code: import argparse
  import json
  
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from timm.models import create_model
  
  from transformers import (
      BeitImageProcessor,
      DataaVecVisionConfig,
      DataaVecVisionForImageClassification,
      DataaVecVisionModel,
  )
  def 		snake_case__  (		lowercase       ,							lowercase=False       ,							lowercase=False       ,							lowercase="data2vec."				):
  
     '''simple docstring'''
  
  
  
  
  
     lowerCAmelCase_:       List[str]         =  '''backbone.''' if is_semantic else ''''''
  
     lowerCAmelCase_:       int         =  []
     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'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')				)
        rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')				)
        rename_keys.append(
            (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')				)
        rename_keys.append(
            (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')				)
        rename_keys.append(
            (F'''{prefix}blocks.{i}.norm2.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')				)
        rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')				)
        rename_keys.append(
            (F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')				)
        rename_keys.append(
            (F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')				)
        rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')				)
        rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')				)
  
     # projection layer + position embeddings
     rename_keys.extend(
         [
             (F'''{prefix}cls_token''', F'''{hf_prefix}embeddings.cls_token'''),
             (F'''{prefix}patch_embed.proj.weight''', F'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
             (F'''{prefix}patch_embed.proj.bias''', F'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
         ]				)
  
     if has_lm_head:
        # mask token + shared relative position bias + layernorm
        rename_keys.extend(
            [
                ("mask_token", F'''{hf_prefix}embeddings.mask_token'''),
                (
                    "rel_pos_bias.relative_position_bias_table",
                    F'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
                ),
                (
                    "rel_pos_bias.relative_position_index",
                    F'''{hf_prefix}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", F'''{hf_prefix}pooler.layernorm.weight'''),
                ("fc_norm.bias", F'''{hf_prefix}pooler.layernorm.bias'''),
                ("head.weight", "classifier.weight"),
                ("head.bias", "classifier.bias"),
            ]				)
  
     return rename_keys
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase=False       ,							lowercase=False       ,							lowercase="data2vec_vision."				):
  
     '''simple docstring'''
  
  
  
  
  
     for i in range(config.num_hidden_layers				):
        lowerCAmelCase_:       str         =  '''backbone.''' if is_semantic else ''''''
        # queries, keys and values
        lowerCAmelCase_:       Union[str, Any]         =  state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''				)
        lowerCAmelCase_:       Tuple         =  state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''				)
        lowerCAmelCase_:       Dict         =  state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''				)
  
        lowerCAmelCase_:       List[str]         =  in_proj_weight[
            : config.hidden_size, :
        ]
        lowerCAmelCase_:       Union[str, Any]         =  q_bias
        lowerCAmelCase_:       Dict         =  in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        lowerCAmelCase_:       Union[str, Any]         =  in_proj_weight[
            -config.hidden_size :, :
        ]
        lowerCAmelCase_:       Union[str, Any]         =  v_bias
  
        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        lowerCAmelCase_:       List[Any]         =  state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''				)
        lowerCAmelCase_:       str         =  state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''				)
  
        lowerCAmelCase_:       Optional[Any]         =  gamma_a
        lowerCAmelCase_:       Optional[Any]         =  gamma_a
  
        # relative_position bias table + index
        if not has_lm_head:
           # each layer has its own relative position bias
           lowerCAmelCase_:       Optional[int]         =  state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''				)
           lowerCAmelCase_:       Optional[Any]         =  state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''				)
  
           lowerCAmelCase_:       Union[str, Any]         =  table
           lowerCAmelCase_:       List[str]         =  index
  def 		snake_case__  (		):
  
     '''simple docstring'''
  
  
  
  
  
     lowerCAmelCase_:       Optional[Any]         =  argparse.ArgumentParser(
         "Convert Data2VecVision to HF for image classification and pretraining"       ,							add_help=UpperCAmelCase_				)
     parser.add_argument("--hf_checkpoint_name"       ,							type=UpperCAmelCase_				)
     parser.add_argument("--input_size"       ,							default=224       ,							type=UpperCAmelCase_       ,							help="images input size"				)
     parser.add_argument("--beit_checkpoint"       ,							default=""       ,							help="beit checkpoint"				)
  
     return parser.parse_args()
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase				):
  
