
from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
_A  					= TypeVar("T", bound="Enum")







@unique
class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Any           =		80
     UpperCAmelCase__				:							Optional[int]           =		70
     UpperCAmelCase__				:							str           =		60
     UpperCAmelCase__				:							Tuple           =		50
     UpperCAmelCase__				:							int           =		40
     UpperCAmelCase__				:							Any           =		30
     UpperCAmelCase__				:							Optional[Any]           =		20
     UpperCAmelCase__				:							Dict           =		10







@unique
class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Optional[int]           =		24
     UpperCAmelCase__				:							Tuple           =		21
     UpperCAmelCase__				:							Dict           =		18
     UpperCAmelCase__				:							Optional[Any]           =		15
     UpperCAmelCase__				:							List[str]           =		12
     UpperCAmelCase__				:							List[Any]           =		9
     UpperCAmelCase__				:							Any           =		6
     UpperCAmelCase__				:							List[Any]           =		3
     UpperCAmelCase__				:							List[str]           =		2
     UpperCAmelCase__				:							Optional[int]           =		1
     UpperCAmelCase__				:							int           =		-1
     UpperCAmelCase__				:							int           =		-2
     UpperCAmelCase__				:							List[str]           =		-3
     UpperCAmelCase__				:							List[Any]           =		-6
     UpperCAmelCase__				:							List[Any]           =		-9
     UpperCAmelCase__				:							List[Any]           =		-12
     UpperCAmelCase__				:							Optional[int]           =		-15
     UpperCAmelCase__				:							Any           =		-18
     UpperCAmelCase__				:							Dict           =		-21
     UpperCAmelCase__				:							Dict           =		-24





     @classmethod
     def 		UpperCAmelCase(cls      :    type[T]   )						->						dict:
         return {unit.name: unit.value for unit in cls if unit.value > 0}





     @classmethod
     def 		UpperCAmelCase(cls      :    type[T]   )						->						dict:
         return {unit.name: unit.value for unit in cls if unit.value < 0}



def 						lowercase_			(   A__		)							->							str:
    """simple docstring"""

    snake_case      =					SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
    for name_prefix, value_prefix in prefixes.items():
        snake_case      =					value / (10**value_prefix)
        if numerical_part > 1:
            return F'{numerical_part!s} {name_prefix}'
    return str(A__		)



def 						lowercase_			(   A__		)							->							str:
    """simple docstring"""

    for prefix in BinaryUnit:
        snake_case      =					value / (2**prefix.value)
        if numerical_part > 1:
            return F'{numerical_part!s} {prefix.name}'
    return str(A__		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch







class      lowerCamelCase		(  unittest.TestCase  ):





     @patch("sys.argv"				,    ["fakeprogrampath", "env"]   )
     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						Dict:
         # test transformers-cli env
         import transformers.commands.transformers_cli

         with CaptureStd() as cs:
             transformers.commands.transformers_cli.main()
         self.assertIn("Python version"				,    cs.out   )
         self.assertIn("Platform"				,    cs.out   )
         self.assertIn("Using distributed or parallel set-up in script?"				,    cs.out   )





     @is_pt_tf_cross_test
     @patch(
         "sys.argv"				,    ["fakeprogrampath", "pt-to-tf", "--model-name", "hf-internal-testing/tiny-random-gptj", "--no-pr"]   )
     def 		UpperCAmelCase(self      :    Any   )						->						int:
         import transformers.commands.transformers_cli

         shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"				,    ignore_errors=_A   )  # cleans potential past runs
         transformers.commands.transformers_cli.main()

         # The original repo has no TF weights -- if they exist, they were created by the CLI
         self.assertTrue(os.path.exists("/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"   )   )





     @require_torch
     @patch("sys.argv"				,    ["fakeprogrampath", "download", "hf-internal-testing/tiny-random-gptj", "--cache-dir", "/tmp"]   )
     def 		UpperCAmelCase(self      :    Dict   )						->						List[Any]:
         import transformers.commands.transformers_cli

         # # remove any previously downloaded model to start clean
         shutil.rmtree("/tmp/models--hf-internal-testing--tiny-random-gptj"				,    ignore_errors=_A   )

         # run the command
         transformers.commands.transformers_cli.main()

         # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"   )   )
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/refs"   )   )
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"   )   )





     @require_torch
     @patch(
         "sys.argv"				,    [
             "fakeprogrampath",
             "download",
             "hf-internal-testing/test_dynamic_model_with_tokenizer",
             "--trust-remote-code",
             "--cache-dir",
             "/tmp",
         ]				,    )
     def 		UpperCAmelCase(self      :    Any   )						->						List[str]:
         import transformers.commands.transformers_cli

         # # remove any previously downloaded model to start clean
         shutil.rmtree("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"				,    ignore_errors=_A   )

         # run the command
         transformers.commands.transformers_cli.main()

         # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"   )   )
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"   )   )
         self.assertTrue(
             os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"   )   )

