




"""simple docstring"""

import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs

class        lowercase      (    __UpperCAmelCase):







				def __init__(							self   :					List[Any]      ,     _lowerCamelCase   :					Optional[int]      ,     _lowerCamelCase   :					Optional[Any]=None      ,     _lowerCamelCase   :					Optional[int]=True      ,     _lowerCamelCase   :					Optional[int]=None      ,     **_lowerCamelCase   :					str       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		parent
					A_  :				int	   =		config_class
					A_  :				List[Any]	   =		has_text_modality
					A_  :				Any	   =		kwargs
					A_  :				Optional[int]	   =		common_properties







				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		self.config_class(**self.inputs_dict       )
					A_  :				Optional[Any]	   =		(
					    ['''hidden_size''', '''num_attention_heads''', '''num_hidden_layers''']
					    if self.common_properties is None
					    else self.common_properties
					)

					# Add common fields for text models
					if self.has_text_modality:
						common_properties.extend(['''vocab_size''']       )

					# Test that config has the common properties as getters
					for prop in common_properties:
						self.parent.assertTrue(hasattr(_lowerCamelCase      ,     _lowerCamelCase       )      ,     msg=F"""`{prop}` does not exist"""       )

					# Test that config has the common properties as setter
					for idx, name in enumerate(_lowerCamelCase       ):
						try:
							setattr(_lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase       )
							self.parent.assertEqual(
							    getattr(_lowerCamelCase      ,     _lowerCamelCase       )      ,     _lowerCamelCase      ,     msg=F"""`{name} value {idx} expected, but was {getattr(_lowerCamelCase      ,     _lowerCamelCase       )}"""       )
						except NotImplementedError:
							# Some models might not be able to implement setters for common_properties
							# In that case, a NotImplementedError is raised
							pass

        # Test if config class can be called with Config(prop_name=..)
					for idx, name in enumerate(_lowerCamelCase       ):
						try:
							A_  :				Union[str, Any]	   =		self.config_class(**{name: idx}       )
							self.parent.assertEqual(
							    getattr(_lowerCamelCase      ,     _lowerCamelCase       )      ,     _lowerCamelCase      ,     msg=F"""`{name} value {idx} expected, but was {getattr(_lowerCamelCase      ,     _lowerCamelCase       )}"""       )
						except NotImplementedError:
							# Some models might not be able to implement setters for common_properties
							# In that case, a NotImplementedError is raised
							pass







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		self.config_class(**self.inputs_dict       )
					A_  :				int	   =		json.loads(config.to_json_string()       )
					for key, value in self.inputs_dict.items():
						self.parent.assertEqual(obj[key]      ,     _lowerCamelCase       )







				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		self.config_class(**self.inputs_dict       )

					with tempfile.TemporaryDirectory() as tmpdirname:
						A_  :				Tuple	   =		os.path.join(_lowerCamelCase      ,     '''config.json'''       )
						config_first.to_json_file(_lowerCamelCase       )
						A_  :				Optional[int]	   =		self.config_class.from_json_file(_lowerCamelCase       )

					self.parent.assertEqual(config_second.to_dict()      ,     config_first.to_dict()       )







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		self.config_class(**self.inputs_dict       )

					with tempfile.TemporaryDirectory() as tmpdirname:
						config_first.save_pretrained(_lowerCamelCase       )
						A_  :				Any	   =		self.config_class.from_pretrained(_lowerCamelCase       )

					self.parent.assertEqual(config_second.to_dict()      ,     config_first.to_dict()       )







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					A_  :				Any	   =		self.config_class(**self.inputs_dict       )

					A_  :				List[str]	   =		'''test'''
					with tempfile.TemporaryDirectory() as tmpdirname:
						A_  :				Optional[int]	   =		os.path.join(_lowerCamelCase      ,     _lowerCamelCase       )
						config_first.save_pretrained(_lowerCamelCase       )
						A_  :				Any	   =		self.config_class.from_pretrained(_lowerCamelCase      ,     subfolder=_lowerCamelCase       )

					self.parent.assertEqual(config_second.to_dict()      ,     config_first.to_dict()       )







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				str	   =		self.config_class(**self.inputs_dict      ,     num_labels=5       )
					self.parent.assertEqual(len(config.idalabel       )      ,     5       )
					self.parent.assertEqual(len(config.labelaid       )      ,     5       )

