


'''simple docstring'''


import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


lowerCamelCase__    	=     '<<<<<<< This should probably be modified because it mentions: '

lowerCamelCase__    	=     '=======\n>>>>>>>\n'

lowerCamelCase__    	=     [
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

lowerCamelCase__    	=     [
    # (pattern, replacement)
    # Order is important here for some replacements
    (r'tfds\.core', r'datasets'),
    (r'tf\.io\.gfile\.GFile', r'open'),
    (r'tf\.([\w\d]+)', r'datasets.Value(\'\1\')'),
    (r'tfds\.features\.Text\(\)', r'datasets.Value(\'string\')'),
    (r'tfds\.features\.Text\(', r'datasets.Value(\'string\'),'),
    (r'features\s*=\s*tfds.features.FeaturesDict\(', r'features=datasets.Features('),
    (r'tfds\.features\.FeaturesDict\(', r'dict('),
    (r'The TensorFlow Datasets Authors', r'The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'),
    (r'tfds\.', r'datasets.'),
    (r'dl_manager\.manual_dir', r'self.config.data_dir'),
    (r'self\.builder_config', r'self.config'),
]






def  __lowerCAmelCase (__lowerCAmelCase   ):
		return ConvertCommand(args.tfds_path      , args.datasets_directory   )






class 	lowerCAmelCase__			(			UpperCAmelCase__	):




			@staticmethod
			def 							lowerCAmelCase__    (		lowerCamelCase__  :     ArgumentParser )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      parser.add_parser(
					    "convert"	,  help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."	,  )
					train_parser.add_argument(
					    "--tfds_path"	,  type=lowerCamelCase__	,  required=lowerCamelCase__	,  help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."	,  )
					train_parser.add_argument(
					    "--datasets_directory"	,  type=lowerCamelCase__	,  required=lowerCamelCase__	,  help="Path to the HuggingFace Datasets folder." )
					train_parser.set_defaults(func=lowerCamelCase__ )




			def __init__(		self  :     int	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     str	,  *lowerCamelCase__  :     Any )   ->int:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      get_logger("datasets-cli/converting" )

					_UpperCAmelCase		:       List[str]								=      tfds_path
					_UpperCAmelCase		:       str								=      datasets_directory






			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->Any:



					'''simple docstring'''







					if os.path.isdir(self._tfds_path ):
							_UpperCAmelCase		:       Optional[Any]								=      os.path.abspath(self._tfds_path )
					elif os.path.isfile(self._tfds_path ):
							_UpperCAmelCase		:       List[Any]								=      os.path.dirname(self._tfds_path )
					else:
							raise ValueError("--tfds_path is neither a directory nor a file. Please check path." )

					_UpperCAmelCase		:       Tuple								=      os.path.abspath(self._datasets_directory )

					self._logger.info(F"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}""" )

					_UpperCAmelCase		:       List[str]								=      []
					_UpperCAmelCase		:       Optional[Any]								=      []
					_UpperCAmelCase		:       int								=      {}

					if os.path.isdir(self._tfds_path ):
							_UpperCAmelCase		:       Dict								=      os.listdir(lowerCamelCase__ )
					else:
							_UpperCAmelCase		:       Union[str, Any]								=      [os.path.basename(self._tfds_path )]

					for f_name in file_names:
							self._logger.info(F"""Looking at file {f_name}""" )
							_UpperCAmelCase		:       int								=      os.path.join(lowerCamelCase__	,  lowerCamelCase__ )
							_UpperCAmelCase		:       str								=      os.path.join(lowerCamelCase__	,  lowerCamelCase__ )

							if not os.path.isfile(lowerCamelCase__ ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
									self._logger.info("Skipping file" )
									continue

							with open(lowerCamelCase__	,  encoding="utf-8" ) as f:
									_UpperCAmelCase		:       Any								=      f.readlines()

							_UpperCAmelCase		:       List[Any]								=      []
							_UpperCAmelCase		:       Union[str, Any]								=      False
							_UpperCAmelCase		:       Union[str, Any]								=      False
							_UpperCAmelCase		:       Any								=      []
							for line in lines:
									_UpperCAmelCase		:       str								=      line

