




"""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      lowerCamelCase__      (					unittest.TestCase     ):


			@patch("sys.argv"			,	["fakeprogrampath", "env"]							)
			def 							lowerCamelCase_			(       self							):



					"""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 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					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 							lowerCamelCase_			(       self							):



					"""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=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 							lowerCamelCase_			(       self							):



					"""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=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


__A							=     {
    "configuration_flava": [
        "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "FlavaConfig",
        "FlavaImageCodebookConfig",
        "FlavaImageConfig",
        "FlavaMultimodalConfig",
        "FlavaTextConfig",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     ["FlavaFeatureExtractor"]
							__A							=     ["FlavaImageProcessor"]
							__A							=     ["FlavaProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "FlavaForPreTraining",
							    "FlavaImageCodebook",
							    "FlavaImageModel",
							    "FlavaModel",
							    "FlavaMultimodalModel",
							    "FlavaPreTrainedModel",
							    "FlavaTextModel",
							]

if TYPE_CHECKING:
							from .configuration_flava import (
							    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    FlavaConfig,
							    FlavaImageCodebookConfig,
							    FlavaImageConfig,
							    FlavaMultimodalConfig,
							    FlavaTextConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_flava import FlavaFeatureExtractor
														from .image_processing_flava import FlavaImageProcessor
														from .processing_flava import FlavaProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flava import (
														    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlavaForPreTraining,
														    FlavaImageCodebook,
														    FlavaImageModel,
														    FlavaModel,
														    FlavaMultimodalModel,
														    FlavaPreTrainedModel,
														    FlavaTextModel,
														)

else:
							import sys

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

