






"""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        UpperCamelCase	(	self					:							str )					->		str:
						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					:							Optional[Any] )					->		Any:
						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        UpperCamelCase	(	self					:							int )					->		Optional[int]:
						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        UpperCamelCase	(	self					:							int )					->		Dict:
						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


_SCREAMING_SNAKE_CASE :    Union[str, Any]    =						{
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE :    Optional[Any]    =						[
      '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''DecisionTransformerGPT2Model''',
      '''DecisionTransformerGPT2PreTrainedModel''',
      '''DecisionTransformerModel''',
      '''DecisionTransformerPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_decision_transformer import (
      DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DecisionTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_decision_transformer import (
        DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DecisionTransformerGPTaModel,
        DecisionTransformerGPTaPreTrainedModel,
        DecisionTransformerModel,
        DecisionTransformerPreTrainedModel,
    )


else:
  import sys

  _SCREAMING_SNAKE_CASE :    List[str]    =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




