"""simple docstring"""





import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402


_UpperCamelCase   :						Any      =    {
    "return_dict": False,
    "output_hidden_states": True,
    "output_attentions": True,
    "torchscript": True,
    "torch_dtype": "float16",
    "use_bfloat16": True,
    "tf_legacy_loss": True,
    "pruned_heads": {"a": 1},
    "tie_word_embeddings": False,
    "is_decoder": True,
    "cross_attention_hidden_size": 1_28,
    "add_cross_attention": True,
    "tie_encoder_decoder": True,
    "max_length": 50,
    "min_length": 3,
    "do_sample": True,
    "early_stopping": True,
    "num_beams": 3,
    "num_beam_groups": 3,
    "diversity_penalty": 0.5,
    "temperature": 2.0,
    "top_k": 10,
    "top_p": 0.7,
    "typical_p": 0.2,
    "repetition_penalty": 0.8,
    "length_penalty": 0.8,
    "no_repeat_ngram_size": 5,
    "encoder_no_repeat_ngram_size": 5,
    "bad_words_ids": [1, 2, 3],
    "num_return_sequences": 3,
    "chunk_size_feed_forward": 5,
    "output_scores": True,
    "return_dict_in_generate": True,
    "forced_bos_token_id": 2,
    "forced_eos_token_id": 3,
    "remove_invalid_values": True,
    "architectures": ["BertModel"],
    "finetuning_task": "translation",
    "id2label": {0: "label"},
    "label2id": {"label": "0"},
    "tokenizer_class": "BertTokenizerFast",
    "prefix": "prefix",
    "bos_token_id": 6,
    "pad_token_id": 7,
    "eos_token_id": 8,
    "sep_token_id": 9,
    "decoder_start_token_id": 10,
    "exponential_decay_length_penalty": (5, 1.0_1),
    "suppress_tokens": [0, 1],
    "begin_suppress_tokens": 2,
    "task_specific_params": {"translation": "some_params"},
    "problem_type": "regression",
}


@is_staging_test
class      UpperCAmelCase_	(    unittest.TestCase):


      @classmethod
      def 		_UpperCAmelCase       (   cls							)       ->					Dict:
             lowercase__			:		Union[str, Any]       =	TOKEN
             HfFolder.save_token(a							)


      @classmethod
      def 		_UpperCAmelCase       (   cls							)       ->					Union[str, Any]:
             try:
                    delete_repo(token=cls._token		,			repo_id='test-config'							)
             except HTTPError:
                    pass

             try:
                    delete_repo(token=cls._token		,			repo_id='valid_org/test-config-org'							)
             except HTTPError:
                    pass

             try:
                    delete_repo(token=cls._token		,			repo_id='test-dynamic-config'							)
             except HTTPError:
                    pass


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		List[Any]       =	BertConfig(
                 vocab_size=9_9		,			hidden_size=3_2		,			num_hidden_layers=5		,			num_attention_heads=4		,			intermediate_size=3_7							)
             config.push_to_hub('test-config'		,			use_auth_token=self._token							)

             lowercase__			:		Any       =	BertConfig.from_pretrained(f"""{USER}/test-config"""							)
             for k, v in config.to_dict().items():
                    if k != "transformers_version":
                           self.assertEqual(a		,			getattr(a		,			a							)							)

        # Reset repo
             delete_repo(token=self._token		,			repo_id='test-config'							)

             # Push to hub via save_pretrained
             with tempfile.TemporaryDirectory() as tmp_dir:
                    config.save_pretrained(a		,			repo_id='test-config'		,			push_to_hub=a		,			use_auth_token=self._token							)

             lowercase__			:		Union[str, Any]       =	BertConfig.from_pretrained(f"""{USER}/test-config"""							)
             for k, v in config.to_dict().items():
                    if k != "transformers_version":
                           self.assertEqual(a		,			getattr(a		,			a							)							)


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		str       =	BertConfig(
                 vocab_size=9_9		,			hidden_size=3_2		,			num_hidden_layers=5		,			num_attention_heads=4		,			intermediate_size=3_7							)
             config.push_to_hub('valid_org/test-config-org'		,			use_auth_token=self._token							)

             lowercase__			:		Tuple       =	BertConfig.from_pretrained('valid_org/test-config-org'							)
             for k, v in config.to_dict().items():
                    if k != "transformers_version":
                           self.assertEqual(a		,			getattr(a		,			a							)							)

        # Reset repo
             delete_repo(token=self._token		,			repo_id='valid_org/test-config-org'							)

             # Push to hub via save_pretrained
             with tempfile.TemporaryDirectory() as tmp_dir:
                    config.save_pretrained(
                        a		,			repo_id='valid_org/test-config-org'		,			push_to_hub=a		,			use_auth_token=self._token							)

             lowercase__			:		Any       =	BertConfig.from_pretrained('valid_org/test-config-org'							)
             for k, v in config.to_dict().items():
                    if k != "transformers_version":
                           self.assertEqual(a		,			getattr(a		,			a							)							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             CustomConfig.register_for_auto_class()
             lowercase__			:		Optional[Any]       =	CustomConfig(attribute=4_2							)

             config.push_to_hub('test-dynamic-config'		,			use_auth_token=self._token							)

             # This has added the proper auto_map field to the config
             self.assertDictEqual(config.auto_map		,			{'AutoConfig': 'custom_configuration.CustomConfig'}							)

             lowercase__			:		int       =	AutoConfig.from_pretrained(f"""{USER}/test-dynamic-config"""		,			trust_remote_code=a							)
             # Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
             self.assertEqual(new_config.__class__.__name__		,			'CustomConfig'							)
             self.assertEqual(new_config.attribute		,			4_2							)





class      UpperCAmelCase_	(    unittest.TestCase):


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__			:		List[Any]       =	GPTaConfig()

             # attempt to modify each of int/float/bool/str config records and verify they were updated
             lowercase__			:		Optional[int]       =	c.n_embd + 1  # int
             lowercase__			:		Optional[int]       =	c.resid_pdrop + 1.0  # float
             lowercase__			:		List[Any]       =	not c.scale_attn_weights  # bool
             lowercase__			:		Union[str, Any]       =	c.summary_type + 'foo'  # str
             c.update_from_string(
                 f"""n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}"""							)
             self.assertEqual(a		,			c.n_embd		,			'mismatch for key: n_embd'							)
             self.assertEqual(a		,			c.resid_pdrop		,			'mismatch for key: resid_pdrop'							)
             self.assertEqual(a		,			c.scale_attn_weights		,			'mismatch for key: scale_attn_weights'							)
             self.assertEqual(a		,			c.summary_type		,			'mismatch for key: summary_type'							)


      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             lowercase__			:		List[Any]       =	PretrainedConfig()
             lowercase__			:		Dict       =	[key for key in base_config.__dict__ if key not in config_common_kwargs]
             # If this part of the test fails, you have arguments to addin config_common_kwargs above.
             self.assertListEqual(
                 a		,			['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version']							)
             lowercase__			:		Dict       =	[key for key, value in config_common_kwargs.items() if value == getattr(a		,			a							)]
             if len(a							) > 0:
                    raise ValueError(
                        'The following keys are set with the default values in'
                        ' `test_configuration_common.config_common_kwargs` pick another value for them:'
                        f""" {", ".join(a							)}."""							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             with self.assertRaises(a							):
                    # config is in subfolder, the following should not work without specifying the subfolder
                    lowercase__			:		Union[str, Any]       =	BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'							)

             lowercase__			:		str       =	BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'		,			subfolder='bert'							)

             self.assertIsNotNone(a							)


      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             # A mock response for an HTTP head request to emulate server down
             lowercase__			:		List[Any]       =	mock.Mock()
             lowercase__			:		Optional[int]       =	5_0_0
             lowercase__			:		Any       =	{}
             lowercase__			:		Union[str, Any]       =	HTTPError
             lowercase__			:		Any       =	{}

             # Download this model to make sure it's in the cache.
             lowercase__			:		Optional[Any]       =	BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert'							)

             # Under the mock environment we get a 500 error when trying to reach the model.
             with mock.patch('requests.Session.request'		,			return_value=a							) as mock_head:
                    lowercase__			:		Any       =	BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert'							)
                    # This check we did call the fake head request
                    mock_head.assert_called()


      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             # This test is for deprecated behavior and can be removed in v5
             lowercase__			:		Union[str, Any]       =	BertConfig.from_pretrained(
                 'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json'							)


