




'''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     UpperCamelCase					(  unittest.TestCase					):



  @patch("""sys.argv"""		,    ["""fakeprogrampath""", """env"""]   )
  def 						a_       ( self   ):







      '''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 						a_       ( self   ):







      '''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 						a_       ( self   ):







      '''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 						a_       ( self   ):







      '''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"""   )   )








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


a		:  Dict   			=			{
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:  List[str]   			=			[
      '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MobileViTV2ForImageClassification''',
      '''MobileViTV2ForSemanticSegmentation''',
      '''MobileViTV2Model''',
      '''MobileViTV2PreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mobilevitva import (
      MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileViTVaConfig,
      MobileViTVaOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileViTVaForImageClassification,
        MobileViTVaForSemanticSegmentation,
        MobileViTVaModel,
        MobileViTVaPreTrainedModel,
    )

else:
  import sys

  a		:  Dict   			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


