code: 
  
  
  
  
  
  from typing import Optional, Tuple
  
  import flax.linen as nn
  import jax
  import jax.numpy as jnp
  from configuration_hybrid_clip import HybridCLIPConfig
  from flax.core.frozen_dict import FrozenDict
  
  from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
  from transformers.modeling_flax_utils import FlaxPreTrainedModel
  from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
  from transformers.utils import logging
  
  
  lowerCamelCase										=  logging.get_logger(__name__)
  
  class 					snake_case_							(       nn.Module      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						42
     __lowerCAmelCase      =						jnp.floataa
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.config.text_config
         _lowercase     =	self.config.vision_config
  
         _lowercase     =	self.config.projection_dim
         _lowercase     =	text_config.hidden_size
         _lowercase     =	vision_config.hidden_size
  
         _lowercase     =	FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
         _lowercase     =	FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__   ,       snake_case__	).module_class
  
         _lowercase     =	text_module(snake_case__   ,       dtype=self.dtype	)
         _lowercase     =	vision_module(snake_case__   ,       dtype=self.dtype	)
  
         _lowercase     =	nn.Dense(
             self.projection_dim   ,       dtype=self.dtype   ,       kernel_init=jax.nn.initializers.normal(0.02	)   ,       use_bias=snake_case__   ,       )
         _lowercase     =	nn.Dense(
             self.projection_dim   ,       dtype=self.dtype   ,       kernel_init=jax.nn.initializers.normal(0.02	)   ,       use_bias=snake_case__   ,       )
         _lowercase     =	self.param('logit_scale'   ,       jax.nn.initializers.ones   ,       []	)
  
     def __call__(    self:	Any   ,       snake_case__:	Any=None   ,       snake_case__:	List[Any]=None   ,       snake_case__:	Dict=None   ,       snake_case__:	Dict=None   ,       snake_case__:	List[Any]=None   ,       snake_case__:	bool = True   ,       snake_case__:	Optional[Any]=None   ,       snake_case__:	int=None   ,       snake_case__:	int=None   ,       )  ->		Any:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	return_dict if return_dict is not None else self.config.return_dict
  
         _lowercase     =	self.vision_model(
             pixel_values=snake_case__   ,       deterministic=snake_case__   ,       output_attentions=snake_case__   ,       output_hidden_states=snake_case__   ,       return_dict=snake_case__   ,       )
  
         _lowercase     =	self.text_model(
             input_ids=snake_case__   ,       attention_mask=snake_case__   ,       token_type_ids=snake_case__   ,       position_ids=snake_case__   ,       deterministic=snake_case__   ,       output_attentions=snake_case__   ,       output_hidden_states=snake_case__   ,       return_dict=snake_case__   ,       )
  
         _lowercase     =	vision_outputs[1]
         _lowercase     =	self.visual_projection(snake_case__	)
  
         _lowercase     =	text_outputs[1]
         _lowercase     =	self.text_projection(snake_case__	)
  
         # normalized features
         _lowercase     =	image_embeds / jnp.linalg.norm(snake_case__   ,       axis=-1   ,       keepdims=snake_case__	)
         _lowercase     =	text_embeds / jnp.linalg.norm(snake_case__   ,       axis=-1   ,       keepdims=snake_case__	)
  
         # cosine similarity as logits
         _lowercase     =	jnp.exp(self.logit_scale	)
         _lowercase     =	jnp.matmul(snake_case__   ,       image_embeds.T	) * logit_scale
         _lowercase     =	logits_per_text.T
  
         if not return_dict:
             return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
  
         return FlaxCLIPOutput(
             logits_per_image=snake_case__   ,       logits_per_text=snake_case__   ,       text_embeds=snake_case__   ,       image_embeds=snake_case__   ,       text_model_output=snake_case__   ,       vision_model_output=snake_case__   ,       )
  
  
  
  
  
  
  class 					snake_case_							(       lowercase_      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						HybridCLIPConfig
     __lowerCAmelCase      =						FlaxHybridCLIPModule
  
     def __init__(    self:	List[Any]   ,       snake_case__:	HybridCLIPConfig   ,       snake_case__:	Optional[Tuple] = None   ,       snake_case__:	int = 0   ,       snake_case__:	jnp.dtype = jnp.floataa   ,       **snake_case__:	Tuple   ,       )  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         if input_shape is None:
             _lowercase     =	((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))
  
         _lowercase     =	self.module_class(config=snake_case__   ,       dtype=snake_case__   ,       **snake_case__	)
         super().__init__(snake_case__   ,       snake_case__   ,       input_shape=snake_case__   ,       seed=snake_case__   ,       dtype=snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple   ,       snake_case__:	jax.random.PRNGKey   ,       snake_case__:	Tuple   ,       snake_case__:	FrozenDict = None	)  ->		FrozenDict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	jnp.zeros(input_shape[0]   ,       dtype='i4'	)
         _lowercase     =	jnp.broadcast_to(jnp.arange(jnp.atleast_ad(snake_case__	).shape[-1]	)   ,       input_shape[0]	)
         _lowercase     =	jnp.ones_like(snake_case__	)
         _lowercase     =	jnp.ones_like(snake_case__	)
  
         _lowercase     =	jax.random.normal(snake_case__   ,       input_shape[1]	)
  
         _lowercase ,    _lowercase     =	jax.random.split(snake_case__	)
         _lowercase     =	{'params': params_rng, 'dropout': dropout_rng}
  
         return self.module.init(snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__	)["params"]
  
     def __call__(    self:	Tuple   ,       snake_case__:	Optional[int]   ,       snake_case__:	Tuple   ,       snake_case__:	List[str]=None   ,       snake_case__:	Optional[int]=None   ,       snake_case__:	int=None   ,       snake_case__:	dict = None   ,       snake_case__:	jax.random.PRNGKey = None   ,       snake_case__:	bool = False   ,       snake_case__:	Optional[bool] = None   ,       snake_case__:	Optional[bool] = None   ,       snake_case__:	Optional[bool] = None   ,       )  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	output_attentions if output_attentions is not None else self.config.output_attentions
         _lowercase     =	(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         _lowercase     =	return_dict if return_dict is not None else self.config.return_dict
  
         if position_ids is None:
             _lowercase     =	jnp.broadcast_to(jnp.arange(jnp.atleast_ad(snake_case__	).shape[-1]	)   ,       input_ids.shape	)
  
         if token_type_ids is None:
             _lowercase     =	jnp.zeros_like(snake_case__	)
  
         if attention_mask is None:
             _lowercase     =	jnp.ones_like(snake_case__	)
  
         # Handle any PRNG if needed
         _lowercase     =	{}
         if dropout_rng is not None:
             _lowercase     =	dropout_rng
  
         return self.module.apply(
             {'params': params or self.params}   ,       jnp.array(snake_case__   ,       dtype='i4'	)   ,       jnp.array(snake_case__   ,       dtype=jnp.floataa	)   ,       jnp.array(snake_case__   ,       dtype='i4'	)   ,       jnp.array(snake_case__   ,       dtype='i4'	)   ,       jnp.array(snake_case__   ,       dtype='i4'	)   ,       not train   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       rngs=snake_case__   ,       )
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]   ,       snake_case__:	Optional[Any]   ,       snake_case__:	Optional[int]=None   ,       snake_case__:	int=None   ,       snake_case__:	List[str]=None   ,       snake_case__:	dict = None   ,       snake_case__:	jax.random.PRNGKey = None   ,       snake_case__:	Optional[Any]=False   ,       )  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         if position_ids is None:
             _lowercase     =	jnp.broadcast_to(jnp.arange(jnp.atleast_ad(snake_case__	).shape[-1]	)   ,       input_ids.shape	)
  
         if token_type_ids is None:
             _lowercase     =	jnp.zeros_like(snake_case__	)
  
         if attention_mask is None:
             _lowercase     =	jnp.ones_like(snake_case__	)
  
         # Handle any PRNG if needed
         _lowercase     =	{}
         if dropout_rng is not None:
             _lowercase     =	dropout_rng
  
         def _get_features(snake_case__:	Tuple   ,       snake_case__:	List[Any]   ,       snake_case__:	Optional[Any]   ,       snake_case__:	Tuple   ,       snake_case__:	Union[str, Any]   ,       snake_case__:	Union[str, Any]	):
             _lowercase     =	module.text_model(
                 input_ids=snake_case__   ,       attention_mask=snake_case__   ,       position_ids=snake_case__   ,       token_type_ids=snake_case__   ,       deterministic=snake_case__   ,       )
             _lowercase     =	text_outputs[1]
             _lowercase     =	module.text_projection(snake_case__	)
             return text_features
  
         return self.module.apply(
             {'params': params or self.params}   ,       jnp.array(snake_case__   ,       dtype='i4'	)   ,       jnp.array(snake_case__   ,       dtype='i4'	)   ,       jnp.array(snake_case__   ,       dtype='i4'	)   ,       jnp.array(snake_case__   ,       dtype='i4'	)   ,       not train   ,       method=_get_features   ,       rngs=snake_case__   ,       )
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict   ,       snake_case__:	str   ,       snake_case__:	dict = None   ,       snake_case__:	jax.random.PRNGKey = None   ,       snake_case__:	Optional[int]=False	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	{}
         if dropout_rng is not None:
             _lowercase     =	dropout_rng
  
         def _get_features(snake_case__:	Any   ,       snake_case__:	Dict   ,       snake_case__:	Any	):
             _lowercase     =	module.vision_model(pixel_values=snake_case__   ,       deterministic=snake_case__	)
             _lowercase     =	vision_outputs[1]  # pooled_output
             _lowercase     =	module.visual_projection(snake_case__	)
             return image_features
  
         return self.module.apply(
             {'params': params or self.params}   ,       jnp.array(snake_case__   ,       dtype=jnp.floataa	)   ,       not train   ,       method=_get_features   ,       rngs=snake_case__   ,       )
  
     @classmethod
     def 			SCREAMING_SNAKE_CASE__				(    cls:	Optional[Any]   ,       snake_case__:	str = None   ,       snake_case__:	str = None   ,       *snake_case__:	List[str]   ,       **snake_case__:	Optional[Any]   ,       )  ->		FlaxPreTrainedModel:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	{
             argument[len('text_'	) :]: value for argument, value in kwargs.items() if argument.startswith('text_'	)
         }
  
         _lowercase     =	{
             argument[len('vision_'	) :]: value for argument, value in kwargs.items() if argument.startswith('vision_'	)
         }
  
         # remove text, vision kwargs from kwargs
         for key in kwargs_text.keys():
             del kwargs["text_" + key]
         for key in kwargs_vision.keys():
             del kwargs["vision_" + key]
  
         # Load and initialize the text and vision model
         _lowercase     =	kwargs_text.pop('model'   ,       snake_case__	)
         if text_model is None:
             assert (
                 text_model_name_or_path is not None
             ), "If `model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
             from transformers import FlaxAutoModel
  
             if "config" not in kwargs_text:
                 from transformers import AutoConfig
  
                 _lowercase     =	AutoConfig.from_pretrained(snake_case__	)
                 _lowercase     =	text_config
  
             _lowercase     =	FlaxAutoModel.from_pretrained(snake_case__   ,       *snake_case__   ,       **snake_case__	)
  
         _lowercase     =	kwargs_vision.pop('model'   ,       snake_case__	)
         if vision_model is None:
             assert (
                 vision_model_name_or_path is not None
             ), "If `model` is not defined as an argument, a `vision_model_name_or_path` has to be defined"
             from transformers import FlaxAutoModel
  
             if "config" not in kwargs_vision:
                 from transformers import AutoConfig
  
                 _lowercase     =	AutoConfig.from_pretrained(snake_case__	)
                 _lowercase     =	vision_config
  
             _lowercase     =	FlaxAutoModel.from_pretrained(snake_case__   ,       *snake_case__   ,       **snake_case__	)
  
         # instantiate config with corresponding kwargs
         _lowercase     =	kwargs.pop('dtype'   ,       jnp.floataa	)
         _lowercase     =	HybridCLIPConfig.from_text_vision_configs(text_model.config   ,       vision_model.config   ,       **snake_case__	)
  
         # init model
         _lowercase     =	cls(snake_case__   ,       *snake_case__   ,       dtype=snake_case__   ,       **snake_case__	)
  
         if vision_config.model_type == "clip":
             _lowercase     =	vision_model.params['vision_model']
             _lowercase     =	vision_model.params['visual_projection']['kernel']
         else:
             _lowercase     =	vision_model.params
  
         _lowercase     =	text_model.params
  
         return model
  
style_context: 
  
  
  
  
  
  
  import argparse
  import json
  from pathlib import Path
  
  import requests
  import torch
  from datasets import load_dataset
  from huggingface_hub import hf_hub_download
  from PIL import Image
  
  from transformers import (
      BeitConfig,
      BeitForImageClassification,
      BeitForMaskedImageModeling,
      BeitForSemanticSegmentation,
      BeitImageProcessor,
  )
  from transformers.image_utils import PILImageResampling
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  lowerCamelCase										=  logging.get_logger(__name__)
  def        UpperCAmelCase      (   _lowercase,      _lowercase=False,      _lowercase=False      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	'backbone.' if is_semantic else ''
  
      _lowercase     =	[]
      for i in range(config.num_hidden_layers      ):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''')      )
          rename_keys.append(
              (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''')      )
          rename_keys.append(
              (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''')      )
  
      # projection layer + position embeddings
      rename_keys.extend(
          [
              (f'''{prefix}cls_token''', 'beit.embeddings.cls_token'),
              (f'''{prefix}patch_embed.proj.weight''', 'beit.embeddings.patch_embeddings.projection.weight'),
              (f'''{prefix}patch_embed.proj.bias''', 'beit.embeddings.patch_embeddings.projection.bias'),
          ]      )
  
      if has_lm_head:
          # mask token + shared relative position bias + layernorm
          rename_keys.extend(
              [
                  ('mask_token', 'beit.embeddings.mask_token'),
                  (
                      'rel_pos_bias.relative_position_bias_table',
                      'beit.encoder.relative_position_bias.relative_position_bias_table',
                  ),
                  (
                      'rel_pos_bias.relative_position_index',
                      'beit.encoder.relative_position_bias.relative_position_index',
                  ),
                  ('norm.weight', 'layernorm.weight'),
                  ('norm.bias', 'layernorm.bias'),
              ]      )
      elif is_semantic:
          # semantic segmentation classification heads
          rename_keys.extend(
              [
                  ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
                  ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
                  ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
                  ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
              ]      )
      else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
                  ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
                  ('head.weight', 'classifier.weight'),
                  ('head.bias', 'classifier.bias'),
              ]      )
  
      return rename_keys
  def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase=False,      _lowercase=False      ):
  
  
      '''simple docstring'''
  
  
  
      for i in range(config.num_hidden_layers      ):
          _lowercase     =	'backbone.' if is_semantic else ''
          # queries, keys and values
          _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''      )
          _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''      )
          _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''      )
  
          _lowercase     =	in_proj_weight[
              : config.hidden_size, :
          ]
          _lowercase     =	q_bias
          _lowercase     =	in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          _lowercase     =	in_proj_weight[
              -config.hidden_size :, :
          ]
          _lowercase     =	v_bias
  
          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''      )
          _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''      )
  
          _lowercase     =	gamma_a
          _lowercase     =	gamma_a
  
          # relative_position bias table + index
          if not has_lm_head:
              # each layer has its own relative position bias
              _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_bias_table'''      )
              _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_index'''      )
  
