

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Union[str, Any]             =       (DPMSolverSinglestepScheduler,)
							UpperCAmelCase__			:		Any             =       (("num_inference_steps", 25),)


							def 							__lowercase							(     self			,				**_a			)       ->       List[str]:
												_a      :       List[str]   =						{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''solver_order''': 2,
												    '''prediction_type''': '''epsilon''',
												    '''thresholding''': False,
												    '''sample_max_value''': 1.0,
												    '''algorithm_type''': '''dpmsolver++''',
												    '''solver_type''': '''midpoint''',
												    '''lambda_min_clipped''': -float('''inf'''			),
												    '''variance_type''': None,
												}

												config.update(**_a			)
												return config


							def 							__lowercase							(     self			,				_a=0			,				**_a			)       ->       Any:
												_a      :       List[str]   =						dict(self.forward_default_kwargs			)
												_a      :       Optional[Any]   =						kwargs.pop('''num_inference_steps'''			,				_a			)
												_a      :       Dict   =						self.dummy_sample
												_a      :       Any   =						0.1 * sample
												_a      :       Optional[Any]   =						[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																	_a      :       List[str]   =						self.get_scheduler_config(**_a			)
																	_a      :       str   =						scheduler_class(**_a			)
																	scheduler.set_timesteps(_a			)
																	# copy over dummy past residuals
																	_a      :       Tuple   =						dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_a			)
																						_a      :       List[str]   =						scheduler_class.from_pretrained(_a			)
																						new_scheduler.set_timesteps(_a			)
																						# copy over dummy past residuals
																						_a      :       int   =						dummy_past_residuals[: new_scheduler.config.solver_order]

																	_a ,   _a      :       Any   =						sample, sample
																	for t in range(_a			,				time_step + scheduler.config.solver_order + 1			):
																						_a      :       Dict   =						scheduler.step(_a			,				_a			,				_a			,				**_a			).prev_sample
																						_a      :       List[str]   =						new_scheduler.step(_a			,				_a			,				_a			,				**_a			).prev_sample

																						assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"


							def 							__lowercase							(     self			)       ->       Dict:
												pass


							def 							__lowercase							(     self			,				_a=0			,				**_a			)       ->       List[Any]:
												_a      :       List[str]   =						dict(self.forward_default_kwargs			)
												_a      :       Union[str, Any]   =						kwargs.pop('''num_inference_steps'''			,				_a			)
												_a      :       Union[str, Any]   =						self.dummy_sample
												_a      :       List[str]   =						0.1 * sample
												_a      :       Optional[Any]   =						[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																	_a      :       int   =						self.get_scheduler_config()
																	_a      :       Optional[int]   =						scheduler_class(**_a			)
																	scheduler.set_timesteps(_a			)

																	# copy over dummy past residuals (must be after setting timesteps)
																	_a      :       str   =						dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_a			)
																						_a      :       str   =						scheduler_class.from_pretrained(_a			)
																						# copy over dummy past residuals
																						new_scheduler.set_timesteps(_a			)

																						# copy over dummy past residual (must be after setting timesteps)
																						_a      :       Optional[Any]   =						dummy_past_residuals[: new_scheduler.config.solver_order]

																	_a      :       int   =						scheduler.step(_a			,				_a			,				_a			,				**_a			).prev_sample
																	_a      :       int   =						new_scheduler.step(_a			,				_a			,				_a			,				**_a			).prev_sample

																	assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"


							def 							__lowercase							(     self			,				_a=None			,				**_a			)       ->       Dict:
												if scheduler is None:
																	_a      :       Optional[Any]   =						self.scheduler_classes[0]
																	_a      :       List[str]   =						self.get_scheduler_config(**_a			)
																	_a      :       Union[str, Any]   =						scheduler_class(**_a			)

												_a      :       int   =						self.scheduler_classes[0]
												_a      :       List[Any]   =						self.get_scheduler_config(**_a			)
												_a      :       Union[str, Any]   =						scheduler_class(**_a			)

												_a      :       Optional[Any]   =						1_0
												_a      :       List[Any]   =						self.dummy_model()
												_a      :       Union[str, Any]   =						self.dummy_sample_deter
												scheduler.set_timesteps(_a			)

												for i, t in enumerate(scheduler.timesteps			):
																	_a      :       Any   =						model(_a			,				_a			)
																	_a      :       Dict   =						scheduler.step(_a			,				_a			,				_a			).prev_sample

												return sample


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Optional[int]   =						DPMSolverSinglestepScheduler(**self.get_scheduler_config()			)
												_a      :       Union[str, Any]   =						5_0
												_a      :       Any   =						self.dummy_model()
												_a      :       Tuple   =						self.dummy_sample_deter
												scheduler.set_timesteps(_a			)

												# make sure that the first t is uneven
												for i, t in enumerate(scheduler.timesteps[3:]			):
																	_a      :       List[Any]   =						model(_a			,				_a			)
																	_a      :       Optional[int]   =						scheduler.step(_a			,				_a			,				_a			).prev_sample

												_a      :       Union[str, Any]   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.2574			) < 1e-3


							def 							__lowercase							(     self			)       ->       int:
												for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=_a			)


							def 							__lowercase							(     self			)       ->       List[Any]:
												# make sure that iterating over schedulers with same config names gives same results
												# for defaults
												_a      :       Dict   =						DPMSolverSinglestepScheduler(**self.get_scheduler_config()			)
												_a      :       Dict   =						self.full_loop(scheduler=_a			)
												_a      :       Optional[Any]   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.2791			) < 1e-3

