
'''simple docstring'''





import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


__lowerCAmelCase					:			List[Any]			      =		datasets.utils.logging.get_logger(__name__)

__lowerCAmelCase					:			List[Any]			      =		["names", "prefix"]
__lowerCAmelCase					:			Any			      =		["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
__lowerCAmelCase					:			Dict			      =		["encoding_errors", "on_bad_lines"]
__lowerCAmelCase					:			Union[str, Any]			      =		["date_format"]


@dataclass
class 						__lowerCAmelCase	(	datasets.BuilderConfig      ):



      """simple docstring"""







      A__	:			str        =    ","
      A__	:			Optional[str]        =    None
      A__	:			Optional[Union[int, List[int], str]]        =    "infer"
      A__	:			Optional[List[str]]        =    None
      A__	:			Optional[List[str]]        =    None
      A__	:			Optional[Union[int, str, List[int], List[str]]]        =    None
      A__	:			Optional[Union[List[int], List[str]]]        =    None
      A__	:			Optional[str]        =    None
      A__	:			bool        =    True
      A__	:			Optional[Literal["c", "python", "pyarrow"]]        =    None
      A__	:			Dict[Union[int, str], Callable[[Any], Any]]        =    None
      A__	:			Optional[list]        =    None
      A__	:			Optional[list]        =    None
      A__	:			bool        =    False
      A__	:			Optional[Union[int, List[int]]]        =    None
      A__	:			Optional[int]        =    None
      A__	:			Optional[Union[str, List[str]]]        =    None
      A__	:			bool        =    True
      A__	:			bool        =    True
      A__	:			bool        =    False
      A__	:			bool        =    True
      A__	:			Optional[str]        =    None
      A__	:			str        =    "."
      A__	:			Optional[str]        =    None
      A__	:			str        =    '"'
      A__	:			int        =    0
      A__	:			Optional[str]        =    None
      A__	:			Optional[str]        =    None
      A__	:			Optional[str]        =    None
      A__	:			Optional[str]        =    None
      A__	:			bool        =    True
      A__	:			bool        =    True
      A__	:			int        =    0
      A__	:			bool        =    True
      A__	:			bool        =    False
      A__	:			Optional[str]        =    None
      A__	:			int        =    1_0_0_0_0
      A__	:			Optional[datasets.Features]        =    None
      A__	:			Optional[str]        =    "strict"
      A__	:			Literal["error", "warn", "skip"]        =    "error"
      A__	:			Optional[str]        =    None




      def 		snake_case_			(      self		:       List[Any]				):
          if self.delimiter is not None:
              __lowercase		:			int	    =       self.delimiter
          if self.column_names is not None:
              __lowercase		:			Any	    =       self.column_names




      @property
      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Union[str, Any]	    =       {
              '''sep''': self.sep,
              '''header''': self.header,
              '''names''': self.names,
              '''index_col''': self.index_col,
              '''usecols''': self.usecols,
              '''prefix''': self.prefix,
              '''mangle_dupe_cols''': self.mangle_dupe_cols,
              '''engine''': self.engine,
              '''converters''': self.converters,
              '''true_values''': self.true_values,
              '''false_values''': self.false_values,
              '''skipinitialspace''': self.skipinitialspace,
              '''skiprows''': self.skiprows,
              '''nrows''': self.nrows,
              '''na_values''': self.na_values,
              '''keep_default_na''': self.keep_default_na,
              '''na_filter''': self.na_filter,
              '''verbose''': self.verbose,
              '''skip_blank_lines''': self.skip_blank_lines,
              '''thousands''': self.thousands,
              '''decimal''': self.decimal,
              '''lineterminator''': self.lineterminator,
              '''quotechar''': self.quotechar,
              '''quoting''': self.quoting,
              '''escapechar''': self.escapechar,
              '''comment''': self.comment,
              '''encoding''': self.encoding,
              '''dialect''': self.dialect,
              '''error_bad_lines''': self.error_bad_lines,
              '''warn_bad_lines''': self.warn_bad_lines,
              '''skipfooter''': self.skipfooter,
              '''doublequote''': self.doublequote,
              '''memory_map''': self.memory_map,
              '''float_precision''': self.float_precision,
              '''chunksize''': self.chunksize,
              '''encoding_errors''': self.encoding_errors,
              '''on_bad_lines''': self.on_bad_lines,
              '''date_format''': self.date_format,
          }

