





import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


UpperCamelCase__											= logging.get_logger(__name__)

class        __SCREAMING_SNAKE_CASE					(     _a       ):






							def __init__(						self  , __lowerCAmelCase  ):
														super().__init__()
														UpperCamelCase__				     =			nn.ModuleList(__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase = None  , __lowerCAmelCase = None  , __lowerCAmelCase = None  , __lowerCAmelCase = None  , __lowerCAmelCase = False  , __lowerCAmelCase = True  , ):
														for i, (image, scale, controlnet) in enumerate(zip(__lowerCAmelCase  , __lowerCAmelCase  , self.nets  )  ):
																					UpperCamelCase__     ,     UpperCamelCase__				     =			controlnet(
																					    __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , )

																					# merge samples
																					if i == 0:
																												UpperCamelCase__     ,     UpperCamelCase__				     =			down_samples, mid_sample
																					else:
																												UpperCamelCase__				     =			[
																												    samples_prev + samples_curr
																												    for samples_prev, samples_curr in zip(__lowerCAmelCase  , __lowerCAmelCase  )
																												]
																												mid_block_res_sample += mid_sample

														return down_block_res_samples, mid_block_res_sample






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase = True  , __lowerCAmelCase = None  , __lowerCAmelCase = False  , __lowerCAmelCase = None  , ):
														UpperCamelCase__				     =			0
														UpperCamelCase__				     =			save_directory
														for controlnet in self.nets:
																					controlnet.save_pretrained(
																					    __lowerCAmelCase  , is_main_process=__lowerCAmelCase  , save_function=__lowerCAmelCase  , safe_serialization=__lowerCAmelCase  , variant=__lowerCAmelCase  , )

																					idx += 1
																					UpperCamelCase__				     =			model_path_to_save + f"""_{idx}"""







							@classmethod
							def 				_lowerCamelCase		(						cls  , __lowerCAmelCase  , **__lowerCAmelCase  ):
														UpperCamelCase__				     =			0
														UpperCamelCase__				     =			[]

														# load controlnet and append to list until no controlnet directory exists anymore
														# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
														# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
														UpperCamelCase__				     =			pretrained_model_path
														while os.path.isdir(__lowerCAmelCase  ):
																					UpperCamelCase__				     =			ControlNetModel.from_pretrained(__lowerCAmelCase  , **__lowerCAmelCase  )
																					controlnets.append(__lowerCAmelCase  )

																					idx += 1
																					UpperCamelCase__				     =			pretrained_model_path + f"""_{idx}"""

														logger.info(f"""{len(__lowerCAmelCase  )} controlnets loaded from {pretrained_model_path}."""  )

														if len(__lowerCAmelCase  ) == 0:
																					raise ValueError(
																					    f"""No ControlNets found under {os.path.dirname(__lowerCAmelCase  )}. Expected at least {pretrained_model_path + "_0"}."""  )

														return cls(__lowerCAmelCase  )











import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
					import jax

					from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
					from transformers.models.bert.modeling_flax_bert import FlaxBertModel
					from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel

@require_flax
class        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["bert-base-cased", "bert-large-uncased"]:
																					with self.subTest(__lowerCAmelCase  ):
																												UpperCamelCase__				     =			AutoConfig.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )

																												UpperCamelCase__				     =			FlaxAutoModel.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["roberta-base", "roberta-large"]:
																					with self.subTest(__lowerCAmelCase  ):
																												UpperCamelCase__				     =			AutoConfig.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )

																												UpperCamelCase__				     =			FlaxAutoModel.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["bert-base-cased", "bert-large-uncased"]:
																					UpperCamelCase__				     =			AutoTokenizer.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			FlaxBertModel.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			tokenizer("""Do you support jax jitted function?"""  , return_tensors=TensorType.JAX  )

																					@jax.jit
																					def eval(**__lowerCAmelCase  ):
																												return model(**__lowerCAmelCase  )

																					eval(**__lowerCAmelCase  ).block_until_ready()






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["roberta-base", "roberta-large"]:
																					UpperCamelCase__				     =			AutoTokenizer.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			FlaxRobertaModel.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			tokenizer("""Do you support jax jitted function?"""  , return_tensors=TensorType.JAX  )

																					@jax.jit
																					def eval(**__lowerCAmelCase  ):
																												return model(**__lowerCAmelCase  )

																					eval(**__lowerCAmelCase  ).block_until_ready()






							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(
														    __lowerCAmelCase  , """bert-base is not a local folder and is not a valid model identifier"""  ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained("""bert-base"""  )






							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(
														    __lowerCAmelCase  , r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""  ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained(__lowerCAmelCase  , revision="""aaaaaa"""  )






							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(
														    __lowerCAmelCase  , """hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack"""  , ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained("""hf-internal-testing/config-no-model"""  )







							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(__lowerCAmelCase  , """Use `from_pt=True` to load this model"""  ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained("""hf-internal-testing/tiny-bert-pt-only"""  )




