




'''simple docstring'''






from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor






class        lowercase_      :





     def __init__(     self     :					Optional[Any]     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Tuple=13     ,							__lowerCamelCase     :					List[str]=30     ,							__lowerCamelCase     :					Any=2     ,							__lowerCamelCase     :					Any=3     ,							__lowerCamelCase     :					str=True     ,							__lowerCamelCase     :					Optional[Any]=True     ,							__lowerCamelCase     :					Optional[int]=32     ,							__lowerCamelCase     :					Optional[Any]=2     ,							__lowerCamelCase     :					int=4     ,							__lowerCamelCase     :					Optional[int]=37     ,							__lowerCamelCase     :					Tuple="gelu"     ,							__lowerCamelCase     :					List[str]=0.1     ,							__lowerCamelCase     :					str=0.1     ,							__lowerCamelCase     :					str=10     ,							__lowerCamelCase     :					Optional[Any]=0.0_2     ,							__lowerCamelCase     :					Optional[int]=3     ,							__lowerCamelCase     :					Tuple=0.6     ,							__lowerCamelCase     :					Any=None     ,							):
           snake_case__       :   Union[str, Any]	      =    parent
           snake_case__       :   str	      =    batch_size
           snake_case__       :   Tuple	      =    image_size
           snake_case__       :   Dict	      =    patch_size
           snake_case__       :   Optional[Any]	      =    num_channels
           snake_case__       :   str	      =    is_training
           snake_case__       :   List[Any]	      =    use_labels
           snake_case__       :   str	      =    hidden_size
           snake_case__       :   Optional[int]	      =    num_hidden_layers
           snake_case__       :   List[Any]	      =    num_attention_heads
           snake_case__       :   Dict	      =    intermediate_size
           snake_case__       :   Union[str, Any]	      =    hidden_act
           snake_case__       :   Dict	      =    hidden_dropout_prob
           snake_case__       :   List[Any]	      =    attention_probs_dropout_prob
           snake_case__       :   Dict	      =    type_sequence_label_size
           snake_case__       :   Any	      =    initializer_range
           snake_case__       :   Union[str, Any]	      =    mask_ratio
           snake_case__       :   List[str]	      =    scope

           # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
           # (we add 1 for the [CLS] token)
           snake_case__       :   Optional[Any]	      =    (image_size // patch_size) ** 2
           snake_case__       :   List[Any]	      =    int(math.ceil((1 - mask_ratio) * (num_patches + 1)			)			)





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   Optional[int]	      =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

           snake_case__       :   Optional[Any]	      =    None
           if self.use_labels:
                 snake_case__       :   List[Any]	      =    ids_tensor([self.batch_size]     ,							self.type_sequence_label_size			)

           snake_case__       :   Optional[int]	      =    self.get_config()

           return config, pixel_values, labels





     def 			_lowerCAmelCase      (     self     :					str			):
           return ViTMAEConfig(
               image_size=self.image_size     ,							patch_size=self.patch_size     ,							num_channels=self.num_channels     ,							hidden_size=self.hidden_size     ,							num_hidden_layers=self.num_hidden_layers     ,							num_attention_heads=self.num_attention_heads     ,							intermediate_size=self.intermediate_size     ,							decoder_hidden_size=self.hidden_size     ,							decoder_num_hidden_layers=self.num_hidden_layers     ,							decoder_num_attention_heads=self.num_attention_heads     ,							decoder_intermediate_size=self.intermediate_size     ,							hidden_act=self.hidden_act     ,							hidden_dropout_prob=self.hidden_dropout_prob     ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,							is_decoder=snake_case__     ,							initializer_range=self.initializer_range     ,							mask_ratio=self.mask_ratio     ,							)





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Optional[int]			):
           snake_case__       :   Dict	      =    TFViTMAEModel(config=snake_case__			)
           snake_case__       :   Optional[Any]	      =    model(snake_case__     ,							training=snake_case__			)
           self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)			)





     def 			_lowerCAmelCase      (     self     :					Optional[Any]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					Tuple			):
           snake_case__       :   Tuple	      =    TFViTMAEForPreTraining(snake_case__			)
           snake_case__       :   str	      =    model(snake_case__     ,							training=snake_case__			)
           # expected sequence length = num_patches
           snake_case__       :   Optional[int]	      =    (self.image_size // self.patch_size) ** 2
           snake_case__       :   Any	      =    self.patch_size**2 * self.num_channels
           self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, num_patches, expected_num_channels)			)