          # some kwargs must not be passed if they don't have a default value
          # some others are deprecated and we can also not pass them if they are the default value
          for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
              if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()				,       _snake_case				):
                  del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
          if not (datasets.config.PANDAS_VERSION.major >= 2):
              for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
                  del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
          if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
              for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
                  del pd_read_csv_kwargs[pd_read_csv_parameter]

          return pd_read_csv_kwargs






class 						__lowerCAmelCase	(	datasets.ArrowBasedBuilder      ):



      """simple docstring"""







      A__	:			Optional[int]        =    CsvConfig




      def 		snake_case_			(      self		:       Optional[Any]				):
          return datasets.DatasetInfo(features=self.config.features				)




      def 		snake_case_			(      self		:       Any				,       _snake_case		:       Tuple				):
          if not self.config.data_files:
              raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}'				)
          __lowercase		:			Tuple	    =       dl_manager.download_and_extract(self.config.data_files				)
          if isinstance(_snake_case				,       (str, list, tuple)				):
              __lowercase		:			Union[str, Any]	    =       data_files
              if isinstance(_snake_case				,       _snake_case				):
                  __lowercase		:			List[str]	    =       [files]
              __lowercase		:			Any	    =       [dl_manager.iter_files(_snake_case				) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,       gen_kwargs={'''files''': files}				)]
          __lowercase		:			Optional[int]	    =       []
          for split_name, files in data_files.items():
              if isinstance(_snake_case				,       _snake_case				):
                  __lowercase		:			Optional[int]	    =       [files]
              __lowercase		:			Optional[Any]	    =       [dl_manager.iter_files(_snake_case				) for file in files]
              splits.append(datasets.SplitGenerator(name=_snake_case				,       gen_kwargs={'''files''': files}				)				)
          return splits




      def 		snake_case_			(      self		:       Optional[Any]				,       _snake_case		:       pa.Table				):
          if self.config.features is not None:
              __lowercase		:			Tuple	    =       self.config.features.arrow_schema
              if all(not require_storage_cast(_snake_case				) for feature in self.config.features.values()				):
                  # cheaper cast
                  __lowercase		:			Optional[int]	    =       pa.Table.from_arrays([pa_table[field.name] for field in schema]				,       schema=_snake_case				)
              else:
                  # more expensive cast; allows str <-> int/float or str to Audio for example
                  __lowercase		:			Tuple	    =       table_cast(_snake_case				,       _snake_case				)
          return pa_table




      def 		snake_case_			(      self		:       Optional[Any]				,       _snake_case		:       Dict				):
          __lowercase		:			str	    =       self.config.features.arrow_schema if self.config.features else None
          # dtype allows reading an int column as str
          __lowercase		:			int	    =       (
              {
                  name: dtype.to_pandas_dtype() if not require_storage_cast(_snake_case				) else object
                  for name, dtype, feature in zip(schema.names				,       schema.types				,       self.config.features.values()				)
              }
              if schema is not None
              else None
          )
          for file_idx, file in enumerate(itertools.chain.from_iterable(_snake_case				)				):
              __lowercase		:			Tuple	    =       pd.read_csv(_snake_case				,       iterator=_snake_case				,       dtype=_snake_case				,       **self.config.pd_read_csv_kwargs				)
              try:
                  for batch_idx, df in enumerate(_snake_case				):
                      __lowercase		:			Any	    =       pa.Table.from_pandas(_snake_case				)
                      # Uncomment for debugging (will print the Arrow table size and elements)
                      # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                      # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                      yield (file_idx, batch_idx), self._cast_table(_snake_case				)
              except ValueError as e:
                  logger.error(F'Failed to read file \'{file}\' with error {type(_snake_case				)}: {e}'				)
                  raise




