




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


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

lowerCamelCase__					:  Tuple      							=				{
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "swinv2"

 lowercase_							       =       {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }



 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						Optional[Any]=224							,							_lowerCAmelCase    :						Optional[int]=4							,							_lowerCAmelCase    :						Tuple=3							,							_lowerCAmelCase    :						Tuple=96							,							_lowerCAmelCase    :						Dict=[2, 2, 6, 2]							,							_lowerCAmelCase    :						Optional[Any]=[3, 6, 12, 24]							,							_lowerCAmelCase    :						str=7							,							_lowerCAmelCase    :						List[Any]=4.0							,							_lowerCAmelCase    :						List[str]=True							,							_lowerCAmelCase    :						List[Any]=0.0							,							_lowerCAmelCase    :						List[Any]=0.0							,							_lowerCAmelCase    :						Any=0.1							,							_lowerCAmelCase    :						List[Any]="gelu"							,							_lowerCAmelCase    :						str=False							,							_lowerCAmelCase    :						str=0.02							,							_lowerCAmelCase    :						List[Any]=1E-5							,							_lowerCAmelCase    :						str=32							,							**_lowerCAmelCase    :						List[Any]							,							):
       super().__init__(**_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     image_size
       SCREAMING_SNAKE_CASE_      		=     patch_size
       SCREAMING_SNAKE_CASE_      		=     num_channels
       SCREAMING_SNAKE_CASE_      		=     embed_dim
       SCREAMING_SNAKE_CASE_      		=     depths
       SCREAMING_SNAKE_CASE_      		=     len(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     num_heads
       SCREAMING_SNAKE_CASE_      		=     window_size
       SCREAMING_SNAKE_CASE_      		=     mlp_ratio
       SCREAMING_SNAKE_CASE_      		=     qkv_bias
       SCREAMING_SNAKE_CASE_      		=     hidden_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     drop_path_rate
       SCREAMING_SNAKE_CASE_      		=     hidden_act
       SCREAMING_SNAKE_CASE_      		=     use_absolute_embeddings
       SCREAMING_SNAKE_CASE_      		=     layer_norm_eps
       SCREAMING_SNAKE_CASE_      		=     initializer_range
       SCREAMING_SNAKE_CASE_      		=     encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       SCREAMING_SNAKE_CASE_      		=     int(embed_dim * 2 ** (len(_lowerCAmelCase		) - 1)		)
       SCREAMING_SNAKE_CASE_      		=     (0, 0, 0, 0)





import subprocess
import sys

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





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 @require_torch
 def  lowerCAmelCase_    (				self    :						int		):
       # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
       # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
       # while running an external program

       # python one-liner segments

       # this must be loaded before socket.socket is monkey-patched
       SCREAMING_SNAKE_CASE_      		=     '\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

       SCREAMING_SNAKE_CASE_      		=     '\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        '

       SCREAMING_SNAKE_CASE_      		=     '\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
       SCREAMING_SNAKE_CASE_      		=     'hf-internal-testing/tiny-random-bert'
       BertConfig.from_pretrained(_lowerCAmelCase		)
       BertModel.from_pretrained(_lowerCAmelCase		)
       BertTokenizer.from_pretrained(_lowerCAmelCase		)
       pipeline(task='fill-mask'							,							model=_lowerCAmelCase		)

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

       # should succeed
       SCREAMING_SNAKE_CASE_      		=     self.get_env()
       # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
       SCREAMING_SNAKE_CASE_      		=     '1'
       SCREAMING_SNAKE_CASE_      		=     subprocess.run(_lowerCAmelCase							,							env=_lowerCAmelCase							,							check=_lowerCAmelCase							,							capture_output=_lowerCAmelCase		)
       self.assertEqual(result.returncode							,							0							,							result.stderr		)
       self.assertIn('success'							,							result.stdout.decode()		)



 @require_torch
 def  lowerCAmelCase_    (				self    :						Tuple		):
       # python one-liner segments
       # this must be loaded before socket.socket is monkey-patched
       SCREAMING_SNAKE_CASE_      		=     '\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

