"""simple docstring"""
import glob
import os
from typing import Dict, List, Union

import torch

from diffusers.utils import is_safetensors_available


if is_safetensors_available():
 import safetensors.torch

from huggingface_hub import snapshot_download

from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME




class   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self)      ->     List[Any]:





        '''simple docstring'''





        self.register_to_config()
        super().__init__()



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        if dicta == dicta:
          return True
        else:
          a__				,       a__:	List[str]     				=      self._remove_meta_keys(lowercase)
          a__				,       a__:	List[str]     				=      self._remove_meta_keys(lowercase)
          if configa == configa:
            print(f'Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.')
            return True
        return False



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Optional[Any]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      []
        a__:	Optional[int]     				=      config_dict.copy()
        for key in config_dict.keys():
          if key.startswith('_'):
            temp_dict.pop(lowercase)
            meta_keys.append(lowercase)
        return (temp_dict, meta_keys)



      @torch.no_grad()
      def     lowerCamelCase_  (		self       ,     lowercase       ,     **lowercase)      ->     int:





        '''simple docstring'''





        a__:	List[Any]     				=      kwargs.pop('cache_dir'       ,     lowercase)
        a__:	Union[str, Any]     				=      kwargs.pop('resume_download'       ,     lowercase)
        a__:	Any     				=      kwargs.pop('force_download'       ,     lowercase)
        a__:	str     				=      kwargs.pop('proxies'       ,     lowercase)
        a__:	Any     				=      kwargs.pop('local_files_only'       ,     lowercase)
        a__:	int     				=      kwargs.pop('use_auth_token'       ,     lowercase)
        a__:	Any     				=      kwargs.pop('revision'       ,     lowercase)
        a__:	Tuple     				=      kwargs.pop('torch_dtype'       ,     lowercase)
        a__:	Any     				=      kwargs.pop('device_map'       ,     lowercase)

        a__:	Dict     				=      kwargs.pop('alpha'       ,     0.5)
        a__:	Optional[int]     				=      kwargs.pop('interp'       ,     lowercase)

        print('Received list'       ,     lowercase)
        print(f'Combining with alpha={alpha}, interpolation mode={interp}')

        a__:	Optional[Any]     				=      len(lowercase)
        # Ignore result from model_index_json comparision of the two checkpoints
        a__:	Tuple     				=      kwargs.pop('force'       ,     lowercase)

        # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
        if checkpoint_count > 3 or checkpoint_count < 2:
          raise ValueError(
              'Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being'
              ' passed.')

        print('Received the right number of checkpoints')
        # chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
        # chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None

        # Validate that the checkpoints can be merged
        # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
        a__:	Optional[Any]     				=      []
        for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
          a__:	List[str]     				=      DiffusionPipeline.load_config(
              lowercase       ,     cache_dir=lowercase       ,     resume_download=lowercase       ,     force_download=lowercase       ,     proxies=lowercase       ,     local_files_only=lowercase       ,     use_auth_token=lowercase       ,     revision=lowercase       ,     )
          config_dicts.append(lowercase)

        a__:	Optional[int]     				=      True
        for idx in range(1       ,     len(lowercase)):
          comparison_result &= self._compare_model_configs(config_dicts[idx - 1]       ,     config_dicts[idx])
          if not force and comparison_result is False:
            raise ValueError('Incompatible checkpoints. Please check model_index.json for the models.')
            print(config_dicts[0]       ,     config_dicts[1])
        print('Compatible model_index.json files found')
        # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
        a__:	List[str]     				=      []
        for pretrained_model_name_or_path, config_dict in zip(lowercase       ,     lowercase):
          a__:	Tuple     				=      [k for k in config_dict.keys() if not k.startswith('_')]
          a__:	int     				=      [os.path.join(lowercase       ,     '*') for k in folder_names]
          allow_patterns += [
              WEIGHTS_NAME,
              SCHEDULER_CONFIG_NAME,
              CONFIG_NAME,
              ONNX_WEIGHTS_NAME,
              DiffusionPipeline.config_name,
          ]
          a__:	Tuple     				=      config_dict.get('_class_name')
          a__:	List[Any]     				=      {'diffusers': __version__, 'pipeline_class': requested_pipeline_class}

          a__:	Optional[Any]     				=      (
              pretrained_model_name_or_path
              if os.path.isdir(lowercase)
              else snapshot_download(
                  lowercase       ,     cache_dir=lowercase       ,     resume_download=lowercase       ,     proxies=lowercase       ,     local_files_only=lowercase       ,     use_auth_token=lowercase       ,     revision=lowercase       ,     allow_patterns=lowercase       ,     user_agent=lowercase       ,     )
          )
          print('Cached Folder'       ,     lowercase)
          cached_folders.append(lowercase)

