





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__       :			Optional[int]	      =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :			Dict	      =						{
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}







class   __lowerCAmelCase			(					__SCREAMING_SNAKE_CASE  ):
   _UpperCamelCase :						Optional[Any]     		=				"""donut-swin"""

   _UpperCamelCase :						Optional[int]     		=				{
       """num_attention_heads""": """num_heads""",
       """num_hidden_layers""": """num_layers""",
   }





   def __init__( self					,			snake_case=224					,			snake_case=4					,			snake_case=3					,			snake_case=96					,			snake_case=[2, 2, 6, 2]					,			snake_case=[3, 6, 12, 24]					,			snake_case=7					,			snake_case=4.0					,			snake_case=True					,			snake_case=0.0					,			snake_case=0.0					,			snake_case=0.1					,			snake_case="gelu"					,			snake_case=False					,			snake_case=0.02					,			snake_case=1E-5					,			**snake_case					,			)				->  Optional[Any]:
      """simple docstring"""





      super().__init__(**__snake_case      )

      a__	:						Optional[Any]      		=  image_size
      a__	:						Union[str, Any]      		=  patch_size
      a__	:						Optional[Any]      		=  num_channels
      a__	:						Dict      		=  embed_dim
      a__	:						Optional[int]      		=  depths
      a__	:						List[Any]      		=  len(__snake_case      )
      a__	:						Any      		=  num_heads
      a__	:						int      		=  window_size
      a__	:						Dict      		=  mlp_ratio
      a__	:						str      		=  qkv_bias
      a__	:						int      		=  hidden_dropout_prob
      a__	:						List[Any]      		=  attention_probs_dropout_prob
      a__	:						List[Any]      		=  drop_path_rate
      a__	:						Union[str, Any]      		=  hidden_act
      a__	:						str      		=  use_absolute_embeddings
      a__	:						Dict      		=  layer_norm_eps
      a__	:						Optional[int]      		=  initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      a__	:						Optional[int]      		=  int(embed_dim * 2 ** (len(__snake_case      ) - 1)      )








import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch







class   __lowerCAmelCase			(					_UpperCamelCase  ):





   @require_torch
   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__	:						Tuple      		=  "\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

      a__	:						str      		=  "\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n        "

      a__	:						Optional[Any]      		=  "\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError(\"Offline mode is enabled, we shouldn't access internet\")\nsocket.socket = offline_socket\n        "

      # Force fetching the files so that we can use the cache
      a__	:						Tuple      		=  "hf-internal-testing/tiny-random-bert"
      BertConfig.from_pretrained(snake_case      )
      BertModel.from_pretrained(snake_case      )
      BertTokenizer.from_pretrained(snake_case      )
      pipeline(task="fill-mask"					,			model=snake_case      )

      # baseline - just load from_pretrained with normal network
      a__	:						Optional[int]      		=  [sys.executable, "-c", "\n".join([load, run, mock]      )]

      # should succeed
      a__	:						Dict      		=  self.get_env()
      # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
      a__	:						Tuple      		=  "1"
      a__	:						List[Any]      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			0					,			result.stderr      )
      self.assertIn("success"					,			result.stdout.decode()      )





   @require_torch
   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      a__	:						Tuple      		=  "\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

      a__	:						Optional[Any]      		=  "\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n        "

      a__	:						Tuple      		=  "\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Faking flaky internet\")\nsocket.socket = offline_socket\n        "

      # Force fetching the files so that we can use the cache
      a__	:						List[Any]      		=  "hf-internal-testing/tiny-random-bert"
      BertConfig.from_pretrained(snake_case      )
      BertModel.from_pretrained(snake_case      )
      BertTokenizer.from_pretrained(snake_case      )
      pipeline(task="fill-mask"					,			model=snake_case      )

      # baseline - just load from_pretrained with normal network
      a__	:						Tuple      		=  [sys.executable, "-c", "\n".join([load, run, mock]      )]

      # should succeed
      a__	:						Any      		=  self.get_env()
      a__	:						int      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			0					,			result.stderr      )
      self.assertIn("success"					,			result.stdout.decode()      )





   @require_torch
   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						List[Any]      		=  "\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        "

      a__	:						Dict      		=  "\nmname = \"hf-internal-testing/tiny-random-bert-sharded\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint(\"success\")\n        "

      a__	:						int      		=  "\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n        "

      # baseline - just load from_pretrained with normal network
      a__	:						List[Any]      		=  [sys.executable, "-c", "\n".join([load, run]      )]

      # should succeed
      a__	:						str      		=  self.get_env()
      a__	:						List[str]      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			0					,			result.stderr      )
      self.assertIn("success"					,			result.stdout.decode()      )

      # next emulate no network
      a__	:						List[Any]      		=  [sys.executable, "-c", "\n".join([load, mock, run]      )]

      # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
      # env["TRANSFORMERS_OFFLINE"] = "0"
      # result = subprocess.run(cmd, env=env, check=False, capture_output=True)
      # self.assertEqual(result.returncode, 1, result.stderr)

      # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
      a__	:						Union[str, Any]      		=  "1"
      a__	:						Tuple      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			0					,			result.stderr      )
      self.assertIn("success"					,			result.stdout.decode()      )





   @require_torch
   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						Optional[Any]      		=  "\nfrom transformers import pipeline\n        "
      a__	:						int      		=  "\nmname = \"hf-internal-testing/tiny-random-bert\"\npipe = pipeline(model=mname)\n        "

      a__	:						Dict      		=  "\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n        "
      a__	:						List[str]      		=  self.get_env()
      a__	:						Union[str, Any]      		=  "1"
      a__	:						List[str]      		=  [sys.executable, "-c", "\n".join([load, mock, run]      )]
      a__	:						Any      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			1					,			result.stderr      )
      self.assertIn(
          "You cannot infer task automatically within `pipeline` when using offline mode"					,			result.stderr.decode().replace("\n"					,			""      )					,			)





   @require_torch
   def 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





      a__	:						Any      		=  "\nfrom transformers import AutoModel\n        "
      a__	:						Any      		=  "\nmname = \"hf-internal-testing/test_dynamic_model\"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint(\"success\")\n        "

      # baseline - just load from_pretrained with normal network
      a__	:						List[str]      		=  [sys.executable, "-c", "\n".join([load, run]      )]

      # should succeed
      a__	:						Optional[Any]      		=  self.get_env()
      a__	:						Union[str, Any]      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			0					,			result.stderr      )
      self.assertIn("success"					,			result.stdout.decode()      )

      # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
      a__	:						Dict      		=  "1"
      a__	:						Union[str, Any]      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			0					,			result.stderr      )
      self.assertIn("success"					,			result.stdout.decode()      )