     '''simple docstring'''
  
  
  
  
  
     def load_state_dict(lowercase       ,							lowercase       ,							lowercase=""       ,							lowercase="relative_position_index"				):
        lowerCAmelCase_:       List[str]         =  []
        lowerCAmelCase_:       List[str]         =  []
        lowerCAmelCase_:       int         =  []
        # copy state_dict so _load_from_state_dict can modify it
        lowerCAmelCase_:       Union[str, Any]         =  getattr(UpperCAmelCase_       ,							"_metadata"       ,							UpperCAmelCase_				)
        lowerCAmelCase_:       List[str]         =  state_dict.copy()
        if metadata is not None:
           lowerCAmelCase_:       Optional[Any]         =  metadata
  
        def load(lowercase       ,							lowercase=""				):
           lowerCAmelCase_:       Union[str, Any]         =  {} if metadata is None else metadata.get(prefix[:-1]       ,							{}				)
           module._load_from_state_dict(
               UpperCAmelCase_       ,							UpperCAmelCase_       ,							UpperCAmelCase_       ,							UpperCAmelCase_       ,							UpperCAmelCase_       ,							UpperCAmelCase_       ,							UpperCAmelCase_				)
           for name, child in module._modules.items():
              if child is not None:
                 load(UpperCAmelCase_       ,							prefix + name + "."				)
  
        load(UpperCAmelCase_       ,							prefix=UpperCAmelCase_				)
  
        lowerCAmelCase_:       List[str]         =  []
        lowerCAmelCase_:       Union[str, Any]         =  []
        for key in missing_keys:
           lowerCAmelCase_:       Union[str, Any]         =  True
           for ignore_key in ignore_missing.split("|"				):
              if ignore_key in key:
                 lowerCAmelCase_:       int         =  False
                 break
           if keep_flag:
              warn_missing_keys.append(UpperCAmelCase_				)
           else:
              ignore_missing_keys.append(UpperCAmelCase_				)
  
        lowerCAmelCase_:       List[Any]         =  warn_missing_keys
  
        if len(UpperCAmelCase_				) > 0:
           print(
               "Weights of {} not initialized from pretrained model: {}".format(
                   model.__class__.__name__       ,							UpperCAmelCase_				)				)
        if len(UpperCAmelCase_				) > 0:
           print("Weights from pretrained model not used in {}: {}".format(model.__class__.__name__       ,							UpperCAmelCase_				)				)
        if len(UpperCAmelCase_				) > 0:
           print(
               "Ignored weights of {} not initialized from pretrained model: {}".format(
                   model.__class__.__name__       ,							UpperCAmelCase_				)				)
        if len(UpperCAmelCase_				) > 0:
           print("\n".join(UpperCAmelCase_				)				)
  
     lowerCAmelCase_:       int         =  {
         '''pretrained''': False,
         '''use_shared_rel_pos_bias''': True,
         '''use_abs_pos_emb''': False,
         '''init_values''': 0.1,
     }
  
     if is_finetuned:
        model_kwargs.update(
            {
                "num_classes": 1000,
                "use_mean_pooling": True,
                "init_scale": 0.001,
                "use_rel_pos_bias": True,
            }				)
  
     lowerCAmelCase_:       List[Any]         =  create_model(
         "beit_large_patch16_224" if is_large else "beit_base_patch16_224"       ,							**UpperCAmelCase_       ,							)
     lowerCAmelCase_:       Dict         =  model.patch_embed.patch_size
     lowerCAmelCase_:       Optional[int]         =  (args.input_size // patch_size[0], args.input_size // patch_size[1])
     lowerCAmelCase_:       Union[str, Any]         =  torch.load(args.beit_checkpoint       ,							map_location="cpu"				)
  
     print(F'''Load ckpt from {args.beit_checkpoint}'''				)
     lowerCAmelCase_:       List[Any]         =  None
     for model_key in ("model", "module"):
        if model_key in checkpoint:
           lowerCAmelCase_:       Union[str, Any]         =  checkpoint[model_key]
           print(F'''Load state_dict by model_key = {model_key}'''				)
           break
  
     lowerCAmelCase_:       int         =  list(checkpoint_model.keys()				)
     for key in all_keys:
        if "relative_position_index" in key:
           checkpoint_model.pop(UpperCAmelCase_				)
  
