





import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

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



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =pa.array(TypedSequence([1, 2, 3]))
   self.assertEqual(arr.type       , pa.intaa())

 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->str:



   '''simple docstring'''



   with self.assertRaises(UpperCAmelCase_):
     lowerCamelCase__:      str			  =pa.array(TypedSequence([1, 2, 3])       , type=pa.intaa())

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Dict:



   '''simple docstring'''



   with self.assertRaises(UpperCAmelCase_):
     lowerCamelCase__:      str			  =pa.array(TypedSequence([1, 2, 3]       , try_type=Value("bool")       , type=Value("int64")))

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =pa.array(TypedSequence([1, 2, 3]       , type=Value("int32")))
   self.assertEqual(arr.type       , pa.intaa())

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Dict:



   '''simple docstring'''



   with self.assertRaises((TypeError, pa.lib.ArrowInvalid)):
     lowerCamelCase__:      Tuple			  =pa.array(TypedSequence(["foo", "bar"]       , type=Value("int64")))

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =pa.array(TypedSequence([1, 2, 3]       , try_type=Value("int32")))
   self.assertEqual(arr.type       , pa.intaa())

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =pa.array(TypedSequence(["foo", "bar"]       , try_type=Value("int64")))
   self.assertEqual(arr.type       , pa.string())

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =pa.array(TypedSequence([[[1, 2, 3]]]       , type=ArrayaD((1, 3)       , "int64")))
   self.assertEqual(arr.type       , ArrayaDExtensionType((1, 3)       , "int64"))

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



   '''simple docstring'''



   with self.assertRaises((TypeError, pa.lib.ArrowInvalid)):
     lowerCamelCase__:      Tuple			  =pa.array(TypedSequence(["foo", "bar"]       , type=ArrayaD((1, 3)       , "int64")))

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =pa.array(TypedSequence([[[1, 2, 3]]]       , try_type=ArrayaD((1, 3)       , "int64")))
   self.assertEqual(arr.type       , ArrayaDExtensionType((1, 3)       , "int64"))

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =pa.array(TypedSequence(["foo", "bar"]       , try_type=ArrayaD((1, 3)       , "int64")))
   self.assertEqual(arr.type       , pa.string())







 @require_pil
 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int])       ->List[str]:



   '''simple docstring'''



   import PIL.Image

   lowerCamelCase__:      Any			  =PIL.Image.fromarray(np.arange(10       , dtype=np.uinta).reshape(2       , 5))
   with patch(
       "datasets.arrow_writer.cast_to_python_objects"       , side_effect=UpperCAmelCase_) as mock_cast_to_python_objects:
     lowerCamelCase__:      Optional[int]			  =pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image]       , type=Image()))
     lowerCamelCase__  ,					lowerCamelCase__:      Dict			  =mock_cast_to_python_objects.call_args_list[-1]
     self.assertIn("optimize_list_casting"       , UpperCAmelCase_)
     self.assertFalse(kwargs["optimize_list_casting"])





def      lowerCAmelCase_ (       __a	,    __a		)     ->		List[str]:







  """simple docstring"""





  lowerCamelCase__:      Dict			  =pa.BufferReader(__a		) if isinstance(__a	,    pa.Buffer		) else pa.memory_map(__a		)
  lowerCamelCase__:      Union[str, Any]			  =pa.ipc.open_stream(__a		)
  lowerCamelCase__:      pa.Table			  =f.read_all()
  assert len(pa_table.to_batches()		) == expected_num_chunks
  assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
  del pa_table





@pytest.mark.parametrize("writer_batch_size"	,    [None, 1, 10]		)
@pytest.mark.parametrize(
    "fields"	,    [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]		)
def      lowerCAmelCase_ (       __a	,    __a		)     ->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:      List[Any]			  =pa.BufferOutputStream()
  lowerCamelCase__:      List[Any]			  =pa.schema(__a		) if fields else None
  with ArrowWriter(stream=__a	,    schema=__a	,    writer_batch_size=__a		) as writer:
    writer.write({"col_1": "foo", "col_2": 1}		)
    writer.write({"col_1": "bar", "col_2": 2}		)
    lowerCamelCase__  ,					lowerCamelCase__:      int			  =writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    lowerCamelCase__:      Union[str, Any]			  ={"col_1": pa.string(), "col_2": pa.intaa()}
  assert writer._schema == pa.schema(__a	,    metadata=writer._schema.metadata		)
  _check_output(output.getvalue()	,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1		)





def      lowerCAmelCase_ (       )     ->		Tuple:







