

'''simple docstring'''



import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_vision_available():
  from PIL import Image

  from transformers import FlavaImageProcessor, FlavaProcessor
  from transformers.models.flava.image_processing_flava import (
      FLAVA_CODEBOOK_MEAN,
      FLAVA_CODEBOOK_STD,
      FLAVA_IMAGE_MEAN,
      FLAVA_IMAGE_STD,
  )







@require_vision
class 		lowerCAmelCase__     (   unittest.TestCase      ):



 """simple docstring"""



 def    UpperCAmelCase__	(	self      : str    )						->				Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   tempfile.mkdtemp()

      # fmt: off
      __SCREAMING_SNAKE_CASE						     =   ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
      # fmt: on
      __SCREAMING_SNAKE_CASE						     =   os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES["""vocab_file"""]    )

      with open(self.vocab_file      ,							"""w"""      ,							encoding="""utf-8"""    ) as fp:
           fp.write("""""".join([x + """\n""" for x in vocab_tokens]    )    )

      __SCREAMING_SNAKE_CASE						     =   {
          """image_mean""": FLAVA_IMAGE_MEAN,
          """image_std""": FLAVA_IMAGE_STD,
          """do_normalize""": True,
          """do_resize""": True,
          """size""": 224,
          """do_center_crop""": True,
          """crop_size""": 224,
          """input_size_patches""": 14,
          """total_mask_patches""": 75,
          """mask_group_max_patches""": None,
          """mask_group_min_patches""": 16,
          """mask_group_min_aspect_ratio""": 0.3,
          """mask_group_max_aspect_ratio""": None,
          """codebook_do_resize""": True,
          """codebook_size""": 112,
          """codebook_do_center_crop""": True,
          """codebook_crop_size""": 112,
          """codebook_do_map_pixels""": True,
          """codebook_do_normalize""": True,
          """codebook_image_mean""": FLAVA_CODEBOOK_MEAN,
          """codebook_image_std""": FLAVA_CODEBOOK_STD,
      }

      __SCREAMING_SNAKE_CASE						     =   os.path.join(self.tmpdirname      ,							__SCREAMING_SNAKE_CASE    )
      with open(self.image_processor_file      ,							"""w"""      ,							encoding="""utf-8"""    ) as fp:
           json.dump(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Optional[int]      ,							**__SCREAMING_SNAKE_CASE      : Dict    )						->				Tuple:


      """simple docstring"""




      return BertTokenizer.from_pretrained(self.tmpdirname      ,							**__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Any      ,							**__SCREAMING_SNAKE_CASE      : Tuple    )						->				str:


      """simple docstring"""




      return BertTokenizerFast.from_pretrained(self.tmpdirname      ,							**__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : List[str]      ,							**__SCREAMING_SNAKE_CASE      : Any    )						->				Optional[int]:


      """simple docstring"""




      return FlavaImageProcessor.from_pretrained(self.tmpdirname      ,							**__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : str    )						->				str:


      """simple docstring"""




      shutil.rmtree(self.tmpdirname    )
 def    UpperCAmelCase__	(	self      : int    )						->				Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   [np.random.randint(255      ,							size=(3, 30, 400)      ,							dtype=np.uinta    )]

      __SCREAMING_SNAKE_CASE						     =   [Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE      ,							0      ,							-1    )    ) for x in image_inputs]

      return image_inputs
 def    UpperCAmelCase__	(	self      : Dict    )						->				List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.get_tokenizer()
      __SCREAMING_SNAKE_CASE						     =   self.get_rust_tokenizer()
      __SCREAMING_SNAKE_CASE						     =   self.get_image_processor()

      __SCREAMING_SNAKE_CASE						     =   FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,							image_processor=__SCREAMING_SNAKE_CASE    )
      processor_slow.save_pretrained(self.tmpdirname    )
      __SCREAMING_SNAKE_CASE						     =   FlavaProcessor.from_pretrained(self.tmpdirname      ,							use_fast=__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,							image_processor=__SCREAMING_SNAKE_CASE    )
      processor_fast.save_pretrained(self.tmpdirname    )
      __SCREAMING_SNAKE_CASE						     =   FlavaProcessor.from_pretrained(self.tmpdirname    )

      self.assertEqual(processor_slow.tokenizer.get_vocab()      ,							tokenizer_slow.get_vocab()    )
      self.assertEqual(processor_fast.tokenizer.get_vocab()      ,							tokenizer_fast.get_vocab()    )
      self.assertEqual(tokenizer_slow.get_vocab()      ,							tokenizer_fast.get_vocab()    )
      self.assertIsInstance(processor_slow.tokenizer      ,							__SCREAMING_SNAKE_CASE    )
      self.assertIsInstance(processor_fast.tokenizer      ,							__SCREAMING_SNAKE_CASE    )

      self.assertEqual(processor_slow.image_processor.to_json_string()      ,							image_processor.to_json_string()    )
      self.assertEqual(processor_fast.image_processor.to_json_string()      ,							image_processor.to_json_string()    )
      self.assertIsInstance(processor_slow.image_processor      ,							__SCREAMING_SNAKE_CASE    )
      self.assertIsInstance(processor_fast.image_processor      ,							__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : str    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   FlavaProcessor(tokenizer=self.get_tokenizer()      ,							image_processor=self.get_image_processor()    )
      processor.save_pretrained(self.tmpdirname    )

      __SCREAMING_SNAKE_CASE						     =   self.get_tokenizer(bos_token="""(BOS)"""      ,							eos_token="""(EOS)"""    )
      __SCREAMING_SNAKE_CASE						     =   self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE      ,							padding_value=1.0    )

