


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				str		     =					os.path.join(args.tf_model_dir ,			'''parameters.json'''       )
  A_      :				List[Any]		     =					json.loads(open(SCREAMING_SNAKE_CASE       ).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'''       ):
    A_      :				List[str]		     =					args.output + '''.pt'''
  A_      :				List[str]		     =					OrderedDict()
  with tf.device('''/CPU:0'''       ):
    A_      :				str		     =					tf.train.load_checkpoint(args.tf_model_dir       )
    A_      :				Dict		     =					reader.get_variable_to_shape_map()
    for key_name in shapes.keys():
      A_      :				Optional[Any]		     =					reader.get_tensor(SCREAMING_SNAKE_CASE       ).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'''       ):
          A_      :				Any		     =					int(key_name[9]       )
        elif key_name.startswith('''pasts/out'''       ):
          A_      :				int		     =					8
        A_      :				str		     =					'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
        A_      :				Union[str, Any]		     =					vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
        A_      :				str		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
      elif key_name.startswith('''model/moe'''       ):
        A_      :				Optional[int]		     =					int(key_name[9:].split('''/'''       )[0]       )
        if key_name.endswith('''/switch_gating/kernel'''       ):
          A_      :				str		     =					'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
          A_      :				List[Any]		     =					vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
          A_      :				Optional[Any]		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
        elif key_name.endswith('''/softmlp/kernel'''       ):
          A_      :				str		     =					'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
          A_      :				Dict		     =					vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
          A_      :				Dict		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
        elif key_name.endswith('''/wo/kernel'''       ) or key_name.endswith('''/wi/kernel'''       ):
          A_      :				str		     =					key_name[-9:-7]
          for i in range(16       ):
            A_      :				Union[str, Any]		     =					'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
            A_      :				str		     =					(
                vnp[i].transpose([1, 0]       ).copy()
            )  # In Mesh-Tensorflow, it is one array, so it is divided
            A_      :				Optional[int]		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
      elif key_name.startswith('''model/mlp'''       ):
        A_      :				Any		     =					int(key_name[9:].split('''/'''       )[0]       )
        if key_name.endswith('''/p1/kernel'''       ):
          A_      :				Tuple		     =					'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
          A_      :				str		     =					vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
          A_      :				Optional[int]		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
        elif key_name.endswith('''/p1/bias'''       ):
          A_      :				Optional[int]		     =					'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
          A_      :				Optional[int]		     =					vnp.copy()  # same because it is one dimensional
          A_      :				Optional[Any]		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
        elif key_name.endswith('''/p2/kernel'''       ):
          A_      :				Optional[int]		     =					'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
          A_      :				Tuple		     =					vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
          A_      :				List[Any]		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
        elif key_name.endswith('''/p2/bias'''       ):
          A_      :				Any		     =					'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
          A_      :				Optional[int]		     =					vnp.copy()  # same because it is one dimensional
          A_      :				Union[str, Any]		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
      elif key_name.startswith('''model/ln'''       ):
        A_      :				str		     =					int(key_name[8:].split('''/'''       )[0]       )
        if key_name.endswith('''/b'''       ):
          A_      :				Optional[int]		     =					'''model.blocks.%d.feed_forward.norm.bias''' % player
          A_      :				Dict		     =					vnp.copy()  # same because it is one dimensional
          A_      :				Optional[Any]		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
        elif key_name.endswith('''/g'''       ):