           # test greyscale images
           snake_case__       :   List[Any]	      =    1
           snake_case__       :   Any	      =    TFViTMAEForPreTraining(snake_case__			)

           snake_case__       :   Union[str, Any]	      =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
           snake_case__       :   str	      =    model(snake_case__     ,							training=snake_case__			)
           snake_case__       :   List[str]	      =    self.patch_size**2
           self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, num_patches, expected_num_channels)			)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   Any	      =    self.prepare_config_and_inputs()
           (snake_case__)       :   str	      =    config_and_inputs
           snake_case__       :   Dict	      =    {"pixel_values": pixel_values}
           return config, inputs_dict






@require_tf
class        lowercase_      (						lowerCAmelCase_						,       lowerCAmelCase_						,       unittest.TestCase							):
     A_											=			(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
     A_											=			{"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

     A_											=			False
     A_											=			False
     A_											=			False
     A_											=			False





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   str	      =    TFViTMAEModelTester(self			)
           snake_case__       :   Any	      =    ConfigTester(self     ,							config_class=snake_case__     ,							has_text_modality=snake_case__     ,							hidden_size=37			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           self.config_tester.run_common_tests()





     @unittest.skip(reason='ViTMAE does not use inputs_embeds'			)
     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           pass





     def 			_lowerCAmelCase      (     self     :					str			):
           snake_case__       :   Tuple	      =    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 snake_case__       :   Dict	      =    model_class(snake_case__			)
                 self.assertIsInstance(model.get_input_embeddings()     ,							(tf.keras.layers.Layer)			)
                 snake_case__       :   str	      =    model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(snake_case__     ,							tf.keras.layers.Layer			)			)





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   Optional[int]	      =    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 snake_case__       :   str	      =    model_class(snake_case__			)
                 snake_case__       :   Dict	      =    inspect.signature(model.call			)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 snake_case__       :   Tuple	      =    [*signature.parameters.keys()]

                 snake_case__       :   Optional[int]	      =    ["pixel_values"]
                 self.assertListEqual(arg_names[:1]     ,							snake_case__			)





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   List[Any]	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case__			)





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   int	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_pretraining(*snake_case__			)





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           np.random.seed(2			)

           snake_case__       :   Dict	      =    self.model_tester.prepare_config_and_inputs_for_common()

           snake_case__       :   Optional[Any]	      =    int((config.image_size // config.patch_size) ** 2			)
           snake_case__       :   Dict	      =    np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)

           for model_class in self.all_model_classes:
                 snake_case__       :   Optional[Any]	      =    model_class(snake_case__			)
                 snake_case__       :   Any	      =    self._prepare_for_class(snake_case__     ,							snake_case__			)

                 snake_case__       :   Optional[Any]	      =    model(snake_case__     ,							noise=snake_case__			)

                 snake_case__       :   Optional[Any]	      =    copy.deepcopy(self._prepare_for_class(snake_case__     ,							snake_case__			)			)
                 snake_case__       :   List[Any]	      =    model(**snake_case__     ,							noise=snake_case__			)
                 snake_case__       :   Tuple	      =    outputs_dict[0].numpy()
                 snake_case__       :   Any	      =    outputs_keywords[0].numpy()

                 self.assertLess(np.sum(np.abs(output_dict - output_keywords			)			)     ,							1E-6			)





     def 			_lowerCAmelCase      (     self     :					int			):
           np.random.seed(2			)

           snake_case__       :   List[Any]	      =    self.model_tester.prepare_config_and_inputs_for_common()

           snake_case__       :   str	      =    int((config.image_size // config.patch_size) ** 2			)
           snake_case__       :   int	      =    np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)

           def prepare_numpy_arrays(__lowerCamelCase     :					Any			):
                 snake_case__       :   Optional[Any]	      =    {}
                 for k, v in inputs_dict.items():
                       if tf.is_tensor(snake_case__			):
                             snake_case__       :   Optional[Any]	      =    v.numpy()
                       else:
                             snake_case__       :   Any	      =    np.array(snake_case__			)

