
'''simple docstring'''

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__ :			Dict											=		logging.get_logger(__name__)






class  A  (						SCREAMING_SNAKE_CASE__     ):





	def __init__(       self      :							Optional[int]   ,				__magic_name__      :							Union[List[ControlNetModel], Tuple[ControlNetModel]]				):


				"""simple docstring"""

				super().__init__()
				lowerCAmelCase__										=				nn.ModuleList(__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]   ,				__magic_name__      :							torch.FloatTensor   ,				__magic_name__      :							Union[torch.Tensor, float, int]   ,				__magic_name__      :							torch.Tensor   ,				__magic_name__      :							List[torch.tensor]   ,				__magic_name__      :							List[float]   ,				__magic_name__      :							Optional[torch.Tensor] = None   ,				__magic_name__      :							Optional[torch.Tensor] = None   ,				__magic_name__      :							Optional[torch.Tensor] = None   ,				__magic_name__      :							Optional[Dict[str, Any]] = None   ,				__magic_name__      :							bool = False   ,				__magic_name__      :							bool = True   ,				):


				"""simple docstring"""

				for i, (image, scale, controlnet) in enumerate(zip(__magic_name__   ,				__magic_name__   ,				self.nets				)				):
							lowerCAmelCase__						,lowerCAmelCase__										=				controlnet(
							    __magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				)

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

				return down_block_res_samples, mid_block_res_sample





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]   ,				__magic_name__      :							Union[str, os.PathLike]   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Callable = None   ,				__magic_name__      :							bool = False   ,				__magic_name__      :							Optional[str] = None   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				0
				lowerCAmelCase__										=				save_directory
				for controlnet in self.nets:
							controlnet.save_pretrained(
							    __magic_name__   ,				is_main_process=__magic_name__   ,				save_function=__magic_name__   ,				safe_serialization=__magic_name__   ,				variant=__magic_name__   ,				)

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







	@classmethod
	def 				__SCREAMING_SNAKE_CASE	(       cls      :							str   ,				__magic_name__      :							Optional[Union[str, os.PathLike]]   ,				**__magic_name__      :							Tuple				):


				"""simple docstring"""

				lowerCAmelCase__										=				0
				lowerCAmelCase__										=				[]

				# 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`, ...
				lowerCAmelCase__										=				pretrained_model_path
				while os.path.isdir(__magic_name__				):
							lowerCAmelCase__										=				ControlNetModel.from_pretrained(__magic_name__   ,				**__magic_name__				)
							controlnets.append(__magic_name__				)

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

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

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

				return cls(__magic_name__				)













"""simple docstring"""
import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.distilbert.modeling_flax_distilbert import (
			    FlaxDistilBertForMaskedLM,
			    FlaxDistilBertForMultipleChoice,
			    FlaxDistilBertForQuestionAnswering,
			    FlaxDistilBertForSequenceClassification,
			    FlaxDistilBertForTokenClassification,
			    FlaxDistilBertModel,
			)







class      UpperCamelCase_			(unittest.TestCase	):