												_a      :       int   =						DEISMultistepScheduler.from_config(scheduler.config			)
												_a      :       Union[str, Any]   =						DPMSolverMultistepScheduler.from_config(scheduler.config			)
												_a      :       List[Any]   =						UniPCMultistepScheduler.from_config(scheduler.config			)
												_a      :       Union[str, Any]   =						DPMSolverSinglestepScheduler.from_config(scheduler.config			)

												_a      :       Optional[int]   =						self.full_loop(scheduler=_a			)
												_a      :       List[str]   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.2791			) < 1e-3


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												self.check_over_configs(thresholding=_a			)
												for order in [1, 2, 3]:
																	for solver_type in ["midpoint", "heun"]:
																						for threshold in [0.5, 1.0, 2.0]:
																											for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    thresholding=_a			,				prediction_type=_a			,				sample_max_value=_a			,				algorithm_type='''dpmsolver++'''			,				solver_order=_a			,				solver_type=_a			,				)


							def 							__lowercase							(     self			)       ->       Tuple:
												for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=_a			)


							def 							__lowercase							(     self			)       ->       Dict:
												for algorithm_type in ["dpmsolver", "dpmsolver++"]:
																	for solver_type in ["midpoint", "heun"]:
																						for order in [1, 2, 3]:
																											for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    solver_order=_a			,				solver_type=_a			,				prediction_type=_a			,				algorithm_type=_a			,				)
																																_a      :       List[Any]   =						self.full_loop(
																																    solver_order=_a			,				solver_type=_a			,				prediction_type=_a			,				algorithm_type=_a			,				)
																																assert not torch.isnan(_a			).any(), "Samples have nan numbers"


							def 							__lowercase							(     self			)       ->       List[Any]:
												self.check_over_configs(lower_order_final=_a			)
												self.check_over_configs(lower_order_final=_a			)


							def 							__lowercase							(     self			)       ->       List[Any]:
												self.check_over_configs(lambda_min_clipped=-float('''inf'''			)			)
												self.check_over_configs(lambda_min_clipped=-5.1			)


							def 							__lowercase							(     self			)       ->       int:
												self.check_over_configs(variance_type=_a			)
												self.check_over_configs(variance_type='''learned_range'''			)


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_forward(num_inference_steps=_a			,				time_step=0			)


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Any   =						self.full_loop()
												_a      :       Optional[Any]   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.2791			) < 1e-3


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       Optional[Any]   =						self.full_loop(use_karras_sigmas=_a			)
												_a      :       int   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.2248			) < 1e-3


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Dict   =						self.full_loop(prediction_type='''v_prediction'''			)
												_a      :       Optional[int]   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.1453			) < 1e-3


							def 							__lowercase							(     self			)       ->       str:
												_a      :       List[str]   =						self.full_loop(prediction_type='''v_prediction'''			,				use_karras_sigmas=_a			)
												_a      :       Tuple   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.0649			) < 1e-3




							def 							__lowercase							(     self			)       ->       List[str]:
												_a      :       Union[str, Any]   =						self.scheduler_classes[0]
												_a      :       Dict   =						self.get_scheduler_config(thresholding=_a			,				dynamic_thresholding_ratio=0			)
												_a      :       str   =						scheduler_class(**_a			)

												_a      :       Dict   =						1_0
												_a      :       Optional[Any]   =						self.dummy_model()
												_a      :       List[str]   =						self.dummy_sample_deter.half()
												scheduler.set_timesteps(_a			)

												for i, t in enumerate(scheduler.timesteps			):
																	_a      :       Any   =						model(_a			,				_a			)
																	_a      :       int   =						scheduler.step(_a			,				_a			,				_a			).prev_sample

												assert sample.dtype == torch.floataa










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__      :     Tuple         =		'Hello, World!'
a__      :     Any         =		'en_XX'

def      UpperCAmelCase_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:bool				)	->				Optional[Any]:
     '''simple docstring'''


     A_          =       Path('''data_bin'''				)
     A_          =       FairseqXmodModel.from_pretrained(
         model_name_or_path=str(Path(_UpperCAmelCase				).parent				)  ,						checkpoint_file=Path(_UpperCAmelCase				).name  ,						_name='''xmod_base'''  ,						arch='''xmod_base'''  ,						task='''multilingual_masked_lm'''  ,						data_name_or_path=str(_UpperCAmelCase				)  ,						bpe='''sentencepiece'''  ,						sentencepiece_model=str(Path(_UpperCAmelCase				).parent / '''sentencepiece.bpe.model'''				)  ,						src_dict=str(data_dir / '''dict.txt'''				)  ,						)
     xmod.eval()  # disable dropout
     print(_UpperCAmelCase				)

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

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

     A_          =       XmodForSequenceClassification(_UpperCAmelCase				) if classification_head else XmodForMaskedLM(_UpperCAmelCase				)
     model.eval()

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

     A_          =       xmod_sent_encoder.layernorm_embedding.weight
     A_          =       xmod_sent_encoder.layernorm_embedding.bias

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

          # self attention
          A_          =       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_          =       xmod_layer.self_attn.q_proj.weight
          A_          =       xmod_layer.self_attn.q_proj.bias
          A_          =       xmod_layer.self_attn.k_proj.weight
          A_          =       xmod_layer.self_attn.k_proj.bias
          A_          =       xmod_layer.self_attn.v_proj.weight
          A_          =       xmod_layer.self_attn.v_proj.bias

          # self-attention output
          A_          =       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_          =       xmod_layer.self_attn.out_proj.weight
          A_          =       xmod_layer.self_attn.out_proj.bias
          A_          =       xmod_layer.self_attn_layer_norm.weight
          A_          =       xmod_layer.self_attn_layer_norm.bias

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

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

          # end of layer

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

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

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

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

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


if __name__ == "__main__":
      a__      :     List[str]         =		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__      :     Tuple         =		parser.parse_args()
      convert_xmod_checkpoint_to_pytorch(
          args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )



