






"""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 _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	checkpoints.load_tax_checkpoint(_snake_case							)
      UpperCAmelCase          =	flatten_dict(_snake_case							)
      return flax_params

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	{}

      UpperCAmelCase          =	{
          """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""",
      }

      UpperCAmelCase          =	{
          """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
                  UpperCAmelCase          =	""".""".join(key[1:]							)

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

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

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

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

                  UpperCAmelCase          =	flax_dict[key]

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

      return converted_torch_dict

def _a			(		_snake_case				,	_snake_case				,	_snake_case=False				,	_snake_case=False							):







      """simple docstring"""






      UpperCAmelCase          =	get_flax_param(_snake_case							)

      if not use_large:
            UpperCAmelCase          =	PixaStructVisionConfig()
            UpperCAmelCase          =	PixaStructTextConfig()
      else:
            UpperCAmelCase          =	PixaStructVisionConfig(
                hidden_size=1536				,	d_ff=3968				,	num_attention_heads=24				,	num_hidden_layers=18							)
            UpperCAmelCase          =	PixaStructTextConfig(hidden_size=1536				,	d_ff=3968				,	num_heads=24				,	num_layers=18							)
      UpperCAmelCase          =	PixaStructConfig(
          vision_config=encoder_config.to_dict()				,	text_config=decoder_config.to_dict()				,	is_vqa=_snake_case							)

      UpperCAmelCase          =	PixaStructForConditionalGeneration(_snake_case							)

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

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

      if use_large:
            UpperCAmelCase          =	4096

      UpperCAmelCase          =	True

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

      model.save_pretrained(_snake_case							)
      processor.save_pretrained(_snake_case							)

      print("""Model saved in {}""".format(_snake_case							)							)


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
  )











"""simple docstring"""







import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_UpperCamelCase					      =  get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
  _UpperCamelCase					      =  json.load(f)







@require_torch
class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self     ,A ):
          return FSMTTokenizer.from_pretrained(A )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	FSMTForConditionalGeneration.from_pretrained(A ).to(A )
          if torch_device == "cuda":
                model.half()
          return model






    @parameterized.expand(
        [
            ["""en-ru""", 26.0],
            ["""ru-en""", 22.0],
            ["""en-de""", 22.0],
            ["""de-en""", 29.0],
        ] )
    @slow
    def     _UpperCamelCase							(			self     ,A     ,A ):
          # note: this test is not testing the best performance since it only evals a small batch
          # but it should be enough to detect a regression in the output quality
          UpperCAmelCase          =	F'''facebook/wmt19-{pair}'''
          UpperCAmelCase          =	self.get_tokenizer(A )
          UpperCAmelCase          =	self.get_model(A )

          UpperCAmelCase          =	bleu_data[pair]["""src"""]
          UpperCAmelCase          =	bleu_data[pair]["""tgt"""]

          UpperCAmelCase          =	tokenizer(A     ,return_tensors="""pt"""     ,truncation=A     ,padding="""longest""" ).to(A )
          UpperCAmelCase          =	model.generate(
              input_ids=batch.input_ids     ,num_beams=8     ,)
          UpperCAmelCase          =	tokenizer.batch_decode(
              A     ,skip_special_tokens=A     ,clean_up_tokenization_spaces=A )
          UpperCAmelCase          =	calculate_bleu(A     ,A )
          print(A )
          self.assertGreaterEqual(scores["""bleu"""]     ,A )



