



"""simple docstring"""

import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union

from .. import config
from .filelock import FileLock
from .logging import get_logger


lowercase__					:						List[str]												=			get_logger(__name__)




class  _UpperCAmelCase :

 def __init__(  self	: int						,  lowercase_	: Optional[str] = None       ):
     snake_case_				:  Any       =			(
         os.path.join(lowercase_						,  config.EXTRACTED_DATASETS_DIR       ) if cache_dir else config.EXTRACTED_DATASETS_PATH
     )
     snake_case_				:  Union[str, Any]       =			Extractor

 def 		_snake_case      (  self	: Union[str, Any]						,  lowercase_	: str       ):
     from .file_utils import hash_url_to_filename

     # Path where we extract compressed archives
     # We extract in the cache dir, and get the extracted path name by hashing the original path"
     snake_case_				:  Dict       =			os.path.abspath(lowercase_       )
     return os.path.join(self.extract_dir						,  hash_url_to_filename(lowercase_       )       )

 def 		_snake_case      (  self	: Dict						,  lowercase_	: str						,  lowercase_	: bool       ):
     return force_extract or (
         not os.path.isfile(lowercase_       ) and not (os.path.isdir(lowercase_       ) and os.listdir(lowercase_       ))
     )






 def 		_snake_case      (  self	: Any						,  lowercase_	: str						,  lowercase_	: bool = False       ):
     snake_case_				:  List[str]       =			self.extractor.infer_extractor_format(lowercase_       )
     if not extractor_format:
         return input_path
     snake_case_				:  Tuple       =			self._get_output_path(lowercase_       )
     if self._do_extract(lowercase_						,  lowercase_       ):
         self.extractor.extract(lowercase_						,  lowercase_						,  lowercase_       )
     return output_path





class  _UpperCAmelCase (    lowerCAmelCase__):

 @classmethod
 @abstractmethod
 def 		_snake_case      (  cls	: Union[str, Any]						,  lowercase_	: Union[Path, str]						,  **lowercase_	: List[str]       ):
     ...






 @staticmethod
 @abstractmethod
 def 		_snake_case      (  lowercase_	: Union[Path, str]						,  lowercase_	: Union[Path, str]       ):
     ...





class  _UpperCAmelCase (    lowerCAmelCase__					,  lowerCAmelCase__):
 _lowerCAmelCase     :    List[bytes]	      =	[]

 @staticmethod
 def 		_snake_case      (  lowercase_	: Union[Path, str]						,  lowercase_	: int       ):
     with open(lowercase_						,  '''rb'''       ) as f:
         return f.read(lowercase_       )






 @classmethod
 def 		_snake_case      (  cls	: List[Any]						,  lowercase_	: Union[Path, str]						,  lowercase_	: bytes = b""       ):
     if not magic_number:
         snake_case_				:  List[str]       =			max(len(lowercase_       ) for cls_magic_number in cls.magic_numbers       )
         try:
             snake_case_				:  Any       =			cls.read_magic_number(lowercase_						,  lowercase_       )
         except OSError:
             return False
     return any(magic_number.startswith(lowercase_       ) for cls_magic_number in cls.magic_numbers       )





class  _UpperCAmelCase (    lowerCAmelCase__):

 @classmethod
 def 		_snake_case      (  cls	: List[str]						,  lowercase_	: Union[Path, str]						,  **lowercase_	: Union[str, Any]       ):
     return tarfile.is_tarfile(lowercase_       )

 @staticmethod
 def 		_snake_case      (  lowercase_	: List[str]						,  lowercase_	: List[Any]       ):

     def resolved(lowercase_	: str       ) -> str:
         return os.path.realpath(os.path.abspath(lowercase_       )       )

     def badpath(lowercase_	: str						,  lowercase_	: str       ) -> bool:
         # joinpath will ignore base if path is absolute
         return not resolved(os.path.join(lowercase_						,  lowercase_       )       ).startswith(lowercase_       )

     def badlink(lowercase_	: Optional[Any]						,  lowercase_	: str       ) -> bool:
         # Links are interpreted relative to the directory containing the link
         snake_case_				:  Union[str, Any]       =			resolved(os.path.join(lowercase_						,  os.path.dirname(info.name       )       )       )
         return badpath(info.linkname						,  base=lowercase_       )