                 return inputs_np_dict

           for model_class in self.all_model_classes:
                 snake_case__       :   Dict	      =    model_class(snake_case__			)

                 snake_case__       :   Dict	      =    self._prepare_for_class(snake_case__     ,							snake_case__			)
                 snake_case__       :   int	      =    prepare_numpy_arrays(snake_case__			)

                 snake_case__       :   List[str]	      =    model(snake_case__     ,							noise=snake_case__			)
                 snake_case__       :   Union[str, Any]	      =    model(**snake_case__     ,							noise=snake_case__			)
                 self.assert_outputs_same(snake_case__     ,							snake_case__			)





     def 			_lowerCAmelCase      (     self     :					int     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					int			):
           np.random.seed(2			)

           snake_case__       :   Union[str, Any]	      =    int((tf_model.config.image_size // tf_model.config.patch_size) ** 2			)
           snake_case__       :   List[Any]	      =    np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)
           snake_case__       :   Optional[Any]	      =    tf.constant(snake_case__			)

           # Add `noise` argument.
           # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
           snake_case__       :   int	      =    tf_noise

           super().check_pt_tf_models(snake_case__     ,							snake_case__     ,							snake_case__			)





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           np.random.seed(2			)

           snake_case__       :   Any	      =    self.model_tester.prepare_config_and_inputs_for_common()

           snake_case__       :   Any	      =    {
               module_member
               for model_class in self.all_model_classes
               for module in (import_module(model_class.__module__			),)
               for module_member_name in dir(snake_case__			)
               if module_member_name.endswith('MainLayer'			)
               # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
               and module_member_name[: -len('MainLayer'			)] == model_class.__name__[: -len('Model'			)]
               for module_member in (getattr(snake_case__     ,							snake_case__			),)
               if isinstance(snake_case__     ,							snake_case__			)
               and tf.keras.layers.Layer in module_member.__bases__
               and getattr(snake_case__     ,							'_keras_serializable'     ,							snake_case__			)
           }

           snake_case__       :   Union[str, Any]	      =    int((config.image_size // config.patch_size) ** 2			)
           snake_case__       :   Optional[Any]	      =    np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)
           snake_case__       :   Tuple	      =    tf.convert_to_tensor(snake_case__			)
           inputs_dict.update({'noise': noise}			)

           for main_layer_class in tf_main_layer_classes:
                 snake_case__       :   List[Any]	      =    main_layer_class(snake_case__			)

                 snake_case__       :   Tuple	      =    {
                     name: tf.keras.Input(tensor.shape[1:]     ,							dtype=tensor.dtype			) for name, tensor in inputs_dict.items()
                 }

                 snake_case__       :   int	      =    tf.keras.Model(snake_case__     ,							outputs=main_layer(snake_case__			)			)
                 snake_case__       :   str	      =    model(snake_case__			)

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       snake_case__       :   List[str]	      =    os.path.join(snake_case__     ,							'keras_model.h5'			)
                       model.save(snake_case__			)
                       snake_case__       :   str	      =    tf.keras.models.load_model(
                           snake_case__     ,							custom_objects={main_layer_class.__name__: main_layer_class}			)
                       assert isinstance(snake_case__     ,							tf.keras.Model			)
                       snake_case__       :   Union[str, Any]	      =    model(snake_case__			)
                       self.assert_outputs_same(snake_case__     ,							snake_case__			)





     @slow
     def 			_lowerCAmelCase      (     self     :					Dict			):
           np.random.seed(2			)

           snake_case__       :   List[str]	      =    self.model_tester.prepare_config_and_inputs_for_common()

           snake_case__       :   Any	      =    int((config.image_size // config.patch_size) ** 2			)
           snake_case__       :   Any	      =    np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)

           for model_class in self.all_model_classes:
                 snake_case__       :   Optional[Any]	      =    model_class(snake_case__			)
                 snake_case__       :   Optional[int]	      =    self._prepare_for_class(snake_case__     ,							snake_case__			)
                 snake_case__       :   List[str]	      =    model(snake_case__     ,							noise=snake_case__			)

                 if model_class.__name__ == "TFViTMAEModel":
                       snake_case__       :   Dict	      =    outputs.last_hidden_state.numpy()
                       snake_case__       :   Tuple	      =    0
                 else:
                       snake_case__       :   Tuple	      =    outputs.logits.numpy()
                       snake_case__       :   int	      =    0

