




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



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


if __name__ == "__main__":
     lowerCAmelCase_            =			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""")
     lowerCAmelCase_            =			parser.parse_args()
     convert_tf_gptsan_to_pt(args)








import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class   lowerCAmelCase				(   unittest.TestCase     ):
 def 	A_      ( self    : str      )     ->      Dict:
     lowerCamelCase__       : Optional[Any]   					=						tempfile.mkdtemp()

     # fmt: off
     lowerCamelCase__       : Tuple   					=						['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
     # fmt: on
     lowerCamelCase__       : int   					=						os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES['vocab_file']      )
     with open(self.vocab_file     ,	'w'     ,	encoding='utf-8'      ) as vocab_writer:
         vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )

     lowerCamelCase__       : Any   					=						{
         'do_resize': True,
         'size': {'height': 18, 'width': 18},
         'do_normalize': True,
         'image_mean': [0.5, 0.5, 0.5],
         'image_std': [0.5, 0.5, 0.5],
     }
     lowerCamelCase__       : int   					=						os.path.join(self.tmpdirname     ,	UpperCAmelCase      )
     with open(self.image_processor_file     ,	'w'     ,	encoding='utf-8'      ) as fp:
         json.dump(UpperCAmelCase     ,	UpperCAmelCase      )
 def 	A_      ( self    : List[str]     ,	**UpperCAmelCase    : str      )     ->      int:
     return BertTokenizer.from_pretrained(self.tmpdirname     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Optional[int]     ,	**UpperCAmelCase    : List[str]      )     ->      List[str]:
     return ViTImageProcessor.from_pretrained(self.tmpdirname     ,	**UpperCAmelCase      )
 def 	A_      ( self    : int      )     ->      Any:
     shutil.rmtree(self.tmpdirname      )
 def 	A_      ( self    : str      )     ->      str:
     lowerCamelCase__       : List[Any]   					=						[np.random.randint(255     ,	size=(3, 30, 400)     ,	dtype=np.uinta      )]

     lowerCamelCase__       : Union[str, Any]   					=						[Image.fromarray(np.moveaxis(UpperCAmelCase     ,	0     ,	-1      )      ) for x in image_inputs]

     return image_inputs
 def 	A_      ( self    : Dict      )     ->      Dict:
     lowerCamelCase__       : List[Any]   					=						self.get_tokenizer()
     lowerCamelCase__       : Tuple   					=						self.get_image_processor()

     lowerCamelCase__       : Union[str, Any]   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     processor.save_pretrained(self.tmpdirname      )
     lowerCamelCase__       : List[str]   					=						VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname      )

     self.assertEqual(processor.tokenizer.get_vocab()     ,	tokenizer.get_vocab()      )
     self.assertIsInstance(processor.tokenizer     ,	(BertTokenizer, BertTokenizerFast)      )

     self.assertEqual(processor.image_processor.to_json_string()     ,	image_processor.to_json_string()      )
     self.assertIsInstance(processor.image_processor     ,	UpperCAmelCase      )
 def 	A_      ( self    : List[str]      )     ->      Tuple:
     lowerCamelCase__       : List[Any]   					=						VisionTextDualEncoderProcessor(
         tokenizer=self.get_tokenizer()     ,	image_processor=self.get_image_processor()      )
     processor.save_pretrained(self.tmpdirname      )

     lowerCamelCase__       : Tuple   					=						self.get_tokenizer(bos_token='(BOS)'     ,	eos_token='(EOS)'      )
     lowerCamelCase__       : int   					=						self.get_image_processor(do_normalize=UpperCAmelCase     ,	padding_value=1.0      )

     lowerCamelCase__       : Union[str, Any]   					=						VisionTextDualEncoderProcessor.from_pretrained(
         self.tmpdirname     ,	bos_token='(BOS)'     ,	eos_token='(EOS)'     ,	do_normalize=UpperCAmelCase     ,	padding_value=1.0      )

     self.assertEqual(processor.tokenizer.get_vocab()     ,	tokenizer_add_kwargs.get_vocab()      )
     self.assertIsInstance(processor.tokenizer     ,	(BertTokenizer, BertTokenizerFast)      )

     self.assertEqual(processor.image_processor.to_json_string()     ,	image_processor_add_kwargs.to_json_string()      )
     self.assertIsInstance(processor.image_processor     ,	UpperCAmelCase      )
 def 	A_      ( self    : Any      )     ->      Tuple:
     lowerCamelCase__       : List[Any]   					=						self.get_image_processor()
     lowerCamelCase__       : Any   					=						self.get_tokenizer()

     lowerCamelCase__       : Optional[int]   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : Optional[int]   					=						self.prepare_image_inputs()

     lowerCamelCase__       : int   					=						image_processor(UpperCAmelCase     ,	return_tensors='np'      )
     lowerCamelCase__       : List[str]   					=						processor(images=UpperCAmelCase     ,	return_tensors='np'      )

     for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum()     ,	input_processor[key].sum()     ,	delta=1e-2      )
 def 	A_      ( self    : Tuple      )     ->      List[Any]:
     lowerCamelCase__       : Dict   					=						self.get_image_processor()
     lowerCamelCase__       : List[str]   					=						self.get_tokenizer()

     lowerCamelCase__       : str   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : Tuple   					=						'lower newer'

     lowerCamelCase__       : Tuple   					=						processor(text=UpperCAmelCase      )

     lowerCamelCase__       : Any   					=						tokenizer(UpperCAmelCase      )

     for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]     ,	encoded_processor[key]      )
 def 	A_      ( self    : Optional[int]      )     ->      Any:
     lowerCamelCase__       : Optional[int]   					=						self.get_image_processor()
     lowerCamelCase__       : Tuple   					=						self.get_tokenizer()

     lowerCamelCase__       : Tuple   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : str   					=						'lower newer'
     lowerCamelCase__       : Dict   					=						self.prepare_image_inputs()

     lowerCamelCase__       : int   					=						processor(text=UpperCAmelCase     ,	images=UpperCAmelCase      )

     self.assertListEqual(list(inputs.keys()      )     ,	['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']      )

     # test if it raises when no input is passed
     with self.assertRaises(UpperCAmelCase      ):
         processor()
 def 	A_      ( self    : Optional[Any]      )     ->      List[str]:
     lowerCamelCase__       : Dict   					=						self.get_image_processor()
     lowerCamelCase__       : Union[str, Any]   					=						self.get_tokenizer()

     lowerCamelCase__       : List[str]   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : List[str]   					=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     lowerCamelCase__       : Dict   					=						processor.batch_decode(UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						tokenizer.batch_decode(UpperCAmelCase      )

     self.assertListEqual(UpperCAmelCase     ,	UpperCAmelCase      )







 def 	A_      ( self    : Optional[int]      )     ->      List[str]:
     lowerCamelCase__       : Tuple   					=						self.get_image_processor()
     lowerCamelCase__       : List[str]   					=						self.get_tokenizer()

     lowerCamelCase__       : Union[str, Any]   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						'lower newer'
     lowerCamelCase__       : List[Any]   					=						self.prepare_image_inputs()

     lowerCamelCase__       : Union[str, Any]   					=						processor(text=UpperCAmelCase     ,	images=UpperCAmelCase      )

     self.assertListEqual(list(inputs.keys()      )     ,	processor.model_input_names      )





