




import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 (
      TFEfficientFormerForImageClassification,
      TFEfficientFormerForImageClassificationWithTeacher,
      TFEfficientFormerModel,
  )
  from transformers.models.efficientformer.modeling_tf_efficientformer import (
      TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  )


if is_vision_available():
  from PIL import Image

  from transformers import EfficientFormerImageProcessor


class 				UpperCamelCase_    :

    '''simple docstring'''






    def __init__(			self      :	Optional[int]					, UpperCAmelCase__      :	List[Any]					, UpperCAmelCase__      :	int = 13					, UpperCAmelCase__      :	int = 64					, UpperCAmelCase__      :	int = 2					, UpperCAmelCase__      :	int = 3					, UpperCAmelCase__      :	int = 3					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	int = 128					, UpperCAmelCase__      :	Optional[Any]=[16, 32, 64, 128]					, UpperCAmelCase__      :	int = 7					, UpperCAmelCase__      :	int = 4					, UpperCAmelCase__      :	int = 37					, UpperCAmelCase__      :	str = "gelu"					, UpperCAmelCase__      :	float = 0.1					, UpperCAmelCase__      :	float = 0.1					, UpperCAmelCase__      :	int = 10					, UpperCAmelCase__      :	float = 0.02					, UpperCAmelCase__      :	int = 2					, UpperCAmelCase__      :	int = 1					, UpperCAmelCase__      :	int = 128					, UpperCAmelCase__      :	List[int] = [2, 2, 2, 2]					, UpperCAmelCase__      :	int = 2					, UpperCAmelCase__      :	int = 2					, )						->List[Any]:





           '''simple docstring'''

           A__       =    parent
           A__       =    batch_size
           A__       =    image_size
           A__       =    patch_size
           A__       =    num_channels
           A__       =    is_training
           A__       =    use_labels
           A__       =    hidden_size
           A__       =    num_hidden_layers
           A__       =    num_attention_heads
           A__       =    intermediate_size
           A__       =    hidden_act
           A__       =    hidden_dropout_prob
           A__       =    attention_probs_dropout_prob
           A__       =    type_sequence_label_size
           A__       =    initializer_range
           A__       =    encoder_stride
           A__       =    num_attention_outputs
           A__       =    embed_dim
           A__       =    embed_dim + 1
           A__       =    resolution
           A__       =    depths
           A__       =    hidden_sizes
           A__       =    dim
           A__       =    mlp_expansion_ratio


    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->str:





           '''simple docstring'''

           A__       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           A__       =    None
           if self.use_labels:
                  A__       =    ids_tensor([self.batch_size]					, self.type_sequence_label_size)

           A__       =    self.get_config()

           return config, pixel_values, labels


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->str:





           '''simple docstring'''

           return EfficientFormerConfig(
               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					, hidden_act=self.hidden_act					, hidden_dropout_prob=self.hidden_dropout_prob					, attention_probs_dropout_prob=self.attention_probs_dropout_prob					, is_decoder=UpperCAmelCase__					, initializer_range=self.initializer_range					, encoder_stride=self.encoder_stride					, resolution=self.resolution					, depths=self.depths					, hidden_sizes=self.hidden_sizes					, dim=self.dim					, mlp_expansion_ratio=self.mlp_expansion_ratio					, )


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int]					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	List[str]					, UpperCAmelCase__      :	Dict)						->Dict:





           '''simple docstring'''

           A__       =    TFEfficientFormerModel(config=UpperCAmelCase__)
           A__       =    model(UpperCAmelCase__					, training=UpperCAmelCase__)
           self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size))


    def        SCREAMING_SNAKE_CASE							(			self      :	int					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	str)						->Union[str, Any]:





           '''simple docstring'''

           A__       =    self.type_sequence_label_size
           A__       =    TFEfficientFormerForImageClassification(UpperCAmelCase__)
           A__       =    model(UpperCAmelCase__					, labels=UpperCAmelCase__					, training=UpperCAmelCase__)
           self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.type_sequence_label_size))

           # test greyscale images
           A__       =    1
           A__       =    TFEfficientFormerForImageClassification(UpperCAmelCase__)

           A__       =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           A__       =    model(UpperCAmelCase__					, labels=UpperCAmelCase__)
           self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.type_sequence_label_size))