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       model.save_pretrained(snake_case__     ,							saved_model=snake_case__			)
                       snake_case__       :   Dict	      =    model_class.from_pretrained(snake_case__			)
                       snake_case__       :   List[str]	      =    model(snake_case__     ,							noise=snake_case__			)

                       if model_class.__name__ == "TFViTMAEModel":
                             snake_case__       :   List[str]	      =    after_outputs["last_hidden_state"].numpy()
                             snake_case__       :   List[str]	      =    0
                       else:
                             snake_case__       :   List[str]	      =    after_outputs["logits"].numpy()
                             snake_case__       :   List[Any]	      =    0

                       snake_case__       :   List[Any]	      =    np.amax(np.abs(out_a - out_a			)			)
                       self.assertLessEqual(snake_case__     ,							1E-5			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           np.random.seed(2			)

           snake_case__       :   Any	      =    self.model_tester.prepare_config_and_inputs_for_common()

           snake_case__       :   int	      =    int((config.image_size // config.patch_size) ** 2			)
           snake_case__       :   Optional[int]	      =    np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)

           for model_class in self.all_model_classes:
                 snake_case__       :   Any	      =    model_class(snake_case__			)
                 snake_case__       :   List[Any]	      =    self._prepare_for_class(snake_case__     ,							snake_case__			)

                 snake_case__       :   Optional[Any]	      =    model(snake_case__     ,							noise=snake_case__			)
                 snake_case__       :   List[str]	      =    model.get_config()
                 # make sure that returned config is jsonifiable, which is required by keras
                 json.dumps(snake_case__			)
                 snake_case__       :   str	      =    model_class.from_config(model.get_config()			)
                 # make sure it also accepts a normal config
                 snake_case__       :   List[str]	      =    model_class.from_config(model.config			)
                 snake_case__       :   Union[str, Any]	      =    new_model(snake_case__			)  # Build model
                 new_model.set_weights(model.get_weights()			)
                 snake_case__       :   List[Any]	      =    new_model(snake_case__     ,							noise=snake_case__			)

                 self.assert_outputs_same(snake_case__     ,							snake_case__			)





     @unittest.skip(
         reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'			)
     def 			_lowerCAmelCase      (     self     :					int			):
           pass





     @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'			)
     def 			_lowerCAmelCase      (     self     :					Any			):
           pass





     @slow
     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   Optional[int]	      =    TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'			)
           self.assertIsNotNone(snake_case__			)






def 		UpperCamelCase__      (		)    ->   Any:
      snake_case__       :   str	      =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
      return image






@require_tf
@require_vision
class        lowercase_      (						unittest.TestCase							):





     @cached_property
     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'			) if is_vision_available() else None





     @slow
     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           np.random.seed(2			)

           snake_case__       :   Dict	      =    TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'			)

           snake_case__       :   int	      =    self.default_image_processor
           snake_case__       :   Union[str, Any]	      =    prepare_img()
           snake_case__       :   Tuple	      =    image_processor(images=snake_case__     ,							return_tensors='tf'			)

           # prepare a noise vector that will be also used for testing the TF model
           # (this way we can ensure that the PT and TF models operate on the same inputs)
           snake_case__       :   Any	      =    ViTMAEConfig()
           snake_case__       :   Any	      =    int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2			)
           snake_case__       :   List[Any]	      =    np.random.uniform(size=(1, num_patches)			)

           # forward pass
           snake_case__       :   str	      =    model(**snake_case__     ,							noise=snake_case__			)

           # verify the logits
           snake_case__       :   Union[str, Any]	      =    tf.convert_to_tensor([1, 196, 768]			)
           self.assertEqual(outputs.logits.shape     ,							snake_case__			)

           snake_case__       :   Optional[Any]	      =    tf.convert_to_tensor(
               [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]			)

           tf.debugging.assert_near(outputs.logits[0, :3, :3]     ,							snake_case__     ,							atol=1E-4			)