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		:       Dict				,       _snake_case		:       Optional[int]				,       _snake_case		:       List[Any]=13				,       _snake_case		:       int=32				,       _snake_case		:       int=3				,       _snake_case		:       Any=4				,       _snake_case		:       Optional[int]=[10, 20, 30, 40]				,       _snake_case		:       Optional[Any]=[2, 2, 3, 2]				,       _snake_case		:       Dict=True				,       _snake_case		:       List[Any]=True				,       _snake_case		:       int=37				,       _snake_case		:       Union[str, Any]="gelu"				,       _snake_case		:       Tuple=10				,       _snake_case		:       Tuple=0.02				,       _snake_case		:       List[str]=["stage2", "stage3", "stage4"]				,       _snake_case		:       Tuple=3				,       _snake_case		:       int=None				,       ):
          __lowercase		:			List[Any]	    =       parent
          __lowercase		:			Union[str, Any]	    =       batch_size
          __lowercase		:			Optional[Any]	    =       image_size
          __lowercase		:			Optional[Any]	    =       num_channels
          __lowercase		:			List[str]	    =       num_stages
          __lowercase		:			Union[str, Any]	    =       hidden_sizes
          __lowercase		:			Optional[Any]	    =       depths
          __lowercase		:			List[Any]	    =       is_training
          __lowercase		:			List[str]	    =       use_labels
          __lowercase		:			Tuple	    =       intermediate_size
          __lowercase		:			Union[str, Any]	    =       hidden_act
          __lowercase		:			Dict	    =       type_sequence_label_size
          __lowercase		:			Dict	    =       initializer_range
          __lowercase		:			str	    =       out_features
          __lowercase		:			Tuple	    =       num_labels
          __lowercase		:			Tuple	    =       scope
          __lowercase		:			Optional[Any]	    =       num_stages




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Optional[int]	    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

          __lowercase		:			Dict	    =       None
          if self.use_labels:
              __lowercase		:			List[Any]	    =       ids_tensor([self.batch_size]				,       self.type_sequence_label_size				)

          __lowercase		:			Union[str, Any]	    =       self.get_config()

          return config, pixel_values, labels




      def 		snake_case_			(      self		:       List[str]				):
          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		:       int				):
          return UperNetConfig(
              backbone_config=self.get_backbone_config()				,       hidden_size=512				,       pool_scales=[1, 2, 3, 6]				,       use_auxiliary_head=_snake_case				,       auxiliary_loss_weight=0.4				,       auxiliary_in_channels=40				,       auxiliary_channels=256				,       auxiliary_num_convs=1				,       auxiliary_concat_input=_snake_case				,       loss_ignore_index=255				,       num_labels=self.num_labels				,       )




      def 		snake_case_			(      self		:       Tuple				,       _snake_case		:       Optional[Any]				,       _snake_case		:       List[Any]				,       _snake_case		:       List[str]				):
          __lowercase		:			Optional[int]	    =       UperNetForSemanticSegmentation(config=_snake_case				)
          model.to(_snake_case				)
          model.eval()
          __lowercase		:			Optional[Any]	    =       model(_snake_case				)
          self.parent.assertEqual(
              result.logits.shape				,       (self.batch_size, self.num_labels, self.image_size, self.image_size)				)




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Optional[int]	    =       self.prepare_config_and_inputs()
          (
              (
              __lowercase
          )    ,      (
              __lowercase
          )    ,      (
              __lowercase
          )    ,      
          )		:			Optional[int]	    =       config_and_inputs
          __lowercase		:			List[Any]	    =       {'''pixel_values''': pixel_values}
          return config, inputs_dict






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



      """simple docstring"""







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




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			str	    =       UperNetModelTester(self				)
          __lowercase		:			List[Any]	    =       ConfigTester(self				,       config_class=_snake_case				,       has_text_modality=_snake_case				,       hidden_size=37				)




      def 		snake_case_			(      self		:       str				):
          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		:       str				):
          return




      def 		snake_case_			(      self		:       str				):
          __lowercase    ,      __lowercase		:			Optional[Any]	    =       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __lowercase		:			Optional[Any]	    =       model_class(_snake_case				)
              __lowercase		:			Union[str, Any]	    =       inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              __lowercase		:			Dict	    =       [*signature.parameters.keys()]

