






'''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         =						logging.get_logger(__name__)






class   _SCREAMING_SNAKE_CASE   (   __a     ):



 def __init__(   self		:   Optional[int]			,				a__		:   Union[List[ControlNetModel], Tuple[ControlNetModel]]      ):
      super().__init__()
      __magic_name__						       =    nn.ModuleList(a__      )



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   torch.FloatTensor			,				a__		:   Union[torch.Tensor, float, int]			,				a__		:   torch.Tensor			,				a__		:   List[torch.tensor]			,				a__		:   List[float]			,				a__		:   Optional[torch.Tensor] = None			,				a__		:   Optional[torch.Tensor] = None			,				a__		:   Optional[torch.Tensor] = None			,				a__		:   Optional[Dict[str, Any]] = None			,				a__		:   bool = False			,				a__		:   bool = True			,				):
      for i, (image, scale, controlnet) in enumerate(zip(a__			,				a__			,				self.nets      )      ):
           __magic_name__       ,  __magic_name__						       =    controlnet(
               a__			,				a__			,				a__			,				a__			,				a__			,				a__			,				a__			,				a__			,				a__			,				a__			,				a__			,				)

           # merge samples
           if i == 0:
                __magic_name__       ,  __magic_name__						       =    down_samples, mid_sample
           else:
                __magic_name__						       =    [
                    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		:   List[str]			,				a__		:   Union[str, os.PathLike]			,				a__		:   bool = True			,				a__		:   Callable = None			,				a__		:   bool = False			,				a__		:   Optional[str] = None			,				):
      __magic_name__						       =    0
      __magic_name__						       =    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
           __magic_name__						       =    model_path_to_save + F'''_{idx}'''



 @classmethod
 def 			snake_case__  (   cls		:   Optional[int]			,				a__		:   Optional[Union[str, os.PathLike]]			,				**a__		:   List[Any]      ):
      __magic_name__						       =    0
      __magic_name__						       =    []

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

           idx += 1
           __magic_name__						       =    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'''




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   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 __SCREAMING_SNAKE_CASE					:Any      	=					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



 def 			snake_case__  (   self		:   Tuple			,				a__		:   Tuple			,				a__		:   int			,				a__		:   int      ):
      __magic_name__						       =    TextaTextGenerationPipeline(model=a__			,				tokenizer=a__      )
      return generator, ["Something to write", "Something else"]



 def 			snake_case__  (   self		:   List[str]			,				a__		:   List[Any]			,				a__		:   List[str]      ):
      __magic_name__						       =    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'''      )      )

      __magic_name__						       =    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__      )}],
          ]			,				)

      __magic_name__						       =    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 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    pipeline('''text2text-generation'''			,				model='''patrickvonplaten/t5-tiny-random'''			,				framework='''pt'''      )
      # do_sample=False necessary for reproducibility
      __magic_name__						       =    generator('''Something there'''			,				do_sample=a__      )
      self.assertEqual(a__			,				[{'''generated_text''': ''''''}]      )

      __magic_name__						       =    3
      __magic_name__						       =    generator(
          '''Something there'''			,				num_return_sequences=a__			,				num_beams=a__			,				)
      __magic_name__						       =    [
          {'''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__      )

      __magic_name__						       =    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      )},
          ]			,				)
      __magic_name__						       =    generator.model.config.eos_token_id
      __magic_name__						       =    '''<pad>'''
      __magic_name__						       =    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 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    pipeline('''text2text-generation'''			,				model='''patrickvonplaten/t5-tiny-random'''			,				framework='''tf'''      )
      # do_sample=False necessary for reproducibility
      __magic_name__						       =    generator('''Something there'''			,				do_sample=a__      )
      self.assertEqual(a__			,				[{'''generated_text''': ''''''}]      )