"""simple docstring"""



import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowerCAmelCase	:	Dict				=      {
    """sample_size""": 32,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 1_000,
    """block_out_channels""": [32, 64],
    """attention_head_dim""": 8,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

_lowerCAmelCase	:	str				=      {
    """sample_size""": 64,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 1_000,
    """block_out_channels""": [192, 192 * 2, 192 * 3, 192 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

_lowerCAmelCase	:	Dict				=      {
    """sample_size""": 256,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """default""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

_lowerCAmelCase	:	int				=      {
    """num_train_timesteps""": 40,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

_lowerCAmelCase	:	List[Any]				=      {
    """num_train_timesteps""": 201,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

_lowerCAmelCase	:	List[Any]				=      {
    """num_train_timesteps""": 151,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str				)->		str:






     '''simple docstring'''



     if isinstance(snake_case			,					snake_case				):
          return v
     if v.lower() in ("yes", "true", "t", "y", "1"):
          return True
     elif v.lower() in ("no", "false", "f", "n", "0"):
          return False
     else:
          raise argparse.ArgumentTypeError("boolean value expected"				)


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any			,					snake_case       : Optional[Any]			,					snake_case       : List[str]			,					snake_case       : Any			,					snake_case       : str=False				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    Dict							=							checkpoint[f'{old_prefix}.in_layers.0.weight']
     UpperCAmelCase__						:    List[Any]							=							checkpoint[f'{old_prefix}.in_layers.0.bias']
     UpperCAmelCase__						:    Dict							=							checkpoint[f'{old_prefix}.in_layers.2.weight']
     UpperCAmelCase__						:    List[Any]							=							checkpoint[f'{old_prefix}.in_layers.2.bias']
     UpperCAmelCase__						:    Optional[int]							=							checkpoint[f'{old_prefix}.emb_layers.1.weight']
     UpperCAmelCase__						:    Dict							=							checkpoint[f'{old_prefix}.emb_layers.1.bias']
     UpperCAmelCase__						:    Any							=							checkpoint[f'{old_prefix}.out_layers.0.weight']
     UpperCAmelCase__						:    List[Any]							=							checkpoint[f'{old_prefix}.out_layers.0.bias']
     UpperCAmelCase__						:    int							=							checkpoint[f'{old_prefix}.out_layers.3.weight']
     UpperCAmelCase__						:    int							=							checkpoint[f'{old_prefix}.out_layers.3.bias']

     if has_skip:
          UpperCAmelCase__						:    str							=							checkpoint[f'{old_prefix}.skip_connection.weight']
          UpperCAmelCase__						:    Any							=							checkpoint[f'{old_prefix}.skip_connection.bias']

     return new_checkpoint


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]			,					snake_case       : Union[str, Any]			,					snake_case       : Any			,					snake_case       : Tuple			,					snake_case       : Dict=None				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[Any]							=							checkpoint[f'{old_prefix}.qkv.weight'].chunk(3			,					dim=0				)
     UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							checkpoint[f'{old_prefix}.qkv.bias'].chunk(3			,					dim=0				)

     UpperCAmelCase__						:    Dict							=							checkpoint[f'{old_prefix}.norm.weight']
     UpperCAmelCase__						:    Tuple							=							checkpoint[f'{old_prefix}.norm.bias']

     UpperCAmelCase__						:    Tuple							=							weight_q.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    int							=							bias_q.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Optional[Any]							=							weight_k.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Optional[int]							=							bias_k.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Tuple							=							weight_v.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Optional[Any]							=							bias_v.squeeze(-1				).squeeze(-1				)

     UpperCAmelCase__						:    Union[str, Any]							=							(
         checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1				).squeeze(-1				)
     )
     UpperCAmelCase__						:    List[str]							=							checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1				).squeeze(-1				)

     return new_checkpoint


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : Union[str, Any]				)->		List[str]:






     '''simple docstring'''



     UpperCAmelCase__						:    Any							=							torch.load(snake_case			,					map_location="cpu"				)
     UpperCAmelCase__						:    Any							=							{}

