
'''simple docstring'''





import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a__		: Optional[int]  							=							logging.get_logger(__name__)





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







      """simple docstring"""
      __A													=      tf.image.resize(UpperCAmelCase   ,	(3_4_6, 3_4_6)						)
      __A													=      tf.image.crop_to_bounding_box(UpperCAmelCase   ,	(3_4_6 - 2_8_9) // 2   ,	(3_4_6 - 2_8_9) // 2   ,	2_8_9   ,	2_8_9						)
      return image





def 							snake_case				(		)->      List[Any]:







      """simple docstring"""
      __A													=      EfficientNetConfig.from_pretrained('google/efficientnet-b7'						)
      __A													=      2_8_9
      __A													=      6_4_0
      __A													=      {'0': 'LABEL_0', '1': 'LABEL_1'}
      __A													=      {'LABEL_0': 0, 'LABEL_1': 1}
      __A													=      []

      __A													=      BertConfig()
      __A													=      AlignConfig.from_text_vision_configs(
          text_config=UpperCAmelCase   ,	vision_config=UpperCAmelCase   ,	projection_dim=6_4_0						)
      return config





def 							snake_case				(		)->      Any:







      """simple docstring"""
      __A													=      'http://images.cocodataset.org/val2017/000000039769.jpg'
      __A													=      Image.open(requests.get(UpperCAmelCase   ,	stream=UpperCAmelCase						).raw						)
      return im





def 							snake_case				(		)->      List[Any]:







      """simple docstring"""
      __A													=      EfficientNetImageProcessor(
          do_center_crop=UpperCAmelCase   ,	rescale_factor=1 / 127.5   ,	rescale_offset=UpperCAmelCase   ,	do_normalize=UpperCAmelCase   ,	include_top=UpperCAmelCase   ,	resample=Image.BILINEAR   ,	)
      __A													=      BertTokenizer.from_pretrained('bert-base-uncased'						)
      __A													=      6_4
      __A													=      AlignProcessor(image_processor=UpperCAmelCase   ,	tokenizer=UpperCAmelCase						)
      return processor





def 							snake_case				(		UpperCAmelCase						)->      Any:







      """simple docstring"""
      # EfficientNet image encoder
      __A													=      [v.split('_'						)[0].split('block'						)[1] for v in original_param_names if v.startswith('block'						)]
      __A													=      list(set(UpperCAmelCase						)						)
      __A													=      sorted(UpperCAmelCase						)
      __A													=      len(UpperCAmelCase						)
      __A													=      {b: str(UpperCAmelCase						) for b, i in zip(UpperCAmelCase   ,	range(UpperCAmelCase						)						)}

      __A													=      []
      rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')						)
      rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')						)
      rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')						)
      rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')						)
      rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')						)

      for b in block_names:
            __A													=      block_name_mapping[b]
            rename_keys.append((f'block{b}_expand_conv/kernel:0', f'encoder.blocks.{hf_b}.expansion.expand_conv.weight')						)
            rename_keys.append((f'block{b}_expand_bn/gamma:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.weight')						)
            rename_keys.append((f'block{b}_expand_bn/beta:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.bias')						)
            rename_keys.append(
                (f'block{b}_expand_bn/moving_mean:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean')						)
            rename_keys.append(
                (f'block{b}_expand_bn/moving_variance:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.running_var')						)
            rename_keys.append(
                (f'block{b}_dwconv/depthwise_kernel:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight')						)
            rename_keys.append((f'block{b}_bn/gamma:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight')						)
            rename_keys.append((f'block{b}_bn/beta:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias')						)
            rename_keys.append(
                (f'block{b}_bn/moving_mean:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean')						)
            rename_keys.append(
                (f'block{b}_bn/moving_variance:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var')						)