									# Convert imports
									if "import tensorflow.compat.v2 as tf" in out_line:
											continue
									elif "@tfds.core" in out_line:
											continue
									elif "builder=self" in out_line:
											continue
									elif "import tensorflow_datasets.public_api as tfds" in out_line:
											_UpperCAmelCase		:       List[Any]								=      "import datasets\n"
									elif "import tensorflow" in out_line:
											# order is important here
											_UpperCAmelCase		:       Tuple								=      ""
											continue
									elif "from absl import logging" in out_line:
											_UpperCAmelCase		:       Optional[Any]								=      "from datasets import logging\n"
									elif "getLogger" in out_line:
											_UpperCAmelCase		:       str								=      out_line.replace("getLogger"	,  "get_logger" )
									elif any(expression in out_line for expression in TO_HIGHLIGHT ):
											_UpperCAmelCase		:       int								=      True
											_UpperCAmelCase		:       Optional[int]								=      list(filter(lambda lowerCamelCase__ : e in out_line	,  lowerCamelCase__ ) )
											out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(lowerCamelCase__ ) + "\n" )
											out_lines.append(lowerCamelCase__ )
											out_lines.append(lowerCamelCase__ )
											continue
									else:
											for pattern, replacement in TO_CONVERT:
													_UpperCAmelCase		:       Dict								=      re.sub(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )

                # Take care of saving utilities (to later move them together with main script)
									if "tensorflow_datasets" in out_line:
											_UpperCAmelCase		:       Optional[int]								=      re.match(R"from\stensorflow_datasets.*import\s([^\.\r\n]+)"	,  lowerCamelCase__ )
											tfds_imports.extend(imp.strip() for imp in match.group(1 ).split("," ) )
											_UpperCAmelCase		:       List[str]								=      "from . import " + match.group(1 )

									# Check we have not forget anything
									if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
											raise ValueError(F"""Error converting {out_line.strip()}""" )

									if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
											_UpperCAmelCase		:       int								=      True
									out_lines.append(lowerCamelCase__ )

							if is_builder or "wmt" in f_name:
									# We create a new directory for each dataset
									_UpperCAmelCase		:       List[str]								=      f_name.replace(".py"	,  "" )
									_UpperCAmelCase		:       Optional[Any]								=      os.path.join(lowerCamelCase__	,  lowerCamelCase__ )
									_UpperCAmelCase		:       List[Any]								=      os.path.join(lowerCamelCase__	,  lowerCamelCase__ )
									os.makedirs(lowerCamelCase__	,  exist_ok=lowerCamelCase__ )
									self._logger.info(F"""Adding directory {output_dir}""" )
									imports_to_builder_map.update({imp: output_dir for imp in tfds_imports} )
							else:
									# Utilities will be moved at the end
									utils_files.append(lowerCamelCase__ )

							if needs_manual_update:
									with_manual_update.append(lowerCamelCase__ )

							with open(lowerCamelCase__	,  "w"	,  encoding="utf-8" ) as f:
									f.writelines(lowerCamelCase__ )
							self._logger.info(F"""Converted in {output_file}""" )

					for utils_file in utils_files:
							try:
									_UpperCAmelCase		:       List[Any]								=      os.path.basename(lowerCamelCase__ )
									_UpperCAmelCase		:       Optional[int]								=      imports_to_builder_map[f_name.replace(".py"	,  "" )]
									self._logger.info(F"""Moving {dest_folder} to {utils_file}""" )
									shutil.copy(lowerCamelCase__	,  lowerCamelCase__ )
							except KeyError:
									self._logger.error(F"""Cannot find destination folder for {utils_file}. Please copy manually.""" )

					if with_manual_update:
							for file_path in with_manual_update:
									self._logger.warning(
									    F"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'.""" )







'''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 	lowerCAmelCase__			:




			def __init__(		self  :     Union[str, Any]	,  lowerCamelCase__  :     Optional[int]	,  lowerCamelCase__  :     List[Any]=2	,  lowerCamelCase__  :     List[Any]=True	,  lowerCamelCase__  :     Union[str, Any]=False	,  lowerCamelCase__  :     List[Any]=10	,  lowerCamelCase__  :     Optional[Any]=3	,  lowerCamelCase__  :     Tuple=32 * 8	,  lowerCamelCase__  :     int=32 * 8	,  lowerCamelCase__  :     Dict=4	,  lowerCamelCase__  :     Any=64	,  )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      parent
					_UpperCAmelCase		:       Tuple								=      batch_size
					_UpperCAmelCase		:       Dict								=      is_training
					_UpperCAmelCase		:       Optional[Any]								=      use_auxiliary_loss
					_UpperCAmelCase		:       Dict								=      num_queries
					_UpperCAmelCase		:       Dict								=      num_channels
					_UpperCAmelCase		:       Union[str, Any]								=      min_size
					_UpperCAmelCase		:       Optional[int]								=      max_size
					_UpperCAmelCase		:       str								=      num_labels
					_UpperCAmelCase		:       Optional[int]								=      hidden_dim
					_UpperCAmelCase		:       Any								=      hidden_dim




			def 							lowerCAmelCase__    (		self  :     str )   ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
					    lowerCamelCase__ )

