


import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
 raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
 raise Exception("""requires fairseq < v2""")

logging.set_verbosity_info()
__a     =			logging.get_logger(__name__)

__a     =			"""Hello, World!"""
__a     =			"""en_XX"""
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Optional[int]:
   UpperCAmelCase							=     Path("""data_bin"""   )
   UpperCAmelCase							=     FairseqXmodModel.from_pretrained(
       model_name_or_path=str(Path(lowerCAmelCase_   ).parent   )  ,						checkpoint_file=Path(lowerCAmelCase_   ).name  ,						_name="""xmod_base"""  ,						arch="""xmod_base"""  ,						task="""multilingual_masked_lm"""  ,						data_name_or_path=str(lowerCAmelCase_   )  ,						bpe="""sentencepiece"""  ,						sentencepiece_model=str(Path(lowerCAmelCase_   ).parent / """sentencepiece.bpe.model"""   )  ,						src_dict=str(data_dir / """dict.txt"""   )  ,						)
   xmod.eval()  # disable dropout
   print(lowerCAmelCase_   )

   UpperCAmelCase							=     xmod.model.encoder.sentence_encoder
   UpperCAmelCase							=     XmodConfig(
       vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings  ,						hidden_size=xmod.cfg.model.encoder_embed_dim  ,						num_hidden_layers=xmod.cfg.model.encoder_layers  ,						num_attention_heads=xmod.cfg.model.encoder_attention_heads  ,						intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim  ,						max_position_embeddings=5_1_4  ,						type_vocab_size=1  ,						layer_norm_eps=1e-5  ,						pre_norm=xmod.cfg.model.encoder_normalize_before  ,						adapter_reduction_factor=getattr(xmod.cfg.model  ,						"""bottleneck"""  ,						2   )  ,						adapter_layer_norm=xmod.cfg.model.adapter_layer_norm  ,						adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm  ,						ln_before_adapter=xmod.cfg.model.ln_before_adapter  ,						languages=xmod.cfg.model.languages  ,						)
   if classification_head:
      UpperCAmelCase							=     xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

   print("""Our X-MOD config:"""  ,						lowerCAmelCase_   )

   UpperCAmelCase							=     XmodForSequenceClassification(lowerCAmelCase_   ) if classification_head else XmodForMaskedLM(lowerCAmelCase_   )
   model.eval()

   # Now let's copy all the weights.
   # Embeddings
   UpperCAmelCase							=     xmod_sent_encoder.embed_tokens.weight
   UpperCAmelCase							=     xmod_sent_encoder.embed_positions.weight
   UpperCAmelCase							=     torch.zeros_like(
       model.roberta.embeddings.token_type_embeddings.weight   )  # just zero them out b/c xmod doesn't use them.

   UpperCAmelCase							=     xmod_sent_encoder.layernorm_embedding.weight
   UpperCAmelCase							=     xmod_sent_encoder.layernorm_embedding.bias

   for i in range(config.num_hidden_layers   ):
      # Encoder: start of layer
      UpperCAmelCase							=     model.roberta.encoder.layer[i]
      UpperCAmelCase							=     xmod_sent_encoder.layers[i]

      # self attention
      UpperCAmelCase							=     layer.attention.self
      if not (
          xmod_layer.self_attn.k_proj.weight.data.shape
          == xmod_layer.self_attn.q_proj.weight.data.shape
          == xmod_layer.self_attn.v_proj.weight.data.shape
          == torch.Size((config.hidden_size, config.hidden_size)   )
      ):
         raise AssertionError("""Dimensions of self-attention weights do not match."""   )

      UpperCAmelCase							=     xmod_layer.self_attn.q_proj.weight
      UpperCAmelCase							=     xmod_layer.self_attn.q_proj.bias
      UpperCAmelCase							=     xmod_layer.self_attn.k_proj.weight
      UpperCAmelCase							=     xmod_layer.self_attn.k_proj.bias
      UpperCAmelCase							=     xmod_layer.self_attn.v_proj.weight
      UpperCAmelCase							=     xmod_layer.self_attn.v_proj.bias

      # self-attention output
      UpperCAmelCase							=     layer.attention.output
      if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
         raise AssertionError("""Dimensions of self-attention output weights do not match."""   )
      UpperCAmelCase							=     xmod_layer.self_attn.out_proj.weight
      UpperCAmelCase							=     xmod_layer.self_attn.out_proj.bias
      UpperCAmelCase							=     xmod_layer.self_attn_layer_norm.weight
      UpperCAmelCase							=     xmod_layer.self_attn_layer_norm.bias

