

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   _a					(			snake_case_				):




      """simple docstring"""

      def 		__A	(   self							:				Tuple	):
            A_						  =				pa.array(TypedSequence([1, 2, 3]	)	)
            self.assertEqual(arr.type						,			pa.intaa()	)





      def 		__A	(   self							:				int	):
            with self.assertRaises(UpperCAmelCase	):
                  A_						  =				pa.array(TypedSequence([1, 2, 3]	)						,			type=pa.intaa()	)





      def 		__A	(   self							:				Optional[int]	):
            with self.assertRaises(UpperCAmelCase	):
                  A_						  =				pa.array(TypedSequence([1, 2, 3]						,			try_type=Value("bool"	)						,			type=Value("int64"	)	)	)





      def 		__A	(   self							:				Optional[Any]	):
            A_						  =				pa.array(TypedSequence([1, 2, 3]						,			type=Value("int32"	)	)	)
            self.assertEqual(arr.type						,			pa.intaa()	)





      def 		__A	(   self							:				int	):
            with self.assertRaises((TypeError, pa.lib.ArrowInvalid)	):
                  A_						  =				pa.array(TypedSequence(["foo", "bar"]						,			type=Value("int64"	)	)	)





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





      def 		__A	(   self							:				Optional[int]	):
            A_						  =				pa.array(TypedSequence(["foo", "bar"]						,			try_type=Value("int64"	)	)	)
            self.assertEqual(arr.type						,			pa.string()	)





      def 		__A	(   self							:				Tuple	):
            A_						  =				pa.array(TypedSequence([[[1, 2, 3]]]						,			type=ArrayaD((1, 3)						,			"int64"	)	)	)
            self.assertEqual(arr.type						,			ArrayaDExtensionType((1, 3)						,			"int64"	)	)





      def 		__A	(   self							:				Optional[Any]	):
            with self.assertRaises((TypeError, pa.lib.ArrowInvalid)	):
                  A_						  =				pa.array(TypedSequence(["foo", "bar"]						,			type=ArrayaD((1, 3)						,			"int64"	)	)	)





      def 		__A	(   self							:				List[str]	):
            A_						  =				pa.array(TypedSequence([[[1, 2, 3]]]						,			try_type=ArrayaD((1, 3)						,			"int64"	)	)	)
            self.assertEqual(arr.type						,			ArrayaDExtensionType((1, 3)						,			"int64"	)	)





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







      @require_pil
      def 		__A	(   self							:				int	):
            import PIL.Image

            A_						  =				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:
                  A_						  =				pa.array(TypedSequence([{"path": None, "bytes": B"image_bytes"}, pil_image]						,			type=Image()	)	)
                  A_  ,     A_						  =				mock_cast_to_python_objects.call_args_list[-1]
                  self.assertIn("optimize_list_casting"						,			UpperCAmelCase	)
                  self.assertFalse(kwargs["optimize_list_casting"]	)




def       __snake_case						( __UpperCamelCase	: str				,__UpperCamelCase	: int    ):



      """simple docstring"""

      A_						  =				pa.BufferReader(__UpperCamelCase    ) if isinstance(__UpperCamelCase				,pa.Buffer    ) else pa.memory_map(__UpperCamelCase    )
      A_						  =				pa.ipc.open_stream(__UpperCamelCase    )
      A_						  =				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       __snake_case						( __UpperCamelCase	: Union[str, Any]				,__UpperCamelCase	: List[str]    ):



      """simple docstring"""

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

def       __snake_case						( ):



      """simple docstring"""

      A_						  =				pa.BufferOutputStream()
      A_						  =				Features({"labels": ClassLabel(names=["neg", "pos"]    )}    )
      with ArrowWriter(stream=__UpperCamelCase				,features=__UpperCamelCase    ) as writer:
            writer.write({"labels": 0}    )
            writer.write({"labels": 1}    )
            A_  ,     A_						  =				writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      assert writer._schema == features.arrow_schema
      assert writer._schema.metadata == features.arrow_schema.metadata
      A_						  =				pa.BufferReader(output.getvalue()    )
      A_						  =				pa.ipc.open_stream(__UpperCamelCase    )
      A_						  =				f.read_all()
      A_						  =				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(__UpperCamelCase    )

@pytest.mark.parametrize("writer_batch_size"				,[None, 1, 10]    )
def       __snake_case						( __UpperCamelCase	: List[str]    ):