            rename_keys.append((f'block{b}_se_reduce/kernel:0', f'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight')						)
            rename_keys.append((f'block{b}_se_reduce/bias:0', f'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias')						)
            rename_keys.append((f'block{b}_se_expand/kernel:0', f'encoder.blocks.{hf_b}.squeeze_excite.expand.weight')						)
            rename_keys.append((f'block{b}_se_expand/bias:0', f'encoder.blocks.{hf_b}.squeeze_excite.expand.bias')						)
            rename_keys.append(
                (f'block{b}_project_conv/kernel:0', f'encoder.blocks.{hf_b}.projection.project_conv.weight')						)
            rename_keys.append((f'block{b}_project_bn/gamma:0', f'encoder.blocks.{hf_b}.projection.project_bn.weight')						)
            rename_keys.append((f'block{b}_project_bn/beta:0', f'encoder.blocks.{hf_b}.projection.project_bn.bias')						)
            rename_keys.append(
                (f'block{b}_project_bn/moving_mean:0', f'encoder.blocks.{hf_b}.projection.project_bn.running_mean')						)
            rename_keys.append(
                (f'block{b}_project_bn/moving_variance:0', f'encoder.blocks.{hf_b}.projection.project_bn.running_var')						)

      __A													=      {}
      for item in rename_keys:
            if item[0] in original_param_names:
                  __A													=      'vision_model.' + item[1]

    # BERT text encoder
      __A													=      []
      __A													=      'tf_bert_model/bert'
      __A													=      'text_model'
      for i in range(1_2						):
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/self/query/kernel:0',
                    f'{new}.encoder.layer.{i}.attention.self.query.weight',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/self/query/bias:0',
                    f'{new}.encoder.layer.{i}.attention.self.query.bias',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/self/key/kernel:0',
                    f'{new}.encoder.layer.{i}.attention.self.key.weight',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/self/key/bias:0',
                    f'{new}.encoder.layer.{i}.attention.self.key.bias',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/self/value/kernel:0',
                    f'{new}.encoder.layer.{i}.attention.self.value.weight',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/self/value/bias:0',
                    f'{new}.encoder.layer.{i}.attention.self.value.bias',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/output/dense/kernel:0',
                    f'{new}.encoder.layer.{i}.attention.output.dense.weight',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/output/dense/bias:0',
                    f'{new}.encoder.layer.{i}.attention.output.dense.bias',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0',
                    f'{new}.encoder.layer.{i}.attention.output.LayerNorm.weight',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0',
                    f'{new}.encoder.layer.{i}.attention.output.LayerNorm.bias',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/intermediate/dense/kernel:0',
                    f'{new}.encoder.layer.{i}.intermediate.dense.weight',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/intermediate/dense/bias:0',
                    f'{new}.encoder.layer.{i}.intermediate.dense.bias',
                )						)
            rename_keys.append(
                (f'{old}/encoder/layer_._{i}/output/dense/kernel:0', f'{new}.encoder.layer.{i}.output.dense.weight')						)
            rename_keys.append(
                (f'{old}/encoder/layer_._{i}/output/dense/bias:0', f'{new}.encoder.layer.{i}.output.dense.bias')						)
            rename_keys.append(
                (f'{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0', f'{new}.encoder.layer.{i}.output.LayerNorm.weight')						)
            rename_keys.append(
                (f'{old}/encoder/layer_._{i}/output/LayerNorm/beta:0', f'{new}.encoder.layer.{i}.output.LayerNorm.bias')						)

      rename_keys.append((f'{old}/embeddings/word_embeddings/weight:0', f'{new}.embeddings.word_embeddings.weight')						)
      rename_keys.append(
          (f'{old}/embeddings/position_embeddings/embeddings:0', f'{new}.embeddings.position_embeddings.weight')						)
      rename_keys.append(
          (f'{old}/embeddings/token_type_embeddings/embeddings:0', f'{new}.embeddings.token_type_embeddings.weight')						)
      rename_keys.append((f'{old}/embeddings/LayerNorm/gamma:0', f'{new}.embeddings.LayerNorm.weight')						)
      rename_keys.append((f'{old}/embeddings/LayerNorm/beta:0', f'{new}.embeddings.LayerNorm.bias')						)

      rename_keys.append((f'{old}/pooler/dense/kernel:0', f'{new}.pooler.dense.weight')						)
      rename_keys.append((f'{old}/pooler/dense/bias:0', f'{new}.pooler.dense.bias')						)
      rename_keys.append(('dense/kernel:0', 'text_projection.weight')						)
      rename_keys.append(('dense/bias:0', 'text_projection.bias')						)
      rename_keys.append(('dense/bias:0', 'text_projection.bias')						)
      rename_keys.append(('temperature:0', 'temperature')						)

      for item in rename_keys:
            if item[0] in original_param_names:
                  __A													=      item[1]
      return key_mapping





