






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCAmelCase							={"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "RegNetForImageClassification",
						    "RegNetModel",
						    "RegNetPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFRegNetForImageClassification",
						    "TFRegNetModel",
						    "TFRegNetPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "FlaxRegNetForImageClassification",
						    "FlaxRegNetModel",
						    "FlaxRegNetPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_regnet import (
												    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RegNetForImageClassification,
												    RegNetModel,
												    RegNetPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_regnet import (
												    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRegNetForImageClassification,
												    TFRegNetModel,
												    TFRegNetPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_regnet import (
												    FlaxRegNetForImageClassification,
												    FlaxRegNetModel,
												    FlaxRegNetPreTrainedModel,
												)


else:
						import sys

						__lowerCAmelCase							=_LazyModule(__name__, globals()["__file__"], _import_structure)









'''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  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										# 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  __SCREAMING_SNAKE_CASE (     self     )      ->	List[str]:
										import transformers.commands.transformers_cli

										shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'   , ignore_errors=UpperCAmelCase__     )  # 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  __SCREAMING_SNAKE_CASE (     self     )      ->	List[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=UpperCAmelCase__     )

										# 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  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										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=UpperCAmelCase__     )

										# 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'     )     )