      """simple docstring"""

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

@pytest.mark.parametrize("writer_batch_size"				,[None, 2, 10]    )
def       __snake_case						( __UpperCamelCase	: Tuple    ):



      """simple docstring"""

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

@pytest.mark.parametrize("writer_batch_size"				,[None, 2, 10]    )
def       __snake_case						( __UpperCamelCase	: List[Any]    ):



      """simple docstring"""

      A_						  =				pa.BufferOutputStream()
      with ArrowWriter(
          stream=__UpperCamelCase				,writer_batch_size=__UpperCamelCase				,hash_salt="split_name"				,check_duplicates=__UpperCamelCase				,) as writer:
            writer.write({"col_1": "foo", "col_2": 1}				,key=1    )
            writer.write({"col_1": "bar", "col_2": 2}				,key=2    )
            A_  ,     A_						  =				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       __snake_case						( __UpperCamelCase	: Optional[int]				,__UpperCamelCase	: int    ):



      """simple docstring"""

      A_						  =				pa.BufferOutputStream()
      A_						  =				pa.schema(__UpperCamelCase    ) if fields else None
      with ArrowWriter(stream=__UpperCamelCase				,schema=__UpperCamelCase				,writer_batch_size=__UpperCamelCase    ) as writer:
            writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}    )
            writer.write_batch({"col_1": [], "col_2": []}    )
            A_  ,     A_						  =				writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            A_						  =				{"col_1": pa.string(), "col_2": pa.intaa()}
      assert writer._schema == pa.schema(__UpperCamelCase				,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       __snake_case						( __UpperCamelCase	: str				,__UpperCamelCase	: Tuple    ):



      """simple docstring"""

      A_						  =				pa.BufferOutputStream()
      A_						  =				pa.schema(__UpperCamelCase    ) if fields else None
      with ArrowWriter(stream=__UpperCamelCase				,schema=__UpperCamelCase				,writer_batch_size=__UpperCamelCase    ) as writer:
            writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}    )    )
            A_  ,     A_						  =				writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            A_						  =				{"col_1": pa.string(), "col_2": pa.intaa()}
      assert writer._schema == pa.schema(__UpperCamelCase				,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       __snake_case						( __UpperCamelCase	: Any				,__UpperCamelCase	: Tuple    ):



      """simple docstring"""

      A_						  =				pa.BufferOutputStream()
      A_						  =				pa.schema(__UpperCamelCase    ) if fields else None
      with ArrowWriter(stream=__UpperCamelCase				,schema=__UpperCamelCase				,writer_batch_size=__UpperCamelCase    ) 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]}    )    )
            A_  ,     A_						  =				writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            A_						  =				{"col_1": pa.string(), "col_2": pa.intaa()}
      assert writer._schema == pa.schema(__UpperCamelCase				,metadata=writer._schema.metadata    )
      _check_output(output.getvalue()				,expected_num_chunks=num_examples if writer_batch_size == 1 else 1    )

def       __snake_case						( ):



      """simple docstring"""

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

def       __snake_case						( __UpperCamelCase	: Optional[int]    ):



      """simple docstring"""

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

def       __snake_case						( __UpperCamelCase	: Tuple				,__UpperCamelCase	: Dict    ):



      """simple docstring"""

      if isinstance(lst[0]				,__UpperCamelCase    ):
            change_first_primitive_element_in_list(lst[0]				,__UpperCamelCase    )
      else:
            A_						  =				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       __snake_case						( __UpperCamelCase	: Optional[Any]				,__UpperCamelCase	: Dict				,__UpperCamelCase	: str    ):



      """simple docstring"""

      A_						  =				pa.array(TypedSequence(__UpperCamelCase				,optimized_int_type=__UpperCamelCase    )    )
      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       __snake_case						( __UpperCamelCase	: List[str]				,__UpperCamelCase	: Tuple				,__UpperCamelCase	: Optional[int]    ):



      """simple docstring"""

      A_						  =				pa.array(OptimizedTypedSequence(__UpperCamelCase				,col=__UpperCamelCase    )    )
      assert get_base_dtype(arr.type    ) == expected_dtype

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

@pytest.mark.parametrize("raise_exception"				,[False, True]    )
def       __snake_case						( __UpperCamelCase	: Optional[int]				,__UpperCamelCase	: List[Any]    ):