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







      """simple docstring"""
      list(hf_params.keys()						)

      for key, value in tf_params.items():
            if key not in key_mapping:
                  continue

            __A													=      key_mapping[key]
            if "_conv" in key and "kernel" in key:
                  __A													=      torch.from_numpy(UpperCAmelCase						).permute(3   ,	2   ,	0   ,	1						)
            elif "embeddings" in key:
                  __A													=      torch.from_numpy(UpperCAmelCase						)
            elif "depthwise_kernel" in key:
                  __A													=      torch.from_numpy(UpperCAmelCase						).permute(2   ,	3   ,	0   ,	1						)
            elif "kernel" in key:
                  __A													=      torch.from_numpy(np.transpose(UpperCAmelCase						)						)
            elif "temperature" in key:
                  __A													=      value
            elif "bn/gamma" or "bn/beta" in key:
                  __A													=      torch.from_numpy(np.transpose(UpperCAmelCase						)						).squeeze()
            else:
                  __A													=      torch.from_numpy(UpperCAmelCase						)

            # Replace HF parameters with original TF model parameters
            hf_params[hf_key].copy_(UpperCAmelCase						)





@torch.no_grad()
def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Any:







      """simple docstring"""
      __A													=      6_4
      __A													=      Tokenizer(UpperCAmelCase						)
      __A													=      align.Align('efficientnet-b7'   ,	'bert-base'   ,	6_4_0   ,	UpperCAmelCase   ,	tok.get_vocab_size()						)
      original_model.compile()
      original_model.load_weights(UpperCAmelCase						)

      __A													=      original_model.trainable_variables
      __A													=      original_model.non_trainable_variables
      __A													=      {param.name: param.numpy() for param in tf_params}
      for param in tf_non_train_params:
            __A													=      param.numpy()
      __A													=      list(tf_params.keys()						)

      # Load HuggingFace model
      __A													=      get_align_config()
      __A													=      AlignModel(UpperCAmelCase						).eval()
      __A													=      hf_model.state_dict()

      # Create src-to-dst parameter name mapping dictionary
      print('Converting parameters...'						)
      __A													=      rename_keys(UpperCAmelCase						)
      replace_params(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)

      # Initialize processor
      __A													=      get_processor()
      __A													=      processor(
          images=prepare_img()   ,	text='A picture of a cat'   ,	padding='max_length'   ,	max_length=6_4   ,	return_tensors='pt'						)

      # HF model inference
      hf_model.eval()
      with torch.no_grad():
            __A													=      hf_model(**UpperCAmelCase						)

      __A													=      outputs.image_embeds.detach().numpy()
      __A													=      outputs.text_embeds.detach().numpy()

      # Original model inference
      __A													=      False
      __A													=      EfficientNetImageProcessor(
          do_center_crop=UpperCAmelCase   ,	do_rescale=UpperCAmelCase   ,	do_normalize=UpperCAmelCase   ,	include_top=UpperCAmelCase   ,	resample=Image.BILINEAR   ,	)
      __A													=      tf_image_processor(images=prepare_img()   ,	return_tensors='tf'   ,	data_format='channels_last'						)['pixel_values']
      __A													=      tok(tf.constant(['A picture of a cat']						)						)

      __A													=      original_model.image_encoder(UpperCAmelCase   ,	training=UpperCAmelCase						)
      __A													=      original_model.text_encoder(UpperCAmelCase   ,	training=UpperCAmelCase						)

