
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     ):
         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     ):
         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       UpperCamelCase_							(			self     ):
         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       UpperCamelCase_							(			self     ):
         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"""     )     )

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A		:   int         =    {'''configuration_vilt''': ['''VILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViltConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		:   int         =    ['''ViltFeatureExtractor''']
		A		:   List[Any]         =    ['''ViltImageProcessor''']
		A		:   Dict         =    ['''ViltProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		:   Tuple         =    [
		    '''VILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViltForImageAndTextRetrieval''',
		    '''ViltForImagesAndTextClassification''',
		    '''ViltForTokenClassification''',
		    '''ViltForMaskedLM''',
		    '''ViltForQuestionAnswering''',
		    '''ViltLayer''',
		    '''ViltModel''',
		    '''ViltPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_vilt import ViltFeatureExtractor
				from .image_processing_vilt import ViltImageProcessor
				from .processing_vilt import ViltProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vilt import (
				    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViltForImageAndTextRetrieval,
				    ViltForImagesAndTextClassification,
				    ViltForMaskedLM,
				    ViltForQuestionAnswering,
				    ViltForTokenClassification,
				    ViltLayer,
				    ViltModel,
				    ViltPreTrainedModel,
				)


else:
		import sys

		A		:   Dict         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)