"""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			):


	"""simple docstring"""







	@patch("sys.argv"       ,     ["fakeprogrampath", "env"] )
	def       A    (			self )    ->	List[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       A    (			self )    ->	List[str]:
							import transformers.commands.transformers_cli

							shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"       ,     ignore_errors=SCREAMING_SNAKE_CASE_ )  # 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 )    ->	Any:
							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=SCREAMING_SNAKE_CASE_ )

							# 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:
							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=SCREAMING_SNAKE_CASE_ )

							# 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


UpperCamelCase     					=     {'configuration_levit': ['LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LevitConfig', 'LevitOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     ['LevitFeatureExtractor']
				UpperCamelCase     					=     ['LevitImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     [
				    'LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'LevitForImageClassification',
				    'LevitForImageClassificationWithTeacher',
				    'LevitModel',
				    'LevitPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_levit import LevitFeatureExtractor
								from .image_processing_levit import LevitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_levit import (
								    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LevitForImageClassification,
								    LevitForImageClassificationWithTeacher,
								    LevitModel,
								    LevitPreTrainedModel,
								)
else:
				import sys

				UpperCamelCase     					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