      """simple docstring"""

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

def       __snake_case						( __UpperCamelCase	: Dict    ):



      """simple docstring"""

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

def       __snake_case						( ):



      """simple docstring"""

      A_						  =				pa.BufferOutputStream()
      with ParquetWriter(stream=__UpperCamelCase    ) as writer:
            writer.write({"col_1": "foo", "col_2": 1}    )
            writer.write({"col_1": "bar", "col_2": 2}    )
            A_  ,     A_						  =				writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      A_						  =				pa.BufferReader(output.getvalue()    )
      A_						  =				pq.read_table(__UpperCamelCase    )
      assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}

@require_pil
@pytest.mark.parametrize("embed_local_files"				,[False, True]    )
def       __snake_case						( __UpperCamelCase	: Any				,__UpperCamelCase	: str    ):



      """simple docstring"""

      import PIL.Image

      A_						  =				str(tmp_path / "test_image_rgb.jpg"    )
      PIL.Image.fromarray(np.zeros((5, 5)				,dtype=np.uinta    )    ).save(__UpperCamelCase				,format="png"    )
      A_						  =				pa.BufferOutputStream()
      with ParquetWriter(
          stream=__UpperCamelCase				,features=Features({"image": Image()}    )				,embed_local_files=__UpperCamelCase    ) as writer:
            writer.write({"image": image_path}    )
            writer.finalize()
      A_						  =				pa.BufferReader(output.getvalue()    )
      A_						  =				pq.read_table(__UpperCamelCase    )
      A_						  =				pa_table.to_pydict()
      if embed_local_files:
            assert isinstance(out["image"][0]["path"]				,__UpperCamelCase    )
            with open(__UpperCamelCase				,"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       __snake_case						( ):



      """simple docstring"""

      A_						  =				pa.schema([pa.field("col_1"				,pa.string()				,nullable=__UpperCamelCase    )]    )
      A_						  =				pa.BufferOutputStream()
      with ArrowWriter(stream=__UpperCamelCase    ) as writer:
            writer._build_writer(inferred_schema=__UpperCamelCase    )
            assert writer._schema == pa.schema([pa.field("col_1"				,pa.string()    )]    )




"""simple docstring"""






import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


snake_case   					=				logging.get_logger(__name__)

snake_case   					=				OrderedDict(
    [
        ('''align''', '''EfficientNetImageProcessor'''),
        ('''beit''', '''BeitImageProcessor'''),
        ('''bit''', '''BitImageProcessor'''),
        ('''blip''', '''BlipImageProcessor'''),
        ('''blip-2''', '''BlipImageProcessor'''),
        ('''bridgetower''', '''BridgeTowerImageProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPImageProcessor'''),
        ('''clip''', '''CLIPImageProcessor'''),
        ('''clipseg''', '''ViTImageProcessor'''),
        ('''conditional_detr''', '''ConditionalDetrImageProcessor'''),
        ('''convnext''', '''ConvNextImageProcessor'''),
        ('''convnextv2''', '''ConvNextImageProcessor'''),
        ('''cvt''', '''ConvNextImageProcessor'''),
        ('''data2vec-vision''', '''BeitImageProcessor'''),
        ('''deformable_detr''', '''DeformableDetrImageProcessor'''),
        ('''deit''', '''DeiTImageProcessor'''),
        ('''deta''', '''DetaImageProcessor'''),
        ('''detr''', '''DetrImageProcessor'''),
        ('''dinat''', '''ViTImageProcessor'''),
        ('''donut-swin''', '''DonutImageProcessor'''),
        ('''dpt''', '''DPTImageProcessor'''),
        ('''efficientformer''', '''EfficientFormerImageProcessor'''),
        ('''efficientnet''', '''EfficientNetImageProcessor'''),
        ('''flava''', '''FlavaImageProcessor'''),
        ('''focalnet''', '''BitImageProcessor'''),
        ('''git''', '''CLIPImageProcessor'''),
        ('''glpn''', '''GLPNImageProcessor'''),
        ('''groupvit''', '''CLIPImageProcessor'''),
        ('''imagegpt''', '''ImageGPTImageProcessor'''),
        ('''instructblip''', '''BlipImageProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2ImageProcessor'''),
        ('''layoutlmv3''', '''LayoutLMv3ImageProcessor'''),
        ('''levit''', '''LevitImageProcessor'''),
        ('''mask2former''', '''Mask2FormerImageProcessor'''),
        ('''maskformer''', '''MaskFormerImageProcessor'''),
        ('''mgp-str''', '''ViTImageProcessor'''),
        ('''mobilenet_v1''', '''MobileNetV1ImageProcessor'''),
        ('''mobilenet_v2''', '''MobileNetV2ImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevitv2''', '''MobileViTImageProcessor'''),
        ('''nat''', '''ViTImageProcessor'''),
        ('''oneformer''', '''OneFormerImageProcessor'''),
        ('''owlvit''', '''OwlViTImageProcessor'''),
        ('''perceiver''', '''PerceiverImageProcessor'''),
        ('''pix2struct''', '''Pix2StructImageProcessor'''),
        ('''poolformer''', '''PoolFormerImageProcessor'''),
        ('''regnet''', '''ConvNextImageProcessor'''),
        ('''resnet''', '''ConvNextImageProcessor'''),
        ('''sam''', '''SamImageProcessor'''),
        ('''segformer''', '''SegformerImageProcessor'''),
        ('''swiftformer''', '''ViTImageProcessor'''),
        ('''swin''', '''ViTImageProcessor'''),
        ('''swin2sr''', '''Swin2SRImageProcessor'''),
        ('''swinv2''', '''ViTImageProcessor'''),
        ('''table-transformer''', '''DetrImageProcessor'''),
        ('''timesformer''', '''VideoMAEImageProcessor'''),
        ('''tvlt''', '''TvltImageProcessor'''),
        ('''upernet''', '''SegformerImageProcessor'''),
        ('''van''', '''ConvNextImageProcessor'''),
        ('''videomae''', '''VideoMAEImageProcessor'''),
        ('''vilt''', '''ViltImageProcessor'''),
        ('''vit''', '''ViTImageProcessor'''),
        ('''vit_hybrid''', '''ViTHybridImageProcessor'''),
        ('''vit_mae''', '''ViTImageProcessor'''),
        ('''vit_msn''', '''ViTImageProcessor'''),
        ('''xclip''', '''CLIPImageProcessor'''),
        ('''yolos''', '''YolosImageProcessor'''),
    ]
)

snake_case   					=				_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)




def 			snake_case		(		lowerCAmelCase_	)						->				str:
  for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
    if class_name in extractors:
      _snake_case   	=      model_type_to_module_name(lowerCAmelCase_	)

      _snake_case   	=      importlib.import_module(f""".{module_name}"""  ,						'''transformers.models'''	)
      try:
        return getattr(lowerCAmelCase_  ,						lowerCAmelCase_	)
      except AttributeError:
        continue

  for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
    if getattr(lowerCAmelCase_  ,						'''__name__'''  ,						lowerCAmelCase_	) == class_name:
      return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
  _snake_case   	=      importlib.import_module('''transformers'''	)
  if hasattr(lowerCAmelCase_  ,						lowerCAmelCase_	):
    return getattr(lowerCAmelCase_  ,						lowerCAmelCase_	)

  return None




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = False  ,						lowerCAmelCase_ = False  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = False  ,						**lowerCAmelCase_  ,						)						->				int:
  _snake_case   	=      get_file_from_repo(
      lowerCAmelCase_  ,						lowerCAmelCase_  ,						cache_dir=lowerCAmelCase_  ,						force_download=lowerCAmelCase_  ,						resume_download=lowerCAmelCase_  ,						proxies=lowerCAmelCase_  ,						use_auth_token=lowerCAmelCase_  ,						revision=lowerCAmelCase_  ,						local_files_only=lowerCAmelCase_  ,						)
  if resolved_config_file is None:
    logger.info(
        '''Could not locate the image processor configuration file, will try to use the model config instead.'''	)
    return {}

  with open(lowerCAmelCase_  ,						encoding='''utf-8'''	) as reader:
    return json.load(lowerCAmelCase_	)







class     UpperCAmelCase    :



   def __init__(       self    :	Any				):



     """simple docstring"""
     raise EnvironmentError(
         '''AutoImageProcessor is designed to be instantiated '''
         '''using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''				)



