



"""simple docstring"""
import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import UperNetForSemanticSegmentation
			from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor


class 				lowerCamelCase_:





					'''simple docstring'''



					def __init__(     self					,							lowerCamelCase__					,							lowerCamelCase__=1_3					,							lowerCamelCase__=3_2					,							lowerCamelCase__=3					,							lowerCamelCase__=4					,							lowerCamelCase__=[1_0, 2_0, 3_0, 4_0]					,							lowerCamelCase__=[2, 2, 3, 2]					,							lowerCamelCase__=True					,							lowerCamelCase__=True					,							lowerCamelCase__=3_7					,							lowerCamelCase__="gelu"					,							lowerCamelCase__=1_0					,							lowerCamelCase__=0.0_2					,							lowerCamelCase__=["stage2", "stage3", "stage4"]					,							lowerCamelCase__=3					,							lowerCamelCase__=None					,							):
									_lowerCamelCase   	=					parent
									_lowerCamelCase   	=					batch_size
									_lowerCamelCase   	=					image_size
									_lowerCamelCase   	=					num_channels
									_lowerCamelCase   	=					num_stages
									_lowerCamelCase   	=					hidden_sizes
									_lowerCamelCase   	=					depths
									_lowerCamelCase   	=					is_training
									_lowerCamelCase   	=					use_labels
									_lowerCamelCase   	=					intermediate_size
									_lowerCamelCase   	=					hidden_act
									_lowerCamelCase   	=					type_sequence_label_size
									_lowerCamelCase   	=					initializer_range
									_lowerCamelCase   	=					out_features
									_lowerCamelCase   	=					num_labels
									_lowerCamelCase   	=					scope
									_lowerCamelCase   	=					num_stages

					def       snake_case__   (     self ):
									_lowerCamelCase   	=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

									_lowerCamelCase   	=					None
									if self.use_labels:
													_lowerCamelCase   	=					ids_tensor([self.batch_size]					,							self.type_sequence_label_size )

									_lowerCamelCase   	=					self.get_config()

									return config, pixel_values, labels

					def       snake_case__   (     self ):
									return ConvNextConfig(
									    num_channels=self.num_channels					,							num_stages=self.num_stages					,							hidden_sizes=self.hidden_sizes					,							depths=self.depths					,							is_training=self.is_training					,							intermediate_size=self.intermediate_size					,							hidden_act=self.hidden_act					,							out_features=self.out_features					,							)

					def       snake_case__   (     self ):
									return UperNetConfig(
									    backbone_config=self.get_backbone_config()					,							hidden_size=5_1_2					,							pool_scales=[1, 2, 3, 6]					,							use_auxiliary_head=__lowerCAmelCase					,							auxiliary_loss_weight=0.4					,							auxiliary_in_channels=4_0					,							auxiliary_channels=2_5_6					,							auxiliary_num_convs=1					,							auxiliary_concat_input=__lowerCAmelCase					,							loss_ignore_index=2_5_5					,							num_labels=self.num_labels					,							)

					def       snake_case__   (     self					,							lowerCamelCase__					,							lowerCamelCase__					,							lowerCamelCase__ ):
									_lowerCamelCase   	=					UperNetForSemanticSegmentation(config=__lowerCAmelCase )
									model.to(__lowerCAmelCase )
									model.eval()
									_lowerCamelCase   	=					model(__lowerCAmelCase )
									self.parent.assertEqual(
									    result.logits.shape					,							(self.batch_size, self.num_labels, self.image_size, self.image_size) )





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


@require_torch
class 				lowerCamelCase_( lowerCAmelCase_,					lowerCAmelCase_,					unittest.TestCase     ):