      # intermediate
      UpperCAmelCase							=     layer.intermediate
      if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
         raise AssertionError("""Dimensions of intermediate weights do not match."""   )
      UpperCAmelCase							=     xmod_layer.fca.weight
      UpperCAmelCase							=     xmod_layer.fca.bias

      # output
      UpperCAmelCase							=     layer.output
      if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
         raise AssertionError("""Dimensions of feed-forward weights do not match."""   )
      UpperCAmelCase							=     xmod_layer.fca.weight
      UpperCAmelCase							=     xmod_layer.fca.bias
      UpperCAmelCase							=     xmod_layer.final_layer_norm.weight
      UpperCAmelCase							=     xmod_layer.final_layer_norm.bias
      if bert_output.adapter_layer_norm is not None:
         UpperCAmelCase							=     xmod_layer.adapter_layer_norm.weight
         UpperCAmelCase							=     xmod_layer.adapter_layer_norm.bias

      if sorted(bert_output.adapter_modules.keys()   ) != sorted(xmod_layer.adapter_modules.keys()   ):
         raise AssertionError("""Lists of language adapters do not match."""   )
      for lang_code, adapter in xmod_layer.adapter_modules.items():
         UpperCAmelCase							=     bert_output.adapter_modules[lang_code]
         UpperCAmelCase							=     xmod_layer.adapter_modules[lang_code]
         UpperCAmelCase							=     from_adapter.fca.weight
         UpperCAmelCase							=     from_adapter.fca.bias
         UpperCAmelCase							=     from_adapter.fca.weight
         UpperCAmelCase							=     from_adapter.fca.bias

      # end of layer

   if xmod_sent_encoder.layer_norm is not None:
      UpperCAmelCase							=     xmod_sent_encoder.layer_norm.weight
      UpperCAmelCase							=     xmod_sent_encoder.layer_norm.bias

   if classification_head:
      UpperCAmelCase							=     xmod.model.classification_heads["""mnli"""].dense.weight
      UpperCAmelCase							=     xmod.model.classification_heads["""mnli"""].dense.bias
      UpperCAmelCase							=     xmod.model.classification_heads["""mnli"""].out_proj.weight
      UpperCAmelCase							=     xmod.model.classification_heads["""mnli"""].out_proj.bias
   else:
      # LM Head
      UpperCAmelCase							=     xmod.model.encoder.lm_head.dense.weight
      UpperCAmelCase							=     xmod.model.encoder.lm_head.dense.bias
      UpperCAmelCase							=     xmod.model.encoder.lm_head.layer_norm.weight
      UpperCAmelCase							=     xmod.model.encoder.lm_head.layer_norm.bias
      UpperCAmelCase							=     xmod.model.encoder.lm_head.weight
      UpperCAmelCase							=     xmod.model.encoder.lm_head.bias

   # Let's check that we get the same results.
   UpperCAmelCase							=     xmod.encode(lowerCAmelCase_   ).unsqueeze(0   )  # batch of size 1
   model.roberta.set_default_language(lowerCAmelCase_   )

   UpperCAmelCase							=     model(lowerCAmelCase_   )[0]
   if classification_head:
      UpperCAmelCase							=     xmod.model.classification_heads["""mnli"""](xmod.extract_features(lowerCAmelCase_   )   )
   else:
      UpperCAmelCase							=     xmod.model(lowerCAmelCase_  ,						lang_id=[SAMPLE_LANGUAGE]   )[0]
   print(our_output.shape  ,						their_output.shape   )
   UpperCAmelCase							=     torch.max(torch.abs(our_output - their_output   )   ).item()
   print(F"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
   UpperCAmelCase							=     torch.allclose(lowerCAmelCase_  ,						lowerCAmelCase_  ,						atol=1e-3   )
   print("""Do both models output the same tensors?"""  ,						"""🔥""" if success else """💩"""   )
   if not success:
      raise Exception("""Something went wRoNg"""   )

   Path(lowerCAmelCase_   ).mkdir(parents=lowerCAmelCase_  ,						exist_ok=lowerCAmelCase_   )
   print(F"""Saving model to {pytorch_dump_folder_path}"""   )
   model.save_pretrained(lowerCAmelCase_   )


if __name__ == "__main__":
 __a     =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 parser.add_argument(
     """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
 )
 __a     =			parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
 import torch


if is_vision_available():
 from PIL import Image

 from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class   __lowercase				(  unittest.TestCase  ):
      UpperCamelCase								=       ViTImageProcessor if is_vision_available() else None





      @property
      def        _lowercase				(		self						:					Tuple       )    ->   int:






         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()





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






         """simple docstring"""







         UpperCAmelCase							=     (3, 3_2, 1_2_8)
         UpperCAmelCase							=     tempfile.mkdtemp()

         # fmt: off
         UpperCAmelCase							=     ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
         # fmt: on
         UpperCAmelCase							=     dict(zip(__lowerCamelCase					,       range(len(__lowerCamelCase       )       )       )       )

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

         UpperCAmelCase							=     {
             """do_normalize""": False,
             """do_resize""": True,
             """image_processor_type""": """ViTImageProcessor""",
             """resample""": 3,
             """size""": {"""height""": 3_2, """width""": 1_2_8},
         }
         UpperCAmelCase							=     os.path.join(self.tmpdirname					,       __lowerCamelCase       )
         with open(self.image_processor_file					,       """w"""					,       encoding="""utf-8"""       ) as fp:
            json.dump(__lowerCamelCase					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]					,       **__lowerCamelCase						:					Union[str, Any]       )    ->   int:






         """simple docstring"""







         return MgpstrTokenizer.from_pretrained(self.tmpdirname					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					int					,       **__lowerCamelCase						:					Optional[int]       )    ->   Optional[int]:






         """simple docstring"""







         return ViTImageProcessor.from_pretrained(self.tmpdirname					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Dict:






         """simple docstring"""







         shutil.rmtree(self.tmpdirname       )





      def        _lowercase				(		self						:					Any       )    ->   str:






         """simple docstring"""







         UpperCAmelCase							=     np.random.randint(2_5_5					,       size=(3, 3_0, 4_0_0)					,       dtype=np.uinta       )

         UpperCAmelCase							=     Image.fromarray(np.moveaxis(__lowerCamelCase					,       0					,       -1       )       )

         return image_input





      def        _lowercase				(		self						:					Tuple       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.get_tokenizer()
         UpperCAmelCase							=     self.get_image_processor()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )
         processor.save_pretrained(self.tmpdirname       )
         UpperCAmelCase							=     MgpstrProcessor.from_pretrained(self.tmpdirname					,       use_fast=__lowerCamelCase       )

         self.assertEqual(processor.char_tokenizer.get_vocab()					,       tokenizer.get_vocab()       )
         self.assertIsInstance(processor.char_tokenizer					,       __lowerCamelCase       )

         self.assertEqual(processor.image_processor.to_json_string()					,       image_processor.to_json_string()       )
         self.assertIsInstance(processor.image_processor					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					int       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.get_tokenizer()
         UpperCAmelCase							=     self.get_image_processor()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )
         processor.save_pretrained(self.tmpdirname       )

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

         UpperCAmelCase							=     MgpstrProcessor.from_pretrained(
             self.tmpdirname					,       bos_token="""(BOS)"""					,       eos_token="""(EOS)"""					,       do_normalize=__lowerCamelCase					,       padding_value=1.0       )

         self.assertEqual(processor.char_tokenizer.get_vocab()					,       tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.char_tokenizer					,       __lowerCamelCase       )

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





      def        _lowercase				(		self						:					int       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     image_processor(__lowerCamelCase					,       return_tensors="""np"""       )
         UpperCAmelCase							=     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        _lowercase				(		self						:					str       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     """test"""

         UpperCAmelCase							=     processor(text=__lowerCamelCase       )

         UpperCAmelCase							=     tokenizer(__lowerCamelCase       )
         for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]					,       encoded_processor[key]       )





      def        _lowercase				(		self						:					Optional[int]       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     """test"""
         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     processor(text=__lowerCamelCase					,       images=__lowerCamelCase       )

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

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





      def        _lowercase				(		self						:					str       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

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

         UpperCAmelCase							=     processor.char_decode(__lowerCamelCase       )
         UpperCAmelCase							=     tokenizer.batch_decode(__lowerCamelCase       )
         UpperCAmelCase							=     [seq.replace(""" """					,       """"""       ) for seq in decoded_tok]

         self.assertListEqual(__lowerCamelCase					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     None
         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     processor(text=__lowerCamelCase					,       images=__lowerCamelCase       )

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





      def        _lowercase				(		self						:					Dict       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     torch.randn(1					,       2_7					,       3_8       )
         UpperCAmelCase							=     torch.randn(1					,       2_7					,       5_0_2_5_7       )
         UpperCAmelCase							=     torch.randn(1					,       2_7					,       3_0_5_2_2       )

         UpperCAmelCase							=     processor.batch_decode([char_input, bpe_input, wp_input]       )

         self.assertListEqual(list(results.keys()       )					,       ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""]       )