              _lowercase     =	table
              _lowercase     =	index
  def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	dct.pop(_lowercase      )
      _lowercase     =	val
  def        UpperCAmelCase      (   ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	'http://images.cocodataset.org/val2017/000000039769.jpg'
      _lowercase     =	Image.open(requests.get(_lowercase,      stream=_lowercase      ).raw      )
      return im
  @torch.no_grad()
  def        UpperCAmelCase      (   _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	BeitConfig()
      _lowercase     =	False
      _lowercase     =	False
      _lowercase     =	'huggingface/label-files'
      # set config parameters based on URL
      if checkpoint_url[-9:-4] == "pt22k":
          # masked image modeling
          _lowercase     =	True
          _lowercase     =	True
          _lowercase     =	True
      elif checkpoint_url[-9:-4] == "ft22k":
          # intermediate fine-tuning on ImageNet-22k
          _lowercase     =	True
          _lowercase     =	21841
          _lowercase     =	'imagenet-22k-id2label.json'
          _lowercase     =	json.load(open(hf_hub_download(_lowercase,      _lowercase,      repo_type='dataset'      ),      'r'      )      )
          _lowercase     =	{int(_lowercase      ): v for k, v in idalabel.items()}
          # this dataset contains 21843 labels but the model only has 21841
          # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
          del idalabel[9205]
          del idalabel[15027]
          _lowercase     =	idalabel
          _lowercase     =	{v: k for k, v in idalabel.items()}
      elif checkpoint_url[-8:-4] == "to1k":
          # fine-tuning on ImageNet-1k
          _lowercase     =	True
          _lowercase     =	1000
          _lowercase     =	'imagenet-1k-id2label.json'
          _lowercase     =	json.load(open(hf_hub_download(_lowercase,      _lowercase,      repo_type='dataset'      ),      'r'      )      )
          _lowercase     =	{int(_lowercase      ): v for k, v in idalabel.items()}
          _lowercase     =	idalabel
          _lowercase     =	{v: k for k, v in idalabel.items()}
          if "384" in checkpoint_url:
              _lowercase     =	384
          if "512" in checkpoint_url:
              _lowercase     =	512
      elif "ade20k" in checkpoint_url:
          # fine-tuning
          _lowercase     =	True
          _lowercase     =	150
          _lowercase     =	'ade20k-id2label.json'
          _lowercase     =	json.load(open(hf_hub_download(_lowercase,      _lowercase,      repo_type='dataset'      ),      'r'      )      )
          _lowercase     =	{int(_lowercase      ): v for k, v in idalabel.items()}
          _lowercase     =	idalabel
          _lowercase     =	{v: k for k, v in idalabel.items()}
          _lowercase     =	640
          _lowercase     =	True
      else:
          raise ValueError('Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''      )
  
      # size of the architecture
      if "base" in checkpoint_url:
          pass
      elif "large" in checkpoint_url:
          _lowercase     =	1024
          _lowercase     =	4096
          _lowercase     =	24
          _lowercase     =	16
          if "ade20k" in checkpoint_url:
              _lowercase     =	640
              _lowercase     =	[7, 11, 15, 23]
      else:
          raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'      )
  
      # load state_dict of original model, remove and rename some keys
      _lowercase     =	torch.hub.load_state_dict_from_url(_lowercase,      map_location='cpu',      check_hash=_lowercase      )
      _lowercase     =	state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']
  
      _lowercase     =	create_rename_keys(_lowercase,      has_lm_head=_lowercase,      is_semantic=_lowercase      )
      for src, dest in rename_keys:
          rename_key(_lowercase,      _lowercase,      _lowercase      )
      read_in_q_k_v(_lowercase,      _lowercase,      has_lm_head=_lowercase,      is_semantic=_lowercase      )
      if is_semantic:
          # add prefix to decoder keys
          for key, val in state_dict.copy().items():
              _lowercase     =	state_dict.pop(_lowercase      )
              if key.startswith('backbone.fpn'      ):
                  _lowercase     =	key.replace('backbone.fpn',      'fpn'      )
              _lowercase     =	val
  
