



"""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__    :  Optional[int]							    =	logging.get_logger(__name__)




class        _UpperCAmelCase       (			_lowerCamelCase):

 def __init__(     self		:       List[str]  ,     lowercase_		:       Tuple				):
     super().__init__()
     snake_case_ :     List[str]				=	nn.ModuleList(a_				)

 def 						_snake_case					(     self		:       Union[str, Any]  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       Tuple  ,     lowercase_		:       Dict  ,     lowercase_		:       Tuple  ,     lowercase_		:       Optional[Any] = None  ,     lowercase_		:       Dict = None  ,     lowercase_		:       List[Any] = None  ,     lowercase_		:       Tuple = None  ,     lowercase_		:       int = False  ,     lowercase_		:       Tuple = True  ,     ):
     for i, (image, scale, controlnet) in enumerate(zip(a_  ,     a_  ,     self.nets				)				):
         snake_case_ :     List[Any]				=	controlnet(
             a_  ,     a_  ,     a_  ,     a_  ,     a_  ,     a_  ,     a_  ,     a_  ,     a_  ,     a_  ,     a_  ,     )

         # merge samples
         if i == 0:
             snake_case_ :     Optional[int]				=	down_samples, mid_sample
         else:
             snake_case_ :     Dict				=	[
                 samples_prev + samples_curr
                 for samples_prev, samples_curr in zip(a_  ,     a_				)
             ]
             mid_block_res_sample += mid_sample

     return down_block_res_samples, mid_block_res_sample

 def 						_snake_case					(     self		:       Dict  ,     lowercase_		:       Any  ,     lowercase_		:       Any = True  ,     lowercase_		:       str = None  ,     lowercase_		:       List[str] = False  ,     lowercase_		:       List[str] = None  ,     ):
     snake_case_ :     List[Any]				=	0
     snake_case_ :     Dict				=	save_directory
     for controlnet in self.nets:
         controlnet.save_pretrained(
             a_  ,     is_main_process=a_  ,     save_function=a_  ,     safe_serialization=a_  ,     variant=a_  ,     )

         idx += 1
         snake_case_ :     Optional[Any]				=	model_path_to_save + f"_{idx}"






 @classmethod
 def 						_snake_case					(     cls		:       Tuple  ,     lowercase_		:       str  ,     **lowercase_		:       Union[str, Any]				):
     snake_case_ :     str				=	0
     snake_case_ :     Union[str, Any]				=	[]

     # 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`, ...
     snake_case_ :     Optional[int]				=	pretrained_model_path
     while os.path.isdir(a_				):
         snake_case_ :     List[str]				=	ControlNetModel.from_pretrained(a_  ,     **a_				)
         controlnets.append(a_				)

         idx += 1
         snake_case_ :     Tuple				=	pretrained_model_path + f"_{idx}"

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

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

     return cls(a_				)










'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel



@require_tf
@require_sentencepiece
@require_tokenizers
class 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 @slow
 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Dict			     = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""							)

        _UpperCAmelCase  :						Optional[int]			     = tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]  ,dtype=tf.intaa  ,)  # J'aime le camembert !"

        _UpperCAmelCase  :						Dict			     = model(a_							)["""last_hidden_state"""]
        _UpperCAmelCase  :						Dict			     = tf.TensorShape((1, 10, 768)							)
        self.assertEqual(output.shape  ,a_							)
        # compare the actual values for a slice.
        _UpperCAmelCase  :						Tuple			     = tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]  ,dtype=tf.floataa  ,)
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,expected_slice.numpy()  ,atol=1E-4							)							)



