

import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


lowerCAmelCase__								=		logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
							import torch_xla.core.xla_model as xm
							import torch_xla.debug.metrics as met







class 						lowercase    (				_UpperCAmelCase							):


		"""simple docstring"""




		def __init__(				self	,       *__snake_case	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       **__snake_case):
			super().__init__(*A_	,       **A_)
			_UpperCamelCase   :		Dict			=						eval_examples
			_UpperCamelCase   :		Tuple			=						post_process_function
			_UpperCamelCase   :		List[Any]			=						quant_trainer_args
			_UpperCamelCase   :		List[Any]			=						1_28  # default number of calibration samples


		def 		A__					(				self	,       __snake_case=None):
			if calib_dataset is None and self.calib_dataset is None:
				raise ValueError('Trainer: calibration requires an calib_dataset.')
			_UpperCamelCase   :		Optional[int]			=						calib_dataset if calib_dataset is not None else self.calib_dataset

			_UpperCamelCase   :		Union[str, Any]			=						self._remove_unused_columns(A_	,       description='Calibration')

			return DataLoader(
			    A_	,       batch_size=self.args.eval_batch_size	,       collate_fn=self.data_collator	,       drop_last=self.args.dataloader_drop_last	,       num_workers=self.args.dataloader_num_workers	,       pin_memory=self.args.dataloader_pin_memory	,       shuffle=A_	,       )


		def 		A__					(				self	,       __snake_case=None):
			_UpperCamelCase   :		Optional[int]			=						self.train_dataset if calib_dataset is None else calib_dataset
			_UpperCamelCase   :		int			=						self.get_calib_dataloader(A_)

			_UpperCamelCase   :		List[str]			=						self.model
			quant_trainer.configure_model(A_	,       self.quant_trainer_args	,       calib=A_)
			model.eval()
			quant_trainer.enable_calibration(A_)

			logger.info('***** Running calibration *****')
			logger.info(f'''  Num examples = {self.calib_num}''')
			logger.info(f'''  Batch size = {calib_dataloader.batch_size}''')

			for step, inputs in enumerate(A_):
				# Prediction step
				_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		Union[str, Any]			=						self.prediction_step(A_	,       A_	,       prediction_loss_only=A_)
				if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
					break

			quant_trainer.finish_calibration(A_	,       self.quant_trainer_args)
			_UpperCamelCase   :		Any			=						model


		def 		A__					(				self	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case = "eval"):
			_UpperCamelCase   :		List[str]			=						self.eval_dataset if eval_dataset is None else eval_dataset
			_UpperCamelCase   :		Union[str, Any]			=						self.get_eval_dataloader(A_)
			_UpperCamelCase   :		Dict			=						self.eval_examples if eval_examples is None else eval_examples

			# Temporarily disable metric computation, we will do it in the loop here.
			_UpperCamelCase   :		Tuple			=						self.compute_metrics
			_UpperCamelCase   :		Union[str, Any]			=						None
			_UpperCamelCase   :		Union[str, Any]			=						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
			try:
				_UpperCamelCase   :		Tuple			=						eval_loop(
				    A_	,       description='Evaluation'	,       prediction_loss_only=True if compute_metrics is None else None	,       ignore_keys=A_	,       )
			finally:
				_UpperCamelCase   :		Optional[int]			=						compute_metrics

			if self.post_process_function is not None and self.compute_metrics is not None:
				_UpperCamelCase   :		int			=						self.post_process_function(A_	,       A_	,       output.predictions)
				_UpperCamelCase   :		int			=						self.compute_metrics(A_)

				# Prefix all keys with metric_key_prefix + '_'
				for key in list(metrics.keys()):
					if not key.startswith(f'''{metric_key_prefix}_'''):
						_UpperCamelCase   :		Tuple			=						metrics.pop(A_)

				self.log(A_)
			else:
				_UpperCamelCase   :		Dict			=						{}

			if self.args.tpu_metrics_debug or self.args.debug:
				# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
				xm.master_print(met.metrics_report())

			_UpperCamelCase   :		str			=						self.callback_handler.on_evaluate(self.args	,       self.state	,       self.control	,       A_)
			return metrics


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case=None	,       __snake_case = "test"):
			_UpperCamelCase   :		Any			=						self.get_test_dataloader(A_)

			# Temporarily disable metric computation, we will do it in the loop here.
			_UpperCamelCase   :		str			=						self.compute_metrics
			_UpperCamelCase   :		Any			=						None
			_UpperCamelCase   :		Tuple			=						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
			try:
				_UpperCamelCase   :		Optional[Any]			=						eval_loop(
				    A_	,       description='Prediction'	,       prediction_loss_only=True if compute_metrics is None else None	,       ignore_keys=A_	,       )
			finally:
				_UpperCamelCase   :		List[Any]			=						compute_metrics

			if self.post_process_function is None or self.compute_metrics is None:
				return output

			_UpperCamelCase   :		List[Any]			=						self.post_process_function(A_	,       A_	,       output.predictions	,       'predict')
			_UpperCamelCase   :		Optional[Any]			=						self.compute_metrics(A_)