        # Step 3:-
        # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
        a__:	Tuple     				=      DiffusionPipeline.from_pretrained(
            cached_folders[0]       ,     torch_dtype=lowercase       ,     device_map=lowercase)
        final_pipe.to(self.device)

        a__:	Tuple     				=      None
        if len(lowercase) > 2:
          a__:	Optional[int]     				=      os.path.join(cached_folders[2])

        if interp == "sigmoid":
          a__:	Optional[int]     				=      CheckpointMergerPipeline.sigmoid
        elif interp == "inv_sigmoid":
          a__:	Optional[int]     				=      CheckpointMergerPipeline.inv_sigmoid
        elif interp == "add_diff":
          a__:	int     				=      CheckpointMergerPipeline.add_difference
        else:
          a__:	Dict     				=      CheckpointMergerPipeline.weighted_sum

        # Find each module's state dict.
        for attr in final_pipe.config.keys():
          if not attr.startswith('_'):
            a__:	List[str]     				=      os.path.join(cached_folders[1]       ,     lowercase)
            if os.path.exists(lowercase):
              a__:	int     				=      [
                  *glob.glob(os.path.join(lowercase       ,     '*.safetensors')),
                  *glob.glob(os.path.join(lowercase       ,     '*.bin')),
              ]
              a__:	int     				=      files[0] if len(lowercase) > 0 else None
            if len(lowercase) < 3:
              a__:	Union[str, Any]     				=      None
            else:
              a__:	List[str]     				=      os.path.join(cached_folders[2]       ,     lowercase)
              if os.path.exists(lowercase):
                a__:	int     				=      [
                    *glob.glob(os.path.join(lowercase       ,     '*.safetensors')),
                    *glob.glob(os.path.join(lowercase       ,     '*.bin')),
                ]
                a__:	Optional[Any]     				=      files[0] if len(lowercase) > 0 else None
                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
            if checkpoint_path_a is None and checkpoint_path_a is None:
              print(f'Skipping {attr}: not present in 2nd or 3d model')
              continue
            try:
              a__:	Optional[int]     				=      getattr(lowercase       ,     lowercase)
              if isinstance(lowercase       ,     lowercase):  # ignore requires_safety_checker boolean
                continue
              a__:	List[str]     				=      getattr(lowercase       ,     'state_dict')
              a__:	Optional[Any]     				=      theta_a()

              a__:	int     				=      getattr(lowercase       ,     'load_state_dict')
              a__:	str     				=      (
                  safetensors.torch.load_file(lowercase)
                  if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'))
                  else torch.load(lowercase       ,     map_location='cpu')
              )
              a__:	str     				=      None
              if checkpoint_path_a:
                a__:	Optional[Any]     				=      (
                    safetensors.torch.load_file(lowercase)
                    if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'))
                    else torch.load(lowercase       ,     map_location='cpu')
                )

              if not theta_a.keys() == theta_a.keys():
                print(f'Skipping {attr}: key mismatch')
                continue
              if theta_a and not theta_a.keys() == theta_a.keys():
                print(f'Skipping {attr}:y mismatch')
            except Exception as e:
              print(f'Skipping {attr} do to an unexpected error: {str(lowercase)}')
              continue
            print(f'MERGING {attr}')

            for key in theta_a.keys():
              if theta_a:
                a__:	Optional[int]     				=      theta_func(theta_a[key]       ,     theta_a[key]       ,     theta_a[key]       ,     lowercase)
              else:
                a__:	List[Any]     				=      theta_func(theta_a[key]       ,     theta_a[key]       ,     lowercase       ,     lowercase)

            del theta_a
            del theta_a
            update_theta_a(lowercase)

            del theta_a
        return final_pipe



      @staticmethod
      def     lowerCamelCase_  (		lowercase       ,     lowercase       ,     lowercase       ,     lowercase)      ->     str:





        '''simple docstring'''





        return ((1 - alpha) * thetaa) + (alpha * thetaa)



      @staticmethod
      def     lowerCamelCase_  (		lowercase       ,     lowercase       ,     lowercase       ,     lowercase)      ->     str:





        '''simple docstring'''





        a__:	Any     				=      alpha * alpha * (3 - (2 * alpha))
        return thetaa + ((thetaa - thetaa) * alpha)



      @staticmethod
      def     lowerCamelCase_  (		lowercase       ,     lowercase       ,     lowercase       ,     lowercase)      ->     Optional[Any]:





        '''simple docstring'''





        import math

        a__:	Any     				=      0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
        return thetaa + ((thetaa - thetaa) * alpha)



      @staticmethod
      def     lowerCamelCase_  (		lowercase       ,     lowercase       ,     lowercase       ,     lowercase)      ->     List[Any]:





        '''simple docstring'''





        return thetaa + (thetaa - thetaa) * (1.0 - alpha)






import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor







class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       def __init__(  self				:     Dict   , __lowerCamelCase				:     int   , __lowerCamelCase				:     List[Any]=3   , __lowerCamelCase				:     Dict=32   , __lowerCamelCase				:     Optional[Any]=3   , __lowerCamelCase				:     List[str]=10   , __lowerCamelCase				:     List[str]=[10, 20, 30, 40]   , __lowerCamelCase				:     List[Any]=[1, 1, 2, 1]   , __lowerCamelCase				:     Any=True   , __lowerCamelCase				:     Optional[int]=True   , __lowerCamelCase				:     int="relu"   , __lowerCamelCase				:     List[str]=3   , __lowerCamelCase				:     Any=None   , ):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    parent
             lowerCamelCase__						:							Optional[int]       =    batch_size
             lowerCamelCase__						:							List[Any]       =    image_size
             lowerCamelCase__						:							Union[str, Any]       =    num_channels
             lowerCamelCase__						:							Any       =    embeddings_size
             lowerCamelCase__						:							Tuple       =    hidden_sizes
             lowerCamelCase__						:							Tuple       =    depths
             lowerCamelCase__						:							List[str]       =    is_training
             lowerCamelCase__						:							Optional[Any]       =    use_labels
             lowerCamelCase__						:							str       =    hidden_act
             lowerCamelCase__						:							Any       =    num_labels
             lowerCamelCase__						:							List[str]       =    scope
             lowerCamelCase__						:							Any       =    len(__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

             lowerCamelCase__						:							Any       =    self.get_config()

             return config, pixel_values





       def 		lowerCAmelCase	(  self				:     Optional[int]			):


             '''simple docstring'''



             return ResNetConfig(
                 num_channels=self.num_channels   , embeddings_size=self.embeddings_size   , hidden_sizes=self.hidden_sizes   , depths=self.depths   , hidden_act=self.hidden_act   , num_labels=self.num_labels   , image_size=self.image_size   , )





       def 		lowerCAmelCase	(  self				:     str   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    FlaxResNetModel(config=__lowerCamelCase			)
             lowerCamelCase__						:							str       =    model(__lowerCamelCase			)

             # Output shape (b, c, h, w)
             self.parent.assertEqual(
                 result.last_hidden_state.shape   , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   , )





       def 		lowerCAmelCase	(  self				:     int   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[Any]       =    self.num_labels
             lowerCamelCase__						:							int       =    FlaxResNetForImageClassification(config=__lowerCamelCase			)
             lowerCamelCase__						:							Optional[int]       =    model(__lowerCamelCase			)
             self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)			)





       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							Tuple       =    self.prepare_config_and_inputs()
             lowerCamelCase__       ,  lowerCamelCase__						:							Tuple       =    config_and_inputs
             lowerCamelCase__						:							Optional[int]       =    {"pixel_values": pixel_values}
             return config, inputs_dict







@require_flax
class      _lowercase					(				lowercase__				,    unittest.TestCase):


