code: 
  '''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 			A							(       unittest.TestCase				):
  
  
  
  			@patch("""sys.argv"""       ,     ["""fakeprogrampath""", """env"""]       )
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''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       )			->		List[Any]:
  
  
  
  
  									'''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       )			->		Dict:
  
  
  
  
  									'''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       )			->		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"""       )       )
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  __A          =    {"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    ["DetrFeatureExtractor"]
  				__A          =    ["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    [
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				__A          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  