




"""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 							snake_case_ (				unittest.TestCase ):







   """simple docstring"""




   @patch('''sys.argv'''					,				['''fakeprogrampath''', '''env'''])
   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       # 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)							->      str:
       import transformers.commands.transformers_cli

       shutil.rmtree('''/tmp/hf-internal-testing/tiny-random-gptj'''					,				ignore_errors=lowerCamelCase_)  # 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)							->      int:
       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=lowerCamelCase_)

       # 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)							->      Dict:
       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=lowerCamelCase_)

       # 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"""







import os
from string import ascii_letters

SCREAMING_SNAKE_CASE_      =					ascii_letters + ' \t\n'





def     __snake_case   (							):


    """simple docstring"""







    UpperCamelCase										=  os.path.split(os.path.realpath(_lowercase						)						)
    UpperCamelCase										=  {}
    with open(path[0] + '''/dictionary.txt'''						) as dictionary_file:
        for word in dictionary_file.read().split('''\n'''						):
            UpperCamelCase										=  None
    return english_words


SCREAMING_SNAKE_CASE_      =					load_dictionary()





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  message.upper()
    UpperCamelCase										=  remove_non_letters(_lowercase						)
    UpperCamelCase										=  message.split()
    UpperCamelCase										=  len([word for word in possible_words if word in ENGLISH_WORDS]						)
    return float(_lowercase						) / len(_lowercase						)





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE						)





def     __snake_case   (							_lowercase ,_lowercase = 20 ,_lowercase = 85						):


    """simple docstring"""







    UpperCamelCase										=  get_english_count(_lowercase						) * 100 >= word_percentage
    UpperCamelCase										=  len(remove_non_letters(_lowercase						)						)
    UpperCamelCase										=  (float(_lowercase						) / len(_lowercase						)) * 100
    UpperCamelCase										=  message_letters_percentage >= letter_percentage
    return words_match and letters_match


if __name__ == "__main__":
  import doctest

  doctest.testmod()