       """simple docstring"""




       A__   		=     (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

       A__   		=     False
       A__   		=     False
       A__   		=     False





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    FlaxResNetModelTester(self			)
             lowerCamelCase__						:							Dict       =    ConfigTester(self   , config_class=__lowerCamelCase   , has_text_modality=__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             self.create_and_test_config_common_properties()
             self.config_tester.create_and_test_config_to_json_string()
             self.config_tester.create_and_test_config_to_json_file()
             self.config_tester.create_and_test_config_from_and_save_pretrained()
             self.config_tester.create_and_test_config_with_num_labels()
             self.config_tester.check_config_can_be_init_without_params()
             self.config_tester.check_config_arguments_init()





       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             return





       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							str       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							str       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase			)





       @unittest.skip(reason="ResNet does not use inputs_embeds"			)
       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             pass





       @unittest.skip(reason="ResNet does not support input and output embeddings"			)
       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             lowerCamelCase__       ,  lowerCamelCase__						:							Tuple       =    self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   lowerCamelCase__						:							Tuple       =    model_class(__lowerCamelCase			)
                   lowerCamelCase__						:							Tuple       =    inspect.signature(model.__call__			)
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   lowerCamelCase__						:							Optional[Any]       =    [*signature.parameters.keys()]

                   lowerCamelCase__						:							List[Any]       =    ["pixel_values"]
                   self.assertListEqual(arg_names[:1]   , __lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             def check_hidden_states_output(__lowerCamelCase				:     List[str]   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     str			):
                   lowerCamelCase__						:							Any       =    model_class(__lowerCamelCase			)

                   lowerCamelCase__						:							Dict       =    model(**self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase			)			)

                   lowerCamelCase__						:							str       =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                   lowerCamelCase__						:							Tuple       =    self.model_tester.num_stages
                   self.assertEqual(len(__lowerCamelCase			)   , expected_num_stages + 1			)





       @unittest.skip(reason="ResNet does not use feedforward chunking"			)
       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             lowerCamelCase__       ,  lowerCamelCase__						:							Dict       =    self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__			):
                         lowerCamelCase__						:							Any       =    self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase			)
                         lowerCamelCase__						:							Optional[int]       =    model_class(__lowerCamelCase			)

                         @jax.jit
                         def model_jitted(__lowerCamelCase				:     Optional[Any]   , **__lowerCamelCase				:     List[Any]			):
                               return model(pixel_values=__lowerCamelCase   , **__lowerCamelCase			)

                         with self.subTest("JIT Enabled"			):
                               lowerCamelCase__						:							Any       =    model_jitted(**__lowerCamelCase			).to_tuple()

                         with self.subTest("JIT Disabled"			):
                               with jax.disable_jit():
                                     lowerCamelCase__						:							Union[str, Any]       =    model_jitted(**__lowerCamelCase			).to_tuple()

                         self.assertEqual(len(__lowerCamelCase			)   , len(__lowerCamelCase			)			)
                         for jitted_output, output in zip(__lowerCamelCase   , __lowerCamelCase			):
                               self.assertEqual(jitted_output.shape   , output.shape			)



def  lowercase_						(						):

      """simple docstring"""




      lowerCamelCase__						:							Optional[Any]       =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
      return image







@require_flax
class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       @cached_property
       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             return AutoImageProcessor.from_pretrained("microsoft/resnet-50"			) if is_vision_available() else None





       @slow
       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							List[Any]       =    FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50"			)

             lowerCamelCase__						:							List[str]       =    self.default_image_processor
             lowerCamelCase__						:							Optional[Any]       =    prepare_img()
             lowerCamelCase__						:							str       =    image_processor(images=__lowerCamelCase   , return_tensors="np"			)

             lowerCamelCase__						:							str       =    model(**__lowerCamelCase			)

             # verify the logits
             lowerCamelCase__						:							int       =    (1, 1000)
             self.assertEqual(outputs.logits.shape   , __lowerCamelCase			)

             lowerCamelCase__						:							Optional[int]       =    jnp.array([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]			)

             self.assertTrue(jnp.allclose(outputs.logits[0, :3]   , __lowerCamelCase   , atol=1E-4			)			)


