

"""simple docstring"""



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()
SCREAMING_SNAKE_CASE_					:	Dict							  =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						'Hello, World!'
SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						'en_XX'





def  _snake_case     (    UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					bool			):
     A__										=  Path("""data_bin"""			)
     A__										=  FairseqXmodModel.from_pretrained(
         model_name_or_path=str(Path(UpperCAmelCase_			).parent			)					,   checkpoint_file=Path(UpperCAmelCase_			).name					,   _name="""xmod_base"""					,   arch="""xmod_base"""					,   task="""multilingual_masked_lm"""					,   data_name_or_path=str(UpperCAmelCase_			)					,   bpe="""sentencepiece"""					,   sentencepiece_model=str(Path(UpperCAmelCase_			).parent / """sentencepiece.bpe.model"""			)					,   src_dict=str(data_dir / """dict.txt"""			)					,   )
     xmod.eval()  # disable dropout
     print(UpperCAmelCase_			)

     A__										=  xmod.model.encoder.sentence_encoder
     A__										=  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=514					,   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:
          A__										=  xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

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

     A__										=  XmodForSequenceClassification(UpperCAmelCase_			) if classification_head else XmodForMaskedLM(UpperCAmelCase_			)
     model.eval()

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

     A__										=  xmod_sent_encoder.layernorm_embedding.weight
     A__										=  xmod_sent_encoder.layernorm_embedding.bias

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

          # self attention
          A__										=  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."""			)

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

          # self-attention output
          A__										=  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."""			)
          A__										=  xmod_layer.self_attn.out_proj.weight
          A__										=  xmod_layer.self_attn.out_proj.bias
          A__										=  xmod_layer.self_attn_layer_norm.weight
          A__										=  xmod_layer.self_attn_layer_norm.bias

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

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

          # end of layer

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

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

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

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

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						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.'
 )
 SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						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 import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
 import torch



@is_pipeline_test
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    UpperCAmelCase						 =					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    UpperCAmelCase						 =					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: str    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: Any       ):






         """simple docstring"""


         A__										=  TextaTextGenerationPipeline(model=UpperCamelCase    ,						tokenizer=UpperCamelCase       )
         return generator, ["Something to write", "Something else"]

    def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: Any    ,						UpperCamelCase: Dict       ):






         """simple docstring"""


         A__										=  generator("""Something there"""       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": ANY(UpperCamelCase       )}]       )
         # These are encoder decoder, they don't just append to incoming string
         self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""       )       )

         A__										=  generator(["""This is great !""", """Something else"""]    ,						num_return_sequences=2    ,						do_sample=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
             ]    ,						)

         A__										=  generator(
             ["""This is great !""", """Something else"""]    ,						num_return_sequences=2    ,						batch_size=2    ,						do_sample=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
             ]    ,						)

         with self.assertRaises(UpperCamelCase       ):
              generator(4       )

    @require_torch
    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  pipeline("""text2text-generation"""    ,						model="""patrickvonplaten/t5-tiny-random"""    ,						framework="""pt"""       )
         # do_sample=False necessary for reproducibility
         A__										=  generator("""Something there"""    ,						do_sample=UpperCamelCase       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": """"""}]       )

         A__										=  3
         A__										=  generator(
             """Something there"""    ,						num_return_sequences=UpperCamelCase    ,						num_beams=UpperCamelCase    ,						)
         A__										=  [
             {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
             {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
             {"""generated_text""": """"""},
         ]
         self.assertEqual(UpperCamelCase    ,						UpperCamelCase       )

         A__										=  generator("""This is a test"""    ,						do_sample=UpperCamelCase    ,						num_return_sequences=2    ,						return_tensors=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 {"""generated_token_ids""": ANY(torch.Tensor       )},
                 {"""generated_token_ids""": ANY(torch.Tensor       )},
             ]    ,						)
         A__										=  generator.model.config.eos_token_id
         A__										=  """<pad>"""
         A__										=  generator(
             ["""This is a test""", """This is a second test"""]    ,						do_sample=UpperCamelCase    ,						num_return_sequences=2    ,						batch_size=2    ,						return_tensors=UpperCamelCase    ,						)
         self.assertEqual(
             UpperCamelCase    ,						[
                 [
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                 ],
                 [
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                 ],
             ]    ,						)





    @require_tf
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  pipeline("""text2text-generation"""    ,						model="""patrickvonplaten/t5-tiny-random"""    ,						framework="""tf"""       )
         # do_sample=False necessary for reproducibility
         A__										=  generator("""Something there"""    ,						do_sample=UpperCamelCase       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": """"""}]       )