        if "relative_position_bias_table" in key:
           lowerCAmelCase_:       List[str]         =  checkpoint_model[key]
           lowerCAmelCase_:       Optional[Any]         =  rel_pos_bias.size()
           lowerCAmelCase_:       Union[str, Any]         =  model.state_dict()[key].size()
           lowerCAmelCase_:       int         =  model.patch_embed.patch_shape
           if dst_patch_shape[0] != dst_patch_shape[1]:
              raise NotImplementedError()
  
     load_state_dict(UpperCAmelCase_       ,							UpperCAmelCase_       ,							prefix=""				)
  
     return model
  def 		snake_case__  (		):
  
     '''simple docstring'''
  
  
  
  
  
     lowerCAmelCase_:       Optional[Any]         =  get_args()
  
     lowerCAmelCase_:       Optional[int]         =  '''ft1k''' in args.hf_checkpoint_name
     lowerCAmelCase_:       Any         =  '''large''' in args.hf_checkpoint_name
  
     if is_finetuned:
        # To convert Beit's data2vec_vision to HF you need to copy
        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
        # into this folder.
        import modeling_finetune  # noqa: F401
     else:
        # To convert Beit's data2vec_vision to HF you need to copy
        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
        # into this folder
        # IMPORTANT: Note that for now we've only converted the down-stream
        # model and not the full pretrained model. This means for the integration
        # test you need to add a `return x` after the following line:
        # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
        # to make the integration test pass.
        import modeling_cyclical  # noqa: F401
  
     # 1. Create model config
     lowerCAmelCase_:       List[Any]         =  DataaVecVisionConfig()
     if is_finetuned:
        lowerCAmelCase_:       Optional[Any]         =  True
        lowerCAmelCase_:       Optional[int]         =  False
        lowerCAmelCase_:       List[str]         =  True
        lowerCAmelCase_:       str         =  1000
  
        lowerCAmelCase_:       Union[str, Any]         =  '''huggingface/label-files'''
        lowerCAmelCase_:       Tuple         =  '''imagenet-1k-id2label.json'''
        lowerCAmelCase_:       List[Any]         =  json.load(open(hf_hub_download(UpperCAmelCase_       ,							UpperCAmelCase_       ,							repo_type="dataset"				)       ,							"r"				)				)
        lowerCAmelCase_:       List[Any]         =  {int(UpperCAmelCase_				): v for k, v in idalabel.items()}
        lowerCAmelCase_:       int         =  idalabel
        lowerCAmelCase_:       int         =  {v: k for k, v in idalabel.items()}
     else:
        lowerCAmelCase_:       Optional[int]         =  False
        lowerCAmelCase_:       Tuple         =  True
        lowerCAmelCase_:       Optional[Any]         =  False
  
     if is_large:
        lowerCAmelCase_:       Tuple         =  1024
        lowerCAmelCase_:       Optional[int]         =  4096
        lowerCAmelCase_:       Optional[int]         =  24
        lowerCAmelCase_:       Any         =  16
  
     # 2. Load Beit model
     lowerCAmelCase_:       Tuple         =  load_beit_model(UpperCAmelCase_       ,							UpperCAmelCase_       ,							UpperCAmelCase_				)
     orig_model.eval()
  
     # 3. Forward Beit model
     lowerCAmelCase_:       Optional[int]         =  BeitImageProcessor(size=config.image_size       ,							do_center_crop=UpperCAmelCase_				)
     lowerCAmelCase_:       str         =  Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png"				)
     lowerCAmelCase_:       Dict         =  image_processor(images=UpperCAmelCase_       ,							return_tensors="pt"				)
     lowerCAmelCase_:       Optional[Any]         =  encoding['''pixel_values''']
  
     lowerCAmelCase_:       Optional[Any]         =  (pixel_values,) if is_finetuned else (pixel_values, None)
     with torch.no_grad():
        lowerCAmelCase_:       Any         =  orig_model(*UpperCAmelCase_				)
  
