import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification




def       __lowerCAmelCase      (						A_   : Tuple					)    ->  Optional[int]:
  __UpperCAmelCase						    =       SwinConfig()
  __UpperCAmelCase						    =       swin_name.split("_"					)

  __UpperCAmelCase						    =       name_split[1]
  __UpperCAmelCase						    =       int(name_split[4]					)
  __UpperCAmelCase						    =       int(name_split[3][-1]					)

  if model_size == "tiny":
    __UpperCAmelCase						    =       96
    __UpperCAmelCase						    =       (2, 2, 6, 2)
    __UpperCAmelCase						    =       (3, 6, 12, 24)
  elif model_size == "small":
    __UpperCAmelCase						    =       96
    __UpperCAmelCase						    =       (2, 2, 18, 2)
    __UpperCAmelCase						    =       (3, 6, 12, 24)
  elif model_size == "base":
    __UpperCAmelCase						    =       1_28
    __UpperCAmelCase						    =       (2, 2, 18, 2)
    __UpperCAmelCase						    =       (4, 8, 16, 32)
  else:
    __UpperCAmelCase						    =       1_92
    __UpperCAmelCase						    =       (2, 2, 18, 2)
    __UpperCAmelCase						    =       (6, 12, 24, 48)

  if "in22k" in swin_name:
    __UpperCAmelCase						    =       2_18_41
  else:
    __UpperCAmelCase						    =       10_00
    __UpperCAmelCase						    =       "huggingface/label-files"
    __UpperCAmelCase						    =       "imagenet-1k-id2label.json"
    __UpperCAmelCase						    =       json.load(open(hf_hub_download(A_						,	A_						,	repo_type="dataset"					)						,	"r"					)					)
    __UpperCAmelCase						    =       {int(A_					): v for k, v in idalabel.items()}
    __UpperCAmelCase						    =       idalabel
    __UpperCAmelCase						    =       {v: k for k, v in idalabel.items()}

  __UpperCAmelCase						    =       img_size
  __UpperCAmelCase						    =       num_classes
  __UpperCAmelCase						    =       embed_dim
  __UpperCAmelCase						    =       depths
  __UpperCAmelCase						    =       num_heads
  __UpperCAmelCase						    =       window_size

  return config




def       __lowerCAmelCase      (						A_   : int					)    ->  Any:
  if "patch_embed.proj" in name:
    __UpperCAmelCase						    =       name.replace("patch_embed.proj"						,	"embeddings.patch_embeddings.projection"					)
  if "patch_embed.norm" in name:
    __UpperCAmelCase						    =       name.replace("patch_embed.norm"						,	"embeddings.norm"					)
  if "layers" in name:
    __UpperCAmelCase						    =       "encoder." + name
  if "attn.proj" in name:
    __UpperCAmelCase						    =       name.replace("attn.proj"						,	"attention.output.dense"					)
  if "attn" in name:
    __UpperCAmelCase						    =       name.replace("attn"						,	"attention.self"					)
  if "norm1" in name:
    __UpperCAmelCase						    =       name.replace("norm1"						,	"layernorm_before"					)
  if "norm2" in name:
    __UpperCAmelCase						    =       name.replace("norm2"						,	"layernorm_after"					)
  if "mlp.fc1" in name:
    __UpperCAmelCase						    =       name.replace("mlp.fc1"						,	"intermediate.dense"					)
  if "mlp.fc2" in name:
    __UpperCAmelCase						    =       name.replace("mlp.fc2"						,	"output.dense"					)

  if name == "norm.weight":
    __UpperCAmelCase						    =       "layernorm.weight"
  if name == "norm.bias":
    __UpperCAmelCase						    =       "layernorm.bias"

  if "head" in name:
    __UpperCAmelCase						    =       name.replace("head"						,	"classifier"					)
  else:
    __UpperCAmelCase						    =       "swin." + name

  return name




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : int					)    ->  Tuple:
  for key in orig_state_dict.copy().keys():
    __UpperCAmelCase						    =       orig_state_dict.pop(A_					)

    if "mask" in key:
      continue
    elif "qkv" in key:
      __UpperCAmelCase						    =       key.split("."					)
      __UpperCAmelCase						    =       int(key_split[1]					)
      __UpperCAmelCase						    =       int(key_split[3]					)
      __UpperCAmelCase						    =       model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        __UpperCAmelCase						    =       val[:dim, :]
        __UpperCAmelCase						    =       val[
            dim : dim * 2, :
        ]
        __UpperCAmelCase						    =       val[-dim:, :]
      else:
        __UpperCAmelCase						    =       val[
            :dim
        ]
        __UpperCAmelCase						    =       val[
            dim : dim * 2
        ]
        __UpperCAmelCase						    =       val[
            -dim:
        ]
    else:
      __UpperCAmelCase						    =       val

  return orig_state_dict




def       __lowerCAmelCase      (						A_   : Optional[Any]						,	A_   : List[Any]					)    ->  Tuple:
  __UpperCAmelCase						    =       timm.create_model(A_						,	pretrained=A_					)
  timm_model.eval()

