





'''simple docstring'''






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 	_lowercase    (				unittest.TestCase					):


   @patch('''sys.argv'''     , ['''fakeprogrampath''', '''env''']       )
   def        snake_case					(			self       ):
       # 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        snake_case					(			self       ):
       import transformers.commands.transformers_cli

       shutil.rmtree('''/tmp/hf-internal-testing/tiny-random-gptj'''     , ignore_errors=_lowercase       )  # 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        snake_case					(			self       ):
       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=_lowercase       )

       # 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        snake_case					(			self       ):
       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=_lowercase       )

       # 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'''       )       )

"""simple docstring"""


from .number_theory.prime_numbers import next_prime
class   UpperCAmelCase_	:
    def __init__(	self			:       str   ,						_lowercase			:       int   ,						_lowercase			:       int | None = None   ,						_lowercase			:       float | None = None   ,						)						->    None:
       _lowercase							     =       size_table
       _lowercase							     =       [None] * self.size_table
       _lowercase							     =       0.75 if lim_charge is None else lim_charge
       _lowercase							     =       1 if charge_factor is None else charge_factor
       _lowercase							     =       []
       _lowercase							     =       {}
    def 		_lowerCamelCase      (	self			:       Any		)						->    Optional[Any]:
       return self._keys
    def 		_lowerCamelCase      (	self			:       Dict		)						->    Dict:
       return sum(1 for slot in self.values if slot is not None		) / (
           self.size_table * self.charge_factor
       )
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       List[str]		)						->    str:
       return key % self.size_table
    def 		_lowerCamelCase      (	self			:       Any   ,						_lowercase			:       Union[str, Any]		)						->    Optional[Any]:
       print(f"""step {step_ord}"""		)
       print(list(range(len(self.values		)		)		)		)
       print(self.values		)
    def 		_lowerCamelCase      (	self			:       str   ,						_lowercase			:       List[Any]		)						->    List[Any]:
       _lowercase							     =       1
       _lowercase							     =       values
       for value in values:
          self.insert_data(_lowercase		)
          self._step_by_step(_lowercase		)
          i += 1
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       Union[str, Any]		)						->    List[Any]:
       _lowercase							     =       data
       _lowercase							     =       data
    def 		_lowerCamelCase      (	self			:       int   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Tuple=None		)						->    Any:
       _lowercase							     =       self.hash_function(key + 1		)

       while self.values[new_key] is not None and self.values[new_key] != key:
          if self.values.count(_lowercase		) > 0:
             _lowercase							     =       self.hash_function(new_key + 1		)
          else:
             _lowercase							     =       None
             break

       return new_key
    def 		_lowerCamelCase      (	self			:       str		)						->    Any:
       _lowercase							     =       [value for value in self.values if value is not None]
       _lowercase							     =       next_prime(self.size_table   ,						factor=2		)
       self._keys.clear()
       _lowercase							     =       [None] * self.size_table  # hell's pointers D: don't DRY ;/
       for value in survivor_values:
          self.insert_data(_lowercase		)




    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Tuple		)						->    Optional[int]:
       _lowercase							     =       self.hash_function(_lowercase		)

       if self.values[key] is None:
          self._set_value(_lowercase   ,						_lowercase		)

       elif self.values[key] == data:
          pass

       else:
          _lowercase							     =       self._collision_resolution(_lowercase   ,						_lowercase		)
          if collision_resolution is not None:
             self._set_value(_lowercase   ,						_lowercase		)
          else:
             self.rehashing()
             self.insert_data(_lowercase		)