  """simple docstring"""





  lowerCamelCase__:      Union[str, Any]			  =pa.BufferOutputStream()
  lowerCamelCase__:      List[str]			  =Features({"labels": ClassLabel(names=["neg", "pos"]		)}		)
  with ArrowWriter(stream=__a	,    features=__a		) as writer:
    writer.write({"labels": 0}		)
    writer.write({"labels": 1}		)
    lowerCamelCase__  ,					lowerCamelCase__:      Any			  =writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert writer._schema == features.arrow_schema
  assert writer._schema.metadata == features.arrow_schema.metadata
  lowerCamelCase__:      Union[str, Any]			  =pa.BufferReader(output.getvalue()		)
  lowerCamelCase__:      Dict			  =pa.ipc.open_stream(__a		)
  lowerCamelCase__:      pa.Table			  =f.read_all()
  lowerCamelCase__:      str			  =pa_table.schema
  assert pa_table.num_rows == 2
  assert schema == features.arrow_schema
  assert schema.metadata == features.arrow_schema.metadata
  assert features == Features.from_arrow_schema(__a		)





@pytest.mark.parametrize("writer_batch_size"	,    [None, 1, 10]		)
def      lowerCAmelCase_ (       __a		)     ->		int:







  """simple docstring"""





  lowerCamelCase__:      Tuple			  =pa.BufferOutputStream()
  with ArrowWriter(
      stream=__a	,    writer_batch_size=__a	,    hash_salt="split_name"	,    check_duplicates=__a	,    ) as writer:
    with pytest.raises(__a		):
      writer.write({"col_1": "foo", "col_2": 1}	,    key=[1, 2]		)
      lowerCamelCase__  ,					lowerCamelCase__:      Union[str, Any]			  =writer.finalize()





@pytest.mark.parametrize("writer_batch_size"	,    [None, 2, 10]		)
def      lowerCAmelCase_ (       __a		)     ->		Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__:      Any			  =pa.BufferOutputStream()
  with ArrowWriter(
      stream=__a	,    writer_batch_size=__a	,    hash_salt="split_name"	,    check_duplicates=__a	,    ) as writer:
    with pytest.raises(__a		):
      writer.write({"col_1": "foo", "col_2": 1}	,    key=10		)
      writer.write({"col_1": "bar", "col_2": 2}	,    key=10		)
      lowerCamelCase__  ,					lowerCamelCase__:      int			  =writer.finalize()





@pytest.mark.parametrize("writer_batch_size"	,    [None, 2, 10]		)
def      lowerCAmelCase_ (       __a		)     ->		Dict:







  """simple docstring"""





  lowerCamelCase__:      List[Any]			  =pa.BufferOutputStream()
  with ArrowWriter(
      stream=__a	,    writer_batch_size=__a	,    hash_salt="split_name"	,    check_duplicates=__a	,    ) as writer:
    writer.write({"col_1": "foo", "col_2": 1}	,    key=1		)
    writer.write({"col_1": "bar", "col_2": 2}	,    key=2		)
    lowerCamelCase__  ,					lowerCamelCase__:      str			  =writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  _check_output(output.getvalue()	,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1		)





@pytest.mark.parametrize("writer_batch_size"	,    [None, 1, 10]		)
@pytest.mark.parametrize(
    "fields"	,    [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]		)
def      lowerCAmelCase_ (       __a	,    __a		)     ->		Dict:







  """simple docstring"""





  lowerCamelCase__:      Tuple			  =pa.BufferOutputStream()
  lowerCamelCase__:      List[Any]			  =pa.schema(__a		) if fields else None
  with ArrowWriter(stream=__a	,    schema=__a	,    writer_batch_size=__a		) as writer:
    writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}		)
    writer.write_batch({"col_1": [], "col_2": []}		)
    lowerCamelCase__  ,					lowerCamelCase__:      List[str]			  =writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    lowerCamelCase__:      Optional[int]			  ={"col_1": pa.string(), "col_2": pa.intaa()}
  assert writer._schema == pa.schema(__a	,    metadata=writer._schema.metadata		)
  _check_output(output.getvalue()	,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1		)





