
import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
						import bitsandbytes as bnb
						import torch
						import torch.nn as nn

						from ..pytorch_utils import ConvaD

if is_accelerate_available():
						from accelerate import init_empty_weights
						from accelerate.utils import find_tied_parameters

__a  			=					logging.get_logger(__name__)




def 				a						(							snake_case__:					Optional[int]						,   snake_case__:					Dict						,   snake_case__:					int						,   snake_case__:					List[str]=None						,   snake_case__:					List[Any]=None					):


				'''simple docstring'''
				# Recurse if needed
				if "." in tensor_name:
								lowercase_       =					tensor_name.split('''.'''					)
								for split in splits[:-1]:
												lowercase_       =					getattr(snake_case__						,   snake_case__					)
												if new_module is None:
																raise ValueError(F'''{module} has no attribute {split}.'''					)
												lowercase_       =					new_module
								lowercase_       =					splits[-1]

				if tensor_name not in module._parameters and tensor_name not in module._buffers:
								raise ValueError(F'''{module} does not have a parameter or a buffer named {tensor_name}.'''					)
				lowercase_       =					tensor_name in module._buffers
				lowercase_       =					getattr(snake_case__						,   snake_case__					)

				if old_value.device == torch.device('''meta'''					) and device not in ["meta", torch.device('''meta'''					)] and value is None:
								raise ValueError(F'''{tensor_name} is on the meta device, we need a `value` to put in on {device}.'''					)

				lowercase_       =					False
				lowercase_       =					False
				if is_buffer or not is_bitsandbytes_available():
								lowercase_       =					False
								lowercase_       =					False
				else:
								lowercase_       =					hasattr(bnb.nn						,   '''Params4bit'''					) and isinstance(module._parameters[tensor_name]						,   bnb.nn.Paramsabit					)
								lowercase_       =					isinstance(module._parameters[tensor_name]						,   bnb.nn.IntaParams					)

				if is_abit or is_abit:
								lowercase_       =					module._parameters[tensor_name]
								if param.device.type != "cuda":
												if value is None:
																lowercase_       =					old_value.to(snake_case__					)
												elif isinstance(snake_case__						,   torch.Tensor					):
																lowercase_       =					value.to('''cpu'''					)
																if value.dtype == torch.inta:
																				lowercase_       =					version.parse(importlib.metadata.version('''bitsandbytes'''					)					) > version.parse(
																				    '''0.37.2'''					)
																				if not is_abit_serializable:
																								raise ValueError(
																								    '''Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. '''
																								    '''Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.'''					)
												else:
																lowercase_       =					torch.tensor(snake_case__						,   device='''cpu'''					)

												# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
												# Since weights are saved in the correct "orientation", we skip transposing when loading.
												if issubclass(module.source_cls						,   snake_case__					) and fpaa_statistics is None:
																lowercase_       =					new_value.T

												lowercase_       =					old_value.__dict__
												if is_abit:
																lowercase_       =					bnb.nn.IntaParams(snake_case__						,   requires_grad=snake_case__						,   **snake_case__					).to(snake_case__					)
												elif is_abit:
																lowercase_       =					bnb.nn.Paramsabit(snake_case__						,   requires_grad=snake_case__						,   **snake_case__					).to(snake_case__					)

												lowercase_       =					new_value
												if fpaa_statistics is not None:
																setattr(module.weight						,   '''SCB'''						,   fpaa_statistics.to(snake_case__					)					)

				else:
								if value is None:
												lowercase_       =					old_value.to(snake_case__					)
								elif isinstance(snake_case__						,   torch.Tensor					):
												lowercase_       =					value.to(snake_case__					)
								else:
												lowercase_       =					torch.tensor(snake_case__						,   device=snake_case__					)

								if is_buffer:
												lowercase_       =					new_value
								else:
												lowercase_       =					nn.Parameter(snake_case__						,   requires_grad=old_value.requires_grad					)
												lowercase_       =					new_value




def 				a						(							snake_case__:					str						,   snake_case__:					Union[str, Any]=None						,   snake_case__:					Any=None						,   snake_case__:					List[str]=None						,   snake_case__:					Optional[Any]=False					):


