




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase     :		Tuple								=       logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_=False						)       ->	Optional[Any]:



       """simple docstring"""

       A__							=   []
       for i in range(config.num_hidden_layers						):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""deit.encoder.layer.{i}.layernorm_before.weight""")						)
              rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""deit.encoder.layer.{i}.layernorm_before.bias""")						)
              rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""deit.encoder.layer.{i}.attention.output.dense.weight""")						)
              rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""deit.encoder.layer.{i}.attention.output.dense.bias""")						)
              rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""deit.encoder.layer.{i}.layernorm_after.weight""")						)
              rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""deit.encoder.layer.{i}.layernorm_after.bias""")						)
              rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""deit.encoder.layer.{i}.intermediate.dense.weight""")						)
              rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""deit.encoder.layer.{i}.intermediate.dense.bias""")						)
              rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""deit.encoder.layer.{i}.output.dense.weight""")						)
              rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""deit.encoder.layer.{i}.output.dense.bias""")						)

       # projection layer + position embeddings
       rename_keys.extend(
           [
               ('''cls_token''', '''deit.embeddings.cls_token'''),
               ('''dist_token''', '''deit.embeddings.distillation_token'''),
               ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
               ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
               ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
           ]						)

       if base_model:
              # layernorm + pooler
              rename_keys.extend(
                  [
                      ('''norm.weight''', '''layernorm.weight'''),
                      ('''norm.bias''', '''layernorm.bias'''),
                      ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
                      ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
                  ]						)

              # if just the base model, we should remove "deit" from all keys that start with "deit"
              A__							=   [(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''						) else pair for pair in rename_keys]
       else:
              # layernorm + classification heads
              rename_keys.extend(
                  [
                      ('''norm.weight''', '''deit.layernorm.weight'''),
                      ('''norm.bias''', '''deit.layernorm.bias'''),
                      ('''head.weight''', '''cls_classifier.weight'''),
                      ('''head.bias''', '''cls_classifier.bias'''),
                      ('''head_dist.weight''', '''distillation_classifier.weight'''),
                      ('''head_dist.bias''', '''distillation_classifier.bias'''),
                  ]						)

       return rename_keys







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_=False						)       ->	int:



       """simple docstring"""

       for i in range(config.num_hidden_layers						):
              if base_model:
                     A__							=   ''''''
              else:
                     A__							=   '''deit.'''
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              A__							=   state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""						)
              A__							=   state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""						)
              # next, add query, keys and values (in that order) to the state dict
              A__							=   in_proj_weight[
                  : config.hidden_size, :
              ]
              A__							=   in_proj_bias[: config.hidden_size]
              A__							=   in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              A__							=   in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              A__							=   in_proj_weight[
                  -config.hidden_size :, :
              ]
              A__							=   in_proj_bias[-config.hidden_size :]







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	int:



       """simple docstring"""

       A__							=   dct.pop(lowercase_						)
       A__							=   val







def SCREAMING_SNAKE_CASE    (	)       ->	int:



       """simple docstring"""

       A__							=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
       A__							=   Image.open(requests.get(lowercase_			,    stream=lowercase_						).raw						)
       return im







@torch.no_grad()
def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Optional[int]:



       """simple docstring"""

       A__							=   DeiTConfig()
       # all deit models have fine-tuned heads
       A__							=   False
       # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
       A__							=   1_000
       A__							=   '''huggingface/label-files'''
       A__							=   '''imagenet-1k-id2label.json'''
       A__							=   json.load(open(hf_hub_download(lowercase_			,    lowercase_			,    repo_type='''dataset'''						)			,    '''r'''						)						)
       A__							=   {int(lowercase_						): v for k, v in idalabel.items()}
       A__							=   idalabel
       A__							=   {v: k for k, v in idalabel.items()}
       A__							=   int(deit_name[-6:-4]						)
       A__							=   int(deit_name[-3:]						)
       # size of the architecture
       if deit_name[9:].startswith('''tiny'''						):
              A__							=   192
              A__							=   768
              A__							=   12
              A__							=   3
       elif deit_name[9:].startswith('''small'''						):
              A__							=   384
              A__							=   1_536
              A__							=   12
              A__							=   6
       if deit_name[9:].startswith('''base'''						):
              pass
       elif deit_name[4:].startswith('''large'''						):
              A__							=   1_024
              A__							=   4_096
              A__							=   24
              A__							=   16

       # load original model from timm
       A__							=   timm.create_model(lowercase_			,    pretrained=lowercase_						)
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       A__							=   timm_model.state_dict()
       A__							=   create_rename_keys(lowercase_			,    lowercase_						)
       for src, dest in rename_keys:
              rename_key(lowercase_			,    lowercase_			,    lowercase_						)
       read_in_q_k_v(lowercase_			,    lowercase_			,    lowercase_						)

       # load HuggingFace model
       A__							=   DeiTForImageClassificationWithTeacher(lowercase_						).eval()
       model.load_state_dict(lowercase_						)

