





'''simple docstring'''





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch





def   lowerCAmelCase_				( __a	)  ->	Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__:   Optional[int]    						=os.path.join(args.tf_model_dir			,					"parameters.json"	)
  lowerCamelCase__:   List[Any]    						=json.loads(open(__a	).read()	)
  if not params:
    raise ValueError(
        F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""	)
  if not args.output.endswith(".pt"	):
    lowerCamelCase__:   Tuple    						=args.output + ".pt"
  lowerCamelCase__:   Any    						=OrderedDict()
  with tf.device("/CPU:0"	):
    lowerCamelCase__:   List[Any]    						=tf.train.load_checkpoint(args.tf_model_dir	)
    lowerCamelCase__:   Any    						=reader.get_variable_to_shape_map()
    for key_name in shapes.keys():
      lowerCamelCase__:   Dict    						=reader.get_tensor(__a	).astype(np.floataa	)
      if key_name.endswith("/adam_m"	) or key_name.endswith("/adam_v"	):
        continue
      if key_name.startswith("pasts/"	):
        if key_name.startswith("pasts/mlp"	):
          lowerCamelCase__:   Any    						=int(key_name[9]	)
        elif key_name.startswith("pasts/out"	):
          lowerCamelCase__:   Dict    						=8
        lowerCamelCase__:   Optional[int]    						="model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
        lowerCamelCase__:   Any    						=vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
        lowerCamelCase__:   Dict    						=torch.tensor(__a	)
      elif key_name.startswith("model/moe"	):
        lowerCamelCase__:   List[Any]    						=int(key_name[9:].split("/"	)[0]	)
        if key_name.endswith("/switch_gating/kernel"	):
          lowerCamelCase__:   int    						="model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
          lowerCamelCase__:   List[str]    						=vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
          lowerCamelCase__:   str    						=torch.tensor(__a	)
        elif key_name.endswith("/softmlp/kernel"	):
          lowerCamelCase__:   Tuple    						="model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
          lowerCamelCase__:   Any    						=vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
          lowerCamelCase__:   str    						=torch.tensor(__a	)
        elif key_name.endswith("/wo/kernel"	) or key_name.endswith("/wi/kernel"	):
          lowerCamelCase__:   List[Any]    						=key_name[-9:-7]
          for i in range(16	):
            lowerCamelCase__:   Optional[int]    						="model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
            lowerCamelCase__:   Tuple    						=(
                vnp[i].transpose([1, 0]	).copy()
            )  # In Mesh-Tensorflow, it is one array, so it is divided
            lowerCamelCase__:   int    						=torch.tensor(__a	)
      elif key_name.startswith("model/mlp"	):
        lowerCamelCase__:   Dict    						=int(key_name[9:].split("/"	)[0]	)
        if key_name.endswith("/p1/kernel"	):
          lowerCamelCase__:   Optional[int]    						="model.blocks.%d.feed_forward.mlp.wi.weight" % player
          lowerCamelCase__:   List[str]    						=vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
          lowerCamelCase__:   Any    						=torch.tensor(__a	)
        elif key_name.endswith("/p1/bias"	):
          lowerCamelCase__:   int    						="model.blocks.%d.feed_forward.mlp.wi.bias" % player
          lowerCamelCase__:   int    						=vnp.copy()  # same because it is one dimensional
          lowerCamelCase__:   str    						=torch.tensor(__a	)
        elif key_name.endswith("/p2/kernel"	):
          lowerCamelCase__:   Optional[int]    						="model.blocks.%d.feed_forward.mlp.wo.weight" % player
          lowerCamelCase__:   List[Any]    						=vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
          lowerCamelCase__:   Optional[Any]    						=torch.tensor(__a	)
        elif key_name.endswith("/p2/bias"	):
          lowerCamelCase__:   List[Any]    						="model.blocks.%d.feed_forward.mlp.wo.bias" % player
          lowerCamelCase__:   int    						=vnp.copy()  # same because it is one dimensional
          lowerCamelCase__:   int    						=torch.tensor(__a	)
      elif key_name.startswith("model/ln"	):
        lowerCamelCase__:   Any    						=int(key_name[8:].split("/"	)[0]	)
        if key_name.endswith("/b"	):
          lowerCamelCase__:   Optional[int]    						="model.blocks.%d.feed_forward.norm.bias" % player
          lowerCamelCase__:   Union[str, Any]    						=vnp.copy()  # same because it is one dimensional
          lowerCamelCase__:   List[Any]    						=torch.tensor(__a	)
        elif key_name.endswith("/g"	):
          lowerCamelCase__:   Optional[Any]    						="model.blocks.%d.feed_forward.norm.weight" % player