     snake_case_				:  Dict       =			resolved(lowercase_       )

     for finfo in members:
         if badpath(finfo.name						,  lowercase_       ):
             logger.error(f"Extraction of {finfo.name} is blocked (illegal path)"       )
         elif finfo.issym() and badlink(lowercase_						,  lowercase_       ):
             logger.error(f"Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}"       )
         elif finfo.islnk() and badlink(lowercase_						,  lowercase_       ):
             logger.error(f"Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}"       )
         else:
             yield finfo






 @staticmethod
 def 		_snake_case      (  lowercase_	: Union[Path, str]						,  lowercase_	: Union[Path, str]       ):
     os.makedirs(lowercase_						,  exist_ok=lowercase_       )
     snake_case_				:  Optional[int]       =			tarfile.open(lowercase_       )
     tar_file.extractall(lowercase_						,  members=TarExtractor.safemembers(lowercase_						,  lowercase_       )       )
     tar_file.close()





class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Any	      =	[B"""\x1F\x8B"""]

 @staticmethod
 def 		_snake_case      (  lowercase_	: Union[Path, str]						,  lowercase_	: Union[Path, str]       ):
     with gzip.open(lowercase_						,  '''rb'''       ) as gzip_file:
         with open(lowercase_						,  '''wb'''       ) as extracted_file:
             shutil.copyfileobj(lowercase_						,  lowercase_       )





class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Optional[int]	      =	[
     B"""PK\x03\x04""",
     B"""PK\x05\x06""",  # empty archive
     B"""PK\x07\x08""",  # spanned archive
 ]

 @classmethod
 def 		_snake_case      (  cls	: Optional[Any]						,  lowercase_	: Union[Path, str]						,  lowercase_	: bytes = b""       ):
     if super().is_extractable(lowercase_						,  magic_number=lowercase_       ):
         return True
     try:
         # Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
         # From: https://github.com/python/cpython/pull/5053
         from zipfile import (
             _CD_SIGNATURE,
             _ECD_DISK_NUMBER,
             _ECD_DISK_START,
             _ECD_ENTRIES_TOTAL,
             _ECD_OFFSET,
             _ECD_SIZE,
             _EndRecData,
             sizeCentralDir,
             stringCentralDir,
             structCentralDir,
         )

         with open(lowercase_						,  '''rb'''       ) as fp:
             snake_case_				:  List[str]       =			_EndRecData(lowercase_       )
             if endrec:
                 if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
                     return True  # Empty zipfiles are still zipfiles
                 elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
                     fp.seek(endrec[_ECD_OFFSET]       )  # Central directory is on the same disk
                     if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
                         snake_case_				:  int       =			fp.read(lowercase_       )  # CD is where we expect it to be
                         if len(lowercase_       ) == sizeCentralDir:
                             snake_case_				:  Optional[int]       =			struct.unpack(lowercase_						,  lowercase_       )  # CD is the right size
                             if centdir[_CD_SIGNATURE] == stringCentralDir:
                                 return True  # First central directory entry  has correct magic number
         return False
     except Exception:  # catch all errors in case future python versions change the zipfile internals
         return False






 @staticmethod
 def 		_snake_case      (  lowercase_	: Union[Path, str]						,  lowercase_	: Union[Path, str]       ):
     os.makedirs(lowercase_						,  exist_ok=lowercase_       )
     with zipfile.ZipFile(lowercase_						,  '''r'''       ) as zip_file:
         zip_file.extractall(lowercase_       )
         zip_file.close()





class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    List[Any]	      =	[B"""\xFD\x37\x7A\x58\x5A\x00"""]

 @staticmethod
 def 		_snake_case      (  lowercase_	: Union[Path, str]						,  lowercase_	: Union[Path, str]       ):
     with lzma.open(lowercase_       ) as compressed_file:
         with open(lowercase_						,  '''wb'''       ) as extracted_file:
             shutil.copyfileobj(lowercase_						,  lowercase_       )





class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Any	      =	[B"""Rar!\x1a\x07\x00""", B"""Rar!\x1a\x07\x01\x00"""]  # RAR_ID  # RAR5_ID

