


'''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     lowercase__  (						_snake_case					):





							'''simple docstring'''


							def __init__(    self    ,					*__snake_case    ,					__snake_case=None    ,					__snake_case=None    ,					**__snake_case  ):
											super().__init__(*__snake_case    ,					**__snake_case  )
											_SCREAMING_SNAKE_CASE :       List[str]     		=      eval_examples
											_SCREAMING_SNAKE_CASE :       str     		=      post_process_function






							def UpperCAmelCase_	(    self    ,					__snake_case = None    ,					__snake_case=None    ,					__snake_case = None    ,					__snake_case = "eval"    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      gen_kwargs.copy()
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      (
											    gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""  ) is not None else self.args.generation_max_length
											)
											_SCREAMING_SNAKE_CASE :       Any     		=      (
											    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""  ) is not None else self.args.generation_num_beams
											)
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      gen_kwargs

											_SCREAMING_SNAKE_CASE :       Dict     		=      self.eval_dataset if eval_dataset is None else eval_dataset
											_SCREAMING_SNAKE_CASE :       Dict     		=      self.get_eval_dataloader(__snake_case  )
											_SCREAMING_SNAKE_CASE :       List[str]     		=      self.eval_examples if eval_examples is None else eval_examples

											# Temporarily disable metric computation, we will do it in the loop here.
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.compute_metrics
											_SCREAMING_SNAKE_CASE :       Tuple     		=      None
											_SCREAMING_SNAKE_CASE :       Dict     		=      time.time()
											_SCREAMING_SNAKE_CASE :       Dict     		=      self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      eval_loop(
															    __snake_case    ,					description="""Evaluation"""    ,					prediction_loss_only=True if compute_metrics is None else None    ,					ignore_keys=__snake_case    ,					metric_key_prefix=__snake_case    ,					)
											finally:
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      compute_metrics
											_SCREAMING_SNAKE_CASE :       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(
											        __snake_case    ,					__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
															_SCREAMING_SNAKE_CASE :       int     		=      self.post_process_function(__snake_case    ,					__snake_case    ,					__snake_case  )
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.compute_metrics(__snake_case  )

															# Prefix all keys with metric_key_prefix + '_'
															for key in list(metrics.keys()  ):
																			if not key.startswith(f"""{metric_key_prefix}_"""  ):
																							_SCREAMING_SNAKE_CASE :       Optional[int]     		=      metrics.pop(__snake_case  )

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

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

											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.callback_handler.on_evaluate(self.args    ,					self.state    ,					self.control    ,					__snake_case  )
											return metrics






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case=None    ,					__snake_case = "test"    ,					**__snake_case  ):
											_SCREAMING_SNAKE_CASE :       str     		=      gen_kwargs.copy()

											_SCREAMING_SNAKE_CASE :       int     		=      self.get_test_dataloader(__snake_case  )

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

											_SCREAMING_SNAKE_CASE :       Dict     		=      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(
											        __snake_case    ,					__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

											_SCREAMING_SNAKE_CASE :       int     		=      self.post_process_function(__snake_case    ,					__snake_case    ,					__snake_case    ,					"""predict"""  )
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.compute_metrics(__snake_case  )

											# Prefix all keys with metric_key_prefix + '_'
											for key in list(metrics.keys()  ):
															if not key.startswith(f"""{metric_key_prefix}_"""  ):
																			_SCREAMING_SNAKE_CASE :       str     		=      metrics.pop(__snake_case  )
											metrics.update(output.metrics  )
											return PredictionOutput(predictions=predictions.predictions    ,					label_ids=predictions.label_ids    ,					metrics=__snake_case  )








'''simple docstring'''


from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


UpperCAmelCase_		:   List[Any]							   =		logging.get_logger(__name__)







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				try:
								with open(SCREAMING_SNAKE_CASE__							,      """rb"""     ) as flax_state_f:
												_SCREAMING_SNAKE_CASE :       Dict     		=      from_bytes(SCREAMING_SNAKE_CASE__							,      flax_state_f.read()     )
				except UnpicklingError as e:
								try:
												with open(SCREAMING_SNAKE_CASE__     ) as f:
																if f.read().startswith("""version"""     ):
																				raise OSError(
																				    """You seem to have cloned a repository without having git-lfs installed. Please"""
																				    """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
																				    """ folder you cloned."""     )
																else:
																				raise ValueError from e
								except (UnicodeDecodeError, ValueError):
												raise EnvironmentError(f"""Unable to convert {model_file} to Flax deserializable object. """     )

				return load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				try:
								import torch  # noqa: F401
				except ImportError:
								logger.error(
								    """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
								    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
								    """ instructions."""     )
								raise