  __UpperCAmelCase						    =       get_swin_config(A_					)
  __UpperCAmelCase						    =       SwinForImageClassification(A_					)
  model.eval()

  __UpperCAmelCase						    =       convert_state_dict(timm_model.state_dict()						,	A_					)
  model.load_state_dict(A_					)

  __UpperCAmelCase						    =       "http://images.cocodataset.org/val2017/000000039769.jpg"

  __UpperCAmelCase						    =       AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"						,	"-"					)					)					)
  __UpperCAmelCase						    =       Image.open(requests.get(A_						,	stream=A_					).raw					)
  __UpperCAmelCase						    =       image_processor(images=A_						,	return_tensors="pt"					)

  __UpperCAmelCase						    =       timm_model(inputs["pixel_values"]					)
  __UpperCAmelCase						    =       model(**A_					).logits

  assert torch.allclose(A_						,	A_						,	atol=1e-3					)

  print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}'''					)
  model.save_pretrained(A_					)

  print(F'''Saving image processor to {pytorch_dump_folder_path}'''					)
  image_processor.save_pretrained(A_					)


if __name__ == "__main__":
       a_							=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--swin_name""",
           default="""swin_tiny_patch4_window7_224""",
           type=str,
           help="""Name of the Swin 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."""
       )

       a_							=      parser.parse_args()
       convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)




import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def       __lowerCAmelCase      (						A_   : int						,	A_   : Optional[Any]						,	A_   : Tuple					)    ->  List[str]:
  return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]




def       __lowerCAmelCase      (						A_   : Optional[int]						,	A_   : Dict						,	A_   : Dict						,	A_   : Optional[int]="attention"					)    ->  Optional[int]:
  __UpperCAmelCase						    =       __UpperCAmelCase						    =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]					)
  __UpperCAmelCase						    =       k_tmp.reshape(k_tmp.shape[0]						,	k_tmp.shape[1] * k_tmp.shape[2]					)
  __UpperCAmelCase						    =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]					)
  __UpperCAmelCase						    =       o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]						,	o_tmp.shape[2]					)
  __UpperCAmelCase						    =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]					)
  __UpperCAmelCase						    =       q_tmp.reshape(q_tmp.shape[0]						,	q_tmp.shape[1] * q_tmp.shape[2]					)
  __UpperCAmelCase						    =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]					)
  __UpperCAmelCase						    =       v_tmp.reshape(v_tmp.shape[0]						,	v_tmp.shape[1] * v_tmp.shape[2]					)
  return k, o, q, v




def       __lowerCAmelCase      (						A_   : Dict						,	A_   : Union[str, Any]						,	A_   : Optional[Any]						,	A_   : int=False					)    ->  List[Any]:
  if split_mlp_wi:
    __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
    __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
    __UpperCAmelCase						    =       (wi_a, wi_a)
  else:
    __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

  __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
  return wi, wo




def       __lowerCAmelCase      (						A_   : Union[str, Any]						,	A_   : int						,	A_   : Optional[Any]						,	A_   : Optional[Any]					)    ->  Tuple:
  return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]




def       __lowerCAmelCase      (						A_   : dict						,	*, A_   : int						,	A_   : bool						,	A_   : bool = False					)    ->  List[str]:
  __UpperCAmelCase						    =       traverse_util.flatten_dict(variables["target"]					)
  __UpperCAmelCase						    =       {"/".join(A_					): v for k, v in old.items()}

  # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
  __UpperCAmelCase						    =       "encoder/encoder/mlp/wi_0/kernel" in old
  print("Split MLP:"						,	A_					)

  __UpperCAmelCase						    =       collections.OrderedDict()

  # Shared embeddings.
  __UpperCAmelCase						    =       old["token_embedder/embedding"]

  # Encoder.
  for i in range(A_					):
    # Block i, layer 0 (Self Attention).
    __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"encoder"						,	"pre_attention_layer_norm"					)
    __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_attention_lookup(A_						,	A_						,	"encoder"						,	"attention"					)
    __UpperCAmelCase						    =       layer_norm
    __UpperCAmelCase						    =       k.T
    __UpperCAmelCase						    =       o.T
    __UpperCAmelCase						    =       q.T
    __UpperCAmelCase						    =       v.T

    # Block i, layer 1 (MLP).
    __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"encoder"						,	"pre_mlp_layer_norm"					)
    __UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_mlp_lookup(A_						,	A_						,	"encoder"						,	A_					)
    __UpperCAmelCase						    =       layer_norm
    if split_mlp_wi:
      __UpperCAmelCase						    =       wi[0].T
      __UpperCAmelCase						    =       wi[1].T
    else:
      __UpperCAmelCase						    =       wi.T
    __UpperCAmelCase						    =       wo.T
    if scalable_attention:
      # convert the rel_embedding of each layer
      __UpperCAmelCase						    =       tax_relpos_bias_lookup(
          A_						,	A_						,	"encoder"					).T

