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    __lowerCAmelCase   (    A_ :			int    ,						A_ :			Tuple    ,						A_ :			int       )     ->	List[str]:
  __UpperCAmelCase         =							Path("data_bin"       )
  __UpperCAmelCase         =							FairseqXmodModel.from_pretrained(
      model_name_or_path=str(Path(a__       ).parent       )    ,						checkpoint_file=Path(a__       ).name    ,						_name="xmod_base"    ,						arch="xmod_base"    ,						task="multilingual_masked_lm"    ,						data_name_or_path=str(a__       )    ,						bpe="sentencepiece"    ,						sentencepiece_model=str(Path(a__       ).parent / "sentencepiece.bpe.model"       )    ,						src_dict=str(data_dir / "dict.txt"       )    ,						)
  xmod.eval()  # disable dropout
  print(a__       )

  __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_14    ,						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:"    ,						a__       )

  __UpperCAmelCase         =							XmodForSequenceClassification(a__       ) if classification_head else XmodForMaskedLM(a__       )
  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(a__       ).unsqueeze(0       )  # batch of size 1
  model.roberta.set_default_language(a__       )

  __UpperCAmelCase         =							model(a__       )[0]
  if classification_head:
    __UpperCAmelCase         =							xmod.model.classification_heads["mnli"](xmod.extract_features(a__       )       )
  else:
    __UpperCAmelCase         =							xmod.model(a__    ,						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(a__    ,						a__    ,						atol=1e-3       )
  print("Do both models output the same tensors?"    ,						"🔥" if success else "💩"       )
  if not success:
    raise Exception("Something went wRoNg"       )

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


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
       )






'''simple docstring'''



import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase					:			int       =	get_tests_dir('fixtures/test_sentencepiece.model')







@require_sentencepiece
class 		lowerCAmelCase__  ( a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = XLMProphetNetTokenizer
 lowerCAmelCase__       = False
 lowerCAmelCase__       = True
 def  UpperCAmelCase__						(						self   :			str     )    ->					Optional[int]:


      """simple docstring"""




      super().setUp()

      # We have a SentencePiece fixture for testing
      __SCREAMING_SNAKE_CASE      = XLMProphetNetTokenizer(__SCREAMING_SNAKE_CASE   ,				keep_accents=__SCREAMING_SNAKE_CASE     )
      tokenizer.save_pretrained(self.tmpdirname     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """[PAD]"""
      __SCREAMING_SNAKE_CASE      = 0

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE     )   ,				__SCREAMING_SNAKE_CASE     )
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE     )   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = list(self.get_tokenizer().get_vocab().keys()     )

      self.assertEqual(vocab_keys[0]   ,				"""[PAD]"""     )
      self.assertEqual(vocab_keys[1]   ,				"""[CLS]"""     )
      self.assertEqual(vocab_keys[-1]   ,				"""j"""     )
      self.assertEqual(len(__SCREAMING_SNAKE_CASE     )   ,				1_012     )
 def  UpperCAmelCase__						(						self   :			Any     )    ->					List[str]:


      """simple docstring"""




      self.assertEqual(self.get_tokenizer().vocab_size   ,				1_012     )
 def  UpperCAmelCase__						(						self   :			int     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = XLMProphetNetTokenizer(__SCREAMING_SNAKE_CASE   ,				keep_accents=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = tokenizer.tokenize("""This is a test"""     )
      self.assertListEqual(__SCREAMING_SNAKE_CASE   ,				["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]     )

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE     )   ,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]   ,				)

      __SCREAMING_SNAKE_CASE      = tokenizer.tokenize("""I was born in 92000, and this is falsé."""     )
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE   ,				[
              SPIECE_UNDERLINE + """I""",
              SPIECE_UNDERLINE + """was""",
              SPIECE_UNDERLINE + """b""",
              """or""",
              """n""",
              SPIECE_UNDERLINE + """in""",
              SPIECE_UNDERLINE + """""",
              """9""",
              """2""",
              """0""",
              """0""",
              """0""",
              """,""",
              SPIECE_UNDERLINE + """and""",
              SPIECE_UNDERLINE + """this""",
              SPIECE_UNDERLINE + """is""",
              SPIECE_UNDERLINE + """f""",
              """al""",
              """s""",
              """é""",
              """.""",
          ]   ,				)
      __SCREAMING_SNAKE_CASE      = tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE     )
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE   ,				[
              value + tokenizer.fairseq_offset
              for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
          ]   ,				)

      __SCREAMING_SNAKE_CASE      = tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE     )
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE   ,				[
              SPIECE_UNDERLINE + """I""",
              SPIECE_UNDERLINE + """was""",
              SPIECE_UNDERLINE + """b""",
              """or""",
              """n""",
              SPIECE_UNDERLINE + """in""",
              SPIECE_UNDERLINE + """""",
              """[UNK]""",
              """2""",
              """0""",
              """0""",
              """0""",
              """,""",
              SPIECE_UNDERLINE + """and""",
              SPIECE_UNDERLINE + """this""",
              SPIECE_UNDERLINE + """is""",
              SPIECE_UNDERLINE + """f""",
              """al""",
              """s""",
              """[UNK]""",
              """.""",
          ]   ,				)
 @cached_property
 def  UpperCAmelCase__						(						self   :			str     )    ->					str:


      """simple docstring"""




      return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""     )
 @slow
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """Hello World!"""
      __SCREAMING_SNAKE_CASE      = [35_389, 6_672, 49, 2]
      self.assertListEqual(__SCREAMING_SNAKE_CASE   ,				self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE     )     )


 @slow
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = {"""input_ids""": [[11_073, 82_783, 18, 26, 82_783, 549, 51_540, 248, 17_209, 1_301, 217, 20, 215_186, 1_325, 147, 17_209, 1_301, 217, 20, 56_370, 53, 122_020, 20, 16_477, 27, 87_355, 4_548, 20, 4_728, 78_392, 17, 159_969, 18, 26, 24_491, 629, 15, 538, 22_704, 5_439, 15, 2_788, 24_491, 9_885, 15, 43_534, 605, 15, 814, 18_403, 33_200, 29, 15, 43_534, 24_458, 12_410, 111, 24_966, 83_669, 9_637, 144_068, 26, 850, 22_346, 27, 147, 24_966, 83_669, 83_490, 26, 39_113, 735, 27, 689, 656, 2_800, 1_339, 4_600, 53, 122_020, 115_785, 34, 816, 1_339, 46_887, 18, 147, 53_905, 1_951, 42_238, 41_170, 17_732, 834, 436, 15, 27_523, 98_733, 217, 147, 5_542, 4_981, 930, 17_347, 16, 2], [20_091, 629, 94, 82_786, 58, 490, 20, 1_528, 84, 53_905, 344, 80_592, 110_128, 18_822, 5_267, 1_306, 62, 152_537, 308, 7_997, 401, 124_427, 549, 35_442, 225, 109, 15_055, 25_748, 147, 7_119, 43_712, 34, 767, 135_366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63_784, 119_466, 17, 147_808, 88_214, 18, 656, 81, 32, 3_296, 10_280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=__SCREAMING_SNAKE_CASE   ,				model_name="""microsoft/xprophetnet-large-wiki100-cased"""   ,				revision="""1acad1643ddd54a44df6a1b797ada8373685d90e"""   ,				)