       # Check outputs on an image, prepared by DeiTImageProcessor
       A__							=   int(
           (256 / 224) * config.image_size						)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
       A__							=   DeiTImageProcessor(size=lowercase_			,    crop_size=config.image_size						)
       A__							=   image_processor(images=prepare_img()			,    return_tensors='''pt'''						)
       A__							=   encoding['''pixel_values''']
       A__							=   model(lowercase_						)

       A__							=   timm_model(lowercase_						)
       assert timm_logits.shape == outputs.logits.shape
       assert torch.allclose(lowercase_			,    outputs.logits			,    atol=1E-3						)

       Path(lowercase_						).mkdir(exist_ok=lowercase_						)
       print(f"""Saving model {deit_name} 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     :		Dict								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--deit_name""",
      default="""vit_deit_base_distilled_patch16_224""",
      type=str,
      help="""Name of the DeiT timm model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  _lowerCamelCase     :		Optional[int]								=       parser.parse_args()
  convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)








import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Tuple:



       """simple docstring"""

       A__							=   AutoConfig.from_pretrained(lowercase_						)
       A__							=   FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase_						)
       A__							=   checkpoints.load_tax_checkpoint(lowercase_						)

       A__							=   '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

       if config.model_type == "t5":
              A__							=   '''SelfAttention'''
       if config.model_type == "longt5" and config.encoder_attention_type == "local":
              A__							=   '''LocalSelfAttention'''
       elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
              A__							=   '''TransientGlobalSelfAttention'''
       else:
              raise ValueError(
                  '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
                  ''' attribute with a value from [\'local\', \'transient-global].'''						)

       # Encoder
       for layer_index in range(config.num_layers						):
              A__							=   f"""layers_{str(lowercase_						)}"""

              # Self-Attention
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

              # Global input layer norm
              if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                     A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

              if split_mlp_wi:
                     A__							=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                     A__							=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
              else:
                     A__							=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

              # Assigning
              A__							=   flax_model.params['''encoder''']['''block'''][str(lowercase_						)]['''layer''']
              A__							=   tax_attention_key
              A__							=   tax_attention_out
              A__							=   tax_attention_query
              A__							=   tax_attention_value

              A__							=   tax_attention_layer_norm

              # Global input layer norm
              if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                     A__							=   tax_global_layer_norm

              if split_mlp_wi:
                     A__							=   tax_mlp_wi_a
                     A__							=   tax_mlp_wi_a
              else:
                     A__							=   tax_mlp_wi

              A__							=   tax_mlp_wo
              A__							=   tax_mlp_layer_norm

              A__							=   flax_model_encoder_layer_block

       # Only for layer 0:
       A__							=   tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
       A__							=   tax_encoder_rel_embedding

       # Side/global relative position_bias + layer norm
       if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
              A__							=   tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
              A__							=   tax_encoder_global_rel_embedding

       # Assigning
       A__							=   tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
       A__							=   tax_encoder_norm

       # Decoder
       for layer_index in range(config.num_layers						):
              A__							=   f"""layers_{str(lowercase_						)}"""

              # Self-Attention
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                  '''scale'''
              ]

              # Encoder-Decoder-Attention
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
              A__							=   tax_enc_dec_attention_module['''key''']['''kernel''']
              A__							=   tax_enc_dec_attention_module['''out''']['''kernel''']
              A__							=   tax_enc_dec_attention_module['''query''']['''kernel''']
              A__							=   tax_enc_dec_attention_module['''value''']['''kernel''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

              # MLP
              if split_mlp_wi:
                     A__							=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                     A__							=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
              else:
                     A__							=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

              # Assigning
              A__							=   flax_model.params['''decoder''']['''block'''][str(lowercase_						)]['''layer''']
              A__							=   tax_attention_key
              A__							=   tax_attention_out
              A__							=   tax_attention_query
              A__							=   tax_attention_value

              A__							=   tax_pre_attention_layer_norm

              A__							=   tax_enc_dec_attention_key
              A__							=   tax_enc_dec_attention_out
              A__							=   tax_enc_dec_attention_query
              A__							=   tax_enc_dec_attention_value

              A__							=   tax_cross_layer_norm

              if split_mlp_wi:
                     A__							=   tax_mlp_wi_a
                     A__							=   tax_mlp_wi_a
              else:
                     A__							=   tax_mlp_wi

              A__							=   tax_mlp_wo

              A__							=   txa_mlp_layer_norm

              A__							=   flax_model_decoder_layer_block

       # Decoder Normalization
       A__							=   tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
       A__							=   txa_decoder_norm

       # Only for layer 0:
       A__							=   tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
       A__							=   tax_decoder_rel_embedding

       # Token Embeddings
       A__							=   tax_model['''target''']['''token_embedder''']['''embedding''']
       A__							=   txa_token_embeddings

       # LM Head (only in v1.1 and LongT5 checkpoints)
       if "logits_dense" in tax_model["target"]["decoder"]:
              A__							=   tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

       flax_model.save_pretrained(lowercase_						)
       print('''T5X Model was sucessfully converted!'''						)


if __name__ == "__main__":
  _lowerCamelCase     :		Any								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
  )
  parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
  parser.add_argument(
      """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
  )
  _lowerCamelCase     :		Tuple								=       parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)