    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->List[str]:





           '''simple docstring'''

           A__       =    self.prepare_config_and_inputs()
           A__ ,							A__ ,							A__       =    config_and_inputs
           A__       =    {'''pixel_values''': pixel_values}
           return config, inputs_dict


@require_tf
class 				UpperCamelCase_    ( UpperCAmelCase__		,					UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      (
        (
            TFEfficientFormerModel,
            TFEfficientFormerForImageClassificationWithTeacher,
            TFEfficientFormerForImageClassification,
        )
        if is_tf_available()
        else ()
    )
    UpperCAmelCase__							  =      (
        {
            '''feature-extraction''': TFEfficientFormerModel,
            '''image-classification''': (
                TFEfficientFormerForImageClassification,
                TFEfficientFormerForImageClassificationWithTeacher,
            ),
        }
        if is_tf_available()
        else {}
    )

    UpperCAmelCase__							  =      False

    UpperCAmelCase__							  =      False
    UpperCAmelCase__							  =      False
    UpperCAmelCase__							  =      False
    UpperCAmelCase__							  =      False


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->List[str]:





           '''simple docstring'''

           A__       =    TFEfficientFormerModelTester(self)
           A__       =    ConfigTester(
               self					, config_class=UpperCAmelCase__					, has_text_modality=UpperCAmelCase__					, hidden_size=37)


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Any:





           '''simple docstring'''

           self.config_tester.run_common_tests()


    @unittest.skip(reason='''EfficientFormer does not use inputs_embeds''')
    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Dict:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''EfficientFormer does not support input and output embeddings''')
    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->Optional[Any]:





           '''simple docstring'''

           pass


    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->Optional[Any]:





           '''simple docstring'''

           A__ ,							A__       =    self.model_tester.prepare_config_and_inputs_for_common()

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

                  A__       =    ['''pixel_values''']
                  self.assertListEqual(arg_names[:1]					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Any:





           '''simple docstring'''

           def check_hidden_states_output(UpperCAmelCase__      :	Optional[Any]					, UpperCAmelCase__      :	Any					, UpperCAmelCase__      :	Dict):
                  A__       =    model_class(UpperCAmelCase__)

                  A__       =    model(**self._prepare_for_class(UpperCAmelCase__					, UpperCAmelCase__)					, training=UpperCAmelCase__)
                  A__       =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                  A__       =    getattr(
                      self.model_tester					, '''expected_num_hidden_layers'''					, self.model_tester.num_hidden_layers + 1)
                  self.assertEqual(len(UpperCAmelCase__)					, UpperCAmelCase__)

                  if hasattr(self.model_tester					, '''encoder_seq_length'''):
                         A__       =    self.model_tester.encoder_seq_length
                         if hasattr(self.model_tester					, '''chunk_length''') and self.model_tester.chunk_length > 1:
                                A__       =    seq_length * self.model_tester.chunk_length
                  else:
                         A__       =    self.model_tester.seq_length

                  self.assertListEqual(
                      list(hidden_states[-1].shape[-2:])					, [seq_length, self.model_tester.hidden_size]					, )

                  if config.is_encoder_decoder:
                         A__       =    outputs.decoder_hidden_states

                         self.asseretIsInstance(UpperCAmelCase__					, (list, tuple))
                         self.assertEqual(len(UpperCAmelCase__)					, UpperCAmelCase__)
                         A__       =    getattr(self.model_tester					, '''seq_length'''					, UpperCAmelCase__)
                         A__       =    getattr(self.model_tester					, '''decoder_seq_length'''					, UpperCAmelCase__)

                         self.assertListEqual(
                             list(hidden_states[-1].shape[-2:])					, [decoder_seq_length, self.model_tester.hidden_size]					, )

           A__ ,							A__       =    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__       =    True
                  check_hidden_states_output(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  A__       =    True

                  check_hidden_states_output(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Dict=False)						->int:





           '''simple docstring'''

           A__       =    super()._prepare_for_class(UpperCAmelCase__					, UpperCAmelCase__					, return_labels=UpperCAmelCase__)

           if return_labels:
                  if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
                         del inputs_dict["labels"]

           return inputs_dict


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Union[str, Any]:





           '''simple docstring'''

           A__       =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase__)


    @unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet''')
    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->str:





           '''simple docstring'''

           A__       =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->Tuple:





           '''simple docstring'''

           A__       =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__)


    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Optional[int]:





           '''simple docstring'''

           for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__       =    TFEfficientFormerModel.from_pretrained(UpperCAmelCase__)
                  self.assertIsNotNone(UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->str:





           '''simple docstring'''

           A__ ,							A__       =    self.model_tester.prepare_config_and_inputs_for_common()
           A__       =    True

           A__       =    getattr(self.model_tester					, '''seq_length'''					, UpperCAmelCase__)
           A__       =    getattr(self.model_tester					, '''encoder_seq_length'''					, UpperCAmelCase__)
           A__       =    getattr(self.model_tester					, '''key_length'''					, UpperCAmelCase__)
           A__       =    getattr(self.model_tester					, '''chunk_length'''					, UpperCAmelCase__)

           if chunk_length is not None and hasattr(self.model_tester					, '''num_hashes'''):
                  A__       =    encoder_seq_length * self.model_tester.num_hashes

           for model_class in self.all_model_classes:
                  A__       =    True
                  A__       =    False
                  A__       =    True
                  A__       =    model_class(UpperCAmelCase__)

                  A__       =    model(**self._prepare_for_class(UpperCAmelCase__					, UpperCAmelCase__)					, training=UpperCAmelCase__)
                  A__       =    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                  self.assertEqual(len(UpperCAmelCase__)					, self.model_tester.num_attention_outputs)

                  # check that output_attentions also work using config
                  del inputs_dict["output_attentions"]
                  A__       =    True
                  A__       =    model_class(UpperCAmelCase__)
                  A__       =    model(**self._prepare_for_class(UpperCAmelCase__					, UpperCAmelCase__)					, training=UpperCAmelCase__)

                  A__       =    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                  self.assertEqual(len(UpperCAmelCase__)					, self.model_tester.num_attention_outputs)

                  if chunk_length is not None:
                         self.assertListEqual(
                             list(attentions[0].shape[-4:])					, [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]					, )
                  else:
                         self.assertListEqual(
                             list(attentions[0].shape[-3:])					, [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]					, )





    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Optional[Any]:





           '''simple docstring'''

           A__ ,							A__       =    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  # Prepare our model
                  A__       =    model_class(UpperCAmelCase__)
                  # These are maximally general inputs for the model, with multiple None dimensions
                  # Hopefully this will catch any conditionals that fail for flexible shapes
                  A__       =    {
                      key: tf.keras.Input(shape=val.shape[1:]					, dtype=val.dtype					, name=UpperCAmelCase__)
                      for key, val in model.input_signature.items()
                      if key in model.dummy_inputs
                  }
                  A__       =    model(UpperCAmelCase__)
                  self.assertTrue(outputs_dict is not None)







def SCREAMING_SNAKE_CASE				(       )   ->       Any:



       """simple docstring"""

       A__       =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
       return image


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

    '''simple docstring'''






    @cached_property
    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->List[str]:





           '''simple docstring'''

           return (
               EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300''')
               if is_vision_available()
               else None
           )


    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Any:





           '''simple docstring'''

           A__       =    TFEfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300''')
           A__       =    self.default_image_processor
           A__       =    prepare_img()
           A__       =    image_processor(images=UpperCAmelCase__					, return_tensors='''tf''')
           # forward pass
           A__       =    model(**UpperCAmelCase__					, training=UpperCAmelCase__)
           # verify the logits
           A__       =    tf.TensorShape((1, 1_000))
           self.assertEqual(outputs.logits.shape					, UpperCAmelCase__)
           A__       =    tf.constant([-0.0555, 0.4825, -0.0852])
           self.assertTrue(np.allclose(outputs.logits[0, :3]					, UpperCAmelCase__					, atol=1e-4))





    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->int:





           '''simple docstring'''