 @staticmethod
 def 		_snake_case      (  lowercase_	: Union[Path, str]						,  lowercase_	: Union[Path, str]       ):
     if not config.RARFILE_AVAILABLE:
         raise ImportError('''Please pip install rarfile'''       )
     import rarfile

     os.makedirs(lowercase_						,  exist_ok=lowercase_       )
     snake_case_				:  List[str]       =			rarfile.RarFile(lowercase_       )
     rf.extractall(lowercase_       )
     rf.close()





class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Union[str, Any]	      =	[B"""\x28\xb5\x2F\xFD"""]

 @staticmethod
 def 		_snake_case      (  lowercase_	: Union[Path, str]						,  lowercase_	: Union[Path, str]       ):
     if not config.ZSTANDARD_AVAILABLE:
         raise ImportError('''Please pip install zstandard'''       )
     import zstandard as zstd

     snake_case_				:  int       =			zstd.ZstdDecompressor()
     with open(lowercase_						,  '''rb'''       ) as ifh, open(lowercase_						,  '''wb'''       ) as ofh:
         dctx.copy_stream(lowercase_						,  lowercase_       )





class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    List[str]	      =	[B"""\x42\x5A\x68"""]

 @staticmethod
 def 		_snake_case      (  lowercase_	: Union[Path, str]						,  lowercase_	: Union[Path, str]       ):
     with bza.open(lowercase_						,  '''rb'''       ) as compressed_file:
         with open(lowercase_						,  '''wb'''       ) as extracted_file:
             shutil.copyfileobj(lowercase_						,  lowercase_       )





class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    int	      =	[B"""\x37\x7A\xBC\xAF\x27\x1C"""]

 @staticmethod
 def 		_snake_case      (  lowercase_	: Union[Path, str]						,  lowercase_	: Union[Path, str]       ):
     if not config.PY7ZR_AVAILABLE:
         raise ImportError('''Please pip install py7zr'''       )
     import pyazr

     os.makedirs(lowercase_						,  exist_ok=lowercase_       )
     with pyazr.SevenZipFile(lowercase_						,  '''r'''       ) as archive:
         archive.extractall(lowercase_       )





class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    int	      =	[B"""\x04\x22\x4D\x18"""]

 @staticmethod
 def 		_snake_case      (  lowercase_	: Union[Path, str]						,  lowercase_	: Union[Path, str]       ):
     if not config.LZ4_AVAILABLE:
         raise ImportError('''Please pip install lz4'''       )
     import lza.frame

     with lza.frame.open(lowercase_						,  '''rb'''       ) as compressed_file:
         with open(lowercase_						,  '''wb'''       ) as extracted_file:
             shutil.copyfileobj(lowercase_						,  lowercase_       )





class  _UpperCAmelCase :
 #  Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
 _lowerCAmelCase     :    Dict[str, Type[BaseExtractor]]	      =	{
     "tar": TarExtractor,
     "gzip": GzipExtractor,
     "zip": ZipExtractor,
     "xz": XzExtractor,
     "rar": RarExtractor,
     "zstd": ZstdExtractor,
     "bz2": BzipaExtractor,
     "7z": SevenZipExtractor,  # <Added version="2.4.0"/>
     "lz4": LzaExtractor,  # <Added version="2.4.0"/>
 }

 @classmethod
 def 		_snake_case      (  cls	: Optional[int]       ):
     return max(
         len(lowercase_       )
         for extractor in cls.extractors.values()
         if issubclass(lowercase_						,  lowercase_       )
         for extractor_magic_number in extractor.magic_numbers       )

 @staticmethod
 def 		_snake_case      (  lowercase_	: Union[Path, str]						,  lowercase_	: int       ):
     try:
         return MagicNumberBaseExtractor.read_magic_number(lowercase_						,  magic_number_length=lowercase_       )
     except OSError:
         return b""

