






'''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()
__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   'Hello, World!'
__lowerCAmelCase									=   'en_XX'





def 		_UpperCAmelCase (				__A				: str    ,		__A				: str    ,		__A				: bool   ):
				a_		:	str									=   Path('''data_bin'''   )
				a_		:	Optional[int]									=   FairseqXmodModel.from_pretrained(
				    model_name_or_path=str(Path(__A   ).parent   )    ,		checkpoint_file=Path(__A   ).name    ,		_name='''xmod_base'''    ,		arch='''xmod_base'''    ,		task='''multilingual_masked_lm'''    ,		data_name_or_path=str(__A   )    ,		bpe='''sentencepiece'''    ,		sentencepiece_model=str(Path(__A   ).parent / '''sentencepiece.bpe.model'''   )    ,		src_dict=str(data_dir / '''dict.txt'''   )    ,		)
				xmod.eval()  # disable dropout
				print(__A   )

				a_		:	Any									=   xmod.model.encoder.sentence_encoder
				a_		:	Optional[Any]									=   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=5_14    ,		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_		:	Optional[int]									=   xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

				a_		:	Optional[int]									=   XmodForSequenceClassification(__A   ) if classification_head else XmodForMaskedLM(__A   )
				model.eval()

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

				a_		:	Optional[int]									=   xmod_sent_encoder.layernorm_embedding.weight
				a_		:	Tuple									=   xmod_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers   ):
								# Encoder: start of layer
								a_		:	Union[str, Any]									=   model.roberta.encoder.layer[i]
								a_		:	Optional[Any]									=   xmod_sent_encoder.layers[i]

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

								# self-attention output
								a_		:	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.'''   )
								a_		:	List[Any]									=   xmod_layer.self_attn.out_proj.weight
								a_		:	Optional[int]									=   xmod_layer.self_attn.out_proj.bias
								a_		:	str									=   xmod_layer.self_attn_layer_norm.weight
								a_		:	int									=   xmod_layer.self_attn_layer_norm.bias

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

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

								# end of layer

				if xmod_sent_encoder.layer_norm is not None:
								a_		:	int									=   xmod_sent_encoder.layer_norm.weight
								a_		:	Optional[int]									=   xmod_sent_encoder.layer_norm.bias

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

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

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

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


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












'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
		import torch


if is_vision_available():
		from PIL import Image

		from transformers import MgpstrProcessor, ViTImageProcessor





@require_torch
@require_vision
class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):
			snake_case__	=					ViTImageProcessor if is_vision_available() else None






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      List[str]:
							return self.image_processor_tester.prepare_image_processor_dict()






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Optional[int]:
							a_		:	Optional[Any]									=   (3, 32, 128)
							a_		:	Optional[Any]									=   tempfile.mkdtemp()

							# fmt: off
							a_		:	Union[str, Any]									=   ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
							# fmt: on
							a_		:	List[Any]									=   dict(zip(__SCREAMING_SNAKE_CASE	,    range(len(__SCREAMING_SNAKE_CASE       )       )       )       )

							a_		:	Union[str, Any]									=   os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES['''vocab_file''']       )
							with open(self.vocab_file	,    '''w'''	,    encoding='''utf-8'''       ) as fp:
											fp.write(json.dumps(__SCREAMING_SNAKE_CASE       ) + '''\n'''       )

							a_		:	Union[str, Any]									=   {
							    '''do_normalize''': False,
							    '''do_resize''': True,
							    '''image_processor_type''': '''ViTImageProcessor''',
							    '''resample''': 3,
							    '''size''': {'''height''': 32, '''width''': 128},
							}
							a_		:	Any									=   os.path.join(self.tmpdirname	,    __SCREAMING_SNAKE_CASE       )
							with open(self.image_processor_file	,    '''w'''	,    encoding='''utf-8'''       ) as fp:
											json.dump(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    **__SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      Dict:
							return MgpstrTokenizer.from_pretrained(self.tmpdirname	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]	,    **__SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      List[Any]:
							return ViTImageProcessor.from_pretrained(self.tmpdirname	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Union[str, Any]:
							shutil.rmtree(self.tmpdirname       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      str:
							a_		:	Optional[Any]									=   np.random.randint(255	,    size=(3, 30, 400)	,    dtype=np.uinta       )

							a_		:	Optional[int]									=   Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE	,    0	,    -1       )       )

