






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 					a_(						UpperCamelCase_	,    UpperCamelCase_	,    unittest.TestCase					):



     """simple docstring"""



     __snake_case						:							str       			=StableDiffusionXLPipeline
     __snake_case						:							Optional[int]       			=TEXT_TO_IMAGE_PARAMS
     __snake_case						:							Dict       			=TEXT_TO_IMAGE_BATCH_PARAMS
     __snake_case						:							List[str]       			=TEXT_TO_IMAGE_IMAGE_PARAMS
     __snake_case						:							str       			=TEXT_TO_IMAGE_IMAGE_PARAMS





     def   __UpperCamelCase		(						self			:					int)    ->					Any:

      """simple docstring"""
      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE							  =						UNetaDConditionModel(
          block_out_channels=(3_2, 6_4)       ,       layers_per_block=2       ,       sample_size=3_2       ,       in_channels=4       ,       out_channels=4       ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,       attention_head_dim=(2, 4)       ,       use_linear_projection=lowercase_       ,       addition_embed_type='text_time'       ,       addition_time_embed_dim=8       ,       transformer_layers_per_block=(1, 2)       ,       projection_class_embeddings_input_dim=8_0       ,       cross_attention_dim=6_4       ,       )
      SCREAMING_SNAKE_CASE							  =						EulerDiscreteScheduler(
          beta_start=0.0_00_85       ,       beta_end=0.0_12       ,       steps_offset=1       ,       beta_schedule='scaled_linear'       ,       timestep_spacing='leading'       ,       )
      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE							  =						AutoencoderKL(
          block_out_channels=[3_2, 6_4]       ,       in_channels=3       ,       out_channels=3       ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,       latent_channels=4       ,       sample_size=1_2_8       ,       )
      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE							  =						CLIPTextConfig(
          bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=3_2       ,       intermediate_size=3_7       ,       layer_norm_eps=1e-05       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=1_0_0_0       ,       hidden_act='gelu'       ,       projection_dim=3_2       ,       )
      SCREAMING_SNAKE_CASE							  =						CLIPTextModel(lowercase_)
      SCREAMING_SNAKE_CASE							  =						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       ,       local_files_only=lowercase_)

      SCREAMING_SNAKE_CASE							  =						CLIPTextModelWithProjection(lowercase_)
      SCREAMING_SNAKE_CASE							  =						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       ,       local_files_only=lowercase_)

      SCREAMING_SNAKE_CASE							  =						{
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'text_encoder_2': text_encoder_a,
          'tokenizer_2': tokenizer_a,
          # "safety_checker": None,
          # "feature_extractor": None,
      }
      return components





     def   __UpperCamelCase		(						self			:					Union[str, Any]       ,       lowerCAmelCase__			:					Optional[Any]       ,       lowerCAmelCase__			:					int=0)    ->					Optional[Any]:

      """simple docstring"""




      if str(lowercase_).startswith('mps'):
       SCREAMING_SNAKE_CASE							  =						torch.manual_seed(lowercase_)
      else:
       SCREAMING_SNAKE_CASE							  =						torch.Generator(device=lowercase_).manual_seed(lowercase_)
      SCREAMING_SNAKE_CASE							  =						{
          'prompt': 'A painting of a squirrel eating a burger',
          'generator': generator,
          'num_inference_steps': 2,
          'guidance_scale': 5.0,
          'output_type': 'numpy',
      }
      return inputs





     def   __UpperCamelCase		(						self			:					Tuple)    ->					List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						'cpu'  # ensure determinism for the device-dependent torch.Generator
      SCREAMING_SNAKE_CASE							  =						self.get_dummy_components()
      SCREAMING_SNAKE_CASE							  =						StableDiffusionXLPipeline(**lowercase_)
      SCREAMING_SNAKE_CASE							  =						sd_pipe.to(lowercase_)
      sd_pipe.set_progress_bar_config(disable=lowercase_)

      SCREAMING_SNAKE_CASE							  =						self.get_dummy_inputs(lowercase_)
      SCREAMING_SNAKE_CASE							  =						sd_pipe(**lowercase_).images
      SCREAMING_SNAKE_CASE							  =						image[0, -3:, -3:, -1]

      assert image.shape == (1, 6_4, 6_4, 3)
      SCREAMING_SNAKE_CASE							  =						np.array([0.58_73, 0.61_28, 0.47_97, 0.51_22, 0.56_74, 0.46_39, 0.52_27, 0.51_49, 0.47_47])