				# check if we have bf16 weights
				_SCREAMING_SNAKE_CASE :       List[Any]     		=      flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE__     : x.dtype == jnp.bfloataa							,      SCREAMING_SNAKE_CASE__     )     ).values()
				if any(SCREAMING_SNAKE_CASE__     ):
								# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

								# and bf16 is not fully supported in PT yet.
								logger.warning(
								    """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
								    """before loading those in PyTorch model."""     )
								_SCREAMING_SNAKE_CASE :       Dict     		=      jax.tree_util.tree_map(
								    lambda SCREAMING_SNAKE_CASE__     : params.astype(np.floataa     ) if params.dtype == jnp.bfloataa else params							,      SCREAMING_SNAKE_CASE__     )

				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      """"""

				_SCREAMING_SNAKE_CASE :       str     		=      flatten_dict(SCREAMING_SNAKE_CASE__							,      sep="""."""     )
				_SCREAMING_SNAKE_CASE :       str     		=      pt_model.state_dict()

				# keep track of unexpected & missing keys
				_SCREAMING_SNAKE_CASE :       Tuple     		=      []
				_SCREAMING_SNAKE_CASE :       int     		=      set(pt_model_dict.keys()     )

				for flax_key_tuple, flax_tensor in flax_state_dict.items():
								_SCREAMING_SNAKE_CASE :       Any     		=      flax_key_tuple.split("""."""     )

								if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
												_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      flax_key_tuple_array[:-1] + ["""weight"""]
												_SCREAMING_SNAKE_CASE :       List[str]     		=      jnp.transpose(SCREAMING_SNAKE_CASE__							,      (3, 2, 0, 1)     )
								elif flax_key_tuple_array[-1] == "kernel":
												_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      flax_key_tuple_array[:-1] + ["""weight"""]
												_SCREAMING_SNAKE_CASE :       Any     		=      flax_tensor.T
								elif flax_key_tuple_array[-1] == "scale":
												_SCREAMING_SNAKE_CASE :       Optional[int]     		=      flax_key_tuple_array[:-1] + ["""weight"""]

								if "time_embedding" not in flax_key_tuple_array:
												for i, flax_key_tuple_string in enumerate(SCREAMING_SNAKE_CASE__     ):
																_SCREAMING_SNAKE_CASE :       Optional[int]     		=      (
																    flax_key_tuple_string.replace("""_0"""							,      """.0"""     )
																    .replace("""_1"""							,      """.1"""     )
																    .replace("""_2"""							,      """.2"""     )
																    .replace("""_3"""							,      """.3"""     )
																    .replace("""_4"""							,      """.4"""     )
																    .replace("""_5"""							,      """.5"""     )
																    .replace("""_6"""							,      """.6"""     )
																    .replace("""_7"""							,      """.7"""     )
																    .replace("""_8"""							,      """.8"""     )
																    .replace("""_9"""							,      """.9"""     )
																)

								_SCREAMING_SNAKE_CASE :       Tuple     		=      """.""".join(SCREAMING_SNAKE_CASE__     )

								if flax_key in pt_model_dict:
												if flax_tensor.shape != pt_model_dict[flax_key].shape:
																raise ValueError(
																    f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
																    f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""     )
												else:
																# add weight to pytorch dict
																_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      np.asarray(SCREAMING_SNAKE_CASE__     ) if not isinstance(SCREAMING_SNAKE_CASE__							,      np.ndarray     ) else flax_tensor
																_SCREAMING_SNAKE_CASE :       int     		=      torch.from_numpy(SCREAMING_SNAKE_CASE__     )
																# remove from missing keys
																missing_keys.remove(SCREAMING_SNAKE_CASE__     )
								else:
												# weight is not expected by PyTorch model
												unexpected_keys.append(SCREAMING_SNAKE_CASE__     )

				pt_model.load_state_dict(SCREAMING_SNAKE_CASE__     )

				# re-transform missing_keys to list
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      list(SCREAMING_SNAKE_CASE__     )

				if len(SCREAMING_SNAKE_CASE__     ) > 0:
								logger.warning(
								    """Some weights of the Flax model were not used when initializing the PyTorch model"""
								    f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
								    f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
								    """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
								    f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
								    """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
								    """ FlaxBertForSequenceClassification model)."""     )
				if len(SCREAMING_SNAKE_CASE__     ) > 0:
								logger.warning(
								    f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
								    f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
								    """ use it for predictions and inference."""     )

				return pt_model