     UpperCAmelCase__						:    Any							=							checkpoint["time_embed.0.weight"]
     UpperCAmelCase__						:    Optional[int]							=							checkpoint["time_embed.0.bias"]
     UpperCAmelCase__						:    Any							=							checkpoint["time_embed.2.weight"]
     UpperCAmelCase__						:    Optional[Any]							=							checkpoint["time_embed.2.bias"]

     if unet_config["num_class_embeds"] is not None:
          UpperCAmelCase__						:    List[Any]							=							checkpoint["label_emb.weight"]

     UpperCAmelCase__						:    Dict							=							checkpoint["input_blocks.0.0.weight"]
     UpperCAmelCase__						:    List[Any]							=							checkpoint["input_blocks.0.0.bias"]

     UpperCAmelCase__						:    Any							=							unet_config["down_block_types"]
     UpperCAmelCase__						:    Tuple							=							unet_config["layers_per_block"]
     UpperCAmelCase__						:    str							=							unet_config["attention_head_dim"]
     UpperCAmelCase__						:    str							=							unet_config["block_out_channels"]
     UpperCAmelCase__						:    Optional[int]							=							1
     UpperCAmelCase__						:    Tuple							=							channels_list[0]

     for i, layer_type in enumerate(snake_case				):
          UpperCAmelCase__						:    List[str]							=							channels_list[i]
          UpperCAmelCase__						:    Optional[int]							=							current_channels != prev_channels
          if layer_type == "ResnetDownsampleBlock2D":
               for j in range(snake_case				):
                    UpperCAmelCase__						:    int							=							f'down_blocks.{i}.resnets.{j}'
                    UpperCAmelCase__						:    Union[str, Any]							=							f'input_blocks.{current_layer}.0'
                    UpperCAmelCase__						:    List[str]							=							True if j == 0 and downsample_block_has_skip else False
                    UpperCAmelCase__						:    Optional[Any]							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case			,					has_skip=snake_case				)
                    current_layer += 1

          elif layer_type == "AttnDownBlock2D":
               for j in range(snake_case				):
                    UpperCAmelCase__						:    List[str]							=							f'down_blocks.{i}.resnets.{j}'
                    UpperCAmelCase__						:    Optional[Any]							=							f'input_blocks.{current_layer}.0'
                    UpperCAmelCase__						:    Optional[int]							=							True if j == 0 and downsample_block_has_skip else False
                    UpperCAmelCase__						:    Any							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case			,					has_skip=snake_case				)
                    UpperCAmelCase__						:    Dict							=							f'down_blocks.{i}.attentions.{j}'
                    UpperCAmelCase__						:    List[Any]							=							f'input_blocks.{current_layer}.1'
                    UpperCAmelCase__						:    Optional[Any]							=							convert_attention(
                        snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case				)
                    current_layer += 1

          if i != len(snake_case				) - 1:
               UpperCAmelCase__						:    Dict							=							f'down_blocks.{i}.downsamplers.0'
               UpperCAmelCase__						:    Optional[int]							=							f'input_blocks.{current_layer}.0'
               UpperCAmelCase__						:    Tuple							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)
               current_layer += 1

          UpperCAmelCase__						:    Dict							=							current_channels

     # hardcoded the mid-block for now
     UpperCAmelCase__						:    int							=							"mid_block.resnets.0"
     UpperCAmelCase__						:    Optional[int]							=							"middle_block.0"
     UpperCAmelCase__						:    Any							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)
     UpperCAmelCase__						:    Optional[int]							=							"mid_block.attentions.0"
     UpperCAmelCase__						:    Tuple							=							"middle_block.1"
     UpperCAmelCase__						:    Any							=							convert_attention(snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case				)
     UpperCAmelCase__						:    Optional[int]							=							"mid_block.resnets.1"
     UpperCAmelCase__						:    Union[str, Any]							=							"middle_block.2"
     UpperCAmelCase__						:    Optional[int]							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)