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





     def   __UpperCamelCase		(						self			:					Tuple)    ->					Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.get_dummy_components()
      SCREAMING_SNAKE_CASE							  =						StableDiffusionXLPipeline(**lowercase_)
      SCREAMING_SNAKE_CASE							  =						sd_pipe.to(lowercase_)
      SCREAMING_SNAKE_CASE							  =						sd_pipe.to(lowercase_)
      sd_pipe.set_progress_bar_config(disable=lowercase_)

      # forward without prompt embeds
      SCREAMING_SNAKE_CASE							  =						self.get_dummy_inputs(lowercase_)
      SCREAMING_SNAKE_CASE							  =						3 * ['this is a negative prompt']
      SCREAMING_SNAKE_CASE							  =						negative_prompt
      SCREAMING_SNAKE_CASE							  =						3 * [inputs['prompt']]

      SCREAMING_SNAKE_CASE							  =						sd_pipe(**lowercase_)
      SCREAMING_SNAKE_CASE							  =						output.images[0, -3:, -3:, -1]

      # forward with prompt embeds
      SCREAMING_SNAKE_CASE							  =						self.get_dummy_inputs(lowercase_)
      SCREAMING_SNAKE_CASE							  =						3 * ['this is a negative prompt']
      SCREAMING_SNAKE_CASE							  =						3 * [inputs.pop('prompt')]

      (
          SCREAMING_SNAKE_CASE					
      )							  =						sd_pipe.encode_prompt(lowercase_       ,       negative_prompt=lowercase_)

      SCREAMING_SNAKE_CASE							  =						sd_pipe(
          **lowercase_       ,       prompt_embeds=lowercase_       ,       negative_prompt_embeds=lowercase_       ,       pooled_prompt_embeds=lowercase_       ,       negative_pooled_prompt_embeds=lowercase_       ,       )
      SCREAMING_SNAKE_CASE							  =						output.images[0, -3:, -3:, -1]

      # make sure that it's equal
      assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()).max() < 1e-4





     def   __UpperCamelCase		(						self			:					int)    ->					Optional[Any]:

      """simple docstring"""
      super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)





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

      """simple docstring"""
      super().test_inference_batch_single_identical(expected_max_diff=3e-3)

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



     """simple docstring"""



     def   __UpperCamelCase		(						self			:					Any)    ->					int:

      """simple docstring"""
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





     def   __UpperCamelCase		(						self			:					int       ,       lowerCAmelCase__			:					Dict       ,       lowerCAmelCase__			:					int="cpu"       ,       lowerCAmelCase__			:					Any=torch.floataa       ,       lowerCAmelCase__			:					List[str]=0)    ->					int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						torch.Generator(device=lowercase_).manual_seed(lowercase_)
      SCREAMING_SNAKE_CASE							  =						np.random.RandomState(lowercase_).standard_normal((1, 4, 6_4, 6_4))
      SCREAMING_SNAKE_CASE							  =						torch.from_numpy(lowercase_).to(device=lowercase_       ,       dtype=lowercase_)
      SCREAMING_SNAKE_CASE							  =						{
          'prompt': 'a photograph of an astronaut riding a horse',
          'latents': latents,
          'generator': generator,
          'num_inference_steps': 3,
          'guidance_scale': 7.5,
          'output_type': 'numpy',
      }
      return inputs





     def   __UpperCamelCase		(						self			:					Dict)    ->					Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base')
      pipe.to(lowercase_)
      pipe.set_progress_bar_config(disable=lowercase_)

