






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()
__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						'Hello, World!'
__UpperCAmelCase								=						'en_XX'




def      lowercase__      (     __snake_case    :							str     ,       __snake_case    :							str     ,       __snake_case    :							bool  ):


					'''simple docstring'''


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

					UpperCAmelCase_    :      Dict            =						xmod.model.encoder.sentence_encoder
					UpperCAmelCase_    :      Dict            =						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:
										UpperCAmelCase_    :      Optional[Any]            =						xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

					print('Our X-MOD config:'     ,       __snake_case  )

					UpperCAmelCase_    :      int            =						XmodForSequenceClassification(__snake_case  ) if classification_head else XmodForMaskedLM(__snake_case  )
					model.eval()

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

					UpperCAmelCase_    :      List[str]            =						xmod_sent_encoder.layernorm_embedding.weight
					UpperCAmelCase_    :      Dict            =						xmod_sent_encoder.layernorm_embedding.bias

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

										# self attention
										UpperCAmelCase_    :      Dict            =						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_    :      Optional[int]            =						xmod_layer.self_attn.q_proj.weight
										UpperCAmelCase_    :      Optional[int]            =						xmod_layer.self_attn.q_proj.bias
										UpperCAmelCase_    :      Tuple            =						xmod_layer.self_attn.k_proj.weight
										UpperCAmelCase_    :      Optional[Any]            =						xmod_layer.self_attn.k_proj.bias
										UpperCAmelCase_    :      Union[str, Any]            =						xmod_layer.self_attn.v_proj.weight
										UpperCAmelCase_    :      List[str]            =						xmod_layer.self_attn.v_proj.bias

										# self-attention output
										UpperCAmelCase_    :      int            =						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_    :      List[str]            =						xmod_layer.self_attn.out_proj.weight
										UpperCAmelCase_    :      List[str]            =						xmod_layer.self_attn.out_proj.bias
										UpperCAmelCase_    :      Any            =						xmod_layer.self_attn_layer_norm.weight
										UpperCAmelCase_    :      Dict            =						xmod_layer.self_attn_layer_norm.bias

										# intermediate
										UpperCAmelCase_    :      Optional[Any]            =						layer.intermediate
										if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
															raise AssertionError('Dimensions of intermediate weights do not match.'  )
										UpperCAmelCase_    :      str            =						xmod_layer.fca.weight
										UpperCAmelCase_    :      str            =						xmod_layer.fca.bias

										# output
										UpperCAmelCase_    :      Tuple            =						layer.output
										if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
															raise AssertionError('Dimensions of feed-forward weights do not match.'  )
										UpperCAmelCase_    :      Dict            =						xmod_layer.fca.weight
										UpperCAmelCase_    :      Union[str, Any]            =						xmod_layer.fca.bias
										UpperCAmelCase_    :      List[Any]            =						xmod_layer.final_layer_norm.weight
										UpperCAmelCase_    :      List[Any]            =						xmod_layer.final_layer_norm.bias
										if bert_output.adapter_layer_norm is not None:
															UpperCAmelCase_    :      Tuple            =						xmod_layer.adapter_layer_norm.weight
															UpperCAmelCase_    :      List[Any]            =						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_    :      Optional[int]            =						bert_output.adapter_modules[lang_code]
															UpperCAmelCase_    :      Any            =						xmod_layer.adapter_modules[lang_code]
															UpperCAmelCase_    :      Any            =						from_adapter.fca.weight
															UpperCAmelCase_    :      str            =						from_adapter.fca.bias
															UpperCAmelCase_    :      List[Any]            =						from_adapter.fca.weight
															UpperCAmelCase_    :      List[str]            =						from_adapter.fca.bias

										# end of layer

					if xmod_sent_encoder.layer_norm is not None:
										UpperCAmelCase_    :      str            =						xmod_sent_encoder.layer_norm.weight
										UpperCAmelCase_    :      List[Any]            =						xmod_sent_encoder.layer_norm.bias

					if classification_head:
										UpperCAmelCase_    :      List[Any]            =						xmod.model.classification_heads['mnli'].dense.weight
										UpperCAmelCase_    :      str            =						xmod.model.classification_heads['mnli'].dense.bias
										UpperCAmelCase_    :      int            =						xmod.model.classification_heads['mnli'].out_proj.weight
										UpperCAmelCase_    :      str            =						xmod.model.classification_heads['mnli'].out_proj.bias
					else:
										# LM Head
										UpperCAmelCase_    :      List[Any]            =						xmod.model.encoder.lm_head.dense.weight
										UpperCAmelCase_    :      Optional[Any]            =						xmod.model.encoder.lm_head.dense.bias
										UpperCAmelCase_    :      List[str]            =						xmod.model.encoder.lm_head.layer_norm.weight
										UpperCAmelCase_    :      Any            =						xmod.model.encoder.lm_head.layer_norm.bias
										UpperCAmelCase_    :      Optional[Any]            =						xmod.model.encoder.lm_head.weight
										UpperCAmelCase_    :      str            =						xmod.model.encoder.lm_head.bias