@pytest.mark.parametrize("writer_batch_size"	,    [None, 1, 10]		)
@pytest.mark.parametrize(
    "fields"	,    [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]		)
def      lowerCAmelCase_ (       __a	,    __a		)     ->		Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__:      Dict			  =pa.BufferOutputStream()
  lowerCamelCase__:      Union[str, Any]			  =pa.schema(__a		) if fields else None
  with ArrowWriter(stream=__a	,    schema=__a	,    writer_batch_size=__a		) as writer:
    writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}		)		)
    lowerCamelCase__  ,					lowerCamelCase__:      Any			  =writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    lowerCamelCase__:      List[str]			  ={"col_1": pa.string(), "col_2": pa.intaa()}
  assert writer._schema == pa.schema(__a	,    metadata=writer._schema.metadata		)
  _check_output(output.getvalue()	,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1		)





@pytest.mark.parametrize("writer_batch_size"	,    [None, 1, 10]		)
@pytest.mark.parametrize(
    "fields"	,    [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]		)
def      lowerCAmelCase_ (       __a	,    __a		)     ->		str:







  """simple docstring"""





  lowerCamelCase__:      Tuple			  =pa.BufferOutputStream()
  lowerCamelCase__:      Any			  =pa.schema(__a		) if fields else None
  with ArrowWriter(stream=__a	,    schema=__a	,    writer_batch_size=__a		) as writer:
    writer.write_row(pa.Table.from_pydict({"col_1": ["foo"], "col_2": [1]}		)		)
    writer.write_row(pa.Table.from_pydict({"col_1": ["bar"], "col_2": [2]}		)		)
    lowerCamelCase__  ,					lowerCamelCase__:      Union[str, Any]			  =writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    lowerCamelCase__:      int			  ={"col_1": pa.string(), "col_2": pa.intaa()}
  assert writer._schema == pa.schema(__a	,    metadata=writer._schema.metadata		)
  _check_output(output.getvalue()	,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1		)





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







  """simple docstring"""





  with tempfile.TemporaryDirectory() as tmp_dir:
    lowerCamelCase__:      List[str]			  ={"col_1": pa.string(), "col_2": pa.intaa()}
    lowerCamelCase__:      Tuple			  =os.path.join(__a	,    "test.arrow"		)
    with ArrowWriter(path=__a	,    schema=pa.schema(__a		)		) as writer:
      writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}		)
      lowerCamelCase__  ,					lowerCamelCase__:      Optional[int]			  =writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert writer._schema == pa.schema(__a	,    metadata=writer._schema.metadata		)
    _check_output(__a	,    1		)





def      lowerCAmelCase_ (       __a		)     ->		Tuple:







  """simple docstring"""





  if pa.types.is_list(__a		):
    return get_base_dtype(arr_type.value_type		)
  else:
    return arr_type





def      lowerCAmelCase_ (       __a	,    __a		)     ->		List[str]:







  """simple docstring"""





  if isinstance(lst[0]	,    __a		):
    change_first_primitive_element_in_list(lst[0]	,    __a		)
  else:
    lowerCamelCase__:      Tuple			  =value





@pytest.mark.parametrize("optimized_int_type, expected_dtype"	,    [(None, pa.intaa()), (Value("int32"		), pa.intaa())]		)
@pytest.mark.parametrize("sequence"	,    [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]		)
def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		str:







  """simple docstring"""





  lowerCamelCase__:      List[Any]			  =pa.array(TypedSequence(__a	,    optimized_int_type=__a		)		)
  assert get_base_dtype(arr.type		) == expected_dtype





@pytest.mark.parametrize(
    "col, expected_dtype"	,    [
        ("attention_mask", pa.inta()),
        ("special_tokens_mask", pa.inta()),
        ("token_type_ids", pa.inta()),
        ("input_ids", pa.intaa()),
        ("other", pa.intaa()),
    ]	,    )
@pytest.mark.parametrize("sequence"	,    [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]		)
def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		Tuple:







  """simple docstring"""





  lowerCamelCase__:      Optional[Any]			  =pa.array(OptimizedTypedSequence(__a	,    col=__a		)		)
  assert get_base_dtype(arr.type		) == expected_dtype

