


'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 OwlViTImageProcessor, OwlViTProcessor





@require_vision
class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  tempfile.mkdtemp()

   # fmt: off
   lowerCamelCase_      =  ["", "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
   # fmt: on
   lowerCamelCase_      =  dict(zip(_lowerCamelCase			,  range(len(_lowerCamelCase     )     )     )     )
   lowerCamelCase_      =  ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
   lowerCamelCase_      =  {"unk_token": "<unk>"}

   lowerCamelCase_      =  os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["vocab_file"]     )
   lowerCamelCase_      =  os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["merges_file"]     )
   with open(self.vocab_file			,  "w"			,  encoding="utf-8"     ) as fp:
    fp.write(json.dumps(_lowerCamelCase     ) + "\n"     )
   with open(self.merges_file			,  "w"			,  encoding="utf-8"     ) as fp:
    fp.write("\n".join(_lowerCamelCase     )     )

   lowerCamelCase_      =  {
       "do_resize": True,
       "size": 20,
       "do_center_crop": True,
       "crop_size": 18,
       "do_normalize": True,
       "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
       "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
   }
   lowerCamelCase_      =  os.path.join(self.tmpdirname			,  _lowerCamelCase     )
   with open(self.image_processor_file			,  "w"			,  encoding="utf-8"     ) as fp:
    json.dump(_lowerCamelCase			,  _lowerCamelCase     )






  def   snake_case		(    self			,  **UpperCamelCase     ):







   """simple docstring"""
   return CLIPTokenizer.from_pretrained(self.tmpdirname			,  pad_token="!"			,  **_lowerCamelCase     )






  def   snake_case		(    self			,  **UpperCamelCase     ):







   """simple docstring"""
   return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,  pad_token="!"			,  **_lowerCamelCase     )






  def   snake_case		(    self			,  **UpperCamelCase     ):







   """simple docstring"""
   return OwlViTImageProcessor.from_pretrained(self.tmpdirname			,  **_lowerCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   shutil.rmtree(self.tmpdirname     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  [np.random.randint(255			,  size=(3, 30, 400)			,  dtype=np.uinta     )]

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

   return image_inputs






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_tokenizer()
   lowerCamelCase_      =  self.get_rust_tokenizer()
   lowerCamelCase_      =  self.get_image_processor()

   lowerCamelCase_      =  OwlViTProcessor(tokenizer=_lowerCamelCase			,  image_processor=_lowerCamelCase     )
   processor_slow.save_pretrained(self.tmpdirname     )
   lowerCamelCase_      =  OwlViTProcessor.from_pretrained(self.tmpdirname			,  use_fast=_lowerCamelCase     )

   lowerCamelCase_      =  OwlViTProcessor(tokenizer=_lowerCamelCase			,  image_processor=_lowerCamelCase     )
   processor_fast.save_pretrained(self.tmpdirname     )
   lowerCamelCase_      =  OwlViTProcessor.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			,  _lowerCamelCase     )
   self.assertIsInstance(processor_fast.tokenizer			,  _lowerCamelCase     )

   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			,  _lowerCamelCase     )
   self.assertIsInstance(processor_fast.image_processor			,  _lowerCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  OwlViTProcessor(tokenizer=self.get_tokenizer()			,  image_processor=self.get_image_processor()     )
   processor.save_pretrained(self.tmpdirname     )

   lowerCamelCase_      =  self.get_tokenizer(bos_token="(BOS)"			,  eos_token="(EOS)"     )
   lowerCamelCase_      =  self.get_image_processor(do_normalize=_lowerCamelCase     )

   lowerCamelCase_      =  OwlViTProcessor.from_pretrained(
       self.tmpdirname			,  bos_token="(BOS)"			,  eos_token="(EOS)"			,  do_normalize=_lowerCamelCase     )

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

   self.assertEqual(processor.image_processor.to_json_string()			,  image_processor_add_kwargs.to_json_string()     )
   self.assertIsInstance(processor.image_processor			,  _lowerCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_image_processor()
   lowerCamelCase_      =  self.get_tokenizer()

   lowerCamelCase_      =  OwlViTProcessor(tokenizer=_lowerCamelCase			,  image_processor=_lowerCamelCase     )

   lowerCamelCase_      =  self.prepare_image_inputs()

   lowerCamelCase_      =  image_processor(_lowerCamelCase			,  return_tensors="np"     )
   lowerCamelCase_      =  processor(images=_lowerCamelCase			,  return_tensors="np"     )

