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  :     int       =  logging.get_logger(__name__)

__a  :     Any       =  """Hello, World!"""
__a  :     Tuple       =  """en_XX"""





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



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

				__lowercase 			=							xmod.model.encoder.sentence_encoder
				__lowercase 			=							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:
								__lowercase 			=							xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

				__lowercase 			=							XmodForSequenceClassification(lowercase							) if classification_head else XmodForMaskedLM(lowercase							)
				model.eval()

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

				__lowercase 			=							xmod_sent_encoder.layernorm_embedding.weight
				__lowercase 			=							xmod_sent_encoder.layernorm_embedding.bias

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

								# self attention
								__lowercase 			=							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.'''							)

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

								# self-attention output
								__lowercase 			=							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.'''							)
								__lowercase 			=							xmod_layer.self_attn.out_proj.weight
								__lowercase 			=							xmod_layer.self_attn.out_proj.bias
								__lowercase 			=							xmod_layer.self_attn_layer_norm.weight
								__lowercase 			=							xmod_layer.self_attn_layer_norm.bias

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

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

								# end of layer

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

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

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

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

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


if __name__ == "__main__":
		__a  :     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."""
		)
		__a  :     Optional[int]       =  parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a  :     Any       =  {
    """configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     Optional[int]       =  ["""BloomTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     str       =  [
		    """BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """BloomForCausalLM""",
		    """BloomModel""",
		    """BloomPreTrainedModel""",
		    """BloomForSequenceClassification""",
		    """BloomForTokenClassification""",
		    """BloomForQuestionAnswering""",
		]

if TYPE_CHECKING:
		from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bloom_fast import BloomTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bloom import (
				    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BloomForCausalLM,
				    BloomForQuestionAnswering,
				    BloomForSequenceClassification,
				    BloomForTokenClassification,
				    BloomModel,
				    BloomPreTrainedModel,
				)

else:
		import sys

		__a  :     Dict       =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)