  # not in range
  if col != "other":
    # avoids errors due to in-place modifications
    lowerCamelCase__:      Any			  =copy.deepcopy(__a		)
    lowerCamelCase__:      Any			  =np.iinfo(expected_dtype.to_pandas_dtype()		).max + 1
    change_first_primitive_element_in_list(__a	,    __a		)
    lowerCamelCase__:      Optional[Any]			  =pa.array(OptimizedTypedSequence(__a	,    col=__a		)		)
    assert get_base_dtype(arr.type		) == pa.intaa()





@pytest.mark.parametrize("raise_exception"	,    [False, True]		)
def      lowerCAmelCase_ (       __a	,    __a		)     ->		Tuple:







  """simple docstring"""





  lowerCamelCase__:      str			  =str(tmp_path / "dataset-train.arrow"		)
  try:
    with ArrowWriter(path=__a		) as writer:
      if raise_exception:
        raise pa.lib.ArrowInvalid()
      else:
        writer.stream.close()
  except pa.lib.ArrowInvalid:
    pass
  finally:
    assert writer.stream.closed





def      lowerCAmelCase_ (       __a		)     ->		List[str]:







  """simple docstring"""





  lowerCamelCase__:      str			  ="mock://dataset-train.arrow"
  with ArrowWriter(path=__a	,    storage_options=mockfs.storage_options		) as writer:
    assert isinstance(writer._fs	,    type(__a		)		)
    assert writer._fs.storage_options == mockfs.storage_options
    writer.write({"col_1": "foo", "col_2": 1}		)
    writer.write({"col_1": "bar", "col_2": 2}		)
    lowerCamelCase__  ,					lowerCamelCase__:      str			  =writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert mockfs.exists(__a		)





def      lowerCAmelCase_ (       )     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      Union[str, Any]			  =pa.BufferOutputStream()
  with ParquetWriter(stream=__a		) as writer:
    writer.write({"col_1": "foo", "col_2": 1}		)
    writer.write({"col_1": "bar", "col_2": 2}		)
    lowerCamelCase__  ,					lowerCamelCase__:      Optional[Any]			  =writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  lowerCamelCase__:      Tuple			  =pa.BufferReader(output.getvalue()		)
  lowerCamelCase__:      pa.Table			  =pq.read_table(__a		)
  assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}





@require_pil
@pytest.mark.parametrize("embed_local_files"	,    [False, True]		)
def      lowerCAmelCase_ (       __a	,    __a		)     ->		Any:







  """simple docstring"""





  import PIL.Image

  lowerCamelCase__:      Tuple			  =str(tmp_path / "test_image_rgb.jpg"		)
  PIL.Image.fromarray(np.zeros((5, 5)	,    dtype=np.uinta		)		).save(__a	,    format="png"		)
  lowerCamelCase__:      Union[str, Any]			  =pa.BufferOutputStream()
  with ParquetWriter(
      stream=__a	,    features=Features({"image": Image()}		)	,    embed_local_files=__a		) as writer:
    writer.write({"image": image_path}		)
    writer.finalize()
  lowerCamelCase__:      Any			  =pa.BufferReader(output.getvalue()		)
  lowerCamelCase__:      pa.Table			  =pq.read_table(__a		)
  lowerCamelCase__:      Tuple			  =pa_table.to_pydict()
  if embed_local_files:
    assert isinstance(out["image"][0]["path"]	,    __a		)
    with open(__a	,    "rb"		) as f:
      assert out["image"][0]["bytes"] == f.read()
  else:
    assert out["image"][0]["path"] == image_path
    assert out["image"][0]["bytes"] is None






def      lowerCAmelCase_ (       )     ->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__:      Union[str, Any]			  =pa.schema([pa.field("col_1"	,    pa.string()	,    nullable=__a		)]		)
  lowerCamelCase__:      Dict			  =pa.BufferOutputStream()
  with ArrowWriter(stream=__a		) as writer:
    writer._build_writer(inferred_schema=__a		)
    assert writer._schema == pa.schema([pa.field("col_1"	,    pa.string()		)]		)



'''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 UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     int							,_a      :     Any							,_a      :     Optional[int]=2							,_a      :     Optional[Any]=True							,_a      :     Dict=False							,_a      :     Dict=10							,_a      :     Any=3							,_a      :     str=32 * 8							,_a      :     Optional[int]=32 * 8							,_a      :     int=4							,_a      :     str=64							,):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	Tuple			=   floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
         _a       )