      SCREAMING_SNAKE_CASE							  =						self.get_inputs(lowercase_)
      SCREAMING_SNAKE_CASE							  =						pipe(**lowercase_).images
      SCREAMING_SNAKE_CASE							  =						image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      SCREAMING_SNAKE_CASE							  =						np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06])
      assert np.abs(image_slice - expected_slice).max() < 7e-3







import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


__UpperCAmelCase      				=							{
    "vit_b32": {
        "embed_dim": 5_1_2,
        "image_resolution": 7_6_8,
        "context_length": 1_6,
        "vocab_size": 4_9_4_0_8,
        "vision_layers": 1_2,
        "vision_width": 7_6_8,
        "vision_patch_size": 3_2,
        "transformer_width": 5_1_2,
        "transformer_heads": 8,
        "transformer_layers": 1_2,
    },
    "vit_b16": {
        "embed_dim": 5_1_2,
        "image_resolution": 7_6_8,
        "context_length": 1_6,
        "vocab_size": 4_9_4_0_8,
        "vision_layers": 1_2,
        "vision_width": 7_6_8,
        "vision_patch_size": 1_6,
        "transformer_width": 5_1_2,
        "transformer_heads": 8,
        "transformer_layers": 1_2,
    },
    "vit_l14": {
        "embed_dim": 7_6_8,
        "image_resolution": 8_4_0,
        "context_length": 1_6,
        "vocab_size": 4_9_4_0_8,
        "vision_layers": 2_4,
        "vision_width": 1_0_2_4,
        "vision_patch_size": 1_4,
        "transformer_width": 7_6_8,
        "transformer_heads": 1_2,
        "transformer_layers": 1_2,
    },
}





def 		A_			(		lowercase_					,       lowercase_=""					,       lowercase_="/"	)							->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						[]

 for k, v in params.items():
  SCREAMING_SNAKE_CASE							  =						parent_key + sep + k if parent_key else k

  if isinstance(lowercase_					,       collections.MutableMapping	):
   items.extend(flatten_nested_dict(lowercase_					,       lowercase_					,       sep=lowercase_	).items()	)
  else:
   items.append((new_key, v)	)
 return dict(lowercase_	)





def 		A_			(		lowercase_	)							->List[Any]:







 """simple docstring"""

 return jax.tree_util.tree_map(lambda lowercase_	: x.astype(jnp.floataa	) if x.dtype == jnp.bfloataa else x					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_	)							->Any:







 """simple docstring"""

 SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						pt_attn_layer.in_proj_weight.chunk(3					,       dim=0	)
 SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						pt_attn_layer.in_proj_bias.chunk(3					,       dim=0	)

 SCREAMING_SNAKE_CASE							  =						pt_attn_layer.out_proj.weight
 SCREAMING_SNAKE_CASE							  =						pt_attn_layer.out_proj.bias

 SCREAMING_SNAKE_CASE							  =						q_proj
 SCREAMING_SNAKE_CASE							  =						q_proj_bias

 SCREAMING_SNAKE_CASE							  =						k_proj
 SCREAMING_SNAKE_CASE							  =						k_proj_bias

 SCREAMING_SNAKE_CASE							  =						v_proj
 SCREAMING_SNAKE_CASE							  =						v_proj_bias

 SCREAMING_SNAKE_CASE							  =						out_proj_weights
 SCREAMING_SNAKE_CASE							  =						out_proj_bias





def 		A_			(		lowercase_					,       lowercase_	)							->List[str]:







 """simple docstring"""

 copy_linear(hf_mlp.fca					,       pt_mlp.c_fc	)
 copy_linear(hf_mlp.fca					,       pt_mlp.c_proj	)





def 		A_			(		lowercase_					,       lowercase_	)							->str:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						pt_linear.weight
 SCREAMING_SNAKE_CASE							  =						pt_linear.bias





def 		A_			(		lowercase_					,       lowercase_	)							->Dict:







 """simple docstring"""

 copy_linear(hf_layer.layer_norma					,       pt_layer.ln_a	)
 copy_linear(hf_layer.layer_norma					,       pt_layer.ln_a	)

 # copy MLP
 copy_mlp(hf_layer.mlp					,       pt_layer.mlp	)

 # copy attn
 copy_attn_layer(hf_layer.self_attn					,       pt_layer.attn	)





def 		A_			(		lowercase_					,       lowercase_	)							->str:







 """simple docstring"""

 for hf_layer, pt_layer in zip(lowercase_					,       lowercase_	):
  copy_layer(lowercase_					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_	)							->Tuple:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						pt_model.token_embedding.weight
 SCREAMING_SNAKE_CASE							  =						pt_model.positional_embedding