					# Let's check that we get the same results.
					UpperCAmelCase_    :      List[Any]            =						xmod.encode(__snake_case  ).unsqueeze(0  )  # batch of size 1
					model.roberta.set_default_language(__snake_case  )

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

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


if __name__ == "__main__":
		__UpperCAmelCase								=						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.'
		)
		__UpperCAmelCase								=						parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)














from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a		:       Tuple			     = logging.get_logger(__name__)

a		:       Dict			     = {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : int							       =	't5'
      a  : Dict							       =	['past_key_values']
      a  : Tuple							       =	{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
      def __init__(      self					:			str		,      __lowercase					:			Optional[int]=32128		,      __lowercase					:			Optional[int]=512		,      __lowercase					:			int=64		,      __lowercase					:			Any=2048		,      __lowercase					:			Tuple=6		,      __lowercase					:			Tuple=None		,      __lowercase					:			int=8		,      __lowercase					:			List[Any]=32		,      __lowercase					:			Dict=128		,      __lowercase					:			Optional[int]=0.1		,      __lowercase					:			int=1e-6		,      __lowercase					:			List[str]=1.0		,      __lowercase					:			List[str]="relu"		,      __lowercase					:			Dict=True		,      __lowercase					:			Optional[Any]=True		,      __lowercase					:			Tuple=0		,      __lowercase					:			List[str]=1		,      **__lowercase					:			Any		,      )							->      str:
        __UpperCAmelCase							:	Tuple           =  vocab_size
        __UpperCAmelCase							:	Optional[int]           =  d_model
        __UpperCAmelCase							:	Union[str, Any]           =  d_kv
        __UpperCAmelCase							:	Union[str, Any]           =  d_ff
        __UpperCAmelCase							:	int           =  num_layers
        __UpperCAmelCase							:	Any           =  (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        __UpperCAmelCase							:	Dict           =  num_heads
        __UpperCAmelCase							:	List[Any]           =  relative_attention_num_buckets
        __UpperCAmelCase							:	List[str]           =  relative_attention_max_distance
        __UpperCAmelCase							:	Union[str, Any]           =  dropout_rate
        __UpperCAmelCase							:	List[str]           =  layer_norm_epsilon
        __UpperCAmelCase							:	str           =  initializer_factor
        __UpperCAmelCase							:	Dict           =  feed_forward_proj
        __UpperCAmelCase							:	Optional[int]           =  use_cache

        __UpperCAmelCase							:	List[Any]           =  self.feed_forward_proj.split("""-"""					)
        __UpperCAmelCase							:	Tuple           =  act_info[-1]
        __UpperCAmelCase							:	int           =  act_info[0] == """gated"""

        if len(__lowercase					) > 1 and act_info[0] != "gated" or len(__lowercase					) > 2:
          raise ValueError(
              f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
              """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
              """'gated-gelu' or 'relu'"""					)

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
          __UpperCAmelCase							:	Dict           =  """gelu_new"""

        super().__init__(
            pad_token_id=__lowercase		,      eos_token_id=__lowercase		,      is_encoder_decoder=__lowercase		,      **__lowercase		,      )


class     a    ( lowercase__   ):



      """simple docstring"""


      @property
      def 	UpperCAmelCase      (      self					:			str					)							->      Mapping[str, Mapping[int, str]]:
        __UpperCAmelCase							:	Union[str, Any]           =  {
            """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
            """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
        }
        if self.use_past:
          __UpperCAmelCase							:	List[Any]           =  """past_encoder_sequence + sequence"""
          __UpperCAmelCase							:	Optional[int]           =  {0: """batch"""}
          __UpperCAmelCase							:	Union[str, Any]           =  {0: """batch""", 1: """past_decoder_sequence + sequence"""}
        else:
          __UpperCAmelCase							:	str           =  {0: """batch""", 1: """decoder_sequence"""}
          __UpperCAmelCase							:	str           =  {0: """batch""", 1: """decoder_sequence"""}

        if self.use_past:
          self.fill_with_past_key_values_(__lowercase		,      direction="""inputs"""					)

        return common_inputs




      @property
      def 	UpperCAmelCase      (      self					:			int					)							->      int:
        return 13