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






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_image_processor()
   lowerCamelCase_      =  self.get_tokenizer()

   lowerCamelCase_      =  OwlViTProcessor(tokenizer=_lowerCamelCase			,  image_processor=_lowerCamelCase     )

   lowerCamelCase_      =  "lower newer"

   lowerCamelCase_      =  processor(text=_lowerCamelCase			,  return_tensors="np"     )

   lowerCamelCase_      =  tokenizer(_lowerCamelCase			,  return_tensors="np"     )

   for key in encoded_tok.keys():
    self.assertListEqual(encoded_tok[key][0].tolist()			,  encoded_processor[key][0].tolist()     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_image_processor()
   lowerCamelCase_      =  self.get_tokenizer()

   lowerCamelCase_      =  OwlViTProcessor(tokenizer=_lowerCamelCase			,  image_processor=_lowerCamelCase     )

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

   lowerCamelCase_      =  processor(text=_lowerCamelCase			,  images=_lowerCamelCase     )

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

   # test if it raises when no input is passed
   with pytest.raises(_lowerCamelCase     ):
    processor()






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  "google/owlvit-base-patch32"
   lowerCamelCase_      =  OwlViTProcessor.from_pretrained(_lowerCamelCase     )

   lowerCamelCase_      =  ["cat", "nasa badge"]
   lowerCamelCase_      =  processor(text=_lowerCamelCase     )

   lowerCamelCase_      =  16
   self.assertListEqual(list(inputs.keys()     )			,  ["input_ids", "attention_mask"]     )
   self.assertEqual(inputs["input_ids"].shape			,  (2, seq_length)     )

   # test if it raises when no input is passed
   with pytest.raises(_lowerCamelCase     ):
    processor()






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  "google/owlvit-base-patch32"
   lowerCamelCase_      =  OwlViTProcessor.from_pretrained(_lowerCamelCase     )

   lowerCamelCase_      =  [["cat", "nasa badge"], ["person"]]
   lowerCamelCase_      =  processor(text=_lowerCamelCase     )

   lowerCamelCase_      =  16
   lowerCamelCase_      =  len(_lowerCamelCase     )
   lowerCamelCase_      =  max([len(_lowerCamelCase     ) for texts in input_texts]     )

   self.assertListEqual(list(inputs.keys()     )			,  ["input_ids", "attention_mask"]     )
   self.assertEqual(inputs["input_ids"].shape			,  (batch_size * num_max_text_queries, seq_length)     )

   # test if it raises when no input is passed
   with pytest.raises(_lowerCamelCase     ):
    processor()






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  "google/owlvit-base-patch32"
   lowerCamelCase_      =  OwlViTProcessor.from_pretrained(_lowerCamelCase     )

   lowerCamelCase_      =  ["cat", "nasa badge"]
   lowerCamelCase_      =  processor(text=_lowerCamelCase     )

   lowerCamelCase_      =  16
   lowerCamelCase_      =  inputs["input_ids"]
   lowerCamelCase_      =  [
       [4_9406, 2368, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [4_9406, 6841, 1_1301, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
   ]

   self.assertListEqual(list(inputs.keys()     )			,  ["input_ids", "attention_mask"]     )
   self.assertEqual(inputs["input_ids"].shape			,  (2, seq_length)     )
   self.assertListEqual(list(input_ids[0]     )			,  predicted_ids[0]     )
   self.assertListEqual(list(input_ids[1]     )			,  predicted_ids[1]     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_image_processor()
   lowerCamelCase_      =  self.get_tokenizer()

   lowerCamelCase_      =  OwlViTProcessor(tokenizer=_lowerCamelCase			,  image_processor=_lowerCamelCase     )

   lowerCamelCase_      =  self.prepare_image_inputs()
   lowerCamelCase_      =  self.prepare_image_inputs()

   lowerCamelCase_      =  processor(images=_lowerCamelCase			,  query_images=_lowerCamelCase     )

   self.assertListEqual(list(inputs.keys()     )			,  ["query_pixel_values", "pixel_values"]     )

   # test if it raises when no input is passed
   with pytest.raises(_lowerCamelCase     ):
    processor()







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_image_processor()
   lowerCamelCase_      =  self.get_tokenizer()

   lowerCamelCase_      =  OwlViTProcessor(tokenizer=_lowerCamelCase			,  image_processor=_lowerCamelCase     )