           A__       =    TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
               '''snap-research/efficientformer-l1-300''')
           A__       =    self.default_image_processor
           A__       =    prepare_img()
           A__       =    image_processor(images=UpperCAmelCase__					, return_tensors='''tf''')
           # forward pass
           A__       =    model(**UpperCAmelCase__					, training=UpperCAmelCase__)
           # verify the logits
           A__       =    tf.TensorShape((1, 1_000))
           self.assertEqual(outputs.logits.shape					, UpperCAmelCase__)
           A__       =    tf.constant([-0.1312, 0.4353, -1.0499])
           self.assertTrue(np.allclose(outputs.logits[0, :3]					, UpperCAmelCase__					, atol=1e-4))








import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)   ->       List[Any]:



       """simple docstring"""

       A__       =    StableDiffusionPipeline.from_pretrained(lowercase_ ,				torch_dtype=torch.floataa						)

       # load LoRA weight from .safetensors
       A__       =    load_file(lowercase_						)

       A__       =    []

       # directly update weight in diffusers model
       for key in state_dict:
              # it is suggested to print out the key, it usually will be something like below
              # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

              # as we have set the alpha beforehand, so just skip
              if ".alpha" in key or key in visited:
                     continue

              if "text" in key:
                     A__       =    key.split('''.'''						)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''						)[-1].split('''_'''						)
                     A__       =    pipeline.text_encoder
              else:
                     A__       =    key.split('''.'''						)[0].split(LORA_PREFIX_UNET + '''_'''						)[-1].split('''_'''						)
                     A__       =    pipeline.unet

              # find the target layer
              A__       =    layer_infos.pop(0						)
              while len(lowercase_						) > -1:
                     try:
                            A__       =    curr_layer.__getattr__(lowercase_						)
                            if len(lowercase_						) > 0:
                                   A__       =    layer_infos.pop(0						)
                            elif len(lowercase_						) == 0:
                                   break
                     except Exception:
                            if len(lowercase_						) > 0:
                                   temp_name += "_" + layer_infos.pop(0						)
                            else:
                                   A__       =    layer_infos.pop(0						)

              A__       =    []
              if "lora_down" in key:
                     pair_keys.append(key.replace('''lora_down''' ,				'''lora_up'''						)						)
                     pair_keys.append(lowercase_						)
              else:
                     pair_keys.append(lowercase_						)
                     pair_keys.append(key.replace('''lora_up''' ,				'''lora_down'''						)						)

              # update weight
              if len(state_dict[pair_keys[0]].shape						) == 4:
                     A__       =    state_dict[pair_keys[0]].squeeze(3						).squeeze(2						).to(torch.floataa						)
                     A__       =    state_dict[pair_keys[1]].squeeze(3						).squeeze(2						).to(torch.floataa						)
                     curr_layer.weight.data += alpha * torch.mm(lowercase_ ,				lowercase_						).unsqueeze(2						).unsqueeze(3						)
              else:
                     A__       =    state_dict[pair_keys[0]].to(torch.floataa						)
                     A__       =    state_dict[pair_keys[1]].to(torch.floataa						)
                     curr_layer.weight.data += alpha * torch.mm(lowercase_ ,				lowercase_						)

              # update visited list
              for item in pair_keys:
                     visited.append(lowercase_						)

       return pipeline


if __name__ == "__main__":
  _lowerCamelCase	:		Tuple							       =   argparse.ArgumentParser()

  parser.add_argument(
      """--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
  )
  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
  )
  parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
  parser.add_argument(
      """--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
  )
  parser.add_argument(
      """--lora_prefix_text_encoder""",
      default="""lora_te""",
      type=str,
      help="""The prefix of text encoder weight in safetensors""",
  )
  parser.add_argument("""--alpha""", default=0.75, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
  parser.add_argument(
      """--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
  )
  parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")

  _lowerCamelCase	:		Tuple							       =   parser.parse_args()

  _lowerCamelCase	:		List[Any]							       =   args.base_model_path
  _lowerCamelCase	:		Optional[int]							       =   args.checkpoint_path
  _lowerCamelCase	:		Dict							       =   args.dump_path
  _lowerCamelCase	:		Optional[Any]							       =   args.lora_prefix_unet
  _lowerCamelCase	:		Optional[int]							       =   args.lora_prefix_text_encoder
  _lowerCamelCase	:		List[Any]							       =   args.alpha

  _lowerCamelCase	:		int							       =   convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

  _lowerCamelCase	:		Tuple							       =   pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