     # 4. Load HF Data2VecVision model
     if is_finetuned:
        lowerCAmelCase_:       Dict         =  DataaVecVisionForImageClassification(UpperCAmelCase_				)
        hf_model.eval()
        lowerCAmelCase_:       List[str]         =  False
        lowerCAmelCase_:       int         =  '''data2vec_vision.'''
     else:
        lowerCAmelCase_:       List[str]         =  DataaVecVisionModel(UpperCAmelCase_				)
        hf_model.eval()
        lowerCAmelCase_:       Optional[int]         =  True
        lowerCAmelCase_:       int         =  ''''''
  
     lowerCAmelCase_:       Optional[int]         =  create_rename_keys(UpperCAmelCase_       ,							hf_prefix=UpperCAmelCase_       ,							has_lm_head=UpperCAmelCase_				)
     lowerCAmelCase_:       Union[str, Any]         =  orig_model.state_dict()
     for src, dest in rename_keys:
        lowerCAmelCase_:       str         =  state_dict.pop(UpperCAmelCase_				)
        lowerCAmelCase_:       str         =  val
  
     read_in_q_k_v(UpperCAmelCase_       ,							UpperCAmelCase_       ,							hf_prefix=UpperCAmelCase_       ,							has_lm_head=UpperCAmelCase_				)
     lowerCAmelCase_:       Dict         =  hf_model.load_state_dict(UpperCAmelCase_       ,							strict=UpperCAmelCase_				)
     print("HF missing"       ,							UpperCAmelCase_				)
     print("HF unexpected_keys"       ,							UpperCAmelCase_				)
  
     # 5. Forward HF Data2VecVision model
     with torch.no_grad():
        lowerCAmelCase_:       List[Any]         =  hf_model(UpperCAmelCase_				)
  
     lowerCAmelCase_:       List[Any]         =  hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state
  
     # 6. Compare
     lowerCAmelCase_:       Any         =  torch.max(torch.abs(hf_output - orig_model_output				)				).item()
  
     print(F'''max_absolute_diff = {max_absolute_diff}'''				)
     lowerCAmelCase_:       Optional[int]         =  torch.allclose(UpperCAmelCase_       ,							UpperCAmelCase_       ,							atol=1E-3				)
     print("Do both models output the same tensors?"       ,							"🔥" if success else "💩"				)
     if not success:
        raise Exception("Something went wRoNg"				)
  
     # 7. Save
     print(F'''Saving to {args.hf_checkpoint_name}'''				)
     hf_model.save_pretrained(args.hf_checkpoint_name				)
     image_processor.save_pretrained(args.hf_checkpoint_name				)
  
  
  if __name__ == "__main__":
   main()
   # Run the following to convert checkpoints
   #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
   #          --beit_checkpoint ./pretrained_base.pt \
   #          --hf_checkpoint_name "./data2vec-vision-base"
   #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
   #          --beit_checkpoint ./finetuned_base.pt \
   #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
   #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
   #          --beit_checkpoint ./pretrained_large.pt \
   #          --hf_checkpoint_name "./data2vec-vision-large"
   #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
   #          --beit_checkpoint ./finetuned_large.pt \
   #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"
style_context: import tempfile
  import unittest
  
  import numpy as np
  
  import transformers
  from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
  from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow
  
  from ...generation.test_flax_utils import FlaxGenerationTesterMixin
  from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  
  
  if is_flax_available():
   import jax
   import jax.numpy as jnp
  
   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )
   from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel
  
