
import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


_lowercase			:  List[Any]   							=	["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
_lowercase			:  Optional[int]   							=	{"bart.large": BartModel, "bart.large.mnli": BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("0.9.0"):
							raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
_lowercase			:  Tuple   							=	logging.get_logger(__name__)

_lowercase			:  Any   							=	" Hello world! cécé herlolip"

_lowercase			:  List[str]   							=	[
    ("model.classification_heads.mnli.dense.weight", "classification_head.dense.weight"),
    ("model.classification_heads.mnli.dense.bias", "classification_head.dense.bias"),
    ("model.classification_heads.mnli.out_proj.weight", "classification_head.out_proj.weight"),
    ("model.classification_heads.mnli.out_proj.bias", "classification_head.out_proj.bias"),
]
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Any							):



						"""simple docstring"""





						lowercase_     :					Optional[Any]													=	[
						    '''encoder.version''',
						    '''decoder.version''',
						    '''model.encoder.version''',
						    '''model.decoder.version''',
						    '''_float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(__a				,				__a							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[int]				,				__SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]							):



						"""simple docstring"""





						lowercase_     :					int													=	dct.pop(__a							)
						lowercase_     :					Any													=	val
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Tuple							):



						"""simple docstring"""





						lowercase_     :					str													=	torch.load(__a				,				map_location='''cpu'''							)
						lowercase_     :					str													=	torch.hub.load('''pytorch/fairseq'''				,				'''bart.large.cnn'''							).eval()
						hub_interface.model.load_state_dict(sd['''model''']							)
						return hub_interface
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[str]							):



						"""simple docstring"""





						lowercase_						,			lowercase_     :					Tuple													=	emb.weight.shape
						lowercase_     :					Optional[int]													=	nn.Linear(__a				,				__a				,				bias=__a							)
						lowercase_     :					Union[str, Any]													=	emb.weight.data
						return lin_layer
@torch.no_grad()
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Tuple				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			str=None							):



						"""simple docstring"""





						if not os.path.exists(__a							):
												lowercase_     :					int													=	torch.hub.load('''pytorch/fairseq'''				,				__a							).eval()
						else:
												lowercase_     :					int													=	load_xsum_checkpoint(__a							)

						bart.model.upgrade_state_dict(bart.model.state_dict()							)
						if hf_checkpoint_name is None:
												lowercase_     :					Optional[int]													=	checkpoint_path.replace('''.'''				,				'''-'''							)
						lowercase_     :					str													=	BartConfig.from_pretrained(__a							)
						lowercase_     :					Tuple													=	bart.encode(__a							).unsqueeze(0							)
						lowercase_     :					Optional[int]													=	BartTokenizer.from_pretrained(__a							).encode(__a				,				return_tensors='''pt'''							).unsqueeze(0							)
						if not torch.eq(__a				,				__a							).all():
												raise ValueError(
												    F'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''							)

						if checkpoint_path == "bart.large.mnli":
												lowercase_     :					List[Any]													=	bart.state_dict()
												remove_ignore_keys_(__a							)
												lowercase_     :					Optional[Any]													=	state_dict['''model.decoder.embed_tokens.weight''']
												for src, dest in mnli_rename_keys:
																		rename_key(__a				,				__a				,				__a							)
												lowercase_     :					str													=	BartForSequenceClassification(__a							).eval()
												model.load_state_dict(__a							)
												lowercase_     :					List[Any]													=	bart.predict('''mnli'''				,				__a				,				return_logits=__a							)
												lowercase_     :					Dict													=	model(__a							)[0]  # logits
						else:  # no classification heads to worry about
												lowercase_     :					Optional[int]													=	bart.model.state_dict()
												remove_ignore_keys_(__a							)
												lowercase_     :					Tuple													=	state_dict['''decoder.embed_tokens.weight''']
												lowercase_     :					Dict													=	bart.extract_features(__a							)
												if hf_checkpoint_name == "facebook/bart-large":
																		lowercase_     :					Dict													=	BartModel(__a							).eval()
																		model.load_state_dict(__a							)
																		lowercase_     :					List[Any]													=	model(__a							).model[0]
												else:
																		lowercase_     :					int													=	BartForConditionalGeneration(__a							).eval()  # an existing summarization ckpt
																		model.model.load_state_dict(__a							)
																		if hasattr(__a				,				'''lm_head'''							):
																								lowercase_     :					List[Any]													=	make_linear_from_emb(model.model.shared							)
																		lowercase_     :					Optional[Any]													=	model.model(__a							)[0]

    # Check results
						if fairseq_output.shape != new_model_outputs.shape:
												raise ValueError(
												    F'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'''							)
						if (fairseq_output != new_model_outputs).any().item():
												raise ValueError('''Some values in `fairseq_output` are different from `new_model_outputs`'''							)
						Path(__a							).mkdir(exist_ok=__a							)
						model.save_pretrained(__a							)


if __name__ == "__main__":
							_lowercase			:  int   							=	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "fairseq_path", type=str, help="bart.large, bart.large.cnn or a 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.")
							parser.add_argument(
							    "--hf_config", default=None, type=str, help="Which huggingface architecture to use: bart-large-xsum"
							)
							_lowercase			:  Optional[Any]   							=	parser.parse_args()
							convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)