					_UpperCAmelCase		:       Optional[int]								=      torch.ones([self.batch_size, self.min_size, self.max_size]	,  device=lowerCamelCase__ )

					_UpperCAmelCase		:       Union[str, Any]								=      (
					    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]	,  device=lowerCamelCase__ ) > 0.5
					).float()
					_UpperCAmelCase		:       int								=      (torch.rand((self.batch_size, self.num_labels)	,  device=lowerCamelCase__ ) > 0.5).long()

					_UpperCAmelCase		:       Dict								=      self.get_config()
					return config, pixel_values, pixel_mask, mask_labels, class_labels




			def 							lowerCAmelCase__    (		self  :     List[str] )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      MaskaFormerConfig(
					    hidden_size=self.hidden_dim	,  )
					_UpperCAmelCase		:       List[str]								=      self.num_queries
					_UpperCAmelCase		:       Any								=      self.num_labels

					_UpperCAmelCase		:       Union[str, Any]								=      [1, 1, 1, 1]
					_UpperCAmelCase		:       Any								=      self.num_channels

					_UpperCAmelCase		:       int								=      64
					_UpperCAmelCase		:       int								=      1_28
					_UpperCAmelCase		:       int								=      self.hidden_dim
					_UpperCAmelCase		:       List[Any]								=      self.hidden_dim
					_UpperCAmelCase		:       Any								=      self.hidden_dim
					return config




			def 							lowerCAmelCase__    (		self  :     Any )   ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       str								=      self.prepare_config_and_inputs()
					_UpperCAmelCase		:       Optional[Any]								=      {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
					return config, inputs_dict




			def 							lowerCAmelCase__    (		self  :     int	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     str )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      output.encoder_hidden_states
					_UpperCAmelCase		:       List[str]								=      output.pixel_decoder_hidden_states
					_UpperCAmelCase		:       Optional[Any]								=      output.transformer_decoder_hidden_states

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




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     Dict=False )   ->str:



					'''simple docstring'''







					with torch.no_grad():
							_UpperCAmelCase		:       List[Any]								=      MaskaFormerModel(config=lowerCamelCase__ )
							model.to(lowerCamelCase__ )
							model.eval()

							_UpperCAmelCase		:       int								=      model(pixel_values=lowerCamelCase__	,  pixel_mask=lowerCamelCase__ )
							_UpperCAmelCase		:       List[str]								=      model(lowerCamelCase__	,  output_hidden_states=lowerCamelCase__ )

					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(lowerCamelCase__	,  lowerCamelCase__ )






			def 							lowerCAmelCase__    (		self  :     List[Any]	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     Optional[Any]	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     Tuple )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      MaskaFormerForUniversalSegmentation(config=lowerCamelCase__ )
					model.to(lowerCamelCase__ )
					model.eval()

					def comm_check_on_output(lowerCamelCase__  :     Dict ):
							# 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		:       Union[str, Any]								=      model(pixel_values=lowerCamelCase__	,  pixel_mask=lowerCamelCase__ )
							_UpperCAmelCase		:       int								=      model(lowerCamelCase__ )

							comm_check_on_output(lowerCamelCase__ )

							_UpperCAmelCase		:       Union[str, Any]								=      model(
							    pixel_values=lowerCamelCase__	,  pixel_mask=lowerCamelCase__	,  mask_labels=lowerCamelCase__	,  class_labels=lowerCamelCase__ )

					comm_check_on_output(lowerCamelCase__ )

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

			lowerCAmelCase :	Optional[Any]          =     False
			lowerCAmelCase :	Any          =     False
			lowerCAmelCase :	List[Any]          =     False
			lowerCAmelCase :	Any          =     False




			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      MaskaFormerModelTester(self )
					_UpperCAmelCase		:       int								=      ConfigTester(self	,  config_class=lowerCamelCase__	,  has_text_modality=lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     int )   ->Any:



					'''simple docstring'''







					self.config_tester.run_common_tests()




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase					,    _UpperCAmelCase		:       Optional[int]								=      self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__	,  **lowerCamelCase__	,  output_hidden_states=lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCamelCase__ )




			@unittest.skip(reason="Mask2Former does not use inputs_embeds" )
			def 							lowerCAmelCase__    (		self  :     Tuple )   ->List[str]:



					'''simple docstring'''







					pass




			@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method" )
			def 							lowerCAmelCase__    (		self  :     str )   ->List[Any]:



					'''simple docstring'''







					pass




			@unittest.skip(reason="Mask2Former is not a generative model" )
			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->Optional[Any]:



					'''simple docstring'''







					pass




			@unittest.skip(reason="Mask2Former does not use token embeddings" )
			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->Any:



					'''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 							lowerCAmelCase__    (		self  :     Dict )   ->str:



					'''simple docstring'''







					pass




			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->str:



					'''simple docstring'''







					pass




			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase					,    _UpperCAmelCase		:       Optional[Any]								=      self.model_tester.prepare_config_and_inputs_for_common()

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

							_UpperCAmelCase		:       Dict								=      ["pixel_values"]
							self.assertListEqual(arg_names[:1]	,  lowerCamelCase__ )




			@slow
			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->Any:



					'''simple docstring'''







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




			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      (self.model_tester.min_size,) * 2
					_UpperCAmelCase		:       Optional[Any]								=      {
					    "pixel_values": torch.randn((2, 3, *size)	,  device=lowerCamelCase__ ),
					    "mask_labels": torch.randn((2, 10, *size)	,  device=lowerCamelCase__ ),
					    "class_labels": torch.zeros(2	,  10	,  device=lowerCamelCase__ ).long(),
					}
					_UpperCAmelCase		:       int								=      self.model_tester.get_config()

					_UpperCAmelCase		:       str								=      MaskaFormerForUniversalSegmentation(lowerCamelCase__ ).to(lowerCamelCase__ )
					_UpperCAmelCase		:       str								=      model(**lowerCamelCase__ )
					self.assertTrue(outputs.loss is not None )




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase					,    _UpperCAmelCase		:       Union[str, Any]								=      self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__	,  **lowerCamelCase__	,  output_hidden_states=lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Union[str, Any]:



					'''simple docstring'''







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

					for model_class in self.all_model_classes:
							_UpperCAmelCase		:       int								=      model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
							_UpperCAmelCase		:       List[str]								=      model(**lowerCamelCase__	,  output_attentions=lowerCamelCase__ )
							self.assertTrue(outputs.attentions is not None )




			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->str:



					'''simple docstring'''







					if not self.model_tester.is_training:
							return

					_UpperCAmelCase		:       Optional[Any]								=      self.all_model_classes[1]
					_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       Any								=      self.model_tester.prepare_config_and_inputs()

					_UpperCAmelCase		:       Optional[int]								=      model_class(lowerCamelCase__ )
					model.to(lowerCamelCase__ )
					model.train()

					_UpperCAmelCase		:       Optional[int]								=      model(lowerCamelCase__	,  mask_labels=lowerCamelCase__	,  class_labels=lowerCamelCase__ ).loss
					loss.backward()






			def 							lowerCAmelCase__    (		self  :     Dict )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      self.all_model_classes[1]
					_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       Optional[Any]								=      self.model_tester.prepare_config_and_inputs()
					_UpperCAmelCase		:       Union[str, Any]								=      True
					_UpperCAmelCase		:       Tuple								=      True

					_UpperCAmelCase		:       List[Any]								=      model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
					model.train()

					_UpperCAmelCase		:       Any								=      model(lowerCamelCase__	,  mask_labels=lowerCamelCase__	,  class_labels=lowerCamelCase__ )

					_UpperCAmelCase		:       Optional[int]								=      outputs.encoder_hidden_states[0]
					encoder_hidden_states.retain_grad()

					_UpperCAmelCase		:       Dict								=      outputs.pixel_decoder_hidden_states[0]
					pixel_decoder_hidden_states.retain_grad()

					_UpperCAmelCase		:       Optional[Any]								=      outputs.transformer_decoder_hidden_states[0]
					transformer_decoder_hidden_states.retain_grad()

					_UpperCAmelCase		:       Union[str, Any]								=      outputs.attentions[0]
					attentions.retain_grad()

					outputs.loss.backward(retain_graph=lowerCamelCase__ )