  if is_torch_available():
   import torch
  
  class 				_lowercase						:
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__=14					,	lowerCamelCase__=7					,	lowerCamelCase__=True					,	lowerCamelCase__=True					,	lowerCamelCase__=False					,	lowerCamelCase__=True					,	lowerCamelCase__=99					,	lowerCamelCase__=32					,	lowerCamelCase__=5					,	lowerCamelCase__=4					,	lowerCamelCase__=37					,	lowerCamelCase__="gelu"					,	lowerCamelCase__=0.1					,	lowerCamelCase__=0.1					,	lowerCamelCase__=512					,	lowerCamelCase__=0.0_2					,	):
         lowerCAmelCase_:       int         =  parent
         lowerCAmelCase_:       Tuple         =  batch_size
         lowerCAmelCase_:       int         =  seq_length
         lowerCAmelCase_:       Any         =  is_training
         lowerCAmelCase_:       Any         =  use_input_mask
         lowerCAmelCase_:       Optional[Any]         =  use_token_type_ids
         lowerCAmelCase_:       str         =  use_labels
         lowerCAmelCase_:       Tuple         =  vocab_size
         lowerCAmelCase_:       List[Any]         =  hidden_size
         lowerCAmelCase_:       Optional[int]         =  num_hidden_layers
         lowerCAmelCase_:       List[str]         =  num_attention_heads
         lowerCAmelCase_:       int         =  intermediate_size
         lowerCAmelCase_:       Tuple         =  hidden_act
         lowerCAmelCase_:       Dict         =  hidden_dropout_prob
         lowerCAmelCase_:       Dict         =  attention_probs_dropout_prob
         lowerCAmelCase_:       Any         =  max_position_embeddings
         lowerCAmelCase_:       Dict         =  initializer_range
         lowerCAmelCase_:       List[Any]         =  None
         lowerCAmelCase_:       List[Any]         =  vocab_size - 1
         lowerCAmelCase_:       Union[str, Any]         =  vocab_size - 1
         lowerCAmelCase_:       int         =  vocab_size - 1
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Tuple         =  ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size   )
  
         lowerCAmelCase_:       Any         =  None
         if self.use_input_mask:
            lowerCAmelCase_:       Union[str, Any]         =  random_attention_mask([self.batch_size, self.seq_length]   )
  
         lowerCAmelCase_:       Union[str, Any]         =  GPTaConfig(
             vocab_size=self.vocab_size					,	n_embd=self.hidden_size					,	n_layer=self.num_hidden_layers					,	n_head=self.num_attention_heads					,	n_positions=self.max_position_embeddings					,	use_cache=lowerCamelCase__					,	bos_token_id=self.bos_token_id					,	eos_token_id=self.eos_token_id					,	pad_token_id=self.pad_token_id					,	)
  
         return (config, input_ids, input_mask)
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       str         =  self.prepare_config_and_inputs()
         lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Dict         =  config_and_inputs
         lowerCAmelCase_:       List[str]         =  {"input_ids": input_ids, "attention_mask": attention_mask}
         return config, inputs_dict
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  self.prepare_config_and_inputs()
  
         lowerCAmelCase_:       List[str]         =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
         lowerCAmelCase_:       Tuple         =  ids_tensor([self.batch_size, self.seq_length]					,	vocab_size=2   )
  
         return (
             config,
             input_ids,
             attention_mask,
             encoder_hidden_states,
             encoder_attention_mask,
         )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Optional[Any]         =  20
         lowerCAmelCase_:       Union[str, Any]         =  model_class_name(lowerCamelCase__   )
  
         lowerCAmelCase_:       List[str]         =  model.init_cache(input_ids.shape[0]					,	lowerCamelCase__   )
         lowerCAmelCase_:       Any         =  jnp.ones((input_ids.shape[0], max_decoder_length)					,	dtype="i4"   )
  
         lowerCAmelCase_:       Union[str, Any]         =  jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1   )[None, :]					,	(input_ids.shape[0], input_ids.shape[-1] - 1)   )
         lowerCAmelCase_:       Optional[Any]         =  model(
             input_ids[:, :-1]					,	attention_mask=lowerCamelCase__					,	past_key_values=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)
  
         lowerCAmelCase_:       Any         =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"   )
         lowerCAmelCase_:       Optional[int]         =  model(
             input_ids[:, -1:]					,	attention_mask=lowerCamelCase__					,	past_key_values=outputs_cache.past_key_values					,	position_ids=lowerCamelCase__					,	)
  
         lowerCAmelCase_:       List[Any]         =  model(lowerCamelCase__   )
  
         lowerCAmelCase_:       List[Any]         =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
         self.parent.assertTrue(diff < 1E-3					,	msg=F'''Max diff is {diff}'''   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Union[str, Any]         =  20
         lowerCAmelCase_:       List[str]         =  model_class_name(lowerCamelCase__   )
  
         lowerCAmelCase_:       Any         =  jnp.concatenate(
             [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])   )]					,	axis=-1					,	)
  
         lowerCAmelCase_:       str         =  model.init_cache(input_ids.shape[0]					,	lowerCamelCase__   )
         lowerCAmelCase_:       List[Any]         =  jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1   )[None, :]					,	(input_ids.shape[0], input_ids.shape[-1] - 1)   )
  