      __A													=      tf.nn.la_normalize(UpperCAmelCase   ,	axis=-1						)
      __A													=      tf.nn.la_normalize(UpperCAmelCase   ,	axis=-1						)

      # Check whether original and HF model outputs match  -> np.allclose
      if not np.allclose(UpperCAmelCase   ,	UpperCAmelCase   ,	atol=1e-3						):
            raise ValueError('The predicted image features are not the same.'						)
      if not np.allclose(UpperCAmelCase   ,	UpperCAmelCase   ,	atol=1e-3						):
            raise ValueError('The predicted text features are not the same.'						)
      print('Model outputs match!'						)

      if save_model:
            # Create folder to save model
            if not os.path.isdir(UpperCAmelCase						):
                  os.mkdir(UpperCAmelCase						)
            # Save converted model and image processor
            hf_model.save_pretrained(UpperCAmelCase						)
            processor.save_pretrained(UpperCAmelCase						)

      if push_to_hub:
            # Push model and image processor to hub
            print('Pushing converted ALIGN to the hub...'						)
            processor.push_to_hub('align-base'						)
            hf_model.push_to_hub('align-base'						)


if __name__ == "__main__":
 a__		: str  							=							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--checkpoint_path",
     default="./weights/model-weights",
     type=str,
     help="Path to the pretrained TF ALIGN checkpoint.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default="hf_model",
     type=str,
     help="Path to the output PyTorch model directory.",
 )
 parser.add_argument("--save_model", action="store_true", help="Save model to local")
 parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")

 a__		: List[Any]  							=							parser.parse_args()
 convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 	_a  (							lowerCamelCase_							,		unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					UnCLIPPipeline
	__SCREAMING_SNAKE_CASE              =					TEXT_TO_IMAGE_PARAMS - {
	    'negative_prompt',
	    'height',
	    'width',
	    'negative_prompt_embeds',
	    'guidance_scale',
	    'prompt_embeds',
	    'cross_attention_kwargs',
	}
	__SCREAMING_SNAKE_CASE              =					TEXT_TO_IMAGE_BATCH_PARAMS
	__SCREAMING_SNAKE_CASE              =					[
	    'generator',
	    'return_dict',
	    'prior_num_inference_steps',
	    'decoder_num_inference_steps',
	    'super_res_num_inference_steps',
	]
	__SCREAMING_SNAKE_CASE              =					False


	@property
	def __lowerCAmelCase      (   self       ):
		return 32


	@property
	def __lowerCAmelCase      (   self       ):
		return 32


	@property
	def __lowerCAmelCase      (   self       ):
		return self.time_input_dim


	@property
	def __lowerCAmelCase      (   self       ):
		return self.time_input_dim * 4


	@property
	def __lowerCAmelCase      (   self       ):
		return 100


	@property
	def __lowerCAmelCase      (   self       ):
		_lowercase   =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )
		return tokenizer


	@property
	def __lowerCAmelCase      (   self       ):
		torch.manual_seed(0       )
		_lowercase   =CLIPTextConfig(
		    bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=self.text_embedder_hidden_size       ,  projection_dim=self.text_embedder_hidden_size       ,  intermediate_size=37       ,  layer_norm_eps=1e-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=1000       ,  )
		return CLIPTextModelWithProjection(lowerCAmelCase_       )


	@property
	def __lowerCAmelCase      (   self       ):
		torch.manual_seed(0       )

		_lowercase   ={
		    "num_attention_heads": 2,
		    "attention_head_dim": 12,
		    "embedding_dim": self.text_embedder_hidden_size,
		    "num_layers": 1,
		}

		_lowercase   =PriorTransformer(**lowerCAmelCase_       )
		return model


	@property
	def __lowerCAmelCase      (   self       ):
		torch.manual_seed(0       )

		_lowercase   ={
		    "clip_embeddings_dim": self.text_embedder_hidden_size,
		    "time_embed_dim": self.time_embed_dim,
		    "cross_attention_dim": self.cross_attention_dim,
		}