 @classmethod
 def 		_snake_case      (  cls	: Tuple						,  lowercase_	: Union[Path, str]						,  lowercase_	: bool = False       ):
     warnings.warn(
         '''Method \'is_extractable\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
         '''Use \'infer_extractor_format\' instead.'''						,  category=lowercase_						,  )
     snake_case_				:  List[Any]       =			cls.infer_extractor_format(lowercase_       )
     if extractor_format:
         return True if not return_extractor else (True, cls.extractors[extractor_format])
     return False if not return_extractor else (False, None)

 @classmethod
 def 		_snake_case      (  cls	: int						,  lowercase_	: Union[Path, str]       ):  # <Added version="2.4.0"/>
     snake_case_				:  Optional[Any]       =			cls._get_magic_number_max_length()
     snake_case_				:  Optional[Any]       =			cls._read_magic_number(lowercase_						,  lowercase_       )
     for extractor_format, extractor in cls.extractors.items():
         if extractor.is_extractable(lowercase_						,  magic_number=lowercase_       ):
             return extractor_format






 @classmethod
 def 		_snake_case      (  cls	: Optional[Any]						,  lowercase_	: Union[Path, str]						,  lowercase_	: Union[Path, str]						,  lowercase_	: Optional[str] = None						,  lowercase_	: Optional[BaseExtractor] = "deprecated"						,  ):
     os.makedirs(os.path.dirname(lowercase_       )						,  exist_ok=lowercase_       )
     # Prevent parallel extractions
     snake_case_				:  Dict       =			str(Path(lowercase_       ).with_suffix('''.lock'''       )       )
     with FileLock(lowercase_       ):
         shutil.rmtree(lowercase_						,  ignore_errors=lowercase_       )
         if extractor_format or extractor != "deprecated":
             if extractor != "deprecated" or not isinstance(lowercase_						,  lowercase_       ):  # passed as positional arg
                 warnings.warn(
                     '''Parameter \'extractor\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
                     '''Use \'extractor_format\' instead.'''						,  category=lowercase_						,  )
                 snake_case_				:  List[Any]       =			extractor if extractor != '''deprecated''' else extractor_format
             else:
                 snake_case_				:  List[str]       =			cls.extractors[extractor_format]
             return extractor.extract(lowercase_						,  lowercase_       )
         else:
             warnings.warn(
                 '''Parameter \'extractor_format\' was made required in version 2.4.0 and not passing it will raise an '''
                 '''exception in 3.0.0.'''						,  category=lowercase_						,  )
             for extractor in cls.extractors.values():
                 if extractor.is_extractable(lowercase_       ):
                     return extractor.extract(lowercase_						,  lowercase_       )










"""simple docstring"""

from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFCvtForImageClassification, TFCvtModel
     from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor




class  _UpperCAmelCase (    lowerCAmelCase__):

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  Any       =			self.config_class(**self.inputs_dict       )
     self.parent.assertTrue(hasattr(lowercase_						,  '''embed_dim'''       )       )
     self.parent.assertTrue(hasattr(lowercase_						,  '''num_heads'''       )       )





class  _UpperCAmelCase :

 def __init__(  self	: List[str]						,  lowercase_	: Optional[Any]						,  lowercase_	: int=13						,  lowercase_	: Optional[int]=64						,  lowercase_	: Any=3						,  lowercase_	: Any=[16, 48, 96]						,  lowercase_	: List[Any]=[1, 3, 6]						,  lowercase_	: Union[str, Any]=[1, 2, 10]						,  lowercase_	: Optional[Any]=[7, 3, 3]						,  lowercase_	: Union[str, Any]=[4, 2, 2]						,  lowercase_	: Tuple=[2, 1, 1]						,  lowercase_	: List[str]=[2, 2, 2]						,  lowercase_	: Union[str, Any]=[False, False, True]						,  lowercase_	: Optional[int]=[0.0, 0.0, 0.0]						,  lowercase_	: str=0.02						,  lowercase_	: Optional[Any]=1E-12						,  lowercase_	: Optional[int]=True						,  lowercase_	: Optional[int]=True						,  lowercase_	: Optional[Any]=2						,  ):
     snake_case_				:  List[Any]       =			parent
     snake_case_				:  int       =			batch_size
     snake_case_				:  Union[str, Any]       =			image_size
     snake_case_				:  Tuple       =			patch_sizes
     snake_case_				:  List[Any]       =			patch_stride
     snake_case_				:  Dict       =			patch_padding
     snake_case_				:  Any       =			is_training
     snake_case_				:  Any       =			use_labels
     snake_case_				:  str       =			num_labels
     snake_case_				:  Optional[Any]       =			num_channels
     snake_case_				:  Optional[Any]       =			embed_dim
     snake_case_				:  int       =			num_heads
     snake_case_				:  List[str]       =			stride_kv
     snake_case_				:  Any       =			depth
     snake_case_				:  Dict       =			cls_token
     snake_case_				:  Dict       =			attention_drop_rate
     snake_case_				:  int       =			initializer_range
     snake_case_				:  Tuple       =			layer_norm_eps

 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  str       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

     snake_case_				:  str       =			None
     if self.use_labels:
         # create a random int32 tensor of given shape
         snake_case_				:  Union[str, Any]       =			ids_tensor([self.batch_size]						,  self.num_labels       )

     snake_case_				:  Dict       =			self.get_config()
     return config, pixel_values, labels

 def 		_snake_case      (  self	: int       ):
     return CvtConfig(
         image_size=self.image_size						,  num_labels=self.num_labels						,  num_channels=self.num_channels						,  embed_dim=self.embed_dim						,  num_heads=self.num_heads						,  patch_sizes=self.patch_sizes						,  patch_padding=self.patch_padding						,  patch_stride=self.patch_stride						,  stride_kv=self.stride_kv						,  depth=self.depth						,  cls_token=self.cls_token						,  attention_drop_rate=self.attention_drop_rate						,  initializer_range=self.initializer_range						,  )

 def 		_snake_case      (  self	: int						,  lowercase_	: List[str]						,  lowercase_	: Optional[Any]						,  lowercase_	: Union[str, Any]       ):
     snake_case_				:  Tuple       =			TFCvtModel(config=lowercase_       )
     snake_case_				:  Tuple       =			model(lowercase_						,  training=lowercase_       )
     snake_case_				:  int       =			(self.image_size, self.image_size)
     snake_case_,			snake_case_				:  List[str]       =			image_size[0], image_size[1]
     for i in range(len(self.depth       )       ):
         snake_case_				:  str       =			floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
         snake_case_				:  str       =			floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
     self.parent.assertEqual(result.last_hidden_state.shape						,  (self.batch_size, self.embed_dim[-1], height, width)       )

 def 		_snake_case      (  self	: Dict						,  lowercase_	: Tuple						,  lowercase_	: Union[str, Any]						,  lowercase_	: List[str]       ):
     snake_case_				:  int       =			self.num_labels
     snake_case_				:  Any       =			TFCvtForImageClassification(lowercase_       )
     snake_case_				:  List[Any]       =			model(lowercase_						,  labels=lowercase_						,  training=lowercase_       )
     self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_labels)       )






 def 		_snake_case      (  self	: Any       ):
     snake_case_				:  Tuple       =			self.prepare_config_and_inputs()
     snake_case_,			snake_case_,			snake_case_				:  List[str]       =			config_and_inputs
     snake_case_				:  Any       =			{'''pixel_values''': pixel_values}
     return config, inputs_dict





@require_tf
class  _UpperCAmelCase (    lowerCAmelCase__					,  lowerCAmelCase__					,  unittest.TestCase):
 _lowerCAmelCase     :    Optional[int]	      =	(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
 _lowerCAmelCase     :    str	      =	(
     {"""feature-extraction""": TFCvtModel, """image-classification""": TFCvtForImageClassification}
     if is_tf_available()
     else {}
 )
 _lowerCAmelCase     :    str	      =	False
 _lowerCAmelCase     :    int	      =	False
 _lowerCAmelCase     :    Tuple	      =	False
 _lowerCAmelCase     :    int	      =	False
 _lowerCAmelCase     :    int	      =	False

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  Optional[int]       =			TFCvtModelTester(self       )
     snake_case_				:  str       =			TFCvtConfigTester(self						,  config_class=lowercase_						,  has_text_modality=lowercase_						,  hidden_size=37       )

 def 		_snake_case      (  self	: int       ):
     self.config_tester.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()