         lowerCAmelCase_:       List[Any]         =  model(
             input_ids[:, :-1]					,	attention_mask=lowerCamelCase__					,	past_key_values=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)
         lowerCAmelCase_:       Dict         =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"   )
         lowerCAmelCase_:       Dict         =  model(
             input_ids[:, -1:]					,	past_key_values=outputs_cache.past_key_values					,	attention_mask=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)
  
         lowerCAmelCase_:       Union[str, Any]         =  model(lowerCamelCase__					,	attention_mask=lowerCamelCase__   )
  
         lowerCAmelCase_:       List[str]         =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
         self.parent.assertTrue(diff < 1E-3					,	msg=F'''Max diff is {diff}'''   )
  
  
  
  
  
  @require_flax
  class 				_lowercase						(    UpperCAmelCase__   ,     UpperCAmelCase__   ,     unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Optional[int]        =						(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
      SCREAMING_SNAKE_CASE:							Union[str, Any]        =						(FlaxGPTaLMHeadModel,) if is_flax_available() else ()
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Any         =  FlaxGPTaModelTester(self   )
  
  
  
  
      def     _a						(						self   ):
         for model_class_name in self.all_model_classes:
            lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Dict         =  self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )
  
  
  
  
      def     _a						(						self   ):
         for model_class_name in self.all_model_classes:
            lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[Any]         =  self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward_with_attn_mask(
                lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )
  
  
  
  
      @slow
      def     _a						(						self   ):
         lowerCAmelCase_:       Any         =  GPTaTokenizer.from_pretrained("gpt2"					,	pad_token="</s>"					,	padding_side="left"   )
         lowerCAmelCase_:       Optional[int]         =  tokenizer(["Hello this is a long string", "Hey"]					,	return_tensors="np"					,	padding=lowerCamelCase__					,	truncation=lowerCamelCase__   )
  
         lowerCAmelCase_:       List[Any]         =  FlaxGPTaLMHeadModel.from_pretrained("gpt2"   )
         lowerCAmelCase_:       List[str]         =  False
         lowerCAmelCase_:       int         =  model.config.eos_token_id
  
         lowerCAmelCase_:       Optional[int]         =  jax.jit(model.generate   )
  
         lowerCAmelCase_:       Any         =  jit_generate(inputs["input_ids"]					,	attention_mask=inputs["attention_mask"]   ).sequences
  
         lowerCAmelCase_:       Any         =  tokenizer.batch_decode(lowerCamelCase__					,	skip_special_tokens=lowerCamelCase__   )
  
         lowerCAmelCase_:       Dict         =  [
             "Hello this is a long string of words. I'm going to start with the first one.\n",
             "Hey, I'm not sure if I'm going to be able to do",
         ]
  
         self.assertListEqual(lowerCamelCase__					,	lowerCamelCase__   )
  
  
  
  
      @is_pt_flax_cross_test
      def     _a						(						self   ):
         lowerCAmelCase_     ,  lowerCAmelCase_:       Union[str, Any]         =  self.model_tester.prepare_config_and_inputs_for_common()
  
         for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__   ):
               # prepare inputs
               lowerCAmelCase_:       Any         =  self._prepare_for_class(lowerCamelCase__					,	lowerCamelCase__   )
               lowerCAmelCase_:       str         =  {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}
  
               # load corresponding PyTorch class
               lowerCAmelCase_:       Dict         =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
               lowerCAmelCase_:       Union[str, Any]         =  getattr(lowerCamelCase__					,	lowerCamelCase__   )
  
               lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  pt_inputs["input_ids"].shape
               lowerCAmelCase_:       str         =  np.random.randint(0					,	seq_length - 1					,	size=(batch_size,)   )
               for batch_idx, start_index in enumerate(lowerCamelCase__   ):
                  lowerCAmelCase_:       List[str]         =  0
                  lowerCAmelCase_:       int         =  1
                  lowerCAmelCase_:       List[Any]         =  0
                  lowerCAmelCase_:       List[Any]         =  1
               lowerCAmelCase_:       Tuple         =  pt_model_class(lowerCamelCase__   ).eval()
               lowerCAmelCase_:       Tuple         =  model_class(lowerCamelCase__					,	dtype=jnp.floataa   )
  