			# Prefix all keys with metric_key_prefix + '_'
			for key in list(metrics.keys()):
				if not key.startswith(f'''{metric_key_prefix}_'''):
					_UpperCamelCase   :		Union[str, Any]			=						metrics.pop(A_)

			return PredictionOutput(predictions=predictions.predictions	,       label_ids=predictions.label_ids	,       metrics=A_)






		def 		A__					(				self	,       __snake_case="./"):
			_UpperCamelCase   :		str			=						self.eval_dataset
			_UpperCamelCase   :		List[str]			=						self.get_eval_dataloader(A_)

			_UpperCamelCase   :		List[Any]			=						next(iter(A_))

			# saving device - to make it consistent
			_UpperCamelCase   :		Tuple			=						torch.device('cuda' if torch.cuda.is_available() else 'cpu')

			# convert to tuple
			_UpperCamelCase   :		Union[str, Any]			=						tuple(v.to(A_) for k, v in batch.items())

			logger.info('Converting model to be onnx compatible')
			from pytorch_quantization.nn import TensorQuantizer

			_UpperCamelCase   :		Dict			=						True

			_UpperCamelCase   :		int			=						self.model.to(A_)

			model.eval()
			model.float()

			_UpperCamelCase   :		List[str]			=						model.module if hasattr(A_	,       'module') else model
			quant_trainer.configure_model(A_	,       self.quant_trainer_args)

			_UpperCamelCase   :		Dict			=						os.path.join(A_	,       'model.onnx')
			logger.info(f'''exporting model to {output_model_file}''')

			_UpperCamelCase   :		Optional[int]			=						{0: 'batch_size', 1: 'seq_len'}

			torch.onnx.export(
			    A_	,       A_	,       A_	,       export_params=A_	,       opset_version=13	,       do_constant_folding=A_	,       input_names=['input_ids', 'attention_mask', 'token_type_ids']	,       output_names=['output_start_logits', 'output_end_logits']	,       dynamic_axes={
			        'input_ids': axes,
			        'attention_mask': axes,
			        'token_type_ids': axes,
			        'output_start_logits': axes,
			        'output_end_logits': axes,
			    }	,       verbose=A_	,       )
			logger.info('onnx export finished')







import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		[
    ["""attention""", """attn"""],
    ["""encoder_attention""", """encoder_attn"""],
    ["""q_lin""", """q_proj"""],
    ["""k_lin""", """k_proj"""],
    ["""v_lin""", """v_proj"""],
    ["""out_lin""", """out_proj"""],
    ["""norm_embeddings""", """layernorm_embedding"""],
    ["""position_embeddings""", """embed_positions"""],
    ["""embeddings""", """embed_tokens"""],
    ["""ffn.lin""", """fc"""],
]






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]	)						->				Optional[int]:




	'''simple docstring'''




	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		_UpperCamelCase   :		List[Any]			=						k.replace(UpperCAmelCase_							,       UpperCAmelCase_	)

	if k.startswith('encoder'	):
		_UpperCamelCase   :		Optional[Any]			=						k.replace('.attn'							,       '.self_attn'	)
		_UpperCamelCase   :		Optional[int]			=						k.replace('norm1'							,       'self_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm2'							,       'final_layer_norm'	)
	elif k.startswith('decoder'	):
		_UpperCamelCase   :		Any			=						k.replace('norm1'							,       'self_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm2'							,       'encoder_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm3'							,       'final_layer_norm'	)
	return k






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict	)						->				Optional[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						[
	    'model.encoder.layernorm_embedding.weight',
	    'model.encoder.layernorm_embedding.bias',
	    'model.decoder.layernorm_embedding.weight',
	    'model.decoder.layernorm_embedding.bias',
	]
	for k in keys:
		_UpperCamelCase   :		Optional[int]			=						sd.pop(UpperCAmelCase_	)
		_UpperCamelCase   :		str			=						k.replace('layernorm_embedding'							,       'layer_norm'	)
		assert new_k not in sd
		_UpperCamelCase   :		Tuple			=						v


lowerCAmelCase__								=		["""START"""]






@torch.no_grad()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     Any	)						->				List[str]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						torch.load(UpperCAmelCase_							,       map_location='cpu'	)
	_UpperCamelCase   :		int			=						model['model']
	_UpperCamelCase   :		List[Any]			=						BlenderbotConfig.from_json_file(UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						BlenderbotForConditionalGeneration(UpperCAmelCase_	)
	_UpperCamelCase   :		int			=						m.model.state_dict().keys()
	_UpperCamelCase   :		Union[str, Any]			=						[]
	_UpperCamelCase   :		int			=						{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		_UpperCamelCase   :		Optional[int]			=						rename_state_dict_key(UpperCAmelCase_	)
		if new_k not in valid_keys:
			failures.append([k, new_k]	)
		else:
			_UpperCamelCase   :		int			=						v
	if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
		rename_layernorm_keys(UpperCAmelCase_	)
	m.model.load_state_dict(UpperCAmelCase_							,       strict=UpperCAmelCase_	)
	m.half()
	m.save_pretrained(UpperCAmelCase_	)


if __name__ == "__main__":
							lowerCAmelCase__								=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument("""--src_path""", type=str, help="""like blenderbot-model.bin""")
							parser.add_argument("""--save_dir""", default="""hf_blenderbot""", type=str, help="""Where to save converted model.""")
							parser.add_argument(
							    """--hf_config_json""", default="""blenderbot-3b-config.json""", type=str, help="""Path to config to use"""
							)
							lowerCAmelCase__								=		parser.parse_args()
							convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)




