



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


a_								=       logging.get_logger(__name__)



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):




 def __init__(    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    super().__init__()
    __lowerCamelCase    				=     nn.ModuleList(__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					__UpperCAmelCase = False						,					__UpperCAmelCase = True						,					):



    '''simple docstring'''
    for i, (image, scale, controlnet) in enumerate(zip(__UpperCAmelCase						,					__UpperCAmelCase						,					self.nets					)					):
       __lowerCamelCase				,__lowerCamelCase    				=     controlnet(
           __UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					)

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

    return down_block_res_samples, mid_block_res_sample




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase = True						,					__UpperCAmelCase = None						,					__UpperCAmelCase = False						,					__UpperCAmelCase = None						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     0
    __lowerCamelCase    				=     save_directory
    for controlnet in self.nets:
       controlnet.save_pretrained(
           __UpperCAmelCase						,					is_main_process=__UpperCAmelCase						,					save_function=__UpperCAmelCase						,					safe_serialization=__UpperCAmelCase						,					variant=__UpperCAmelCase						,					)

       idx += 1
       __lowerCamelCase    				=     model_path_to_save + F"""_{idx}"""




 @classmethod
 def 							lowerCamelCase   (    cls						,					__UpperCAmelCase						,					**__UpperCAmelCase					):



    '''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(__UpperCAmelCase					):
       __lowerCamelCase    				=     ControlNetModel.from_pretrained(__UpperCAmelCase						,					**__UpperCAmelCase					)
       controlnets.append(__UpperCAmelCase					)

       idx += 1
       __lowerCamelCase    				=     pretrained_model_path + F"""_{idx}"""

    logger.info(F"""{len(__UpperCAmelCase					)} controlnets loaded from {pretrained_model_path}."""					)

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

    return cls(__UpperCAmelCase					)











import os
import sys
import unittest


a_								=       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
a_								=       os.path.join(git_repo_path, """src""", """transformers""")

a_								=       """
{0} = None
"""

a_								=       """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


a_								=       """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""



class 			__lowerCAmelCase  (						unittest.TestCase ):




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")'''					)
    self.assertIsNone(__UpperCAmelCase					)

    __lowerCamelCase    				=     find_backend('''    if not is_tokenizers_available():'''					)
    self.assertEqual(__UpperCAmelCase						,					'''tokenizers'''					)

    __lowerCamelCase    				=     find_backend('''    if not is_tensorflow_text_available():'''					)
    self.assertEqual(__UpperCAmelCase						,					'''tensorflow_text'''					)

    __lowerCamelCase    				=     find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):'''					)
    self.assertEqual(__UpperCAmelCase						,					'''sentencepiece_and_tokenizers'''					)

    __lowerCamelCase    				=     find_backend(
        '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'''					)
    self.assertEqual(__UpperCAmelCase						,					'''sentencepiece_and_tensorflow_text'''					)

    __lowerCamelCase    				=     find_backend(
        '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'''					)
    self.assertEqual(__UpperCAmelCase						,					'''sentencepiece_and_tokenizers_and_vision'''					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     read_init()
    # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
    self.assertIn('''torch'''						,					__UpperCAmelCase					)
    self.assertIn('''tensorflow_text'''						,					__UpperCAmelCase					)
    self.assertIn('''sentencepiece_and_tokenizers'''						,					__UpperCAmelCase					)

    # Likewise, we can't assert on the exact content of a key
    self.assertIn('''BertModel'''						,					objects['''torch''']					)
    self.assertIn('''TFBertModel'''						,					objects['''tf''']					)
    self.assertIn('''FlaxBertModel'''						,					objects['''flax''']					)
    self.assertIn('''BertModel'''						,					objects['''torch''']					)
    self.assertIn('''TFBertTokenizer'''						,					objects['''tensorflow_text''']					)
    self.assertIn('''convert_slow_tokenizer'''						,					objects['''sentencepiece_and_tokenizers''']					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     create_dummy_object('''CONSTANT'''						,					'''\'torch\''''					)
    self.assertEqual(__UpperCAmelCase						,					'''\nCONSTANT = None\n'''					)

    __lowerCamelCase    				=     create_dummy_object('''function'''						,					'''\'torch\''''					)
    self.assertEqual(
        __UpperCAmelCase						,					'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''					)

    __lowerCamelCase    				=     '''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
    __lowerCamelCase    				=     create_dummy_object('''FakeClass'''						,					'''\'torch\''''					)
    self.assertEqual(__UpperCAmelCase						,					__UpperCAmelCase					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
    __lowerCamelCase    				=     create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}					)
    self.assertEqual(dummy_files['''torch''']						,					__UpperCAmelCase					)






