


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

__snake_case											=					'''Hello, World!'''
__snake_case											=					'''en_XX'''


def       a				(      __a			, __a			, __a       )  ->     str:



					'''simple docstring'''






					UpperCamelCase__       :Optional[int]							 =			Path('''data_bin'''       )
					UpperCamelCase__       :Tuple							 =			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       )

					UpperCamelCase__       :Any							 =			xmod.model.encoder.sentence_encoder
					UpperCamelCase__       :List[str]							 =			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__       :List[Any]							 =			xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

					UpperCamelCase__       :Tuple							 =			XmodForSequenceClassification(__a       ) if classification_head else XmodForMaskedLM(__a       )
					model.eval()

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

					UpperCamelCase__       :List[Any]							 =			xmod_sent_encoder.layernorm_embedding.weight
					UpperCamelCase__       :Optional[int]							 =			xmod_sent_encoder.layernorm_embedding.bias

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

										# self attention
										UpperCamelCase__       :List[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.'''       )

										UpperCamelCase__       :Tuple							 =			xmod_layer.self_attn.q_proj.weight
										UpperCamelCase__       :int							 =			xmod_layer.self_attn.q_proj.bias
										UpperCamelCase__       :Dict							 =			xmod_layer.self_attn.k_proj.weight
										UpperCamelCase__       :str							 =			xmod_layer.self_attn.k_proj.bias
										UpperCamelCase__       :Optional[int]							 =			xmod_layer.self_attn.v_proj.weight
										UpperCamelCase__       :List[Any]							 =			xmod_layer.self_attn.v_proj.bias

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

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

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

										# end of layer

					if xmod_sent_encoder.layer_norm is not None:
										UpperCamelCase__       :List[Any]							 =			xmod_sent_encoder.layer_norm.weight
										UpperCamelCase__       :int							 =			xmod_sent_encoder.layer_norm.bias

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

					# Let's check that we get the same results.
					UpperCamelCase__       :Union[str, Any]							 =			xmod.encode(__a       ).unsqueeze(0       )  # batch of size 1
					model.roberta.set_default_language(__a       )

					UpperCamelCase__       :Any							 =			model(__a       )[0]
					if classification_head:
										UpperCamelCase__       :str							 =			xmod.model.classification_heads['''mnli'''](xmod.extract_features(__a       )       )
					else:
										UpperCamelCase__       :Optional[Any]							 =			xmod.model(__a			, lang_id=[SAMPLE_LANGUAGE]       )[0]
					print(our_output.shape			, their_output.shape       )
					UpperCamelCase__       :str							 =			torch.max(torch.abs(our_output - their_output       )       ).item()
					print(f'''max_absolute_diff = {max_absolute_diff}'''       )  # ~ 1e-7
					UpperCamelCase__       :List[str]							 =			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__":
		__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
		)



'''simple docstring'''
import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class        lowercase     (							unittest.TestCase ):

		"""simple docstring"""

		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			logging.get_logger()

							# the current default level is logging.WARNING
							UpperCamelCase__       :List[Any]							 =			logging.get_verbosity()

							logging.set_verbosity_error()
							self.assertEqual(logger.getEffectiveLevel()   ,       logging.get_verbosity()							)

							logging.set_verbosity_warning()
							self.assertEqual(logger.getEffectiveLevel()   ,       logging.get_verbosity()							)

							logging.set_verbosity_info()
							self.assertEqual(logger.getEffectiveLevel()   ,       logging.get_verbosity()							)

							logging.set_verbosity_debug()
							self.assertEqual(logger.getEffectiveLevel()   ,       logging.get_verbosity()							)

							# restore to the original level
							logging.set_verbosity(UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			logging.get_verbosity()

							UpperCamelCase__       :Union[str, Any]							 =			logging.get_logger('''transformers.models.bart.tokenization_bart'''							)
							UpperCamelCase__       :Optional[Any]							 =			'''Testing 1, 2, 3'''

							# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
							if level_origin <= logging.WARNING:
												with CaptureLogger(UpperCamelCase_							) as cl:
																	logger.warning(UpperCamelCase_							)
												self.assertEqual(cl.out   ,       msg + '''\n'''							)

							# this is setting the level for all of `transformers.*` loggers
							logging.set_verbosity_error()

							# should not be able to log warnings
							with CaptureLogger(UpperCamelCase_							) as cl:
												logger.warning(UpperCamelCase_							)
							self.assertEqual(cl.out   ,       ''''''							)

							# should be able to log warnings again
							logging.set_verbosity_warning()
							with CaptureLogger(UpperCamelCase_							) as cl:
												logger.warning(UpperCamelCase_							)
							self.assertEqual(cl.out   ,       msg + '''\n'''							)

							# restore to the original level
							logging.set_verbosity(UpperCamelCase_							)




		@mockenv(TRANSFORMERS_VERBOSITY='''error'''							)
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							transformers.utils.logging._reset_library_root_logger()
							# this action activates the env var
							UpperCamelCase__       :Tuple							 =			logging.get_logger('''transformers.models.bart.tokenization_bart'''							)

							UpperCamelCase__       :Any							 =			os.getenv('''TRANSFORMERS_VERBOSITY'''   ,       UpperCamelCase_							)
							UpperCamelCase__       :Optional[Any]							 =			logging.log_levels[env_level_str]

							UpperCamelCase__       :int							 =			logging.get_verbosity()
							self.assertEqual(
							    UpperCamelCase_   ,       UpperCamelCase_   ,       F'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'''   ,       )

							# restore to the original level
							UpperCamelCase__       :Union[str, Any]							 =			''''''
							transformers.utils.logging._reset_library_root_logger()




		@mockenv(TRANSFORMERS_VERBOSITY='''super-error'''							)
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							transformers.utils.logging._reset_library_root_logger()
							UpperCamelCase__       :Dict							 =			logging.logging.getLogger()
							with CaptureLogger(UpperCamelCase_							) as cl:
												# this action activates the env var
												logging.get_logger('''transformers.models.bart.tokenization_bart'''							)
							self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error'''   ,       cl.out							)

							# no need to restore as nothing was changed




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							transformers.utils.logging._reset_library_root_logger()

							UpperCamelCase__       :Optional[int]							 =			logging.get_logger('''transformers.models.bart.tokenization_bart'''							)
							UpperCamelCase__       :int							 =			'''Testing 1, 2, 3'''

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1'''							):
												# nothing should be logged as env var disables this method
												with CaptureLogger(UpperCamelCase_							) as cl:
																	logger.warning_advice(UpperCamelCase_							)
												self.assertEqual(cl.out   ,       ''''''							)

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''''''							):
												# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
												with CaptureLogger(UpperCamelCase_							) as cl:
																	logger.warning_advice(UpperCamelCase_							)
												self.assertEqual(cl.out   ,       msg + '''\n'''							)



def       a				(      )  ->     str:



					'''simple docstring'''






					disable_progress_bar()
					assert are_progress_bars_disabled()

					enable_progress_bar()
					assert not are_progress_bars_disabled()