 @unittest.skip(reason='''Cvt does not output attentions'''       )
 def 		_snake_case      (  self	: Any       ):
     pass

 @unittest.skip(reason='''Cvt does not use inputs_embeds'''       )
 def 		_snake_case      (  self	: str       ):
     pass

 @unittest.skip(reason='''Cvt does not support input and output embeddings'''       )
 def 		_snake_case      (  self	: Union[str, Any]       ):
     pass

 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''       )       ) == 0						,  reason='''TF does not support backprop for grouped convolutions on CPU.'''						,  )
 def 		_snake_case      (  self	: Tuple       ):
     super().test_dataset_conversion()

 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''       )       ) == 0						,  reason='''TF does not support backprop for grouped convolutions on CPU.'''						,  )
 @slow
 def 		_snake_case      (  self	: Tuple       ):
     super().test_keras_fit()

 @unittest.skip(reason='''Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8'''       )
 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  Optional[Any]       =			tf.keras.mixed_precision.Policy('''mixed_float16'''       )
     tf.keras.mixed_precision.set_global_policy(lowercase_       )
     super().test_keras_fit()
     tf.keras.mixed_precision.set_global_policy('''float32'''       )

 def 		_snake_case      (  self	: int       ):
     snake_case_,			snake_case_				:  List[str]       =			self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         snake_case_				:  Dict       =			model_class(lowercase_       )
         snake_case_				:  str       =			inspect.signature(model.call       )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         snake_case_				:  List[str]       =			[*signature.parameters.keys()]

         snake_case_				:  int       =			['''pixel_values''']
         self.assertListEqual(arg_names[:1]						,  lowercase_       )

 def 		_snake_case      (  self	: List[str]       ):
     def check_hidden_states_output(lowercase_	: Optional[int]						,  lowercase_	: Union[str, Any]						,  lowercase_	: str       ):
         snake_case_				:  Any       =			model_class(lowercase_       )

         snake_case_				:  Optional[Any]       =			model(**self._prepare_for_class(lowercase_						,  lowercase_       )       )
         snake_case_				:  Tuple       =			outputs.hidden_states

         snake_case_				:  str       =			len(self.model_tester.depth       )
         self.assertEqual(len(lowercase_       )						,  lowercase_       )

         # verify the first hidden states (first block)
         self.assertListEqual(
             list(hidden_states[0].shape[-3:]       )						,  [
                 self.model_tester.embed_dim[0],
                 self.model_tester.image_size // 4,
                 self.model_tester.image_size // 4,
             ]						,  )

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

     for model_class in self.all_model_classes:
         snake_case_				:  Dict       =			True
         check_hidden_states_output(lowercase_						,  lowercase_						,  lowercase_       )

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

         check_hidden_states_output(lowercase_						,  lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: str       ):
     snake_case_				:  Dict       =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_       )

 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  str       =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase_       )






 @slow
 def 		_snake_case      (  self	: Optional[Any]       ):
     for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_				:  int       =			TFCvtModel.from_pretrained(lowercase_       )
         self.assertIsNotNone(lowercase_       )





def __lowercase    (							):
    snake_case_				:  Tuple       =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
    return image






@require_tf
@require_vision
class  _UpperCAmelCase (    unittest.TestCase):

 @cached_property
 def 		_snake_case      (  self	: List[Any]       ):
     return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )






 @slow
 def 		_snake_case      (  self	: Tuple       ):
     snake_case_				:  int       =			TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

     snake_case_				:  Any       =			self.default_image_processor
     snake_case_				:  Union[str, Any]       =			prepare_img()
     snake_case_				:  int       =			image_processor(images=lowercase_						,  return_tensors='''tf'''       )

     # forward pass
     snake_case_				:  Tuple       =			model(**lowercase_       )

     # verify the logits
     snake_case_				:  Optional[Any]       =			tf.TensorShape((1, 1000)       )
     self.assertEqual(outputs.logits.shape						,  lowercase_       )

     snake_case_				:  Tuple       =			tf.constant([0.92_85, 0.90_15, -0.31_50]       )
     self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()						,  lowercase_						,  atol=1E-4       )       )