      __SCREAMING_SNAKE_CASE						     =   FlavaProcessor.from_pretrained(
          self.tmpdirname      ,							bos_token="""(BOS)"""      ,							eos_token="""(EOS)"""      ,							do_normalize=__SCREAMING_SNAKE_CASE      ,							padding_value=1.0    )

      self.assertEqual(processor.tokenizer.get_vocab()      ,							tokenizer_add_kwargs.get_vocab()    )
      self.assertIsInstance(processor.tokenizer      ,							__SCREAMING_SNAKE_CASE    )

      self.assertEqual(processor.image_processor.to_json_string()      ,							image_processor_add_kwargs.to_json_string()    )
      self.assertIsInstance(processor.image_processor      ,							__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Any    )						->				str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.get_image_processor()
      __SCREAMING_SNAKE_CASE						     =   self.get_tokenizer()

      __SCREAMING_SNAKE_CASE						     =   FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,							image_processor=__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE						     =   image_processor(__SCREAMING_SNAKE_CASE      ,							return_tensors="""np"""    )
      __SCREAMING_SNAKE_CASE						     =   processor(images=__SCREAMING_SNAKE_CASE      ,							return_tensors="""np"""    )

      for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()      ,							input_processor[key].sum()      ,							delta=1E-2    )

      # With rest of the args
      random.seed(1_234    )
      __SCREAMING_SNAKE_CASE						     =   image_processor(
          __SCREAMING_SNAKE_CASE      ,							return_image_mask=__SCREAMING_SNAKE_CASE      ,							return_codebook_pixels=__SCREAMING_SNAKE_CASE      ,							return_tensors="""np"""    )
      random.seed(1_234    )
      __SCREAMING_SNAKE_CASE						     =   processor(
          images=__SCREAMING_SNAKE_CASE      ,							return_image_mask=__SCREAMING_SNAKE_CASE      ,							return_codebook_pixels=__SCREAMING_SNAKE_CASE      ,							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    UpperCAmelCase__	(	self      : Optional[Any]    )						->				Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.get_image_processor()
      __SCREAMING_SNAKE_CASE						     =   self.get_tokenizer()

      __SCREAMING_SNAKE_CASE						     =   FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,							image_processor=__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   """lower newer"""

      __SCREAMING_SNAKE_CASE						     =   processor(text=__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   tokenizer(__SCREAMING_SNAKE_CASE    )

      for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]      ,							encoded_processor[key]    )
 def    UpperCAmelCase__	(	self      : Any    )						->				Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.get_image_processor()
      __SCREAMING_SNAKE_CASE						     =   self.get_tokenizer()

      __SCREAMING_SNAKE_CASE						     =   FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,							image_processor=__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   """lower newer"""
      __SCREAMING_SNAKE_CASE						     =   self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE						     =   processor(text=__SCREAMING_SNAKE_CASE      ,							images=__SCREAMING_SNAKE_CASE    )

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

      # add extra args
      __SCREAMING_SNAKE_CASE						     =   processor(text=__SCREAMING_SNAKE_CASE      ,							images=__SCREAMING_SNAKE_CASE      ,							return_codebook_pixels=__SCREAMING_SNAKE_CASE      ,							return_image_mask=__SCREAMING_SNAKE_CASE    )

      self.assertListEqual(
          list(inputs.keys()    )      ,							[
              """input_ids""",
              """token_type_ids""",
              """attention_mask""",
              """pixel_values""",
              """codebook_pixel_values""",
              """bool_masked_pos""",
          ]      ,							)

      # test if it raises when no input is passed
      with pytest.raises(__SCREAMING_SNAKE_CASE    ):
           processor()
 def    UpperCAmelCase__	(	self      : Tuple    )						->				str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.get_image_processor()
      __SCREAMING_SNAKE_CASE						     =   self.get_tokenizer()

      __SCREAMING_SNAKE_CASE						     =   FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,							image_processor=__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      __SCREAMING_SNAKE_CASE						     =   processor.batch_decode(__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   tokenizer.batch_decode(__SCREAMING_SNAKE_CASE    )

      self.assertListEqual(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )


 def    UpperCAmelCase__	(	self      : Optional[Any]    )						->				str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.get_image_processor()
      __SCREAMING_SNAKE_CASE						     =   self.get_tokenizer()

      __SCREAMING_SNAKE_CASE						     =   FlavaProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,							image_processor=__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   """lower newer"""
      __SCREAMING_SNAKE_CASE						     =   self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE						     =   processor(text=__SCREAMING_SNAKE_CASE      ,							images=__SCREAMING_SNAKE_CASE    )

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









'''simple docstring'''



import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel







class 		lowerCAmelCase__     (   utils.Parser      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "halfcheetah-medium-expert-v2"
 lowerCAmelCase__						      =   "config.offline"







def      a__ (						a__   ,					a__   ,					a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   utils.load_model(a__   ,					a__   ,					a__   ,					epoch=a__   ,					device=a__		)
     __SCREAMING_SNAKE_CASE						     =   TrajectoryTransformerModel(gpt.config		)

     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()		)
     __SCREAMING_SNAKE_CASE						     =   gpt.pos_emb
     trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()		)
     trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()		)
     trajectory_transformer.head.load_state_dict(gpt.head.state_dict()		)

     for i, block in enumerate(gpt.blocks		):
          trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()		)
          trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()		)
          trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()		)

          trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()		)
          trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()		)
          trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()		)
          trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()		)

     torch.save(trajectory_transformer.state_dict()   ,					"""pytorch_model.bin"""		)


if __name__ == "__main__":
  UpperCAmelCase				: Dict         =  Parser().parse_args('plan')
  convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
      args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
  )