       SCREAMING_SNAKE_CASE_      		=     '\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        '

       SCREAMING_SNAKE_CASE_      		=     '\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
       SCREAMING_SNAKE_CASE_      		=     'hf-internal-testing/tiny-random-bert'
       BertConfig.from_pretrained(_lowerCAmelCase		)
       BertModel.from_pretrained(_lowerCAmelCase		)
       BertTokenizer.from_pretrained(_lowerCAmelCase		)
       pipeline(task='fill-mask'							,							model=_lowerCAmelCase		)

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

       # should succeed
       SCREAMING_SNAKE_CASE_      		=     self.get_env()
       SCREAMING_SNAKE_CASE_      		=     subprocess.run(_lowerCAmelCase							,							env=_lowerCAmelCase							,							check=_lowerCAmelCase							,							capture_output=_lowerCAmelCase		)
       self.assertEqual(result.returncode							,							0							,							result.stderr		)
       self.assertIn('success'							,							result.stdout.decode()		)



 @require_torch
 def  lowerCAmelCase_    (				self    :						List[str]		):
       # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
       # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
       # while running an external program

       # python one-liner segments

       # this must be loaded before socket.socket is monkey-patched
       SCREAMING_SNAKE_CASE_      		=     '\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

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

       SCREAMING_SNAKE_CASE_      		=     '\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
       SCREAMING_SNAKE_CASE_      		=     [sys.executable, '-c', '\n'.join([load, run]		)]

       # should succeed
       SCREAMING_SNAKE_CASE_      		=     self.get_env()
       SCREAMING_SNAKE_CASE_      		=     subprocess.run(_lowerCAmelCase							,							env=_lowerCAmelCase							,							check=_lowerCAmelCase							,							capture_output=_lowerCAmelCase		)
       self.assertEqual(result.returncode							,							0							,							result.stderr		)
       self.assertIn('success'							,							result.stdout.decode()		)

       # next emulate no network
       SCREAMING_SNAKE_CASE_      		=     [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
       SCREAMING_SNAKE_CASE_      		=     '1'
       SCREAMING_SNAKE_CASE_      		=     subprocess.run(_lowerCAmelCase							,							env=_lowerCAmelCase							,							check=_lowerCAmelCase							,							capture_output=_lowerCAmelCase		)
       self.assertEqual(result.returncode							,							0							,							result.stderr		)
       self.assertIn('success'							,							result.stdout.decode()		)



 @require_torch
 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     '\nfrom transformers import pipeline\n        '
       SCREAMING_SNAKE_CASE_      		=     '\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

       SCREAMING_SNAKE_CASE_      		=     '\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
       SCREAMING_SNAKE_CASE_      		=     self.get_env()
       SCREAMING_SNAKE_CASE_      		=     '1'
       SCREAMING_SNAKE_CASE_      		=     [sys.executable, '-c', '\n'.join([load, mock, run]		)]
       SCREAMING_SNAKE_CASE_      		=     subprocess.run(_lowerCAmelCase							,							env=_lowerCAmelCase							,							check=_lowerCAmelCase							,							capture_output=_lowerCAmelCase		)
       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  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     '\nfrom transformers import AutoModel\n        '
       SCREAMING_SNAKE_CASE_      		=     '\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
       SCREAMING_SNAKE_CASE_      		=     [sys.executable, '-c', '\n'.join([load, run]		)]

       # should succeed
       SCREAMING_SNAKE_CASE_      		=     self.get_env()
       SCREAMING_SNAKE_CASE_      		=     subprocess.run(_lowerCAmelCase							,							env=_lowerCAmelCase							,							check=_lowerCAmelCase							,							capture_output=_lowerCAmelCase		)
       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
       SCREAMING_SNAKE_CASE_      		=     '1'
       SCREAMING_SNAKE_CASE_      		=     subprocess.run(_lowerCAmelCase							,							env=_lowerCAmelCase							,							check=_lowerCAmelCase							,							capture_output=_lowerCAmelCase		)
       self.assertEqual(result.returncode							,							0							,							result.stderr		)
       self.assertIn('success'							,							result.stdout.decode()		)