					'''simple docstring'''



					lowercase__ :							List[Any]              =			(UperNetForSemanticSegmentation,) if is_torch_available() else ()
					lowercase__ :							Optional[int]              =			{'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
					lowercase__ :							Optional[int]              =			False
					lowercase__ :							int              =			False
					lowercase__ :							Any              =			False
					lowercase__ :							Dict              =			False
					lowercase__ :							List[str]              =			False
					lowercase__ :							Optional[int]              =			False

					def       snake_case__   (     self ):
									_lowerCamelCase   	=					UperNetModelTester(self )
									_lowerCamelCase   	=					ConfigTester(self					,							config_class=__lowerCAmelCase					,							has_text_modality=__lowerCAmelCase					,							hidden_size=3_7 )

					def       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       snake_case__   (     self ):
									return

					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(__lowerCAmelCase )
													_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]					,							__lowerCAmelCase )

					def       snake_case__   (     self ):
									_lowerCamelCase   	=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCAmelCase )

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

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

					@unittest.skip(reason='''UperNet does not have a base model''' )
					def       snake_case__   (     self ):
									pass

					@unittest.skip(reason='''UperNet does not have a base model''' )
					def       snake_case__   (     self ):
									pass

					@require_torch_multi_gpu
					@unittest.skip(reason='''UperNet 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 ):
									def check_hidden_states_output(lowerCamelCase__					,							lowerCamelCase__					,							lowerCamelCase__ ):
													_lowerCamelCase   	=					model_class(__lowerCAmelCase )
													model.to(__lowerCAmelCase )
													model.eval()

													with torch.no_grad():
																	_lowerCamelCase   	=					model(**self._prepare_for_class(__lowerCAmelCase					,							__lowerCAmelCase ) )

													_lowerCamelCase   	=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													_lowerCamelCase   	=					self.model_tester.num_stages
													self.assertEqual(len(__lowerCAmelCase )					,							expected_num_stages + 1 )

													# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:] )					,							[self.model_tester.image_size // 4, self.model_tester.image_size // 4]					,							)

									_lowerCamelCase	,  _lowerCamelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													_lowerCamelCase   	=					True
													check_hidden_states_output(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase )

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

													check_hidden_states_output(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase )

					def       snake_case__   (     self ):
									_lowerCamelCase	,  _lowerCamelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()

									_lowerCamelCase   	=					_config_zero_init(__lowerCAmelCase )
									_lowerCamelCase   	=					_config_zero_init(configs_no_init.backbone_config )
									for model_class in self.all_model_classes:
													_lowerCamelCase   	=					model_class(config=__lowerCAmelCase )
													for name, param in model.named_parameters():
																	if param.requires_grad:
																					self.assertIn(
																					    ((param.data.mean() * 1e9).round() / 1e9).item()					,							[0.0, 1.0]					,							msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""					,							)

					@unittest.skip(reason='''UperNet does not have tied weights''' )
					def       snake_case__   (     self ):
									pass





					@slow
					def       snake_case__   (     self ):
									for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowerCamelCase   	=					UperNetForSemanticSegmentation.from_pretrained(__lowerCAmelCase )
													self.assertIsNotNone(__lowerCAmelCase )



def 							lowerCAmelCase_( )						->							Optional[Any]:
				_lowerCamelCase   	=					hf_hub_download(
				    repo_id='''hf-internal-testing/fixtures_ade20k'''    , repo_type='''dataset'''    , filename='''ADE_val_00000001.jpg'''				)
				_lowerCamelCase   	=					Image.open(lowercase_				).convert('''RGB'''				)
				return image




@require_torch
@require_vision
@slow
class 				lowerCamelCase_( unittest.TestCase     ):





					'''simple docstring'''



					def       snake_case__   (     self ):
									_lowerCamelCase   	=					AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny''' )
									_lowerCamelCase   	=					UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny''' ).to(__lowerCAmelCase )

									_lowerCamelCase   	=					prepare_img()
									_lowerCamelCase   	=					processor(images=__lowerCAmelCase					,							return_tensors='''pt''' ).to(__lowerCAmelCase )

									with torch.no_grad():
													_lowerCamelCase   	=					model(**__lowerCAmelCase )