		_lowercase   =UnCLIPTextProjModel(**lowerCAmelCase_       )
		return model


	@property
	def __lowerCAmelCase      (   self       ):
		torch.manual_seed(0       )

		_lowercase   ={
		    "sample_size": 32,
		    # RGB in channels
		    "in_channels": 3,
		    # Out channels is double in channels because predicts mean and variance
		    "out_channels": 6,
		    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
		    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
		    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
		    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
		    "layers_per_block": 1,
		    "cross_attention_dim": self.cross_attention_dim,
		    "attention_head_dim": 4,
		    "resnet_time_scale_shift": "scale_shift",
		    "class_embed_type": "identity",
		}

		_lowercase   =UNetaDConditionModel(**lowerCAmelCase_       )
		return model


	@property
	def __lowerCAmelCase      (   self       ):
		return {
		    "sample_size": 64,
		    "layers_per_block": 1,
		    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
		    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
		    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
		    "in_channels": 6,
		    "out_channels": 3,
		}


	@property
	def __lowerCAmelCase      (   self       ):
		torch.manual_seed(0       )

		_lowercase   =UNetaDModel(**self.dummy_super_res_kwargs       )
		return model


	@property
	def __lowerCAmelCase      (   self       ):
		# seeded differently to get different unet than `self.dummy_super_res_first`
		torch.manual_seed(1       )

		_lowercase   =UNetaDModel(**self.dummy_super_res_kwargs       )
		return model


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.dummy_prior
		_lowercase   =self.dummy_decoder
		_lowercase   =self.dummy_text_proj
		_lowercase   =self.dummy_text_encoder
		_lowercase   =self.dummy_tokenizer
		_lowercase   =self.dummy_super_res_first
		_lowercase   =self.dummy_super_res_last

		_lowercase   =UnCLIPScheduler(
		    variance_type="fixed_small_log"       ,  prediction_type="sample"       ,  num_train_timesteps=1000       ,  clip_sample_range=5.0       ,  )

		_lowercase   =UnCLIPScheduler(
		    variance_type="learned_range"       ,  prediction_type="epsilon"       ,  num_train_timesteps=1000       ,  )

		_lowercase   =UnCLIPScheduler(
		    variance_type="fixed_small_log"       ,  prediction_type="epsilon"       ,  num_train_timesteps=1000       ,  )

		_lowercase   ={
		    "prior": prior,
		    "decoder": decoder,
		    "text_proj": text_proj,
		    "text_encoder": text_encoder,
		    "tokenizer": tokenizer,
		    "super_res_first": super_res_first,
		    "super_res_last": super_res_last,
		    "prior_scheduler": prior_scheduler,
		    "decoder_scheduler": decoder_scheduler,
		    "super_res_scheduler": super_res_scheduler,
		}

		return components


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=0       ):
		if str(lowerCAmelCase_       ).startswith("mps"       ):
			_lowercase   =torch.manual_seed(lowerCAmelCase_       )
		else:
			_lowercase   =torch.Generator(device=lowerCAmelCase_       ).manual_seed(lowerCAmelCase_       )
		_lowercase   ={
		    "prompt": "horse",
		    "generator": generator,
		    "prior_num_inference_steps": 2,
		    "decoder_num_inference_steps": 2,
		    "super_res_num_inference_steps": 2,
		    "output_type": "numpy",
		}
		return inputs


	def __lowerCAmelCase      (   self       ):
		_lowercase   ="cpu"

		_lowercase   =self.get_dummy_components()

		_lowercase   =self.pipeline_class(**lowerCAmelCase_       )
		_lowercase   =pipe.to(lowerCAmelCase_       )

		pipe.set_progress_bar_config(disable=lowerCAmelCase_       )

		_lowercase   =pipe(**self.get_dummy_inputs(lowerCAmelCase_       )       )
		_lowercase   =output.images

		_lowercase   =pipe(
		    **self.get_dummy_inputs(lowerCAmelCase_       )       ,  return_dict=lowerCAmelCase_       ,  )[0]