              __lowercase		:			Dict	    =       ['''pixel_values''']
              self.assertListEqual(arg_names[:1]				,       _snake_case				)




      def 		snake_case_			(      self		:       List[str]				):
          __lowercase		:			int	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*_snake_case				)




      @unittest.skip(reason='''UperNet does not use inputs_embeds'''				)
      def 		snake_case_			(      self		:       List[Any]				):
          pass




      @unittest.skip(reason='''UperNet does not support input and output embeddings'''				)
      def 		snake_case_			(      self		:       List[Any]				):
          pass




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




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




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




      def 		snake_case_			(      self		:       List[Any]				):
          def check_hidden_states_output(_snake_case		:       int				,       _snake_case		:       Optional[int]				,       _snake_case		:       Dict				):
              __lowercase		:			Dict	    =       model_class(_snake_case				)
              model.to(_snake_case				)
              model.eval()

              with torch.no_grad():
                  __lowercase		:			Optional[int]	    =       model(**self._prepare_for_class(_snake_case				,       _snake_case				)				)

              __lowercase		:			Tuple	    =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              __lowercase		:			Tuple	    =       self.model_tester.num_stages
              self.assertEqual(len(_snake_case				)				,       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]				,       )

          __lowercase    ,      __lowercase		:			str	    =       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __lowercase		:			int	    =       True
              check_hidden_states_output(_snake_case				,       _snake_case				,       _snake_case				)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              __lowercase		:			Any	    =       True

              check_hidden_states_output(_snake_case				,       _snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase    ,      __lowercase		:			List[Any]	    =       self.model_tester.prepare_config_and_inputs_for_common()

          __lowercase		:			Dict	    =       _config_zero_init(_snake_case				)
          __lowercase		:			Dict	    =       _config_zero_init(configs_no_init.backbone_config				)
          for model_class in self.all_model_classes:
              __lowercase		:			Union[str, Any]	    =       model_class(config=_snake_case				)
              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		:       str				):
          pass




      @slow
      def 		snake_case_			(      self		:       Dict				):
          for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowercase		:			List[Any]	    =       UperNetForSemanticSegmentation.from_pretrained(_snake_case				)
              self.assertIsNotNone(_snake_case				)






def     UpperCAmelCase_  (						) ->						Optional[int]:
    __lowercase		:			Optional[int]	    =       hf_hub_download(
        repo_id='''hf-internal-testing/fixtures_ade20k'''	,					repo_type='''dataset'''	,					filename='''ADE_val_00000001.jpg'''	)
    __lowercase		:			int	    =       Image.open(__lowerCAmelCase	).convert('''RGB'''	)
    return image





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



      """simple docstring"""







      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			List[Any]	    =       AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny'''				)
          __lowercase		:			Optional[Any]	    =       UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny'''				).to(_snake_case				)

          __lowercase		:			Tuple	    =       prepare_img()
          __lowercase		:			Optional[int]	    =       processor(images=_snake_case				,       return_tensors='''pt'''				).to(_snake_case				)

          with torch.no_grad():
              __lowercase		:			Dict	    =       model(**_snake_case				)

          __lowercase		:			Optional[Any]	    =       torch.Size((1, model.config.num_labels, 512, 512)				)
          self.assertEqual(outputs.logits.shape				,       _snake_case				)

          __lowercase		:			List[Any]	    =       torch.tensor(
              [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]				).to(_snake_case				)
          self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]				,       _snake_case				,       atol=1E-4				)				)




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			List[str]	    =       AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny'''				)
          __lowercase		:			Dict	    =       UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny'''				).to(_snake_case				)

          __lowercase		:			Any	    =       prepare_img()
          __lowercase		:			Union[str, Any]	    =       processor(images=_snake_case				,       return_tensors='''pt'''				).to(_snake_case				)

          with torch.no_grad():
              __lowercase		:			Tuple	    =       model(**_snake_case				)

          __lowercase		:			Tuple	    =       torch.Size((1, model.config.num_labels, 512, 512)				)
          self.assertEqual(outputs.logits.shape				,       _snake_case				)

          __lowercase		:			List[Any]	    =       torch.tensor(
              [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]				).to(_snake_case				)
          self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]				,       _snake_case				,       atol=1E-4				)				)


