




"""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


lowercase__     						=						logging.get_logger(__name__)






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ):
							super().__init__()
							_lowerCamelCase     :			Optional[Any]    =    nn.ModuleList(lowercase  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = False  ,      lowercase = True  ,      ):


							for i, (image, scale, controlnet) in enumerate(zip(lowercase  ,      lowercase  ,      self.nets  )  ):
											_lowerCamelCase, _lowerCamelCase     :			List[str]    =    controlnet(
											    lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      )

											# merge samples
											if i == 0:
															_lowerCamelCase, _lowerCamelCase     :			Dict    =    down_samples, mid_sample
											else:
															_lowerCamelCase     :			Tuple    =    [
															    samples_prev + samples_curr
															    for samples_prev, samples_curr in zip(lowercase  ,      lowercase  )
															]
															mid_block_res_sample += mid_sample

							return down_block_res_samples, mid_block_res_sample
			def 					A_    (  self  ,      lowercase  ,      lowercase = True  ,      lowercase = None  ,      lowercase = False  ,      lowercase = None  ,      ):
							_lowerCamelCase     :			str    =    0
							_lowerCamelCase     :			Optional[int]    =    save_directory
							for controlnet in self.nets:
											controlnet.save_pretrained(
											    lowercase  ,      is_main_process=lowercase  ,      save_function=lowercase  ,      safe_serialization=lowercase  ,      variant=lowercase  ,      )

											idx += 1
											_lowerCamelCase     :			Optional[Any]    =    model_path_to_save + F'''_{idx}'''






			@classmethod
			def 					A_    (  cls  ,      lowercase  ,      **lowercase  ):
							_lowerCamelCase     :			Tuple    =    0
							_lowerCamelCase     :			int    =    []

							# 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     :			Tuple    =    pretrained_model_path
							while os.path.isdir(lowercase  ):
											_lowerCamelCase     :			Any    =    ControlNetModel.from_pretrained(lowercase  ,      **lowercase  )
											controlnets.append(lowercase  )

											idx += 1
											_lowerCamelCase     :			Optional[int]    =    pretrained_model_path + F'''_{idx}'''

							logger.info(F'''{len(lowercase  )} controlnets loaded from {pretrained_model_path}.'''  )

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

							return cls(lowercase  )

import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
			import jax.numpy as jnp

			from transformers import FlaxXLMRobertaModel




@require_sentencepiece
@require_tokenizers
@require_flax
class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	@slow
	def  A							(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'	)
				_snake_case		=			AutoTokenizer.from_pretrained('xlm-roberta-base'	)
				_snake_case		=			'The dog is cute and lives in the garden house'
				_snake_case		=			jnp.array([tokenizer.encode(lowercase	)]	)

				_snake_case		=			(1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
				_snake_case		=			jnp.array(
				    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]	)

				_snake_case		=			model(lowercase	)['last_hidden_state']
				self.assertEqual(output.shape			,  lowercase	)
				# compare the actual values for a slice of last dim
				self.assertTrue(jnp.allclose(output[:, :, -1]			,  lowercase			,  atol=1E-3	)	)