





'''simple docstring'''







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 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				def __init__(  self     :					List[str]					,				__a     :					str					,				__a     :					int = 13					,				__a     :					int = 64					,				__a     :					int = 2					,				__a     :					int = 3					,				__a     :					int = 3					,				__a     :					bool = True					,				__a     :					bool = True					,				__a     :					int = 1_28					,				__a     :					List[str]=[16, 32, 64, 1_28]					,				__a     :					int = 7					,				__a     :					int = 4					,				__a     :					int = 37					,				__a     :					str = "gelu"					,				__a     :					float = 0.1					,				__a     :					float = 0.1					,				__a     :					int = 10					,				__a     :					float = 0.02					,				__a     :					int = 2					,				__a     :					int = 1					,				__a     :					int = 1_28					,				__a     :					List[int] = [2, 2, 2, 2]					,				__a     :					int = 2					,				__a     :					int = 2					,				):
								_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   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_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   UpperCamelCase__   (  self     :					str					):
								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=__a					,				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   UpperCamelCase__   (  self     :					Any					,				__a     :					str					,				__a     :					Tuple					,				__a     :					Optional[int]					):
								_a								= TFEfficientFormerModel(config=__a					)
								_a								= model(__a					,				training=__a					)
								self.parent.assertEqual(result.last_hidden_state.shape					,				(self.batch_size, self.seq_length, self.hidden_size)					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					Union[str, Any]					,				__a     :					Union[str, Any]					,				__a     :					Tuple					):
								_a								= self.type_sequence_label_size
								_a								= TFEfficientFormerForImageClassification(__a					)
								_a								= model(__a					,				labels=__a					,				training=__a					)
								self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.type_sequence_label_size)					)

								# test greyscale images
								_a								= 1
								_a								= TFEfficientFormerForImageClassification(__a					)

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




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= self.prepare_config_and_inputs()
								_a	,					_a	,					_a								= config_and_inputs
								_a								= {"pixel_values": pixel_values}
								return config, inputs_dict


@require_tf
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_       ,							unittest.TestCase      ):






				"""simple docstring"""







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

				__a            =False

				__a            =False
				__a            =False
				__a            =False
				__a            =False




				def   UpperCamelCase__   (  self     :					Any					):
								_a								= TFEfficientFormerModelTester(self					)
								_a								= ConfigTester(
								    self					,				config_class=__a					,				has_text_modality=__a					,				hidden_size=37					)




				def   UpperCamelCase__   (  self     :					Dict					):
								self.config_tester.run_common_tests()




				@unittest.skip(reason="EfficientFormer does not use inputs_embeds"					)
				def   UpperCamelCase__   (  self     :					List[str]					):
								pass




				@unittest.skip(reason="EfficientFormer does not support input and output embeddings"					)
				def   UpperCamelCase__   (  self     :					str					):
								pass




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a	,					_a								= self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												_a								= model_class(__a					)
												_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]					,				__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								def check_hidden_states_output(__a     :					int					,				__a     :					Union[str, Any]					,				__a     :					Optional[Any]					):
												_a								= model_class(__a					)

												_a								= model(**self._prepare_for_class(__a					,				__a					)					,				training=__a					)
												_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(__a					)					,				__a					)

												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(__a					,				(list, tuple)					)
																self.assertEqual(len(__a					)					,				__a					)
																_a								= getattr(self.model_tester					,				"seq_length"					,				__a					)
																_a								= getattr(self.model_tester					,				"decoder_seq_length"					,				__a					)

																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(__a					,				__a					,				__a					)

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

												check_hidden_states_output(__a					,				__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Any					,				__a     :					List[Any]					,				__a     :					Dict					,				__a     :					List[str]=False					):
								_a								= super()._prepare_for_class(__a					,				__a					,				return_labels=__a					)

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

								return inputs_dict




				def   UpperCamelCase__   (  self     :					Dict					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__a					)




				@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"					)
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*__a					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__a					)




				@slow
				def   UpperCamelCase__   (  self     :					str					):
								for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_a								= TFEfficientFormerModel.from_pretrained(__a					)
												self.assertIsNotNone(__a					)