		_lowercase   =image[0, -3:, -3:, -1]
		_lowercase   =image_from_tuple[0, -3:, -3:, -1]

		assert image.shape == (1, 64, 64, 3)

		_lowercase   =np.array(
		    [
		        0.9_9_9_7,
		        0.9_9_8_8,
		        0.0_0_2_8,
		        0.9_9_9_7,
		        0.9_9_8_4,
		        0.9_9_6_5,
		        0.0_0_2_9,
		        0.9_9_8_6,
		        0.0_0_2_5,
		    ]       )

		assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
		assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2


	def __lowerCAmelCase      (   self       ):
		_lowercase   =torch.device("cpu"       )
		class 	_a  :



			"""simple docstring"""



			__SCREAMING_SNAKE_CASE              =					1

		_lowercase   =self.get_dummy_components()

		_lowercase   =self.pipeline_class(**lowerCAmelCase_       )
		_lowercase   =pipe.to(lowerCAmelCase_       )

		_lowercase   =components["prior"]
		_lowercase   =components["decoder"]
		_lowercase   =components["super_res_first"]
		_lowercase   =components["tokenizer"]
		_lowercase   =components["text_encoder"]

		_lowercase   =torch.Generator(device=lowerCAmelCase_       ).manual_seed(0       )
		_lowercase   =prior.dtype
		_lowercase   =1

		_lowercase   =(batch_size, prior.config.embedding_dim)
		_lowercase   =pipe.prepare_latents(
		    lowerCAmelCase_       ,  dtype=lowerCAmelCase_       ,  device=lowerCAmelCase_       ,  generator=lowerCAmelCase_       ,  latents=lowerCAmelCase_       ,  scheduler=DummyScheduler()       )
		_lowercase   =(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
		_lowercase   =pipe.prepare_latents(
		    lowerCAmelCase_       ,  dtype=lowerCAmelCase_       ,  device=lowerCAmelCase_       ,  generator=lowerCAmelCase_       ,  latents=lowerCAmelCase_       ,  scheduler=DummyScheduler()       )

		_lowercase   =(
		    batch_size,
		    super_res_first.config.in_channels // 2,
		    super_res_first.config.sample_size,
		    super_res_first.config.sample_size,
		)
		_lowercase   =pipe.prepare_latents(
		    lowerCAmelCase_       ,  dtype=lowerCAmelCase_       ,  device=lowerCAmelCase_       ,  generator=lowerCAmelCase_       ,  latents=lowerCAmelCase_       ,  scheduler=DummyScheduler()       )

		pipe.set_progress_bar_config(disable=lowerCAmelCase_       )

		_lowercase   ="this is a prompt example"

		_lowercase   =torch.Generator(device=lowerCAmelCase_       ).manual_seed(0       )
		_lowercase   =pipe(
		    [prompt]       ,  generator=lowerCAmelCase_       ,  prior_num_inference_steps=2       ,  decoder_num_inference_steps=2       ,  super_res_num_inference_steps=2       ,  prior_latents=lowerCAmelCase_       ,  decoder_latents=lowerCAmelCase_       ,  super_res_latents=lowerCAmelCase_       ,  output_type="np"       ,  )
		_lowercase   =output.images

		_lowercase   =tokenizer(
		    lowerCAmelCase_       ,  padding="max_length"       ,  max_length=tokenizer.model_max_length       ,  return_tensors="pt"       ,  )
		_lowercase   =text_encoder(text_inputs.input_ids       )
		_lowercase   =text_inputs.attention_mask

		_lowercase   =torch.Generator(device=lowerCAmelCase_       ).manual_seed(0       )
		_lowercase   =pipe(
		    generator=lowerCAmelCase_       ,  prior_num_inference_steps=2       ,  decoder_num_inference_steps=2       ,  super_res_num_inference_steps=2       ,  prior_latents=lowerCAmelCase_       ,  decoder_latents=lowerCAmelCase_       ,  super_res_latents=lowerCAmelCase_       ,  text_model_output=lowerCAmelCase_       ,  text_attention_mask=lowerCAmelCase_       ,  output_type="np"       ,  )[0]