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__			:		str       =	AutoConfig.from_pretrained('bert-base-cased'							)
             lowercase__			:		List[str]       =	['config.4.0.0.json']

             with tempfile.TemporaryDirectory() as tmp_dir:
                    configuration.save_pretrained(a							)
                    lowercase__			:		str       =	2
                    json.dump(configuration.to_dict()		,			open(os.path.join(a		,			'config.4.0.0.json'							)		,			'w'							)							)

                    # This should pick the new configuration file as the version of Transformers is > 4.0.0
                    lowercase__			:		Tuple       =	AutoConfig.from_pretrained(a							)
                    self.assertEqual(new_configuration.hidden_size		,			2							)

                    # Will need to be adjusted if we reach v42 and this test is still here.
                    # Should pick the old configuration file as the version of Transformers is < 4.42.0
                    lowercase__			:		Any       =	['config.42.0.0.json']
                    lowercase__			:		Optional[int]       =	7_6_8
                    configuration.save_pretrained(a							)
                    shutil.move(os.path.join(a		,			'config.4.0.0.json'							)		,			os.path.join(a		,			'config.42.0.0.json'							)							)
                    lowercase__			:		Dict       =	AutoConfig.from_pretrained(a							)
                    self.assertEqual(new_configuration.hidden_size		,			7_6_8							)


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             # This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
             lowercase__			:		Dict       =	'hf-internal-testing/test-two-configs'

             import transformers as new_transformers

             lowercase__			:		str       =	'v4.0.0'
             lowercase__    ,      lowercase__			:		str       =	new_transformers.models.auto.AutoConfig.from_pretrained(
                 a		,			return_unused_kwargs=a							)
             self.assertEqual(new_configuration.hidden_size		,			2							)
             # This checks `_configuration_file` ia not kept in the kwargs by mistake.
             self.assertDictEqual(a		,			{}							)

             # Testing an older version by monkey-patching the version in the module it's used.
             import transformers as old_transformers

             lowercase__			:		Dict       =	'v3.0.0'
             lowercase__			:		List[str]       =	old_transformers.models.auto.AutoConfig.from_pretrained(a							)
             self.assertEqual(old_configuration.hidden_size		,			7_6_8							)



import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
				__snake_case								=  argparse.ArgumentParser()

				parser.add_argument(
				    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
				)
				parser.add_argument(
				    """--original_config_file""",
				    type=str,
				    required=True,
				    help="""The YAML config file corresponding to the original architecture.""",
				)
				parser.add_argument(
				    """--num_in_channels""",
				    default=None,
				    type=int,
				    help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
				)
				parser.add_argument(
				    """--image_size""",
				    default=5_12,
				    type=int,
				    help=(
				        """The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
				        """ Base. Use 768 for Stable Diffusion v2."""
				    ),
				)
				parser.add_argument(
				    """--extract_ema""",
				    action="""store_true""",
				    help=(
				        """Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
				        """ or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
				        """ higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
				    ),
				)
				parser.add_argument(
				    """--upcast_attention""",
				    action="""store_true""",
				    help=(
				        """Whether the attention computation should always be upcasted. This is necessary when running stable"""
				        """ diffusion 2.1."""
				    ),
				)
				parser.add_argument(
				    """--from_safetensors""",
				    action="""store_true""",
				    help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
				)
				parser.add_argument(
				    """--to_safetensors""",
				    action="""store_true""",
				    help="""Whether to store pipeline in safetensors format or not.""",
				)
				parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
				parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")






				def 							_lowercase							(     UpperCamelCase_		)		->		Dict:
									'''simple docstring'''


									if string == "True":
														return True
									elif string == "False":
														return False
									else:
														raise ValueError(F'could not parse string as bool {string}'		)

				parser.add_argument(
				    """--use_linear_projection""", help="""Override for use linear projection""", required=False, type=parse_bool
				)

				parser.add_argument("""--cross_attention_dim""", help="""Override for cross attention_dim""", required=False, type=int)

				__snake_case								=  parser.parse_args()

				__snake_case								=  download_controlnet_from_original_ckpt(
				    checkpoint_path=args.checkpoint_path,
				    original_config_file=args.original_config_file,
				    image_size=args.image_size,
				    extract_ema=args.extract_ema,
				    num_in_channels=args.num_in_channels,
				    upcast_attention=args.upcast_attention,
				    from_safetensors=args.from_safetensors,
				    device=args.device,
				    use_linear_projection=args.use_linear_projection,
				    cross_attention_dim=args.cross_attention_dim,
				)



				controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