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a	,					_a								= self.model_tester.prepare_config_and_inputs_for_common()
								_a								= True

								_a								= getattr(self.model_tester					,				"seq_length"					,				__a					)
								_a								= getattr(self.model_tester					,				"encoder_seq_length"					,				__a					)
								_a								= getattr(self.model_tester					,				"key_length"					,				__a					)
								_a								= getattr(self.model_tester					,				"chunk_length"					,				__a					)

								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(__a					)

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

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

												_a								= outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
												self.assertEqual(len(__a					)					,				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   UpperCamelCase__   (  self     :					Tuple					):
								# We use a simplified version of this test for EfficientFormer because it requires training=False
								# and Keras refuses to let us force that during functional construction
								_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(__a					)
												# 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=__a					)
												    for key, val in model.input_signature.items()
												    if key in model.dummy_inputs
												}
												_a								= model(__a					)
												self.assertTrue(outputs_dict is not None					)






def  _lowerCamelCase				( )	->	str:
				_a								= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				return image



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






				"""simple docstring"""







				@cached_property
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								return (
								    EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"					)
								    if is_vision_available()
								    else None
								)




				@slow
				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"					)
								_a								= self.default_image_processor
								_a								= prepare_img()
								_a								= image_processor(images=__a					,				return_tensors="tf"					)
								# forward pass
								_a								= model(**__a					,				training=__a					)
								# verify the logits
								_a								= tf.TensorShape((1, 10_00)					)
								self.assertEqual(outputs.logits.shape					,				__a					)
								_a								= tf.constant([-0.0555, 0.4825, -0.0852]					)
								self.assertTrue(np.allclose(outputs.logits[0, :3]					,				__a					,				atol=1e-4					)					)




				@slow
				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
								    "snap-research/efficientformer-l1-300"					)
								_a								= self.default_image_processor
								_a								= prepare_img()
								_a								= image_processor(images=__a					,				return_tensors="tf"					)
								# forward pass
								_a								= model(**__a					,				training=__a					)
								# verify the logits
								_a								= tf.TensorShape((1, 10_00)					)
								self.assertEqual(outputs.logits.shape					,				__a					)
								_a								= tf.constant([-0.1312, 0.4353, -1.0499]					)
								self.assertTrue(np.allclose(outputs.logits[0, :3]					,				__a					,				atol=1e-4					)					)





"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Optional[Any]							):
    _UpperCAmelCase							:  int											=    OmegaConf.load(UpperCamelCase__							)
    _UpperCAmelCase							:  str											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)['''model''']
    _UpperCAmelCase							:  Optional[Any]											=    list(state_dict.keys()							)

    # extract state_dict for VQVAE
    _UpperCAmelCase							:  Any											=    {}
    _UpperCAmelCase							:  Any											=    '''first_stage_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    # extract state_dict for UNetLDM
    _UpperCAmelCase							:  Tuple											=    {}
    _UpperCAmelCase							:  int											=    '''model.diffusion_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    _UpperCAmelCase							:  List[str]											=    config.model.params.first_stage_config.params
    _UpperCAmelCase							:  Union[str, Any]											=    config.model.params.unet_config.params

    _UpperCAmelCase							:  Any											=    VQModel(**UpperCamelCase__							).eval()
    vqvae.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  Union[str, Any]											=    UNetLDMModel(**UpperCamelCase__							).eval()
    unet.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  int											=    DDIMScheduler(
        timesteps=config.model.params.timesteps     ,       beta_schedule='''scaled_linear'''     ,       beta_start=config.model.params.linear_start     ,       beta_end=config.model.params.linear_end     ,       clip_sample=UpperCamelCase__     ,       )

    _UpperCAmelCase							:  Optional[Any]											=    LDMPipeline(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
    pipeline.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  _lowerCAmelCase    :Union[str, Any]     =					argparse.ArgumentParser()
  parser.add_argument('--checkpoint_path', type=str, required=True)
  parser.add_argument('--config_path', type=str, required=True)
  parser.add_argument('--output_path', type=str, required=True)
  _lowerCAmelCase    :List[Any]     =					parser.parse_args()

  convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