				'''simple docstring'''
				for name, module in model.named_children():
								if current_key_name is None:
												lowercase_       =					[]
								current_key_name.append(snake_case__					)

								if (isinstance(snake_case__						,   nn.Linear					) or isinstance(snake_case__						,   snake_case__					)) and name not in modules_to_not_convert:
												# Check if the current key is not in the `modules_to_not_convert`
												if not any(key in '''.'''.join(snake_case__					) for key in modules_to_not_convert					):
																with init_empty_weights():
																				if isinstance(snake_case__						,   snake_case__					):
																								lowercase_  ,	lowercase_       =					module.weight.shape
																				else:
																								lowercase_       =					module.in_features
																								lowercase_       =					module.out_features

																				if quantization_config.quantization_method() == "llm_int8":
																								lowercase_       =					bnb.nn.LinearabitLt(
																								    snake_case__						,   snake_case__						,   module.bias is not None						,   has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight						,   threshold=quantization_config.llm_inta_threshold						,   )
																								lowercase_       =					True
																				else:
																								if (
																								    quantization_config.llm_inta_skip_modules is not None
																								    and name in quantization_config.llm_inta_skip_modules
																								):
																												pass
																								else:
																												lowercase_       =					bnb.nn.Linearabit(
																												    snake_case__						,   snake_case__						,   module.bias is not None						,   quantization_config.bnb_abit_compute_dtype						,   compress_statistics=quantization_config.bnb_abit_use_double_quant						,   quant_type=quantization_config.bnb_abit_quant_type						,   )
																												lowercase_       =					True
                    # Store the module class in case we need to transpose the weight later
																				lowercase_       =					type(snake_case__					)
																				# Force requires grad to False to avoid unexpected errors
																				model._modules[name].requires_grad_(snake_case__					)
								if len(list(module.children()					)					) > 0:
												lowercase_  ,	lowercase_       =					_replace_with_bnb_linear(
												    snake_case__						,   snake_case__						,   snake_case__						,   snake_case__						,   has_been_replaced=snake_case__						,   )
								# Remove the last key for recursion
								current_key_name.pop(-1					)
				return model, has_been_replaced




def 				a						(							snake_case__:					Any						,   snake_case__:					Any=None						,   snake_case__:					Union[str, Any]=None						,   snake_case__:					str=None					):


				'''simple docstring'''
				lowercase_       =					['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
				lowercase_  ,	lowercase_       =					_replace_with_bnb_linear(
				    snake_case__						,   snake_case__						,   snake_case__						,   snake_case__					)

				if not has_been_replaced:
								logger.warning(
								    '''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
								    ''' Please double check your model architecture, or submit an issue on github if you think this is'''
								    ''' a bug.'''					)

				return model




def 				a						(							*snake_case__:					str						,   **snake_case__:					Dict					):


				'''simple docstring'''
				warnings.warn(
				    '''`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead'''						,   snake_case__						,   )
				return replace_with_bnb_linear(*snake_case__						,   **snake_case__					)




def 				a						(							*snake_case__:					Any						,   **snake_case__:					List[Any]					):


				'''simple docstring'''
				warnings.warn(
				    '''`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead'''						,   snake_case__						,   )
				return set_module_quantized_tensor_to_device(*snake_case__						,   **snake_case__					)




def 				a						(							snake_case__:					Optional[Any]					):


				'''simple docstring'''
				lowercase_       =					deepcopy(snake_case__					)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
				tied_model.tie_weights()

				lowercase_       =					find_tied_parameters(snake_case__					)
				# For compatibility with Accelerate < 0.18
				if isinstance(snake_case__						,   snake_case__					):
								lowercase_       =					sum(list(tied_params.values()					)						,   []					) + list(tied_params.keys()					)
				else:
								lowercase_       =					sum(snake_case__						,   []					)
				lowercase_       =					len(snake_case__					) > 0

				# Check if it is a base model
				lowercase_       =					not hasattr(snake_case__						,   model.base_model_prefix					)

				# Ignore this for base models (BertModel, GPT2Model, etc.)
				if (not has_tied_params) and is_base_model:
								return []

				# otherwise they have an attached head
				lowercase_       =					list(model.named_children()					)
				lowercase_       =					[list_modules[-1][0]]

				# add last module together with tied weights
				lowercase_       =					set(snake_case__					) - set(snake_case__					)
				lowercase_       =					list(set(snake_case__					)					) + list(snake_case__					)

