


'''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()
a_						:       Any      			=				logging.get_logger(__name__)

a_						:       List[str]      			=				"""Hello, World!"""
a_						:       List[Any]      			=				"""en_XX"""





def __snake_case      (   UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		bool	):
	lowerCamelCase_  				=						Path("data_bin"	)
	lowerCamelCase_  				=						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__	)

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

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

	lowerCamelCase_  				=						XmodForSequenceClassification(snake_case__	) if classification_head else XmodForMaskedLM(snake_case__	)
	model.eval()

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

	lowerCamelCase_  				=						xmod_sent_encoder.layernorm_embedding.weight
	lowerCamelCase_  				=						xmod_sent_encoder.layernorm_embedding.bias

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

		# self attention
		lowerCamelCase_  				=						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."	)

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

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

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

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

		# end of layer

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

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

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

	lowerCamelCase_  				=						model(snake_case__	)[0]
	if classification_head:
		lowerCamelCase_  				=						xmod.model.classification_heads["mnli"](xmod.extract_features(snake_case__	)	)
	else:
		lowerCamelCase_  				=						xmod.model(snake_case__      ,       lang_id=[SAMPLE_LANGUAGE]	)[0]
	print(our_output.shape      ,       their_output.shape	)
	lowerCamelCase_  				=						torch.max(torch.abs(our_output - their_output	)	).item()
	print(F'''max_absolute_diff = {max_absolute_diff}'''	)  # ~ 1e-7
	lowerCamelCase_  				=						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__":
					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_						:       Union[str, Any]      			=				parser.parse_args()
					convert_xmod_checkpoint_to_pytorch(
					    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_						:       List[Any]      			=				{
    """configuration_owlvit""": [
        """OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """OwlViTConfig""",
        """OwlViTOnnxConfig""",
        """OwlViTTextConfig""",
        """OwlViTVisionConfig""",
    ],
    """processing_owlvit""": ["""OwlViTProcessor"""],
}


try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:       int      			=				["""OwlViTFeatureExtractor"""]
     a_						:       Any      			=				["""OwlViTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:       Tuple      			=				[
         """OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """OwlViTModel""",
         """OwlViTPreTrainedModel""",
         """OwlViTTextModel""",
         """OwlViTVisionModel""",
         """OwlViTForObjectDetection""",
     ]

if TYPE_CHECKING:
     from .configuration_owlvit import (
         OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         OwlViTConfig,
         OwlViTOnnxConfig,
         OwlViTTextConfig,
         OwlViTVisionConfig,
     )
     from .processing_owlvit import OwlViTProcessor

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_owlvit import OwlViTFeatureExtractor
          from .image_processing_owlvit import OwlViTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_owlvit import (
              OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              OwlViTForObjectDetection,
              OwlViTModel,
              OwlViTPreTrainedModel,
              OwlViTTextModel,
              OwlViTVisionModel,
          )

else:
     import sys

     a_						:       Tuple      			=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