							return image_input






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      int:
							a_		:	str									=   self.get_tokenizer()
							a_		:	List[Any]									=   self.get_image_processor()

							a_		:	Union[str, Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )
							processor.save_pretrained(self.tmpdirname       )
							a_		:	Tuple									=   MgpstrProcessor.from_pretrained(self.tmpdirname	,    use_fast=__SCREAMING_SNAKE_CASE       )

							self.assertEqual(processor.char_tokenizer.get_vocab()	,    tokenizer.get_vocab()       )
							self.assertIsInstance(processor.char_tokenizer	,    __SCREAMING_SNAKE_CASE       )

							self.assertEqual(processor.image_processor.to_json_string()	,    image_processor.to_json_string()       )
							self.assertIsInstance(processor.image_processor	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      int:
							a_		:	Any									=   self.get_tokenizer()
							a_		:	Dict									=   self.get_image_processor()

							a_		:	Optional[Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )
							processor.save_pretrained(self.tmpdirname       )

							a_		:	str									=   self.get_tokenizer(bos_token='''(BOS)'''	,    eos_token='''(EOS)'''       )
							a_		:	str									=   self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE	,    padding_value=1.0       )

							a_		:	Optional[int]									=   MgpstrProcessor.from_pretrained(
							    self.tmpdirname	,    bos_token='''(BOS)'''	,    eos_token='''(EOS)'''	,    do_normalize=__SCREAMING_SNAKE_CASE	,    padding_value=1.0       )

							self.assertEqual(processor.char_tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()       )
							self.assertIsInstance(processor.char_tokenizer	,    __SCREAMING_SNAKE_CASE       )

							self.assertEqual(processor.image_processor.to_json_string()	,    image_processor_add_kwargs.to_json_string()       )
							self.assertIsInstance(processor.image_processor	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							a_		:	Dict									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[Any]									=   self.prepare_image_inputs()

							a_		:	List[Any]									=   image_processor(__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''       )
							a_		:	int									=   processor(images=__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''       )

							for key in input_image_proc.keys():
											self.assertAlmostEqual(input_image_proc[key].sum()	,    input_processor[key].sum()	,    delta=1e-2       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							a_		:	Dict									=   self.get_image_processor()
							a_		:	List[Any]									=   self.get_tokenizer()

							a_		:	int									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   '''test'''

							a_		:	Any									=   processor(text=__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[int]									=   tokenizer(__SCREAMING_SNAKE_CASE       )
							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]       )






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      int:
							a_		:	Union[str, Any]									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Optional[Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Union[str, Any]									=   '''test'''
							a_		:	str									=   self.prepare_image_inputs()

							a_		:	Tuple									=   processor(text=__SCREAMING_SNAKE_CASE	,    images=__SCREAMING_SNAKE_CASE       )

							self.assertListEqual(list(inputs.keys()       )	,    ['''pixel_values''', '''labels''']       )

							# test if it raises when no input is passed
							with pytest.raises(__SCREAMING_SNAKE_CASE       ):
											processor()






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      int:
							a_		:	int									=   self.get_image_processor()
							a_		:	List[Any]									=   self.get_tokenizer()

							a_		:	Dict									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

							a_		:	List[Any]									=   processor.char_decode(__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[int]									=   tokenizer.batch_decode(__SCREAMING_SNAKE_CASE       )
							a_		:	Union[str, Any]									=   [seq.replace(''' '''	,    ''''''       ) for seq in decoded_tok]

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      List[Any]:
							a_		:	int									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Dict									=   None
							a_		:	Tuple									=   self.prepare_image_inputs()

							a_		:	Optional[int]									=   processor(text=__SCREAMING_SNAKE_CASE	,    images=__SCREAMING_SNAKE_CASE       )

							self.assertListEqual(list(inputs.keys()       )	,    processor.model_input_names       )







			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Union[str, Any]:
							a_		:	List[str]									=   self.get_image_processor()
							a_		:	str									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	str									=   torch.randn(1	,    27	,    38       )
							a_		:	int									=   torch.randn(1	,    27	,    5_0257       )
							a_		:	Union[str, Any]									=   torch.randn(1	,    27	,    3_0522       )

							a_		:	Tuple									=   processor.batch_decode([char_input, bpe_input, wp_input]       )

							self.assertListEqual(list(results.keys()       )	,    ['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']       )