          lowerCamelCase__:   Any    						=vnp.copy()  # same because it is one dimensional
          lowerCamelCase__:   List[Any]    						=torch.tensor(__a	)
      elif key_name.startswith("model/att"	):
        lowerCamelCase__:   List[str]    						=int(key_name[9:].split("/"	)[0]	)
        if key_name.endswith("/qkv/kernel"	):
          lowerCamelCase__:   Union[str, Any]    						=vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
          lowerCamelCase__:   List[str]    						=state[:, 0, :, :]
          lowerCamelCase__:   Tuple    						=state[:, 1, :, :]
          lowerCamelCase__:   Dict    						=state[:, 2, :, :]
          lowerCamelCase__:   Dict    						=(
              state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]	)
              .transpose([1, 0]	)
              .copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          lowerCamelCase__:   Dict    						=(
              state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]	)
              .transpose([1, 0]	)
              .copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          lowerCamelCase__:   Optional[int]    						=(
              state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]	)
              .transpose([1, 0]	)
              .copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          lowerCamelCase__:   Optional[int]    						="model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
          lowerCamelCase__:   Dict    						=torch.tensor(__a	)
          lowerCamelCase__:   int    						="model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
          lowerCamelCase__:   Optional[int]    						=torch.tensor(__a	)
          lowerCamelCase__:   str    						="model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
          lowerCamelCase__:   int    						=torch.tensor(__a	)
        elif key_name.endswith("/o/kernel"	):
          lowerCamelCase__:   Optional[int]    						="model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
          lowerCamelCase__:   Union[str, Any]    						=(
              vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]	).transpose([1, 0]	).copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          lowerCamelCase__:   Union[str, Any]    						=torch.tensor(__a	)
      elif key_name.startswith("model/an"	):
        lowerCamelCase__:   Optional[Any]    						=int(key_name[8:].split("/"	)[0]	)
        if key_name.endswith("/b"	):
          lowerCamelCase__:   List[str]    						="model.blocks.%d.self_attn.norm.bias" % player
          lowerCamelCase__:   List[Any]    						=vnp.copy()  # same because it is one dimensional
          lowerCamelCase__:   Optional[int]    						=torch.tensor(__a	)
        elif key_name.endswith("/g"	):
          lowerCamelCase__:   List[Any]    						="model.blocks.%d.self_attn.norm.weight" % player
          lowerCamelCase__:   Union[str, Any]    						=vnp.copy()  # same because it is one dimensional
          lowerCamelCase__:   Tuple    						=torch.tensor(__a	)
      elif (
          key_name.startswith("model/wte"	)
          or key_name.startswith("model/wpe"	)
          or key_name.startswith("model/ete"	)
      ):
        lowerCamelCase__:   Optional[Any]    						={"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
            key_name[-3:]
        ]
        lowerCamelCase__:   Union[str, Any]    						="model.%s.weight" % nlayer
        lowerCamelCase__:   Dict    						=vnp.copy()  # same in embedded
        lowerCamelCase__:   int    						=torch.tensor(__a	)
        if key_name.startswith("model/wte"	):
          lowerCamelCase__:   Optional[int]    						="lm_head.weight"
          lowerCamelCase__:   Optional[int]    						=vnp.copy()  # same in embedded
          lowerCamelCase__:   int    						=torch.tensor(__a	)
      elif key_name.startswith("model/wob"	):
        lowerCamelCase__:   int    						="final_logits_bias"
        lowerCamelCase__:   Dict    						=vnp.copy()  # same in embedded
        lowerCamelCase__:   str    						=state.reshape((1, -1)	)
        lowerCamelCase__:   List[str]    						=torch.tensor(__a	)
      elif key_name == "model/dense/kernel":
        lowerCamelCase__:   Optional[int]    						="model.last_project.weight"
        lowerCamelCase__:   Optional[int]    						=vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
        lowerCamelCase__:   Optional[int]    						=torch.tensor(__a	)
      elif key_name == "model/dense_1/bias":
        lowerCamelCase__:   List[Any]    						="model.last_project.bias"
        lowerCamelCase__:   str    						=vnp.copy()  # same because it is one dimensional
        lowerCamelCase__:   Tuple    						=torch.tensor(__a	)
  torch.save(__a			,					args.output	)