'''simple docstring'''







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					(DDPMScheduler,)






					def      _snake_case   (       self    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	{
											    '''num_train_timesteps''': 10_00,
											    '''beta_start''': 0.0_001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											    '''variance_type''': '''fixed_small''',
											    '''clip_sample''': True,
											}

											config.update(**__SCREAMING_SNAKE_CASE		)
											return config






					def      _snake_case   (       self		):


											"""simple docstring"""





											for timesteps in [1, 5, 1_00, 10_00]:
																	self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]    ,							[0.002, 0.02, 0.2, 2]		):
																	self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE    ,							beta_end=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for variance in ["fixed_small", "fixed_large", "other"]:
																	self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE		)
											for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample", "v_prediction"]:
																							self.check_over_configs(
																							    thresholding=__SCREAMING_SNAKE_CASE    ,							prediction_type=__SCREAMING_SNAKE_CASE    ,							sample_max_value=__SCREAMING_SNAKE_CASE    ,							)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for t in [0, 5_00, 9_99]:
																	self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	self.scheduler_classes[0]
											lowercase_     :					int													=	self.get_scheduler_config()
											lowercase_     :					str													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(4_87		) - 0.00_979		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(9_99		) - 0.02		)		) < 1E-5






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	self.scheduler_classes[0]
											lowercase_     :					Any													=	self.get_scheduler_config()
											lowercase_     :					Optional[int]													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Any													=	len(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	self.dummy_model()
											lowercase_     :					Any													=	self.dummy_sample_deter
											lowercase_     :					List[str]													=	torch.manual_seed(0		)

											for t in reversed(range(__SCREAMING_SNAKE_CASE		)		):
																	# 1. predict noise residual
																	lowercase_     :					Optional[int]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

																	# 2. predict previous mean of sample x_t-1
																	lowercase_     :					Union[str, Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	lowercase_     :					List[str]													=	pred_prev_sample

											lowercase_     :					str													=	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Tuple													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_sum.item() - 258.9_606		) < 1E-2
											assert abs(result_mean.item() - 0.3_372		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.scheduler_classes[0]
											lowercase_     :					Tuple													=	self.get_scheduler_config(prediction_type='''v_prediction'''		)
											lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	len(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	self.dummy_model()
											lowercase_     :					int													=	self.dummy_sample_deter
											lowercase_     :					str													=	torch.manual_seed(0		)

											for t in reversed(range(__SCREAMING_SNAKE_CASE		)		):
																	# 1. predict noise residual
																	lowercase_     :					Optional[Any]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

																	# 2. predict previous mean of sample x_t-1
																	lowercase_     :					Optional[Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	lowercase_     :					int													=	pred_prev_sample

											lowercase_     :					str													=	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Tuple													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_sum.item() - 202.0_296		) < 1E-2
											assert abs(result_mean.item() - 0.2_631		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	self.scheduler_classes[0]
											lowercase_     :					int													=	self.get_scheduler_config()
											lowercase_     :					int													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					int													=	[1_00, 87, 50, 1, 0]

											scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	scheduler.timesteps

											for i, timestep in enumerate(__SCREAMING_SNAKE_CASE		):
																	if i == len(__SCREAMING_SNAKE_CASE		) - 1:
																							lowercase_     :					str													=	-1
																	else:
																							lowercase_     :					Any													=	timesteps[i + 1]

																	lowercase_     :					Optional[Any]													=	scheduler.previous_timestep(__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					int													=	prev_t.item()

																	self.assertEqual(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Tuple													=	self.scheduler_classes[0]
											lowercase_     :					List[Any]													=	self.get_scheduler_config()
											lowercase_     :					str													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	[1_00, 87, 50, 51, 0]

											with self.assertRaises(__SCREAMING_SNAKE_CASE    ,							msg='''`custom_timesteps` must be in descending order.'''		):
																	scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.scheduler_classes[0]
											lowercase_     :					str													=	self.get_scheduler_config()
											lowercase_     :					Tuple													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Tuple													=	[1_00, 87, 50, 1, 0]
											lowercase_     :					List[str]													=	len(__SCREAMING_SNAKE_CASE		)

											with self.assertRaises(__SCREAMING_SNAKE_CASE    ,							msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''		):
																	scheduler.set_timesteps(num_inference_steps=__SCREAMING_SNAKE_CASE    ,							timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.scheduler_classes[0]
											lowercase_     :					Optional[int]													=	self.get_scheduler_config()
											lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					str													=	[scheduler.config.num_train_timesteps]

											with self.assertRaises(
											    __SCREAMING_SNAKE_CASE    ,							msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''    ,							):
																	scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)