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




lowerCamelCase__    	=     1e-4






def  __lowerCAmelCase ():
		_UpperCAmelCase		:       Union[str, Any]								=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
		return image






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




			@cached_property
			def 							lowerCAmelCase__    (		self  :     str )   ->str:



					'''simple docstring'''







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




			@cached_property
			def 							lowerCAmelCase__    (		self  :     Tuple )   ->List[str]:



					'''simple docstring'''







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




			def 							lowerCAmelCase__    (		self  :     Any )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ )
					_UpperCAmelCase		:       int								=      self.default_image_processor
					_UpperCAmelCase		:       Optional[Any]								=      prepare_img()
					_UpperCAmelCase		:       str								=      image_processor(lowerCamelCase__	,  return_tensors="pt" ).to(lowerCamelCase__ )
					_UpperCAmelCase		:       Dict								=      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(lowerCamelCase__	,  (1, 3, 3_84, 3_84) )

					with torch.no_grad():
							_UpperCAmelCase		:       str								=      model(**lowerCamelCase__ )

					_UpperCAmelCase		:       List[str]								=      torch.tensor(
					    [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]] ).to(lowerCamelCase__ )
					self.assertTrue(
					    torch.allclose(
					        outputs.encoder_last_hidden_state[0, 0, :3, :3]	,  lowerCamelCase__	,  atol=lowerCamelCase__ ) )

					_UpperCAmelCase		:       List[Any]								=      torch.tensor(
					    [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]] ).to(lowerCamelCase__ )
					self.assertTrue(
					    torch.allclose(
					        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]	,  lowerCamelCase__	,  atol=lowerCamelCase__ ) )

					_UpperCAmelCase		:       Tuple								=      torch.tensor(
					    [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]] ).to(lowerCamelCase__ )
					self.assertTrue(
					    torch.allclose(
					        outputs.transformer_decoder_last_hidden_state[0, :3, :3]	,  lowerCamelCase__	,  atol=lowerCamelCase__ ) )




			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
					_UpperCAmelCase		:       List[Any]								=      self.default_image_processor
					_UpperCAmelCase		:       Union[str, Any]								=      prepare_img()
					_UpperCAmelCase		:       Optional[int]								=      image_processor(lowerCamelCase__	,  return_tensors="pt" ).to(lowerCamelCase__ )
					_UpperCAmelCase		:       List[Any]								=      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(lowerCamelCase__	,  (1, 3, 3_84, 3_84) )

					with torch.no_grad():
							_UpperCAmelCase		:       List[Any]								=      model(**lowerCamelCase__ )
					# masks_queries_logits
					_UpperCAmelCase		:       List[Any]								=      outputs.masks_queries_logits
					self.assertEqual(
					    masks_queries_logits.shape	,  (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
					_UpperCAmelCase		:       List[str]								=      [
					    [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
					    [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
					    [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
					]
					_UpperCAmelCase		:       List[Any]								=      torch.tensor(lowerCamelCase__ ).to(lowerCamelCase__ )
					self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,  lowerCamelCase__	,  atol=lowerCamelCase__ ) )
					# class_queries_logits
					_UpperCAmelCase		:       Dict								=      outputs.class_queries_logits
					self.assertEqual(class_queries_logits.shape	,  (1, model.config.num_queries, model.config.num_labels + 1) )
					_UpperCAmelCase		:       str								=      torch.tensor(
					    [
					        [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
					        [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
					        [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
					    ] ).to(lowerCamelCase__ )
					self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,  lowerCamelCase__	,  atol=lowerCamelCase__ ) )






			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
					_UpperCAmelCase		:       Tuple								=      self.default_image_processor

					_UpperCAmelCase		:       List[str]								=      image_processor(
					    [np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )]	,  segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )]	,  return_tensors="pt"	,  )

					_UpperCAmelCase		:       str								=      inputs["pixel_values"].to(lowerCamelCase__ )
					_UpperCAmelCase		:       List[str]								=      [el.to(lowerCamelCase__ ) for el in inputs["mask_labels"]]
					_UpperCAmelCase		:       List[str]								=      [el.to(lowerCamelCase__ ) for el in inputs["class_labels"]]

					with torch.no_grad():
							_UpperCAmelCase		:       int								=      model(**lowerCamelCase__ )

					self.assertTrue(outputs.loss is not None )



