
'''simple docstring'''



import copy
import re

class  _a					:





       """simple docstring"""




       SCREAMING_SNAKE_CASE   =	"hp"
       SCREAMING_SNAKE_CASE   =	{}
       SCREAMING_SNAKE_CASE   =	None






       @classmethod
       def 							UpperCamelCase					(				cls							,				A__							,				A__   )     ->      Optional[int]:
          _SCREAMING_SNAKE_CASE  			=					prefix
          _SCREAMING_SNAKE_CASE  			=					defaults
          cls.build_naming_info()






       @staticmethod
       def 							UpperCamelCase					(				A__							,				A__   )     ->      Dict:
          if len(a_   ) == 0:
             return ""
          _SCREAMING_SNAKE_CASE  			=					None
          if any(char.isdigit() for char in word   ):
             raise Exception(F"Parameters should not contain numbers: \'{word}\' contains a number"   )
          if word in info["short_word"]:
             return info["short_word"][word]
          for prefix_len in range(1							,				len(a_   ) + 1   ):
             _SCREAMING_SNAKE_CASE  			=					word[:prefix_len]
             if prefix in info["reverse_short_word"]:
                continue
             else:
                _SCREAMING_SNAKE_CASE  			=					prefix
                break

          if short_word is None:
             # Paranoid fallback
             def int_to_alphabetic(A__   ):
                _SCREAMING_SNAKE_CASE  			=					""""""
                while integer != 0:
                   _SCREAMING_SNAKE_CASE  			=					chr(ord("""A"""   ) + integer % 10   ) + s
                   integer //= 10
                return s

             _SCREAMING_SNAKE_CASE  			=					0
             while True:
                _SCREAMING_SNAKE_CASE  			=					word + """#""" + int_to_alphabetic(a_   )
                if sword in info["reverse_short_word"]:
                   continue
                else:
                   _SCREAMING_SNAKE_CASE  			=					sword
                   break

          _SCREAMING_SNAKE_CASE  			=					short_word
          _SCREAMING_SNAKE_CASE  			=					word
          return short_word






       @staticmethod
       def 							UpperCamelCase					(				A__							,				A__   )     ->      List[Any]:
          _SCREAMING_SNAKE_CASE  			=					param_name.split("""_"""   )

          _SCREAMING_SNAKE_CASE  			=					[TrialShortNamer.shortname_for_word(a_							,				a_   ) for word in words]

          # We try to create a separatorless short name, but if there is a collision we have to fallback
          # to a separated short name
          _SCREAMING_SNAKE_CASE  			=					["""""", """_"""]

          for separator in separators:
             _SCREAMING_SNAKE_CASE  			=					separator.join(a_   )
             if shortname not in info["reverse_short_param"]:
                _SCREAMING_SNAKE_CASE  			=					shortname
                _SCREAMING_SNAKE_CASE  			=					param_name
                return shortname

          return param_name






       @staticmethod
       def 							UpperCamelCase					(				A__							,				A__   )     ->      Optional[int]:
          _SCREAMING_SNAKE_CASE  			=					TrialShortNamer.shortname_for_key(a_							,				a_   )
          _SCREAMING_SNAKE_CASE  			=					short_name
          _SCREAMING_SNAKE_CASE  			=					param_name






       @classmethod
       def 							UpperCamelCase					(				cls   )     ->      Tuple:
          if cls.NAMING_INFO is not None:
             return

          _SCREAMING_SNAKE_CASE  			=					{
              """short_word""": {},
              """reverse_short_word""": {},
              """short_param""": {},
              """reverse_short_param""": {},
          }

          _SCREAMING_SNAKE_CASE  			=					list(cls.DEFAULTS.keys()   )

          for k in field_keys:
             cls.add_new_param_name(a_							,				a_   )

          _SCREAMING_SNAKE_CASE  			=					info






       @classmethod
       def 							UpperCamelCase					(				cls							,				A__   )     ->      Union[str, Any]:
          cls.build_naming_info()
          assert cls.PREFIX is not None
          _SCREAMING_SNAKE_CASE  			=					[copy.copy(cls.PREFIX   )]

          for k, v in params.items():
             if k not in cls.DEFAULTS:
                raise Exception(F"You should provide a default value for the param name {k} with value {v}"   )
             if v == cls.DEFAULTS[k]:
                # The default value is not added to the name
                continue