	def __init__( self   :					int      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					Union[str, Any]=13      ,      lowerCAmelCase_   :					Union[str, Any]=7      ,      lowerCAmelCase_   :					Optional[Any]=True      ,      lowerCAmelCase_   :					List[Any]=True      ,      lowerCAmelCase_   :					Any=True      ,      lowerCAmelCase_   :					Tuple=True      ,      lowerCAmelCase_   :					Tuple=99      ,      lowerCAmelCase_   :					str=32      ,      lowerCAmelCase_   :					Optional[int]=5      ,      lowerCAmelCase_   :					List[Any]=4      ,      lowerCAmelCase_   :					List[Any]=37      ,      lowerCAmelCase_   :					str="gelu"      ,      lowerCAmelCase_   :					Optional[Any]=0.1      ,      lowerCAmelCase_   :					Union[str, Any]=0.1      ,      lowerCAmelCase_   :					List[Any]=512      ,      lowerCAmelCase_   :					Dict=16      ,      lowerCAmelCase_   :					int=2      ,      lowerCAmelCase_   :					Union[str, Any]=0.0_2      ,      lowerCAmelCase_   :					Optional[Any]=4      ,      )		->     Tuple:
				UpperCAmelCase_	:					Optional[Any]     	=   parent
				UpperCAmelCase_	:					Tuple     	=   batch_size
				UpperCAmelCase_	:					str     	=   seq_length
				UpperCAmelCase_	:					Optional[int]     	=   is_training
				UpperCAmelCase_	:					Dict     	=   use_attention_mask
				UpperCAmelCase_	:					int     	=   use_token_type_ids
				UpperCAmelCase_	:					Tuple     	=   use_labels
				UpperCAmelCase_	:					Tuple     	=   vocab_size
				UpperCAmelCase_	:					Union[str, Any]     	=   hidden_size
				UpperCAmelCase_	:					int     	=   num_hidden_layers
				UpperCAmelCase_	:					List[str]     	=   num_attention_heads
				UpperCAmelCase_	:					str     	=   intermediate_size
				UpperCAmelCase_	:					List[str]     	=   hidden_act
				UpperCAmelCase_	:					Tuple     	=   hidden_dropout_prob
				UpperCAmelCase_	:					Optional[Any]     	=   attention_probs_dropout_prob
				UpperCAmelCase_	:					Optional[int]     	=   max_position_embeddings
				UpperCAmelCase_	:					Optional[Any]     	=   type_vocab_size
				UpperCAmelCase_	:					Dict     	=   type_sequence_label_size
				UpperCAmelCase_	:					List[Any]     	=   initializer_range
				UpperCAmelCase_	:					Any     	=   num_choices





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     str:
				UpperCAmelCase_	:					List[str]     	=   ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size		)

				UpperCAmelCase_	:					Tuple     	=   None
				if self.use_attention_mask:
							UpperCAmelCase_	:					Dict     	=   random_attention_mask([self.batch_size, self.seq_length]		)

				UpperCAmelCase_	:					Any     	=   DistilBertConfig(
				    vocab_size=self.vocab_size      ,      dim=self.hidden_size      ,      n_layers=self.num_hidden_layers      ,      n_heads=self.num_attention_heads      ,      hidden_dim=self.intermediate_size      ,      hidden_act=self.hidden_act      ,      dropout=self.hidden_dropout_prob      ,      attention_dropout=self.attention_probs_dropout_prob      ,      max_position_embeddings=self.max_position_embeddings      ,      initializer_range=self.initializer_range      ,      tie_weights_=lowerCAmelCase_      ,      )

				return config, input_ids, attention_mask





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Any     	=   self.prepare_config_and_inputs()
				UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					List[str]     	=   config_and_inputs
				UpperCAmelCase_	:					Optional[Any]     	=   {"input_ids": input_ids, "attention_mask": attention_mask}
				return config, inputs_dict







@require_flax
class      UpperCamelCase_			(__A							,		unittest.TestCase	):
	__magic_name__       =							(
	    (
	        FlaxDistilBertModel,
	        FlaxDistilBertForMaskedLM,
	        FlaxDistilBertForMultipleChoice,
	        FlaxDistilBertForQuestionAnswering,
	        FlaxDistilBertForSequenceClassification,
	        FlaxDistilBertForTokenClassification,
	        FlaxDistilBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Optional[Any]:
				UpperCAmelCase_	:					List[Any]     	=   FlaxDistilBertModelTester(self		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     List[str]:
				for model_class_name in self.all_model_classes:
							UpperCAmelCase_	:					str     	=   model_class_name.from_pretrained("distilbert-base-uncased"		)
							UpperCAmelCase_	:					List[str]     	=   model(np.ones((1, 1)		)		)
							self.assertIsNotNone(lowerCAmelCase_		)







@require_flax
class      UpperCamelCase_			(unittest.TestCase	):





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Optional[int]:
				UpperCAmelCase_	:					Any     	=   FlaxDistilBertModel.from_pretrained("distilbert-base-uncased"		)
				UpperCAmelCase_	:					int     	=   np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]		)
				UpperCAmelCase_	:					List[str]     	=   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
				UpperCAmelCase_	:					Any     	=   model(lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_		)[0]
				UpperCAmelCase_	:					Union[str, Any]     	=   (1, 11, 768)
				self.assertEqual(output.shape      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					Dict     	=   np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]]		)

				self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]      ,      lowerCAmelCase_      ,      atol=1e-4		)		)



