
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 UpperCAmelCase     (		unittest.TestCase     ):





   '''simple docstring'''


   @patch('''sys.argv'''    ,    ['''fakeprogrampath''', '''env''']  )
   def 				__magic_name__       (      self  :							List[str]  ):

      """simple docstring"""
      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 				__magic_name__       (      self  :							Optional[int]  ):

      """simple docstring"""
      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 				__magic_name__       (      self  :							Optional[Any]  ):

      """simple docstring"""
      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 				__magic_name__       (      self  :							List[str]  ):

      """simple docstring"""
      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'''  )  )





import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


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






class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__     ):





   '''simple docstring'''


   def __init__(      self  :							str    ,    *lowerCAmelCase_  :							Union[str, Any]    ,    **lowerCAmelCase_  :							Union[str, Any]  ):

      """simple docstring"""
      warnings.warn(
          '''The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
          ''' Please use ConvNextImageProcessor instead.'''    ,    lowerCAmelCase_    ,    )
      super().__init__(*lowerCAmelCase_    ,    **lowerCAmelCase_  )