 # copy layer norm
 copy_linear(hf_encoder.final_layer_norm					,       pt_model.ln_final	)

 # copy hidden layers
 copy_layers(hf_encoder.encoder.layers					,       pt_model.transformer.resblocks	)





def 		A_			(		lowercase_					,       lowercase_	)							->Union[str, Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						pt_model.text_projection.data.T

 # copy text encoder
 copy_encoder(hf_model.text_model					,       lowercase_	)





def 		A_			(		lowercase_					,       lowercase_	)							->Optional[int]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						pt_model.visual.proj.data.T

 # copy layer norms
 copy_linear(hf_model.vision_model.pre_layernorm					,       pt_model.visual.ln_pre	)
 copy_linear(hf_model.vision_model.post_layernorm					,       pt_model.visual.ln_post	)

 # copy embeds
 SCREAMING_SNAKE_CASE							  =						pt_model.visual.conva.weight.data
 SCREAMING_SNAKE_CASE							  =						pt_model.visual.class_embedding
 SCREAMING_SNAKE_CASE							  =						pt_model.visual.positional_embedding.data

 # copy encoder
 copy_layers(hf_model.vision_model.encoder.layers					,       pt_model.visual.transformer.resblocks	)





def 		A_			(		lowercase_					,       lowercase_	)							->Dict:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						flatten_nested_dict(flax_params['backbone']['merged_class_token']	)

 SCREAMING_SNAKE_CASE							  =						torch.from_numpy(flax_class_token_params['scale']	)
 SCREAMING_SNAKE_CASE							  =						torch.from_numpy(flax_class_token_params['bias']	)
 SCREAMING_SNAKE_CASE							  =						nn.Parameter(lowercase_	)
 SCREAMING_SNAKE_CASE							  =						nn.Parameter(lowercase_	)





def 		A_			(		lowercase_					,       lowercase_	)							->int:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						hf_model.state_dict()
 SCREAMING_SNAKE_CASE							  =						{}

 # Rename class prediction head flax params to pytorch HF
 SCREAMING_SNAKE_CASE							  =						flatten_nested_dict(flax_params['class_head']	)

 for flax_key, v in flax_class_params.items():
  SCREAMING_SNAKE_CASE							  =						flax_key.replace('/'					,       '.'	)
  SCREAMING_SNAKE_CASE							  =						torch_key.replace('.kernel'					,       '.weight'	)
  SCREAMING_SNAKE_CASE							  =						torch_key.replace('Dense_0'					,       'dense0'	)
  SCREAMING_SNAKE_CASE							  =						'class_head.' + torch_key

  if "weight" in torch_key and v.ndim == 2:
   SCREAMING_SNAKE_CASE							  =						v.T

  SCREAMING_SNAKE_CASE							  =						nn.Parameter(torch.from_numpy(lowercase_	)	)

 # Rename box prediction box flax params to pytorch HF
 SCREAMING_SNAKE_CASE							  =						flatten_nested_dict(flax_params['obj_box_head']	)

 for flax_key, v in flax_box_params.items():
  SCREAMING_SNAKE_CASE							  =						flax_key.replace('/'					,       '.'	)
  SCREAMING_SNAKE_CASE							  =						torch_key.replace('.kernel'					,       '.weight'	)
  SCREAMING_SNAKE_CASE							  =						torch_key.replace('_'					,       ''	).lower()
  SCREAMING_SNAKE_CASE							  =						'box_head.' + torch_key

  if "weight" in torch_key and v.ndim == 2:
   SCREAMING_SNAKE_CASE							  =						v.T

  SCREAMING_SNAKE_CASE							  =						nn.Parameter(torch.from_numpy(lowercase_	)	)

 # Copy flax params to PyTorch params
 for name, param in new_params.items():
  if name in pt_params.keys():
   pt_params[name].copy_(lowercase_	)





def 		A_			(		lowercase_					,       lowercase_	)							->int:







 """simple docstring"""

 for k, v in flax_attn_params.items():
  if k.startswith('transformer'	):
   SCREAMING_SNAKE_CASE							  =						k.replace('transformer.resblocks'					,       'text_model.encoder.layers'	)
  else:
   SCREAMING_SNAKE_CASE							  =						k.replace('visual.transformer.resblocks'					,       'vision_model.encoder.layers'	)

  SCREAMING_SNAKE_CASE							  =						torch_key.replace('attn'					,       'self_attn'	)
  SCREAMING_SNAKE_CASE							  =						torch_key.replace('key'					,       'k_proj'	)
  SCREAMING_SNAKE_CASE							  =						torch_key.replace('value'					,       'v_proj'	)
  SCREAMING_SNAKE_CASE							  =						torch_key.replace('query'					,       'q_proj'	)
  SCREAMING_SNAKE_CASE							  =						torch_key.replace('out'					,       'out_proj'	)

  if "bias" in torch_key and v.ndim == 2:
   SCREAMING_SNAKE_CASE							  =						v.shape[0] * v.shape[1]
   SCREAMING_SNAKE_CASE							  =						v.reshape(lowercase_	)

  if "weight" in torch_key and "out" in torch_key:
   SCREAMING_SNAKE_CASE							  =						(v.shape[0] * v.shape[1], v.shape[2])
   SCREAMING_SNAKE_CASE							  =						v.reshape(lowercase_	).T

  if "weight" in torch_key and "out" not in torch_key:
   SCREAMING_SNAKE_CASE							  =						(v.shape[0], v.shape[1] * v.shape[2])
   SCREAMING_SNAKE_CASE							  =						v.reshape(lowercase_	).T

  # Copy flax CLIP attn params to HF PyTorch params
  SCREAMING_SNAKE_CASE							  =						torch.from_numpy(lowercase_	)
  hf_backbone.state_dict()[torch_key].copy_(lowercase_	)





def 		A_			(		lowercase_	)							->Any:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						{}
 SCREAMING_SNAKE_CASE							  =						[]

 for k, v in params.items():
  if "attn." in k:
   SCREAMING_SNAKE_CASE							  =						k[: k.rindex('attn.'	) + 5]
   if base in processed_attn_layers:
    continue

   processed_attn_layers.append(lowercase_	)
   SCREAMING_SNAKE_CASE							  =						params[base + 'out.weight'].shape[-1]
   SCREAMING_SNAKE_CASE							  =						params[base + 'out.weight'].reshape(lowercase_					,       lowercase_	).T
   SCREAMING_SNAKE_CASE							  =						params[base + 'out.bias']
  else:
   SCREAMING_SNAKE_CASE							  =						v
 return new_params





def 		A_			(		lowercase_					,       lowercase_	)							->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						CLIP(**lowercase_	)
 torch_model.eval()
 SCREAMING_SNAKE_CASE							  =						torch_model.state_dict()

 SCREAMING_SNAKE_CASE							  =						flatten_nested_dict(flax_params['backbone']['clip']	)
 SCREAMING_SNAKE_CASE							  =						{}

 for flax_key, v in flax_clip_params.items():
  SCREAMING_SNAKE_CASE							  =						flax_key.replace('/'					,       '.'	)
  SCREAMING_SNAKE_CASE							  =						torch_key.replace('text.token_embedding.embedding'					,       'token_embedding.kernel'	)

  if (
      torch_key.startswith('text.transformer'	)
      or torch_key.startswith('text.text_projection'	)
      or torch_key.startswith('text.ln_final'	)
      or torch_key.startswith('text.positional_embedding'	)
  ):
   SCREAMING_SNAKE_CASE							  =						torch_key[5:]

  SCREAMING_SNAKE_CASE							  =						torch_key.replace('text_projection.kernel'					,       'text_projection'	)
  SCREAMING_SNAKE_CASE							  =						torch_key.replace('visual.proj.kernel'					,       'visual.proj'	)
  SCREAMING_SNAKE_CASE							  =						torch_key.replace('.scale'					,       '.weight'	)
  SCREAMING_SNAKE_CASE							  =						torch_key.replace('.kernel'					,       '.weight'	)

  if "conv" in torch_key or "downsample.0.weight" in torch_key:
   SCREAMING_SNAKE_CASE							  =						v.transpose(3					,       2					,       0					,       1	)

  elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
   # Fully connected layers are transposed, embeddings are not
   SCREAMING_SNAKE_CASE							  =						v.T

  SCREAMING_SNAKE_CASE							  =						v

 SCREAMING_SNAKE_CASE							  =						_convert_attn_layers(lowercase_	)
 new_torch_params.update(lowercase_	)
 SCREAMING_SNAKE_CASE							  =						{}

 # Copy flax CLIP backbone params to PyTorch params
 for name, param in new_torch_params.items():
  if name in torch_clip_params.keys():
   SCREAMING_SNAKE_CASE							  =						torch.from_numpy(new_torch_params[name]	)
   torch_clip_params[name].copy_(lowercase_	)
  else:
   SCREAMING_SNAKE_CASE							  =						param

 return torch_clip_params, torch_model, attn_params





@torch.no_grad()
def 		A_			(		lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_=None	)							->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						Repository(lowercase_					,       clone_from=f'''google/{pytorch_dump_folder_path}'''	)
 repo.git_pull()

 if config_path is not None:
  SCREAMING_SNAKE_CASE							  =						OwlViTConfig.from_pretrained(lowercase_	)
 else:
  SCREAMING_SNAKE_CASE							  =						OwlViTConfig()

 SCREAMING_SNAKE_CASE							  =						OwlViTModel(lowercase_	).eval()
 SCREAMING_SNAKE_CASE							  =						OwlViTForObjectDetection(lowercase_	).eval()

 copy_text_model_and_projection(lowercase_					,       lowercase_	)
 copy_vision_model_and_projection(lowercase_					,       lowercase_	)
 SCREAMING_SNAKE_CASE							  =						pt_backbone.logit_scale
 copy_flax_attn_params(lowercase_					,       lowercase_	)

 SCREAMING_SNAKE_CASE							  =						hf_backbone
 copy_class_merge_token(lowercase_					,       lowercase_	)
 copy_class_box_heads(lowercase_					,       lowercase_	)

 # Save HF model
 hf_model.save_pretrained(repo.local_dir	)

 # Initialize image processor
 SCREAMING_SNAKE_CASE							  =						OwlViTImageProcessor(
     size=config.vision_config.image_size					,       crop_size=config.vision_config.image_size	)
 # Initialize tokenizer
 SCREAMING_SNAKE_CASE							  =						CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'					,       pad_token='!'					,       model_max_length=1_6	)

 # Initialize processor
 SCREAMING_SNAKE_CASE							  =						OwlViTProcessor(image_processor=lowercase_					,       tokenizer=lowercase_	)
 image_processor.save_pretrained(repo.local_dir	)
 processor.save_pretrained(repo.local_dir	)

 repo.git_add()
 repo.git_commit('Upload model and processor'	)
 repo.git_push()


if __name__ == "__main__":
      __UpperCAmelCase      				=							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--owlvit_version",
          default=None,
          type=str,
          required=True,
          help="OWL-ViT model name [clip_b16, clip_b32, clip_l14].",
      )
      parser.add_argument(
          "--owlvit_checkpoint", default=None, type=str, required=True, help="Path to flax model checkpoint."
      )
      parser.add_argument("--hf_config", default=None, type=str, required=True, help="Path to HF model config.")
      parser.add_argument(
          "--pytorch_dump_folder_path", default="hf_model", type=str, help="Path to the output PyTorch model."
      )
      __UpperCAmelCase      				=							parser.parse_args()

      # Initialize PyToch clip model
      __UpperCAmelCase      				=							args.owlvit_version
      if model_name == "clip_b16":
            __UpperCAmelCase      				=							CONFIGS["vit_b16"]
      elif model_name == "clip_b32":
            __UpperCAmelCase      				=							CONFIGS["vit_b32"]
      elif model_name == "clip_l14":
            __UpperCAmelCase      				=							CONFIGS["vit_l14"]

      # Load from checkpoint and convert params to float-32
      __UpperCAmelCase      				=							checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["optimizer"]["target"]
      __UpperCAmelCase      				=							jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
      del variables

      # Convert CLIP backbone
      __UpperCAmelCase				,	__UpperCAmelCase				,	__UpperCAmelCase      				=							convert_clip_backbone(flax_params, torch_config)

      convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)