          A_      :				Optional[int]		     =					'''model.blocks.%d.feed_forward.norm.weight''' % player
          A_      :				Dict		     =					vnp.copy()  # same because it is one dimensional
          A_      :				int		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
      elif key_name.startswith('''model/att'''       ):
        A_      :				List[Any]		     =					int(key_name[9:].split('''/'''       )[0]       )
        if key_name.endswith('''/qkv/kernel'''       ):
          A_      :				List[Any]		     =					vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
          A_      :				List[Any]		     =					state[:, 0, :, :]
          A_      :				Union[str, Any]		     =					state[:, 1, :, :]
          A_      :				List[Any]		     =					state[:, 2, :, :]
          A_      :				List[Any]		     =					(
              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
          A_      :				List[str]		     =					(
              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
          A_      :				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
          A_      :				Optional[Any]		     =					'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
          A_      :				Any		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
          A_      :				Union[str, Any]		     =					'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
          A_      :				Optional[int]		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
          A_      :				Union[str, Any]		     =					'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
          A_      :				int		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
        elif key_name.endswith('''/o/kernel'''       ):
          A_      :				Union[str, Any]		     =					'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
          A_      :				Union[str, Any]		     =					(
              vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]       ).transpose([1, 0]       ).copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          A_      :				Any		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
      elif key_name.startswith('''model/an'''       ):
        A_      :				int		     =					int(key_name[8:].split('''/'''       )[0]       )
        if key_name.endswith('''/b'''       ):
          A_      :				str		     =					'''model.blocks.%d.self_attn.norm.bias''' % player
          A_      :				Tuple		     =					vnp.copy()  # same because it is one dimensional
          A_      :				Dict		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
        elif key_name.endswith('''/g'''       ):
          A_      :				Optional[Any]		     =					'''model.blocks.%d.self_attn.norm.weight''' % player
          A_      :				List[str]		     =					vnp.copy()  # same because it is one dimensional
          A_      :				Any		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
      elif (
          key_name.startswith('''model/wte'''       )
          or key_name.startswith('''model/wpe'''       )
          or key_name.startswith('''model/ete'''       )
      ):
        A_      :				Union[str, Any]		     =					{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
            key_name[-3:]
        ]
        A_      :				int		     =					'''model.%s.weight''' % nlayer
        A_      :				Optional[int]		     =					vnp.copy()  # same in embedded
        A_      :				str		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
        if key_name.startswith('''model/wte'''       ):
          A_      :				Tuple		     =					'''lm_head.weight'''
          A_      :				str		     =					vnp.copy()  # same in embedded
          A_      :				Union[str, Any]		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
      elif key_name.startswith('''model/wob'''       ):
        A_      :				Optional[int]		     =					'''final_logits_bias'''
        A_      :				Tuple		     =					vnp.copy()  # same in embedded
        A_      :				Any		     =					state.reshape((1, -1)       )
        A_      :				List[str]		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
      elif key_name == "model/dense/kernel":
        A_      :				Dict		     =					'''model.last_project.weight'''
        A_      :				Any		     =					vnp.transpose([1, 0]       ).copy()  # Mesh-Tensorflow is a diagonal matrix
        A_      :				Optional[int]		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
      elif key_name == "model/dense_1/bias":
        A_      :				List[Any]		     =					'''model.last_project.bias'''
        A_      :				Tuple		     =					vnp.copy()  # same because it is one dimensional
        A_      :				Any		     =					torch.tensor(SCREAMING_SNAKE_CASE       )
  torch.save(SCREAMING_SNAKE_CASE ,			args.output       )