					A_  :				List[Any]	   =		3
					self.parent.assertEqual(len(config.idalabel       )      ,     3       )
					self.parent.assertEqual(len(config.labelaid       )      ,     3       )







				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					if self.config_class.is_composition:
						return
					A_  :				Union[str, Any]	   =		self.config_class()
					self.parent.assertIsNotNone(_lowerCamelCase       )







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					A_  :				Tuple	   =		copy.deepcopy(_lowerCamelCase       )
					A_  :				Union[str, Any]	   =		self.config_class(**_lowerCamelCase       )
					A_  :				int	   =		[]
					for key, value in config_common_kwargs.items():
						if key == "torch_dtype":
							if not is_torch_available():
								continue
							else:
								import torch

								if config.torch_dtype != torch.floataa:
									wrong_values.append(('''torch_dtype''', config.torch_dtype, torch.floataa)       )
						elif getattr(_lowerCamelCase      ,     _lowerCamelCase       ) != value:
							wrong_values.append((key, getattr(_lowerCamelCase      ,     _lowerCamelCase       ), value)       )

					if len(_lowerCamelCase       ) > 0:
						A_  :				List[Any]	   =		'''\n'''.join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values]       )
						raise ValueError(F"""The following keys were not properly set in the config:\n{errors}"""       )







				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					self.create_and_test_config_common_properties()
					self.create_and_test_config_to_json_string()
					self.create_and_test_config_to_json_file()
					self.create_and_test_config_from_and_save_pretrained()
					self.create_and_test_config_from_and_save_pretrained_subfolder()
					self.create_and_test_config_with_num_labels()
					self.check_config_can_be_init_without_params()
					self.check_config_arguments_init()











"""simple docstring"""

import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Optional[Any]	   =		[
	    '''decoder.version''',
	    '''decoder.output_projection.weight''',
	    '''_float_tensor''',
	    '''decoder.embed_positions._float_tensor''',
	]
	for k in ignore_keys:
		state_dict.pop(_UpperCAmelCase  ,      _UpperCAmelCase	)

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_   , A_  :				List[Any]	   =		emb.weight.shape
	A_  :				List[Any]	   =		nn.Linear(_UpperCAmelCase  ,      _UpperCAmelCase  ,      bias=_UpperCAmelCase	)
	A_  :				Any	   =		emb.weight.data
	return lin_layer

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				int	   =		torch.load(_UpperCAmelCase  ,      map_location='''cpu'''	)
	A_  :				Any	   =		Namespace(**checkpoint['''cfg''']['''model''']	)
	A_  :				List[str]	   =		checkpoint['''model''']
	remove_ignore_keys_(_UpperCAmelCase	)
	A_  :				Union[str, Any]	   =		state_dict['''decoder.embed_tokens.weight'''].shape[0]

	A_  :				List[str]	   =		{key.replace('''decoder'''  ,      '''model'''	): val for key, val in state_dict.items()}

	A_  :				int	   =		XGLMConfig(
	    vocab_size=_UpperCAmelCase  ,      max_position_embeddings=args.max_target_positions  ,      num_layers=args.decoder_layers  ,      attention_heads=args.decoder_attention_heads  ,      ffn_dim=args.decoder_ffn_embed_dim  ,      d_model=args.decoder_embed_dim  ,      layerdrop=args.decoder_layerdrop  ,      dropout=args.dropout  ,      attention_dropout=args.attention_dropout  ,      activation_dropout=args.activation_dropout  ,      activation_function='''gelu'''  ,      scale_embedding=not args.no_scale_embedding  ,      tie_word_embeddings=args.share_decoder_input_output_embed  ,      )

	A_  :				Any	   =		XGLMForCausalLM(_UpperCAmelCase	)
	A_  :				int	   =		model.load_state_dict(_UpperCAmelCase  ,      strict=_UpperCAmelCase	)
	print(_UpperCAmelCase	)
	A_  :				int	   =		make_linear_from_emb(model.model.embed_tokens	)

	return model


if __name__ == "__main__":
		_lowerCamelCase  :			Any        =       argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
		parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		_lowerCamelCase  :			int        =       parser.parse_args()
		_lowerCamelCase  :			Optional[Any]        =       convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)