      # load HuggingFace model
      if checkpoint_url[-9:-4] == "pt22k":
          _lowercase     =	BeitForMaskedImageModeling(_lowercase      )
      elif "ade20k" in checkpoint_url:
          _lowercase     =	BeitForSemanticSegmentation(_lowercase      )
      else:
          _lowercase     =	BeitForImageClassification(_lowercase      )
      model.eval()
      model.load_state_dict(_lowercase      )
  
      # Check outputs on an image
      if is_semantic:
          _lowercase     =	BeitImageProcessor(size=config.image_size,      do_center_crop=_lowercase      )
          _lowercase     =	load_dataset('hf-internal-testing/fixtures_ade20k',      split='test'      )
          _lowercase     =	Image.open(ds[0]['file']      )
      else:
          _lowercase     =	BeitImageProcessor(
              size=config.image_size,      resample=PILImageResampling.BILINEAR,      do_center_crop=_lowercase      )
          _lowercase     =	prepare_img()
  
      _lowercase     =	image_processor(images=_lowercase,      return_tensors='pt'      )
      _lowercase     =	encoding['pixel_values']
  
      _lowercase     =	model(_lowercase      )
      _lowercase     =	outputs.logits
  
      # verify logits
      _lowercase     =	torch.Size([1, 1000]      )
      if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'      ):
          _lowercase     =	torch.Size([1, 196, 8192]      )
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'      ):
          _lowercase     =	torch.Size([1, 196, 8192]      )
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'      ):
          _lowercase     =	torch.Size([1, 21841]      )
          _lowercase     =	torch.tensor([2.2_288, 2.4_671, 0.7_395]      )
          _lowercase     =	2397
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'      ):
          _lowercase     =	torch.Size([1, 21841]      )
          _lowercase     =	torch.tensor([1.6_881, -0.2_787, 0.5_901]      )
          _lowercase     =	2396
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'      ):
          _lowercase     =	torch.tensor([0.1_241, 0.0_798, -0.6_569]      )
          _lowercase     =	285
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([-1.2_385, -1.0_987, -1.0_108]      )
          _lowercase     =	281
      elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([-1.5_303, -0.9_484, -0.3_147]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'      ):
          _lowercase     =	torch.tensor([0.4_610, -0.0_928, 0.2_086]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([-0.4_804, 0.6_257, -0.1_837]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([-0.3_062, 0.7_261, 0.4_852]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'      ):
          _lowercase     =	(1, 150, 160, 160)
          _lowercase     =	torch.tensor(
              [
                  [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
                  [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
                  [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
              ]      )
      elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'      ):
          _lowercase     =	(1, 150, 160, 160)
          _lowercase     =	torch.tensor(
              [
                  [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
                  [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
                  [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
              ]      )
      else:
          raise ValueError('Can\'t verify logits as model is not supported'      )
  
      assert logits.shape == expected_shape, "Shape of logits not as expected"
      if not has_lm_head:
          if is_semantic:
              assert torch.allclose(
                  logits[0, :3, :3, :3],      _lowercase,      atol=1e-3      ), "First elements of logits not as expected"
          else:
              print('Predicted class idx:',      logits.argmax(-1      ).item()      )
              assert torch.allclose(
                  logits[0, :3],      _lowercase,      atol=1e-3      ), "First elements of logits not as expected"
              assert logits.argmax(-1      ).item() == expected_class_idx, "Predicted class index not as expected"
  
      Path(_lowercase      ).mkdir(exist_ok=_lowercase      )
      print(f'''Saving model to {pytorch_dump_folder_path}'''      )
      model.save_pretrained(_lowercase      )
      print(f'''Saving image processor to {pytorch_dump_folder_path}'''      )
      image_processor.save_pretrained(_lowercase      )
  
  
  if __name__ == "__main__":
     lowerCamelCase										=  argparse.ArgumentParser()
  
     parser.add_argument(
         '''--checkpoint_url''',
         default='''https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth''',
         type=str,
         help='''URL to the original PyTorch checkpoint (.pth file).''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
     )
     lowerCamelCase										=  parser.parse_args()
     convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
  