if __name__ == "__main__":
     __A             =       argparse.ArgumentParser(
         description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
     )
     parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
     parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
     __A             =       parser.parse_args()
     convert_tf_gptsan_to_pt(args)








from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


__A             =       logging.get_logger(__name__)



class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					):







 '''simple docstring'''
 lowercase_	=    ["pixel_values"]

 def __init__(self       :     List[str] ,				UpperCAmelCase_       :     bool = True ,				UpperCAmelCase_       :     Dict[str, int] = None ,				UpperCAmelCase_       :     float = None ,				UpperCAmelCase_       :     PILImageResampling = PILImageResampling.BILINEAR ,				UpperCAmelCase_       :     bool = True ,				UpperCAmelCase_       :     Union[int, float] = 1 / 255 ,				UpperCAmelCase_       :     bool = True ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				**UpperCAmelCase_       :     List[str] ,				) ->None:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)
   lowerCamelCase__:   int    						=size if size is not None else {"shortest_edge": 384}
   lowerCamelCase__:   Tuple    						=get_size_dict(UpperCAmelCase_ ,				default_to_square=UpperCAmelCase_)

   lowerCamelCase__:   Union[str, Any]    						=do_resize
   lowerCamelCase__:   Union[str, Any]    						=size
   # Default value set here for backwards compatibility where the value in config is None
   lowerCamelCase__:   str    						=crop_pct if crop_pct is not None else 224 / 256
   lowerCamelCase__:   Optional[int]    						=resample
   lowerCamelCase__:   str    						=do_rescale
   lowerCamelCase__:   List[Any]    						=rescale_factor
   lowerCamelCase__:   Dict    						=do_normalize
   lowerCamelCase__:   List[str]    						=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   lowerCamelCase__:   Dict    						=image_std if image_std is not None else IMAGENET_STANDARD_STD

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int] ,				UpperCAmelCase_       :     np.ndarray ,				UpperCAmelCase_       :     Dict[str, int] ,				UpperCAmelCase_       :     float ,				UpperCAmelCase_       :     PILImageResampling = PILImageResampling.BICUBIC ,				UpperCAmelCase_       :     Optional[Union[str, ChannelDimension]] = None ,				**UpperCAmelCase_       :     str ,				) ->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__:   List[str]    						=get_size_dict(UpperCAmelCase_ ,				default_to_square=UpperCAmelCase_)
   if "shortest_edge" not in size:
     raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}""")
   lowerCamelCase__:   Tuple    						=size["shortest_edge"]

   if shortest_edge < 384:
     # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
     lowerCamelCase__:   List[str]    						=int(shortest_edge / crop_pct)
     lowerCamelCase__:   List[str]    						=get_resize_output_image_size(UpperCAmelCase_ ,				size=UpperCAmelCase_ ,				default_to_square=UpperCAmelCase_)
     lowerCamelCase__:   List[str]    						=resize(image=UpperCAmelCase_ ,				size=UpperCAmelCase_ ,				resample=UpperCAmelCase_ ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)
     # then crop to (shortest_edge, shortest_edge)
     return center_crop(image=UpperCAmelCase_ ,				size=(shortest_edge, shortest_edge) ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)
   else:
     # warping (no cropping) when evaluated at 384 or larger
     return resize(
         UpperCAmelCase_ ,				size=(shortest_edge, shortest_edge) ,				resample=UpperCAmelCase_ ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int] ,				UpperCAmelCase_       :     np.ndarray ,				UpperCAmelCase_       :     Union[int, float] ,				UpperCAmelCase_       :     Optional[Union[str, ChannelDimension]] = None ,				**UpperCAmelCase_       :     str ,				) ->Tuple:



   '''simple docstring'''



   return rescale(UpperCAmelCase_ ,				scale=UpperCAmelCase_ ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict ,				UpperCAmelCase_       :     np.ndarray ,				UpperCAmelCase_       :     Union[float, List[float]] ,				UpperCAmelCase_       :     Union[float, List[float]] ,				UpperCAmelCase_       :     Optional[Union[str, ChannelDimension]] = None ,				**UpperCAmelCase_       :     Optional[Any] ,				) ->np.ndarray:



   '''simple docstring'''



   return normalize(UpperCAmelCase_ ,				mean=UpperCAmelCase_ ,				std=UpperCAmelCase_ ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict ,				UpperCAmelCase_       :     ImageInput ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     Dict[str, int] = None ,				UpperCAmelCase_       :     float = None ,				UpperCAmelCase_       :     PILImageResampling = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     float = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				UpperCAmelCase_       :     Optional[Union[str, TensorType]] = None ,				UpperCAmelCase_       :     ChannelDimension = ChannelDimension.FIRST ,				**UpperCAmelCase_       :     Dict ,				) ->PIL.Image.Image:



   '''simple docstring'''



   lowerCamelCase__:   List[Any]    						=do_resize if do_resize is not None else self.do_resize
   lowerCamelCase__:   List[Any]    						=crop_pct if crop_pct is not None else self.crop_pct
   lowerCamelCase__:   str    						=resample if resample is not None else self.resample
   lowerCamelCase__:   str    						=do_rescale if do_rescale is not None else self.do_rescale
   lowerCamelCase__:   Optional[Any]    						=rescale_factor if rescale_factor is not None else self.rescale_factor
   lowerCamelCase__:   int    						=do_normalize if do_normalize is not None else self.do_normalize
   lowerCamelCase__:   Any    						=image_mean if image_mean is not None else self.image_mean
   lowerCamelCase__:   Any    						=image_std if image_std is not None else self.image_std

   lowerCamelCase__:   List[str]    						=size if size is not None else self.size
   lowerCamelCase__:   List[str]    						=get_size_dict(UpperCAmelCase_ ,				default_to_square=UpperCAmelCase_)

   lowerCamelCase__:   Any    						=make_list_of_images(UpperCAmelCase_)

   if not valid_images(UpperCAmelCase_):
     raise ValueError(
         "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
         "torch.Tensor, tf.Tensor or jax.ndarray.")

   if do_resize and size is None or resample is None:
     raise ValueError("Size and resample must be specified if do_resize is True.")

   if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
     raise ValueError("crop_pct must be specified if size < 384.")

   if do_rescale and rescale_factor is None:
     raise ValueError("Rescale factor must be specified if do_rescale is True.")

   if do_normalize and (image_mean is None or image_std is None):
     raise ValueError("Image mean and std must be specified if do_normalize is True.")

   # All transformations expect numpy arrays.
   lowerCamelCase__:   List[Any]    						=[to_numpy_array(UpperCAmelCase_) for image in images]

   if do_resize:
     lowerCamelCase__:   Dict    						=[self.resize(image=UpperCAmelCase_ ,				size=UpperCAmelCase_ ,				crop_pct=UpperCAmelCase_ ,				resample=UpperCAmelCase_) for image in images]

   if do_rescale:
     lowerCamelCase__:   Union[str, Any]    						=[self.rescale(image=UpperCAmelCase_ ,				scale=UpperCAmelCase_) for image in images]

   if do_normalize:
     lowerCamelCase__:   Tuple    						=[self.normalize(image=UpperCAmelCase_ ,				mean=UpperCAmelCase_ ,				std=UpperCAmelCase_) for image in images]

   lowerCamelCase__:   Optional[Any]    						=[to_channel_dimension_format(UpperCAmelCase_ ,				UpperCAmelCase_) for image in images]

   lowerCamelCase__:   Optional[Any]    						={"pixel_values": images}
   return BatchFeature(data=UpperCAmelCase_ ,				tensor_type=UpperCAmelCase_)