               lowerCAmelCase_:       int         =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,	lowerCamelCase__   )
               lowerCAmelCase_:       Optional[int]         =  fx_state
  
               with torch.no_grad():
                  lowerCAmelCase_:       Union[str, Any]         =  pt_model(**lowerCamelCase__   ).to_tuple()
  
               lowerCAmelCase_:       List[str]         =  fx_model(**lowerCamelCase__   ).to_tuple()
               self.assertEqual(len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
               for fx_output, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                  self.assert_almost_equals(fx_output[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )
  
               with tempfile.TemporaryDirectory() as tmpdirname:
                  pt_model.save_pretrained(lowerCamelCase__   )
                  lowerCAmelCase_:       Dict         =  model_class.from_pretrained(lowerCamelCase__					,	from_pt=lowerCamelCase__   )
  
               lowerCAmelCase_:       List[Any]         =  fx_model_loaded(**lowerCamelCase__   ).to_tuple()
               self.assertEqual(
                   len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
               for fx_output_loaded, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                  self.assert_almost_equals(fx_output_loaded[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )
  
  
  
  
      @is_pt_flax_cross_test
      def     _a						(						self   ):
         lowerCAmelCase_     ,  lowerCAmelCase_:       List[str]         =  self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__   ):
               # prepare inputs
               lowerCAmelCase_:       Any         =  self._prepare_for_class(lowerCamelCase__					,	lowerCamelCase__   )
               lowerCAmelCase_:       int         =  {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}
  
               # load corresponding PyTorch class
               lowerCAmelCase_:       List[str]         =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
               lowerCAmelCase_:       Dict         =  getattr(lowerCamelCase__					,	lowerCamelCase__   )
  
               lowerCAmelCase_:       Dict         =  pt_model_class(lowerCamelCase__   ).eval()
               lowerCAmelCase_:       Optional[Any]         =  model_class(lowerCamelCase__					,	dtype=jnp.floataa   )
  
               lowerCAmelCase_:       Optional[Any]         =  load_flax_weights_in_pytorch_model(lowerCamelCase__					,	fx_model.params   )
               lowerCAmelCase_     ,  lowerCAmelCase_:       List[str]         =  pt_inputs["input_ids"].shape
               lowerCAmelCase_:       Optional[Any]         =  np.random.randint(0					,	seq_length - 1					,	size=(batch_size,)   )
               for batch_idx, start_index in enumerate(lowerCamelCase__   ):
                  lowerCAmelCase_:       Dict         =  0
                  lowerCAmelCase_:       List[Any]         =  1
                  lowerCAmelCase_:       Dict         =  0
                  lowerCAmelCase_:       Optional[int]         =  1
  
               # make sure weights are tied in PyTorch
               pt_model.tie_weights()
  
               with torch.no_grad():
                  lowerCAmelCase_:       Any         =  pt_model(**lowerCamelCase__   ).to_tuple()
  
               lowerCAmelCase_:       Union[str, Any]         =  fx_model(**lowerCamelCase__   ).to_tuple()
               self.assertEqual(len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
               for fx_output, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                  self.assert_almost_equals(fx_output[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )
  
               with tempfile.TemporaryDirectory() as tmpdirname:
                  fx_model.save_pretrained(lowerCamelCase__   )
                  lowerCAmelCase_:       str         =  pt_model_class.from_pretrained(lowerCamelCase__					,	from_flax=lowerCamelCase__   )
  
               with torch.no_grad():
                  lowerCAmelCase_:       int         =  pt_model_loaded(**lowerCamelCase__   ).to_tuple()
  
               self.assertEqual(
                   len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
               for fx_output, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                  self.assert_almost_equals(fx_output[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )
  
  
  
  
      @slow
      def     _a						(						self   ):
         for model_class_name in self.all_model_classes:
            lowerCAmelCase_:       Tuple         =  model_class_name.from_pretrained("gpt2"					,	from_pt=lowerCamelCase__   )
            lowerCAmelCase_:       int         =  model(np.ones((1, 1)   )   )
            self.assertIsNotNone(lowerCamelCase__   )