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

   lowerCamelCase_      =  processor.batch_decode(_lowerCamelCase     )
   lowerCamelCase_      =  tokenizer.batch_decode(_lowerCamelCase     )

   self.assertListEqual(_lowerCamelCase			,  _lowerCamelCase     )





"""simple docstring"""



import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Optional[int]:
  _snake_case					     =       checkpoints.load_tax_checkpoint(__lowerCamelCase		)
  _snake_case					     =       flatten_dict(__lowerCamelCase		)
  return flax_params
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict		)     ->					Optional[int]:
  _snake_case					     =       {}

  _snake_case					     =       {
      '''token_embedder''': '''embeddings''',
      '''encoder_norm''': '''layernorm''',
      '''kernel''': '''weight''',
      '''.out''': '''.output''',
      '''scale''': '''weight''',
      '''embedders_0.pos_embedding''': '''row_embedder.weight''',
      '''embedders_1.pos_embedding''': '''column_embedder.weight''',
  }

  _snake_case					     =       {
      '''query''': '''attention.query''',
      '''key''': '''attention.key''',
      '''value''': '''attention.value''',
      '''output.dense''': '''output''',
      '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
      '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
      '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
      '''mlp.''': '''mlp.DenseReluDense.''',
      '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
      '''self_attention.o''': '''self_attention.attention.o''',
      '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
      '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
      '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
      '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
  }

  for key in flax_dict.keys():
    if "target" in key:
      # remove the first prefix from the key
      _snake_case					     =       '''.'''.join(key[1:]		)

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        _snake_case					     =       new_key.replace(__lowerCamelCase ,					__lowerCamelCase		)

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          _snake_case					     =       new_key.replace(__lowerCamelCase ,					__lowerCamelCase		)

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        _snake_case					     =       re.sub(R'''layers_(\d+)''' ,					R'''layer.\1''' ,					__lowerCamelCase		)
        _snake_case					     =       new_key.replace('''encoder''' ,					'''encoder.encoder'''		)

      elif "layers" in new_key and "decoder" in new_key:
        # use regex to replace the layer number
        _snake_case					     =       re.sub(R'''layers_(\d+)''' ,					R'''layer.\1''' ,					__lowerCamelCase		)

      _snake_case					     =       flax_dict[key]

  _snake_case					     =       {}
  # convert converted_dict into torch format
  for key in converted_dict.keys():
    if ("embed_tokens" not in key) and ("embedder" not in key):
      _snake_case					     =       torch.from_numpy(converted_dict[key].T		)
    else:
      _snake_case					     =       torch.from_numpy(converted_dict[key]		)

  return converted_torch_dict
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Any=False ,					__lowerCamelCase   :       Optional[int]=False		)     ->					int:
  _snake_case					     =       get_flax_param(__lowerCamelCase		)

  if not use_large:
    _snake_case					     =       PixaStructVisionConfig()
    _snake_case					     =       PixaStructTextConfig()
  else:
    _snake_case					     =       PixaStructVisionConfig(
        hidden_size=15_36 ,					d_ff=39_68 ,					num_attention_heads=24 ,					num_hidden_layers=18		)
    _snake_case					     =       PixaStructTextConfig(hidden_size=15_36 ,					d_ff=39_68 ,					num_heads=24 ,					num_layers=18		)
  _snake_case					     =       PixaStructConfig(
      vision_config=encoder_config.to_dict() ,					text_config=decoder_config.to_dict() ,					is_vqa=__lowerCamelCase		)

  _snake_case					     =       PixaStructForConditionalGeneration(__lowerCamelCase		)

  _snake_case					     =       rename_and_convert_flax_params(__lowerCamelCase		)
  model.load_state_dict(__lowerCamelCase		)

  _snake_case					     =       AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''		)
  _snake_case					     =       PixaStructImageProcessor()
  _snake_case					     =       PixaStructProcessor(image_processor=__lowerCamelCase ,					tokenizer=__lowerCamelCase		)

  if use_large:
    _snake_case					     =       40_96

  _snake_case					     =       True

  # mkdir if needed
  os.makedirs(__lowerCamelCase ,					exist_ok=__lowerCamelCase		)

  model.save_pretrained(__lowerCamelCase		)
  processor.save_pretrained(__lowerCamelCase		)

  print('''Model saved in {}'''.format(__lowerCamelCase		)		)


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser()
 parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--use_large', action='store_true', help='Use large model.')
 parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
 UpperCAmelCase__          = parser.parse_args()

 convert_pixastruct_original_pytorch_checkpoint_to_hf(
     args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
 )