     _a				:	Optional[Any]			=   torch.ones([self.batch_size, self.min_size, self.max_size]							,device=_a       )

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

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


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	int			=   MaskaFormerConfig(
         hidden_size=self.hidden_dim							,)
     _a				:	str			=   self.num_queries
     _a				:	Union[str, Any]			=   self.num_labels

     _a				:	Tuple			=   [1, 1, 1, 1]
     _a				:	Dict			=   self.num_channels

     _a				:	str			=   64
     _a				:	Tuple			=   128
     _a				:	Optional[Any]			=   self.hidden_dim
     _a				:	Union[str, Any]			=   self.hidden_dim
     _a				:	List[Any]			=   self.hidden_dim
     return config


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     List[str]							,_a      :     Optional[Any]							,_a      :     str       ):


     '''simple docstring'''






     _a				:	str			=   output.encoder_hidden_states
     _a				:	Any			=   output.pixel_decoder_hidden_states
     _a				:	Optional[Any]			=   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   __lowercase		(		self      :     List[str]							,_a      :     str							,_a      :     List[Any]							,_a      :     Any							,_a      :     Union[str, Any]=False       ):


     '''simple docstring'''






     with torch.no_grad():
        _a				:	str			=   MaskaFormerModel(config=_a       )
        model.to(_a       )
        model.eval()

        _a				:	Any			=   model(pixel_values=_a							,pixel_mask=_a       )
        _a				:	Optional[Any]			=   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   __lowercase		(		self      :     Tuple							,_a      :     List[Any]							,_a      :     Union[str, Any]							,_a      :     Tuple							,_a      :     List[str]							,_a      :     Any       ):


     '''simple docstring'''






     _a				:	int			=   MaskaFormerForUniversalSegmentation(config=_a       )
     model.to(_a       )
     model.eval()

     def comm_check_on_output(_a      :     Any       ):
        # 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():
        _a				:	Any			=   model(pixel_values=_a							,pixel_mask=_a       )
        _a				:	Optional[int]			=   model(_a       )

        comm_check_on_output(_a       )

        _a				:	List[str]			=   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 UpperCAmelCase__	( lowercase__    ,				lowercase__    ,				unittest.TestCase	):





  """simple docstring"""

  __UpperCAmelCase				:				Optional[int]   =					(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
  __UpperCAmelCase				:				Dict   =					{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

  __UpperCAmelCase				:				Dict   =					False
  __UpperCAmelCase				:				Tuple   =					False
  __UpperCAmelCase				:				Dict   =					False
  __UpperCAmelCase				:				List[Any]   =					False


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   MaskaFormerModelTester(self       )
     _a				:	Dict			=   ConfigTester(self							,config_class=_a							,has_text_modality=_a       )


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     self.config_tester.run_common_tests()


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a,  _a				:	List[str]			=   self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskaformer_model(_a							,**_a							,output_hidden_states=_a       )


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     _a				:	Dict			=   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   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     pass


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


     '''simple docstring'''






     pass


  @unittest.skip(reason='Mask2Former is not a generative model'       )
  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     pass


  @unittest.skip(reason='Mask2Former does not use token embeddings'       )
  def   __lowercase		(		self      :     Optional[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   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     pass


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


     '''simple docstring'''






     pass


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a,  _a				:	Any			=   self.model_tester.prepare_config_and_inputs_for_common()

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

        _a				:	List[Any]			=   ['pixel_values']
        self.assertListEqual(arg_names[:1]							,_a       )


  @slow
  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     _a				:	int			=   (self.model_tester.min_size,) * 2
     _a				:	Any			=   {
         '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(),
     }
     _a				:	List[Any]			=   self.model_tester.get_config()

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


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a,  _a				:	List[Any]			=   self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskaformer_model(_a							,**_a							,output_hidden_states=_a       )


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a,  _a				:	Dict			=   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        _a				:	Any			=   model_class(_a       ).to(_a       )
        _a				:	Optional[int]			=   model(**_a							,output_attentions=_a       )
        self.assertTrue(outputs.attentions is not None       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     if not self.model_tester.is_training:
        return

     _a				:	List[str]			=   self.all_model_classes[1]
     _a,  _a,  _a,  _a,  _a				:	List[str]			=   self.model_tester.prepare_config_and_inputs()

     _a				:	Any			=   model_class(_a       )
     model.to(_a       )
     model.train()