if __name__ == "__main__":
     UpperCamelCase								=	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""")
     UpperCamelCase								=	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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCamelCase								=	logging.get_logger(__name__)


if is_vision_available():
     import PIL






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    ["pixel_values"]



       def __init__(      self				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = 1 / 255				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = True				,       **_SCREAMING_SNAKE_CASE				,       )->None:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					size if size is not None else {'''shortest_edge''': 224}
         A_      :				Any		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         A_      :				Dict		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       default_to_square=_SCREAMING_SNAKE_CASE				,       param_name='''crop_size'''		)

         A_      :				str		     =					do_resize
         A_      :				Tuple		     =					size
         A_      :				Optional[Any]		     =					resample
         A_      :				Tuple		     =					do_center_crop
         A_      :				List[Any]		     =					crop_size
         A_      :				Optional[int]		     =					do_rescale
         A_      :				Tuple		     =					rescale_factor
         A_      :				Any		     =					do_normalize
         A_      :				int		     =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         A_      :				Any		     =					image_std if image_std is not None else OPENAI_CLIP_STD
         A_      :				Any		     =					do_convert_rgb



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->np.ndarray:




         '''simple docstring'''





         A_      :				Dict		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         if "shortest_edge" not in size:
           raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''		)
         A_      :				Any		     =					get_resize_output_image_size(_SCREAMING_SNAKE_CASE				,       size=size['''shortest_edge''']				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         return resize(_SCREAMING_SNAKE_CASE				,       size=_SCREAMING_SNAKE_CASE				,       resample=_SCREAMING_SNAKE_CASE				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->np.ndarray:




         '''simple docstring'''





         A_      :				str		     =					get_size_dict(_SCREAMING_SNAKE_CASE		)
         if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''		)
         return center_crop(_SCREAMING_SNAKE_CASE				,       size=(size['''height'''], size['''width'''])				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->int:




         '''simple docstring'''





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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->np.ndarray:




         '''simple docstring'''





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







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST				,       **_SCREAMING_SNAKE_CASE				,       )->PIL.Image.Image:




         '''simple docstring'''





         A_      :				Optional[int]		     =					do_resize if do_resize is not None else self.do_resize
         A_      :				int		     =					size if size is not None else self.size
         A_      :				Optional[int]		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       param_name='''size'''				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					resample if resample is not None else self.resample
         A_      :				Any		     =					do_center_crop if do_center_crop is not None else self.do_center_crop
         A_      :				List[str]		     =					crop_size if crop_size is not None else self.crop_size
         A_      :				int		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       param_name='''crop_size'''				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[int]		     =					do_rescale if do_rescale is not None else self.do_rescale
         A_      :				int		     =					rescale_factor if rescale_factor is not None else self.rescale_factor
         A_      :				int		     =					do_normalize if do_normalize is not None else self.do_normalize
         A_      :				Tuple		     =					image_mean if image_mean is not None else self.image_mean
         A_      :				Tuple		     =					image_std if image_std is not None else self.image_std
         A_      :				List[str]		     =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         A_      :				Optional[int]		     =					make_list_of_images(_SCREAMING_SNAKE_CASE		)

         if not valid_images(_SCREAMING_SNAKE_CASE		):
           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:
           raise ValueError('''Size must be specified if do_resize is True.'''		)

         if do_center_crop and crop_size is None:
           raise ValueError('''Crop size must be specified if do_center_crop is True.'''		)

         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.'''		)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
           A_      :				List[str]		     =					[convert_to_rgb(_SCREAMING_SNAKE_CASE		) for image in images]

         # All transformations expect numpy arrays.
         A_      :				int		     =					[to_numpy_array(_SCREAMING_SNAKE_CASE		) for image in images]

         if do_resize:
           A_      :				Tuple		     =					[self.resize(image=_SCREAMING_SNAKE_CASE				,       size=_SCREAMING_SNAKE_CASE				,       resample=_SCREAMING_SNAKE_CASE		) for image in images]

         if do_center_crop:
           A_      :				Union[str, Any]		     =					[self.center_crop(image=_SCREAMING_SNAKE_CASE				,       size=_SCREAMING_SNAKE_CASE		) for image in images]

         if do_rescale:
           A_      :				Tuple		     =					[self.rescale(image=_SCREAMING_SNAKE_CASE				,       scale=_SCREAMING_SNAKE_CASE		) for image in images]

         if do_normalize:
           A_      :				List[Any]		     =					[self.normalize(image=_SCREAMING_SNAKE_CASE				,       mean=_SCREAMING_SNAKE_CASE				,       std=_SCREAMING_SNAKE_CASE		) for image in images]

         A_      :				str		     =					[to_channel_dimension_format(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		) for image in images]

         A_      :				Optional[Any]		     =					{'''pixel_values''': images}
         return BatchFeature(data=_SCREAMING_SNAKE_CASE				,       tensor_type=_SCREAMING_SNAKE_CASE		)