     UpperCAmelCase__						:    Optional[Any]							=							0
     UpperCAmelCase__						:    int							=							unet_config["up_block_types"]

     for i, layer_type in enumerate(snake_case				):
          if layer_type == "ResnetUpsampleBlock2D":
               for j in range(layers_per_block + 1				):
                    UpperCAmelCase__						:    Optional[Any]							=							f'up_blocks.{i}.resnets.{j}'
                    UpperCAmelCase__						:    List[str]							=							f'output_blocks.{current_layer}.0'
                    UpperCAmelCase__						:    List[str]							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case			,					has_skip=snake_case				)
                    current_layer += 1

               if i != len(snake_case				) - 1:
                    UpperCAmelCase__						:    Dict							=							f'up_blocks.{i}.upsamplers.0'
                    UpperCAmelCase__						:    Optional[Any]							=							f'output_blocks.{current_layer-1}.1'
                    UpperCAmelCase__						:    Optional[Any]							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)
          elif layer_type == "AttnUpBlock2D":
               for j in range(layers_per_block + 1				):
                    UpperCAmelCase__						:    Optional[Any]							=							f'up_blocks.{i}.resnets.{j}'
                    UpperCAmelCase__						:    Tuple							=							f'output_blocks.{current_layer}.0'
                    UpperCAmelCase__						:    Union[str, Any]							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case			,					has_skip=snake_case				)
                    UpperCAmelCase__						:    Optional[int]							=							f'up_blocks.{i}.attentions.{j}'
                    UpperCAmelCase__						:    List[Any]							=							f'output_blocks.{current_layer}.1'
                    UpperCAmelCase__						:    Any							=							convert_attention(
                        snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case				)
                    current_layer += 1

               if i != len(snake_case				) - 1:
                    UpperCAmelCase__						:    Dict							=							f'up_blocks.{i}.upsamplers.0'
                    UpperCAmelCase__						:    Any							=							f'output_blocks.{current_layer-1}.2'
                    UpperCAmelCase__						:    int							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)

     UpperCAmelCase__						:    Union[str, Any]							=							checkpoint["out.0.weight"]
     UpperCAmelCase__						:    Any							=							checkpoint["out.0.bias"]
     UpperCAmelCase__						:    int							=							checkpoint["out.2.weight"]
     UpperCAmelCase__						:    str							=							checkpoint["out.2.bias"]

     return new_checkpoint


if __name__ == "__main__":
 _lowerCAmelCase	:	str				=      argparse.ArgumentParser()

 parser.add_argument("""--unet_path""", default=None, type=str, required=True, help="""Path to the unet.pt to convert.""")
 parser.add_argument(
     """--dump_path""", default=None, type=str, required=True, help="""Path to output the converted UNet model."""
 )
 parser.add_argument("""--class_cond""", default=True, type=str, help="""Whether the model is class-conditional.""")

 _lowerCAmelCase	:	int				=      parser.parse_args()
 _lowerCAmelCase	:	Optional[int]				=      strabool(args.class_cond)

 _lowerCAmelCase	:	Optional[Any]				=      os.path.basename(args.unet_path)
 print(F"""Checkpoint: {ckpt_name}""")

 # Get U-Net config
 if "imagenet64" in ckpt_name:
  _lowerCAmelCase	:	Dict				=      IMAGENET_64_UNET_CONFIG
 elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
  _lowerCAmelCase	:	Dict				=      LSUN_256_UNET_CONFIG
 elif "test" in ckpt_name:
  _lowerCAmelCase	:	str				=      TEST_UNET_CONFIG
 else:
  raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

 if not args.class_cond:
  _lowerCAmelCase	:	int				=      None

 _lowerCAmelCase	:	Optional[int]				=      con_pt_to_diffuser(args.unet_path, unet_config)

 _lowerCAmelCase	:	Optional[Any]				=      UNetaDModel(**unet_config)
 image_unet.load_state_dict(converted_unet_ckpt)

 # Get scheduler config
 if "cd" in ckpt_name or "test" in ckpt_name:
  _lowerCAmelCase	:	Optional[int]				=      CD_SCHEDULER_CONFIG
 elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
  _lowerCAmelCase	:	str				=      CT_IMAGENET_64_SCHEDULER_CONFIG
 elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
  _lowerCAmelCase	:	Any				=      CT_LSUN_256_SCHEDULER_CONFIG
 else:
  raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

 _lowerCAmelCase	:	Dict				=      CMStochasticIterativeScheduler(**scheduler_config)

 _lowerCAmelCase	:	Tuple				=      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
 consistency_model.save_pretrained(args.dump_path)