  __UpperCAmelCase						    =       old["encoder/encoder_norm/scale"]

  if not scalable_attention:
    __UpperCAmelCase						    =       tax_relpos_bias_lookup(
        A_						,	0						,	"encoder"					).T
    __UpperCAmelCase						    =       tax_relpos_bias_lookup(
        A_						,	0						,	"decoder"					).T

  if not is_encoder_only:
    # Decoder.
    for i in range(A_					):
      # Block i, layer 0 (Self Attention).
      __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"decoder"						,	"pre_self_attention_layer_norm"					)
      __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_attention_lookup(A_						,	A_						,	"decoder"						,	"self_attention"					)
      __UpperCAmelCase						    =       layer_norm
      __UpperCAmelCase						    =       k.T
      __UpperCAmelCase						    =       o.T
      __UpperCAmelCase						    =       q.T
      __UpperCAmelCase						    =       v.T

      # Block i, layer 1 (Cross Attention).
      __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"decoder"						,	"pre_cross_attention_layer_norm"					)
      __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_attention_lookup(A_						,	A_						,	"decoder"						,	"encoder_decoder_attention"					)
      __UpperCAmelCase						    =       layer_norm
      __UpperCAmelCase						    =       k.T
      __UpperCAmelCase						    =       o.T
      __UpperCAmelCase						    =       q.T
      __UpperCAmelCase						    =       v.T

      # Block i, layer 2 (MLP).
      __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"decoder"						,	"pre_mlp_layer_norm"					)
      __UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_mlp_lookup(A_						,	A_						,	"decoder"						,	A_					)
      __UpperCAmelCase						    =       layer_norm
      if split_mlp_wi:
        __UpperCAmelCase						    =       wi[0].T
        __UpperCAmelCase						    =       wi[1].T
      else:
        __UpperCAmelCase						    =       wi.T
      __UpperCAmelCase						    =       wo.T

      if scalable_attention:
        # convert the rel_embedding of each layer
        __UpperCAmelCase						    =       tax_relpos_bias_lookup(A_						,	A_						,	"decoder"					).T

    __UpperCAmelCase						    =       old["decoder/decoder_norm/scale"]

    # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
    if "decoder/logits_dense/kernel" in old:
      __UpperCAmelCase						    =       old["decoder/logits_dense/kernel"].T

  return new




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : bool					)    ->  List[Any]:
  __UpperCAmelCase						    =       collections.OrderedDict([(k, torch.from_numpy(v.copy()					)) for (k, v) in converted_params.items()]					)

  # Add what is missing.
  if "encoder.embed_tokens.weight" not in state_dict:
    __UpperCAmelCase						    =       state_dict["shared.weight"]

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      __UpperCAmelCase						    =       state_dict["shared.weight"]

    if "lm_head.weight" not in state_dict:  # For old 1.0 models.
      print("Using shared word embeddings as lm_head."					)
      __UpperCAmelCase						    =       state_dict["shared.weight"]

  return state_dict




def       __lowerCAmelCase      (						A_   : Optional[Any]						,	A_   : int						,	A_   : List[Any]						,	A_   : List[Any]						,	A_   : List[Any]					)    ->  Optional[int]:
  __UpperCAmelCase						    =       checkpoints.load_tax_checkpoint(A_					)
  __UpperCAmelCase						    =       convert_tax_to_pytorch(
      A_						,	num_layers=config.num_layers						,	is_encoder_only=A_						,	scalable_attention=A_					)
  __UpperCAmelCase						    =       make_state_dict(A_						,	A_					)
  model.load_state_dict(A_						,	strict=A_					)




def       __lowerCAmelCase      (						A_   : List[str]						,	A_   : List[Any]						,	A_   : List[str]						,	A_   : bool = False						,	A_   : bool = False						,	)    ->  Optional[int]:
  __UpperCAmelCase						    =       MTaConfig.from_json_file(A_					)
  print(F'''Building PyTorch model from configuration: {config}'''					)
  # Non-v1.1 checkpoints could also use T5Model, but this works for all.
  # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
  if is_encoder_only:
    __UpperCAmelCase						    =       UMTaEncoderModel(A_					)
  else:
    __UpperCAmelCase						    =       UMTaForConditionalGeneration(A_					)

  # Load weights from tf checkpoint
  load_tax_weights_in_ta(A_						,	A_						,	A_						,	A_						,	A_					)

  # Save pytorch-model
  print(F'''Save PyTorch model to {pytorch_dump_path}'''					)
  model.save_pretrained(A_					)

  # Verify that we can load the checkpoint.
  model.from_pretrained(A_					)
  print("Done"					)


if __name__ == "__main__":
       a_							=      argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
       # Required parameters
       parser.add_argument(
           """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           required=True,
           help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
       )
       parser.add_argument(
           """--scalable_attention""",
           action="""store_true""",
           help="""Whether the model uses scaled attention (umt5 model)""",
           default=False,
       )
       a_							=      parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path,
           args.config_file,
           args.pytorch_dump_path,
           args.is_encoder_only,
           args.scalable_attention,
       )