									_lowerCamelCase   	=					torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2) )
									self.assertEqual(outputs.logits.shape					,							__lowerCAmelCase )

									_lowerCamelCase   	=					torch.tensor(
									    [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]] ).to(__lowerCAmelCase )
									self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]					,							__lowerCAmelCase					,							atol=1e-4 ) )





					def       snake_case__   (     self ):
									_lowerCamelCase   	=					AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny''' )
									_lowerCamelCase   	=					UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny''' ).to(__lowerCAmelCase )

									_lowerCamelCase   	=					prepare_img()
									_lowerCamelCase   	=					processor(images=__lowerCAmelCase					,							return_tensors='''pt''' ).to(__lowerCAmelCase )

									with torch.no_grad():
													_lowerCamelCase   	=					model(**__lowerCAmelCase )

									_lowerCamelCase   	=					torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2) )
									self.assertEqual(outputs.logits.shape					,							__lowerCAmelCase )

									_lowerCamelCase   	=					torch.tensor(
									    [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]] ).to(__lowerCAmelCase )
									self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]					,							__lowerCAmelCase					,							atol=1e-4 ) )

"""simple docstring"""


import json
import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 							a					(		lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	Dict  				=				MgpstrTokenizer
    _snake_case  :	Any  				=				False
    _snake_case  :	str  				=				{}
    _snake_case  :	Dict  				=				False


    def   lowerCAmelCase_	(       self						:	int       ):
      super().setUp()

      # fmt: off
      _UpperCAmelCase    			=   ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
      # fmt: on
      _UpperCAmelCase    			=   dict(zip(__lowerCAmelCase ,					range(len(__lowerCAmelCase       )       )       )       )

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


    def   lowerCAmelCase_	(       self						:	Tuple ,					**__lowerCAmelCase						:	Dict       ):
      return MgpstrTokenizer.from_pretrained(self.tmpdirname ,					**__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   """tester"""
      _UpperCAmelCase    			=   """tester"""
      return input_text, output_text


    @unittest.skip("""MGP-STR always lower cases letters."""       )
    def   lowerCAmelCase_	(       self						:	Tuple       ):
      pass


    def   lowerCAmelCase_	(       self						:	Any       ):
      _UpperCAmelCase    			=   self.get_tokenizers(do_lower_case=__lowerCAmelCase       )
      for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
          _UpperCAmelCase    			=   """[SPECIAL_TOKEN]"""

          tokenizer.add_special_tokens({"""cls_token""": special_token}       )
          _UpperCAmelCase    			=   tokenizer.encode([special_token] ,					add_special_tokens=__lowerCAmelCase       )
          self.assertEqual(len(__lowerCAmelCase       ) ,					1       )

          _UpperCAmelCase    			=   tokenizer.decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )
          self.assertTrue(special_token not in decoded       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   self.get_tokenizers()
      for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
          _UpperCAmelCase			,					_UpperCAmelCase    			=   self.get_input_output_texts(__lowerCAmelCase       )

          _UpperCAmelCase    			=   tokenizer.tokenize(__lowerCAmelCase       )
          _UpperCAmelCase    			=   tokenizer.convert_tokens_to_ids(__lowerCAmelCase       )
          _UpperCAmelCase    			=   tokenizer.encode(__lowerCAmelCase ,					add_special_tokens=__lowerCAmelCase       )
          self.assertListEqual(__lowerCAmelCase ,					__lowerCAmelCase       )

          _UpperCAmelCase    			=   tokenizer.convert_ids_to_tokens(__lowerCAmelCase       )
          self.assertNotEqual(len(__lowerCAmelCase       ) ,					0       )
          _UpperCAmelCase    			=   tokenizer.decode(__lowerCAmelCase       )
          self.assertIsInstance(__lowerCAmelCase ,					__lowerCAmelCase       )

          self.assertEqual(text_a.replace(""" """ ,					""""""       ) ,					__lowerCAmelCase       )


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


    @unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""       )
    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      pass