				# remove ".weight" from the keys
				lowercase_       =					['''.weight''', '''.bias''']
				lowercase_       =					[]
				for name in list_untouched:
								for name_to_remove in names_to_remove:
												if name_to_remove in name:
																lowercase_       =					name.replace(snake_case__						,   ''''''					)
								filtered_module_names.append(snake_case__					)

				return filtered_module_names






'''simple docstring'''







import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


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






class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							*__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											super().__init__(*__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Tuple													=       eval_examples
											lowercase_	:     Tuple													=       post_process_function






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = "eval"				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       gen_kwargs.copy()
											lowercase_	:     List[str]													=       (
											    gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''		) is not None else self.args.generation_max_length
											)
											lowercase_	:     str													=       (
											    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''		) is not None else self.args.generation_num_beams
											)
											lowercase_	:     Dict													=       gen_kwargs

											lowercase_	:     List[Any]													=       self.eval_dataset if eval_dataset is None else eval_dataset
											lowercase_	:     List[str]													=       self.get_eval_dataloader(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       self.eval_examples if eval_examples is None else eval_examples

											# Temporarily disable metric computation, we will do it in the loop here.
											lowercase_	:     Union[str, Any]													=       self.compute_metrics
											lowercase_	:     Optional[int]													=       None
											lowercase_	:     Tuple													=       time.time()
											lowercase_	:     Tuple													=       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																	lowercase_	:     str													=       eval_loop(
																	    __SCREAMING_SNAKE_CASE				,							description='''Evaluation'''				,							prediction_loss_only=True if compute_metrics is None else None				,							ignore_keys=__SCREAMING_SNAKE_CASE				,							metric_key_prefix=__SCREAMING_SNAKE_CASE				,							)
											finally:
																	lowercase_	:     Any													=       compute_metrics
											lowercase_	:     Any													=       self.args.eval_batch_size * self.args.world_size
											if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
																	start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
											output.metrics.update(
											    speed_metrics(
											        __SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							num_samples=output.num_samples				,							num_steps=math.ceil(output.num_samples / total_batch_size		)				,							)		)

											if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
																	# Only the main node write the results by default
																	lowercase_	:     Optional[Any]													=       self.post_process_function(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     Optional[int]													=       self.compute_metrics(__SCREAMING_SNAKE_CASE		)

																	# Prefix all keys with metric_key_prefix + '_'
																	for key in list(metrics.keys()		):
																							if not key.startswith(F'''{metric_key_prefix}_'''		):
																													lowercase_	:     List[Any]													=       metrics.pop(__SCREAMING_SNAKE_CASE		)

																	metrics.update(output.metrics		)
											else:
																	lowercase_	:     List[Any]													=       output.metrics

											if self.args.should_log:
																	# Only the main node log the results by default
																	self.log(__SCREAMING_SNAKE_CASE		)

											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()		)

											lowercase_	:     List[Any]													=       self.callback_handler.on_evaluate(self.args				,							self.state				,							self.control				,							__SCREAMING_SNAKE_CASE		)
											return metrics






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE = "test"				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       gen_kwargs.copy()

											lowercase_	:     Tuple													=       self.get_test_dataloader(__SCREAMING_SNAKE_CASE		)

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

											lowercase_	:     Tuple													=       self.args.eval_batch_size * self.args.world_size
											if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
																	start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
											output.metrics.update(
											    speed_metrics(
											        __SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							num_samples=output.num_samples				,							num_steps=math.ceil(output.num_samples / total_batch_size		)				,							)		)
											if self.post_process_function is None or self.compute_metrics is None:
																	return output

											lowercase_	:     Any													=       self.post_process_function(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							'''predict'''		)
											lowercase_	:     str													=       self.compute_metrics(__SCREAMING_SNAKE_CASE		)

											# Prefix all keys with metric_key_prefix + '_'
											for key in list(metrics.keys()		):
																	if not key.startswith(F'''{metric_key_prefix}_'''		):
																							lowercase_	:     Optional[int]													=       metrics.pop(__SCREAMING_SNAKE_CASE		)
											metrics.update(output.metrics		)
											return PredictionOutput(predictions=predictions.predictions				,							label_ids=predictions.label_ids				,							metrics=__SCREAMING_SNAKE_CASE		)