		# make sure passing text embeddings manually is identical
		assert np.abs(image - image_from_text       ).max() < 1e-4


	@skip_mps
	def __lowerCAmelCase      (   self       ):
		_lowercase   =torch_device == "cpu"

		self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase_       ,  expected_max_diff=0.0_1       )


	@skip_mps
	def __lowerCAmelCase      (   self       ):
		_lowercase   =torch_device == "cpu"
		_lowercase   =True
		_lowercase   =[
		    "prior_num_inference_steps",
		    "decoder_num_inference_steps",
		    "super_res_num_inference_steps",
		]

		self._test_inference_batch_single_identical(
		    test_max_difference=lowerCAmelCase_       ,  relax_max_difference=lowerCAmelCase_       ,  additional_params_copy_to_batched_inputs=lowerCAmelCase_       ,  )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =[
		    "prior_num_inference_steps",
		    "decoder_num_inference_steps",
		    "super_res_num_inference_steps",
		]

		if torch_device == "mps":
			# TODO: MPS errors with larger batch sizes
			_lowercase   =[2, 3]
			self._test_inference_batch_consistent(
			    batch_sizes=lowerCAmelCase_       ,  additional_params_copy_to_batched_inputs=lowerCAmelCase_       ,  )
		else:
			self._test_inference_batch_consistent(
			    additional_params_copy_to_batched_inputs=lowerCAmelCase_       )


	@skip_mps
	def __lowerCAmelCase      (   self       ):
		return super().test_dict_tuple_outputs_equivalent()


	@skip_mps
	def __lowerCAmelCase      (   self       ):
		return super().test_save_load_local()


	@skip_mps
	def __lowerCAmelCase      (   self       ):
		return super().test_save_load_optional_components()

@nightly
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()


	def __lowerCAmelCase      (   self       ):
		_lowercase   =load_numpy(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
		    "/unclip/karlo_v1_alpha_horse_cpu.npy"       )

		_lowercase   =UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"       )
		pipeline.set_progress_bar_config(disable=lowerCAmelCase_       )

		_lowercase   =torch.manual_seed(0       )
		_lowercase   =pipeline(
		    "horse"       ,  num_images_per_prompt=1       ,  generator=lowerCAmelCase_       ,  output_type="np"       ,  )

		_lowercase   =output.images[0]

		assert image.shape == (256, 256, 3)
		assert np.abs(expected_image - image       ).max() < 1e-1

@slow
@require_torch_gpu
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()


	def __lowerCAmelCase      (   self       ):
		_lowercase   =load_numpy(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
		    "/unclip/karlo_v1_alpha_horse_fp16.npy"       )

		_lowercase   =UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"       ,  torch_dtype=torch.floataa       )
		_lowercase   =pipeline.to(lowerCAmelCase_       )
		pipeline.set_progress_bar_config(disable=lowerCAmelCase_       )

		_lowercase   =torch.Generator(device="cpu"       ).manual_seed(0       )
		_lowercase   =pipeline(
		    "horse"       ,  generator=lowerCAmelCase_       ,  output_type="np"       ,  )

		_lowercase   =output.images[0]

		assert image.shape == (256, 256, 3)

		assert_mean_pixel_difference(lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()

		_lowercase   =UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"       ,  torch_dtype=torch.floataa       )
		_lowercase   =pipe.to(lowerCAmelCase_       )
		pipe.set_progress_bar_config(disable=lowerCAmelCase_       )
		pipe.enable_attention_slicing()
		pipe.enable_sequential_cpu_offload()

		_lowercase   =pipe(
		    "horse"       ,  num_images_per_prompt=1       ,  prior_num_inference_steps=2       ,  decoder_num_inference_steps=2       ,  super_res_num_inference_steps=2       ,  output_type="np"       ,  )

		_lowercase   =torch.cuda.max_memory_allocated()
		# make sure that less than 7 GB is allocated
		assert mem_bytes < 7 * 10**9