             _SCREAMING_SNAKE_CASE  			=					cls.NAMING_INFO["""short_param"""][k]

             if isinstance(a_							,				a_   ):
                _SCREAMING_SNAKE_CASE  			=					1 if v else 0

             _SCREAMING_SNAKE_CASE  			=					"""""" if isinstance(a_							,				(int, float)   ) else """-"""
             _SCREAMING_SNAKE_CASE  			=					F"{key}{sep}{v}"
             name.append(a_   )

          return "_".join(a_   )






       @classmethod
       def 							UpperCamelCase					(				cls							,				A__   )     ->      Optional[Any]:
          _SCREAMING_SNAKE_CASE  			=					repr[len(cls.PREFIX   ) + 1 :]
          if repr == "":
             _SCREAMING_SNAKE_CASE  			=					[]
          else:
             _SCREAMING_SNAKE_CASE  			=					repr.split("""_"""   )

          _SCREAMING_SNAKE_CASE  			=					{}

          for value in values:
             if "-" in value:
                _SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  			=					value.split("""-"""   )
             else:
                _SCREAMING_SNAKE_CASE  			=					re.sub("""[0-9.]"""							,				""""""							,				a_   )
                _SCREAMING_SNAKE_CASE  			=					float(re.sub("""[^0-9.]"""							,				""""""							,				a_   )   )

             _SCREAMING_SNAKE_CASE  			=					cls.NAMING_INFO["""reverse_short_param"""][p_k]

             _SCREAMING_SNAKE_CASE  			=					p_v

          for k in cls.DEFAULTS:
             if k not in parameters:
                _SCREAMING_SNAKE_CASE  			=					cls.DEFAULTS[k]

          return parameters









'''simple docstring'''




import subprocess
import sys

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



class 							lowercase_						(		a			):

  '''simple docstring'''





  @require_torch
  def     snake_case_      (  self  )				->					Optional[Any]:


    """simple docstring"""
    # 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
    UpperCAmelCase    				=						'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

    UpperCAmelCase    				=						'\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        '

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

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

    # should succeed
    UpperCAmelCase    				=						self.get_env()
    # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
    UpperCAmelCase    				=						'1'
    UpperCAmelCase    				=						subprocess.run(a_ ,			env=a_ ,			check=a_ ,			capture_output=a_  )
    self.assertEqual(result.returncode ,			0 ,			result.stderr  )
    self.assertIn('success' ,			result.stdout.decode()  )
  @require_torch
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    # python one-liner segments
    # this must be loaded before socket.socket is monkey-patched
    UpperCAmelCase    				=						'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

    UpperCAmelCase    				=						'\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        '

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

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

    # should succeed
    UpperCAmelCase    				=						self.get_env()
    UpperCAmelCase    				=						subprocess.run(a_ ,			env=a_ ,			check=a_ ,			capture_output=a_  )
    self.assertEqual(result.returncode ,			0 ,			result.stderr  )
    self.assertIn('success' ,			result.stdout.decode()  )
  @require_torch
  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    # 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
    UpperCAmelCase    				=						'\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

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

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

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

    # next emulate no network
    UpperCAmelCase    				=						[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
    UpperCAmelCase    				=						'1'
    UpperCAmelCase    				=						subprocess.run(a_ ,			env=a_ ,			check=a_ ,			capture_output=a_  )
    self.assertEqual(result.returncode ,			0 ,			result.stderr  )
    self.assertIn('success' ,			result.stdout.decode()  )
  @require_torch
  def     snake_case_      (  self  )				->					Optional[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						'\nfrom transformers import pipeline\n        '
    UpperCAmelCase    				=						'\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

    UpperCAmelCase    				=						'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
    UpperCAmelCase    				=						self.get_env()
    UpperCAmelCase    				=						'1'
    UpperCAmelCase    				=						[sys.executable, '-c', '\n'.join([load, mock, run]  )]
    UpperCAmelCase    				=						subprocess.run(a_ ,			env=a_ ,			check=a_ ,			capture_output=a_  )
    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  )				->					str:


    """simple docstring"""
    UpperCAmelCase    				=						'\nfrom transformers import AutoModel\n        '
    UpperCAmelCase    				=						'\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
    UpperCAmelCase    				=						[sys.executable, '-c', '\n'.join([load, run]  )]

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


