase              =						line.split(''' '''      )
                                 words.append(splits[0]      )
                                 if len(A      ) > 1:
                                        labels.append(splits[self.label_idx].replace('''\n''',       ''''''      )      )
                                 else:
                                        # Examples could have no label for mode = "test"
                                        labels.append('''O'''      )
                   if words:
                          examples.append(InputExample(guid=F'''{mode}-{guid_index}''',       words=A,       labels=A      )      )
            return examples

     def 	_UpperCamelCase     (      self,       A,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						0
            for line in test_input_reader:
                   if line.startswith('''-DOCSTART-'''      ) or line == "" or line == "\n":
                          writer.write(A      )
                          if not preds_list[example_id]:
                                 example_id += 1
                   elif preds_list[example_id]:
                          _UpperCamelCase              =						line.split()[0] + ''' ''' + preds_list[example_id].pop(0      ) + '''\n'''
                          writer.write(A      )
                   else:
                          logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.''',       line.split()[0]      )



     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if path:
                   with open(A,       '''r'''      ) as f:
                          _UpperCamelCase              =						f.read().splitlines()
                   if "O" not in labels:
                          _UpperCamelCase              =						['''O'''] + labels
                   return labels
            else:
                   return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]






class  __A(						__UpperCAmelCase ):

     def __init__(      self      ):





            """simple docstring"""

            super().__init__(label_idx=-2      )



     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if path:
                   with open(A,       '''r'''      ) as f:
                          _UpperCamelCase              =						f.read().splitlines()
                   if "O" not in labels:
                          _UpperCamelCase              =						['''O'''] + labels
                   return labels
            else:
                   return [
                       "O",
                       "B-ADVP",
                       "B-INTJ",
                       "B-LST",
                       "B-PRT",
                       "B-NP",
                       "B-SBAR",
                       "B-VP",
                       "B-ADJP",
                       "B-CONJP",
                       "B-PP",
                       "I-ADVP",
                       "I-INTJ",
                       "I-LST",
                       "I-PRT",
                       "I-NP",
                       "I-SBAR",
                       "I-VP",
                       "I-ADJP",
                       "I-CONJP",
                       "I-PP",
                   ]






class  __A(						__UpperCAmelCase ):

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            if isinstance(A,       A      ):
                   _UpperCamelCase              =						mode.value
            _UpperCamelCase              =						os.path.join(A,       F'''{mode}.txt'''      )
            _UpperCamelCase              =						1
            _UpperCamelCase              =						[]

            with open(A,       encoding='''utf-8'''      ) as f:
                   for sentence in parse_incr(A      ):
                          _UpperCamelCase              =						[]
                          _UpperCamelCase              =						[]
                          for token in sentence:
                                 words.append(token['''form''']      )
                                 labels.append(token['''upos''']      )
                          assert len(A      ) == len(A      )
                          if words:
                                 examples.append(InputExample(guid=F'''{mode}-{guid_index}''',       words=A,       labels=A      )      )
                                 guid_index += 1
            return examples

     def 	_UpperCamelCase     (      self,       A,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						0
            for sentence in parse_incr(A      ):
                   _UpperCamelCase              =						preds_list[example_id]
                   _UpperCamelCase              =						''''''
                   for token in sentence:
                          out += F'''{token["form"]} ({token["upos"]}|{s_p.pop(0      )}) '''
                   out += "\n"
                   writer.write(A      )
                   example_id += 1



     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if path:
                   with open(A,       '''r'''      ) as f:
                          return f.read().splitlines()
            else:
                   return [
                       "ADJ",
                       "ADP",
                       "ADV",
                       "AUX",
                       "CCONJ",
                       "DET",
                       "INTJ",
                       "NOUN",
                       "NUM",
                       "PART",
                       "PRON",
                       "PROPN",
                       "PUNCT",
                       "SCONJ",
                       "SYM",
                       "VERB",
                       "X",
                   ]






