





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE   						=		{
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE   						=		["""DebertaV2TokenizerFast"""]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE   						=		[
						    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFDebertaV2ForMaskedLM""",
						    """TFDebertaV2ForQuestionAnswering""",
						    """TFDebertaV2ForSequenceClassification""",
						    """TFDebertaV2ForTokenClassification""",
						    """TFDebertaV2Model""",
						    """TFDebertaV2PreTrainedModel""",
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE   						=		[
						    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """DebertaV2ForMaskedLM""",
						    """DebertaV2ForMultipleChoice""",
						    """DebertaV2ForQuestionAnswering""",
						    """DebertaV2ForSequenceClassification""",
						    """DebertaV2ForTokenClassification""",
						    """DebertaV2Model""",
						    """DebertaV2PreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_deberta_va import (
						    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DebertaVaConfig,
						    DebertaVaOnnxConfig,
						)
						from .tokenization_deberta_va import DebertaVaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deberta_va import (
												    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDebertaVaForMaskedLM,
												    TFDebertaVaForQuestionAnswering,
												    TFDebertaVaForSequenceClassification,
												    TFDebertaVaForTokenClassification,
												    TFDebertaVaModel,
												    TFDebertaVaPreTrainedModel,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deberta_va import (
												    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DebertaVaForMaskedLM,
												    DebertaVaForMultipleChoice,
												    DebertaVaForQuestionAnswering,
												    DebertaVaForSequenceClassification,
												    DebertaVaForTokenClassification,
												    DebertaVaModel,
												    DebertaVaPreTrainedModel,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE   						=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








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 SCREAMING_SNAKE_CASE_    (       unittest.TestCase       ):






						@patch('sys.argv'		, ['fakeprogrampath', 'env']						)
						def UpperCAmelCase_					(   self   :   Tuple						)				->     Dict:







							"""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 UpperCAmelCase_					(   self   :   Union[str, Any]						)				->     Optional[Any]:







							"""simple docstring"""


							import transformers.commands.transformers_cli

							shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'		, ignore_errors=_A						)  # 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 UpperCAmelCase_					(   self   :   List[Any]						)				->     str:







							"""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=_A						)

							# 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 UpperCAmelCase_					(   self   :   Optional[Any]						)				->     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=_A						)

							# 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'						)						)

