





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()
snake_case_           =      logging.get_logger(__name__)

snake_case_           =      'Hello, World!'
snake_case_           =      'en_XX'






def lowerCamelCase__   (							snake_case_     :  str	,				snake_case_     :  str	,				snake_case_     :  bool	)				->    Tuple:
			__snake_case								=       Path('''data_bin'''	)
			__snake_case								=       FairseqXmodModel.from_pretrained(
			    model_name_or_path=str(Path(lowerCAmelCase__	).parent	)	,				checkpoint_file=Path(lowerCAmelCase__	).name	,				_name='''xmod_base'''	,				arch='''xmod_base'''	,				task='''multilingual_masked_lm'''	,				data_name_or_path=str(lowerCAmelCase__	)	,				bpe='''sentencepiece'''	,				sentencepiece_model=str(Path(lowerCAmelCase__	).parent / '''sentencepiece.bpe.model'''	)	,				src_dict=str(data_dir / '''dict.txt'''	)	,				)
			xmod.eval()  # disable dropout
			print(lowerCAmelCase__	)

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

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

			__snake_case								=       XmodForSequenceClassification(lowerCAmelCase__	) if classification_head else XmodForMaskedLM(lowerCAmelCase__	)
			model.eval()

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

			__snake_case								=       xmod_sent_encoder.layernorm_embedding.weight
			__snake_case								=       xmod_sent_encoder.layernorm_embedding.bias

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

						# self attention
						__snake_case								=       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.'''	)

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

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

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

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

						# end of layer

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

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

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

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

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


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








from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

snake_case_           =      logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				int    ,      *a__	:				List[Any]    ,      **a__	:				Dict				):





         """simple docstring"""



         super().__init__(*a__    ,      **a__				)
         requires_backends(self    ,      '''vision'''				)
         self.check_model_type(a__				)


      def __call__(self	:				Optional[Any]    ,      a__	:				Union[str, List[str], "Image.Image", List["Image.Image"]]    ,      **a__	:				List[str]				):





         """simple docstring"""



         return super().__call__(a__    ,      **a__				)


      def        a			(self	:				int    ,      **a__	:				int				):





         """simple docstring"""



         return {}, {}, {}


      def        a			(self	:				Optional[int]    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       load_image(a__				)
         __snake_case								=       image.size
         __snake_case								=       self.image_processor(images=a__    ,      return_tensors=self.framework				)
         return model_inputs


      def        a			(self	:				List[Any]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.model(**a__				)
         return model_outputs


      def        a			(self	:				int    ,      a__	:				str				):





         """simple docstring"""



         __snake_case								=       model_outputs.predicted_depth
         __snake_case								=       torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1				)    ,      size=self.image_size[::-1]    ,      mode='''bicubic'''    ,      align_corners=a__				)
         __snake_case								=       prediction.squeeze().cpu().numpy()
         __snake_case								=       (output * 255 / np.max(a__				)).astype('''uint8'''				)
         __snake_case								=       Image.fromarray(a__				)
         __snake_case								=       {}
         __snake_case								=       predicted_depth
         __snake_case								=       depth
         return output_dict