     _a				:	Union[str, Any]			=   model(_a							,mask_labels=_a							,class_labels=_a       ).loss
     loss.backward()


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	int			=   self.all_model_classes[1]
     _a,  _a,  _a,  _a,  _a				:	List[Any]			=   self.model_tester.prepare_config_and_inputs()
     _a				:	str			=   True
     _a				:	str			=   True

     _a				:	List[str]			=   model_class(_a       ).to(_a       )
     model.train()

     _a				:	Optional[int]			=   model(_a							,mask_labels=_a							,class_labels=_a       )

     _a				:	Tuple			=   outputs.encoder_hidden_states[0]
     encoder_hidden_states.retain_grad()

     _a				:	str			=   outputs.pixel_decoder_hidden_states[0]
     pixel_decoder_hidden_states.retain_grad()

     _a				:	Dict			=   outputs.transformer_decoder_hidden_states[0]
     transformer_decoder_hidden_states.retain_grad()

     _a				:	List[str]			=   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       )


__lowerCAmelCase         =    1e-4





def 					UpperCAmelCase_		():






   """simple docstring"""

   _a				:	int			=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
   return image







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





  """simple docstring"""

  @cached_property
  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






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


  @cached_property
  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	List[str]			=   MaskaFormerModel.from_pretrained(self.model_checkpoints       ).to(_a       )
     _a				:	int			=   self.default_image_processor
     _a				:	Tuple			=   prepare_img()
     _a				:	Any			=   image_processor(_a							,return_tensors='pt'       ).to(_a       )
     _a				:	Union[str, 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(_a							,(1, 3, 384, 384)       )

     with torch.no_grad():
        _a				:	Optional[Any]			=   model(**_a       )

     _a				:	List[Any]			=   torch.tensor(
         [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]       ).to(_a       )
     self.assertTrue(
         torch.allclose(
             outputs.encoder_last_hidden_state[0, 0, :3, :3]							,_a							,atol=_a       )       )

     _a				:	str			=   torch.tensor(
         [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]       ).to(_a       )
     self.assertTrue(
         torch.allclose(
             outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]							,_a							,atol=_a       )       )

     _a				:	Any			=   torch.tensor(
         [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]       ).to(_a       )
     self.assertTrue(
         torch.allclose(
             outputs.transformer_decoder_last_hidden_state[0, :3, :3]							,_a							,atol=_a       )       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[Any]			=   MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(_a       ).eval()
     _a				:	Optional[Any]			=   self.default_image_processor
     _a				:	List[Any]			=   prepare_img()
     _a				:	str			=   image_processor(_a							,return_tensors='pt'       ).to(_a       )
     _a				:	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(_a							,(1, 3, 384, 384)       )

     with torch.no_grad():
        _a				:	Optional[int]			=   model(**_a       )
     # masks_queries_logits
     _a				:	Dict			=   outputs.masks_queries_logits
     self.assertEqual(
         masks_queries_logits.shape							,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       )
     _a				:	Dict			=   [
         [-8.7839, -9.0056, -8.8121],
         [-7.4104, -7.0313, -6.5401],
         [-6.6105, -6.3427, -6.4675],
     ]
     _a				:	Optional[Any]			=   torch.tensor(_a       ).to(_a       )
     self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]							,_a							,atol=_a       )       )
     # class_queries_logits
     _a				:	str			=   outputs.class_queries_logits
     self.assertEqual(class_queries_logits.shape							,(1, model.config.num_queries, model.config.num_labels + 1)       )
     _a				:	str			=   torch.tensor(
         [
             [1.8324, -8.0835, -4.1922],
             [0.8450, -9.0050, -3.6053],
             [0.3045, -7.7293, -3.0275],
         ]       ).to(_a       )
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]							,_a							,atol=_a       )       )


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	Any			=   MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(_a       ).eval()
     _a				:	Tuple			=   self.default_image_processor

     _a				:	Tuple			=   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'							,)

     _a				:	str			=   inputs['pixel_values'].to(_a       )
     _a				:	str			=   [el.to(_a       ) for el in inputs['mask_labels']]
     _a				:	Dict			=   [el.to(_a       ) for el in inputs['class_labels']]

     with torch.no_grad():
        _a				:	List[str]			=   model(**_a       )

     self.assertTrue(outputs.loss is not None       )