   @classmethod
   @replace_list_option_in_docstrings(__lowerCamelCase				)
   def    __UpperCAmelCase    (       cls    :	List[str]							,       __lowerCamelCase    :	List[str]							,       **__lowerCamelCase    :	Dict				):



     """simple docstring"""
     _snake_case   	=      kwargs.pop('''config'''							,       __lowerCamelCase				)
     _snake_case   	=      kwargs.pop('''trust_remote_code'''							,       __lowerCamelCase				)
     _snake_case   	=      True

     _snake_case	,     _snake_case   	=      ImageProcessingMixin.get_image_processor_dict(__lowerCamelCase							,       **__lowerCamelCase				)
     _snake_case   	=      config_dict.get('''image_processor_type'''							,       __lowerCamelCase				)
     _snake_case   	=      None
     if "AutoImageProcessor" in config_dict.get('''auto_map'''							,       {}				):
       _snake_case   	=      config_dict['''auto_map''']['''AutoImageProcessor''']

     # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
     # and if so, infer the image processor class from there.
     if image_processor_class is None and image_processor_auto_map is None:
       _snake_case   	=      config_dict.pop('''feature_extractor_type'''							,       __lowerCamelCase				)
       if feature_extractor_class is not None:
         logger.warning(
             '''Could not find image processor class in the image processor config or the model config. Loading'''
             ''' based on pattern matching with the model\'s feature extractor configuration.'''				)
         _snake_case   	=      feature_extractor_class.replace('''FeatureExtractor'''							,       '''ImageProcessor'''				)
       if "AutoFeatureExtractor" in config_dict.get('''auto_map'''							,       {}				):
         _snake_case   	=      config_dict['''auto_map''']['''AutoFeatureExtractor''']
         _snake_case   	=      feature_extractor_auto_map.replace('''FeatureExtractor'''							,       '''ImageProcessor'''				)
         logger.warning(
             '''Could not find image processor auto map in the image processor config or the model config.'''
             ''' Loading based on pattern matching with the model\'s feature extractor configuration.'''				)

        # If we don't find the image processor class in the image processor config, let's try the model config.
     if image_processor_class is None and image_processor_auto_map is None:
       if not isinstance(__lowerCamelCase							,       __lowerCamelCase				):
         _snake_case   	=      AutoConfig.from_pretrained(__lowerCamelCase							,       **__lowerCamelCase				)
       # It could be in `config.image_processor_type``
       _snake_case   	=      getattr(__lowerCamelCase							,       '''image_processor_type'''							,       __lowerCamelCase				)
       if hasattr(__lowerCamelCase							,       '''auto_map'''				) and "AutoImageProcessor" in config.auto_map:
         _snake_case   	=      config.auto_map['''AutoImageProcessor''']

     if image_processor_class is not None:
       _snake_case   	=      image_processor_class_from_name(__lowerCamelCase				)

     _snake_case   	=      image_processor_auto_map is not None
     _snake_case   	=      image_processor_class is not None or type(__lowerCamelCase				) in IMAGE_PROCESSOR_MAPPING
     _snake_case   	=      resolve_trust_remote_code(
         __lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)

     if has_remote_code and trust_remote_code:
       _snake_case   	=      get_class_from_dynamic_module(
           __lowerCamelCase							,       __lowerCamelCase							,       **__lowerCamelCase				)
       _snake_case   	=      kwargs.pop('''code_revision'''							,       __lowerCamelCase				)
       if os.path.isdir(__lowerCamelCase				):
         image_processor_class.register_for_auto_class()
       return image_processor_class.from_dict(__lowerCamelCase							,       **__lowerCamelCase				)
     elif image_processor_class is not None:
       return image_processor_class.from_dict(__lowerCamelCase							,       **__lowerCamelCase				)
     # Last try: we use the IMAGE_PROCESSOR_MAPPING.
     elif type(__lowerCamelCase				) in IMAGE_PROCESSOR_MAPPING:
       _snake_case   	=      IMAGE_PROCESSOR_MAPPING[type(__lowerCamelCase				)]
       return image_processor_class.from_dict(__lowerCamelCase							,       **__lowerCamelCase				)

     raise ValueError(
         f"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
         f"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
         f"""`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()				)}"""				)





   @staticmethod
   def    __UpperCAmelCase    (       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     IMAGE_PROCESSOR_MAPPING.register(__lowerCamelCase							,       __lowerCamelCase				)





