"""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


lowerCAmelCase__  :  List[Any]						     =	logging.get_logger(__name__)
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def __init__(				self   :	List[str]    ,lowerCamelCase__   :	Union[List[ControlNetModel], Tuple[ControlNetModel]]			):
									super().__init__()
									UpperCAmelCase__             =							nn.ModuleList(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	torch.FloatTensor    ,lowerCamelCase__   :	Union[torch.Tensor, float, int]    ,lowerCamelCase__   :	torch.Tensor    ,lowerCamelCase__   :	List[torch.tensor]    ,lowerCamelCase__   :	List[float]    ,lowerCamelCase__   :	Optional[torch.Tensor] = None    ,lowerCamelCase__   :	Optional[torch.Tensor] = None    ,lowerCamelCase__   :	Optional[torch.Tensor] = None    ,lowerCamelCase__   :	Optional[Dict[str, Any]] = None    ,lowerCamelCase__   :	bool = False    ,lowerCamelCase__   :	bool = 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   :	Union[str, Any]    ,lowerCamelCase__   :	Union[str, os.PathLike]    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Callable = None    ,lowerCamelCase__   :	bool = False    ,lowerCamelCase__   :	Optional[str] = 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   :	str    ,lowerCamelCase__   :	Optional[Union[str, os.PathLike]]    ,**lowerCamelCase__   :	str			):
									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__			)









"""simple docstring"""




import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
					import torch






@is_pipeline_test
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				__UpperCamelCase			  =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					TextaTextGenerationPipeline(model=_a		, tokenizer=_a )
							return generator, ["Something to write", "Something else"]





				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					generator("""Something there""" )
							self.assertEqual(_a		, [{"""generated_text""": ANY(_a )}] )
							# These are encoder decoder, they don't just append to incoming string
							self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there""" ) )

							lowerCamelCase			      =					generator(["""This is great !""", """Something else"""]		, num_return_sequences=2		, do_sample=_a )
							self.assertEqual(
							    _a		, [
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							    ]		, )

							lowerCamelCase			      =					generator(
							    ["""This is great !""", """Something else"""]		, num_return_sequences=2		, batch_size=2		, do_sample=_a )
							self.assertEqual(
							    _a		, [
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							    ]		, )

							with self.assertRaises(_a ):
										generator(4 )





				@require_torch
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""pt""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )

							lowerCamelCase			      =					3
							lowerCamelCase			      =					generator(
							    """Something there"""		, num_return_sequences=_a		, num_beams=_a		, )
							lowerCamelCase			      =					[
							    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
							    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
							    {"""generated_text""": """"""},
							]
							self.assertEqual(_a		, _a )

							lowerCamelCase			      =					generator("""This is a test"""		, do_sample=_a		, num_return_sequences=2		, return_tensors=_a )
							self.assertEqual(
							    _a		, [
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							    ]		, )
							lowerCamelCase			      =					generator.model.config.eos_token_id
							lowerCamelCase			      =					"""<pad>"""
							lowerCamelCase			      =					generator(
							    ["""This is a test""", """This is a second test"""]		, do_sample=_a		, num_return_sequences=2		, batch_size=2		, return_tensors=_a		, )
							self.assertEqual(
							    _a		, [
							        [
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							        ],
							        [
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							        ],
							    ]		, )





				@require_tf
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""tf""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )



