






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


__magic_name__   :  Dict        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			Union[str, Any]:
      """simple docstring"""

      try:
            with open(_UpperCamelCase       ,							'rb') as flax_state_f:
                  UpperCamelCase					       =  from_bytes(_UpperCamelCase       ,							flax_state_f.read())
      except UnpicklingError as e:
            try:
                  with open(_UpperCamelCase) 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(_UpperCamelCase       ,							_UpperCamelCase)
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			Union[str, Any]:
      """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
      UpperCamelCase					       =  flatten_dict(jax.tree_util.tree_map(lambda _UpperCamelCase: x.dtype == jnp.bfloataa       ,							_UpperCamelCase)).values()
      if any(_UpperCamelCase):
            # 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.')
            UpperCamelCase					       =  jax.tree_util.tree_map(
                lambda _UpperCamelCase: params.astype(np.floataa) if params.dtype == jnp.bfloataa else params       ,							_UpperCamelCase)

      UpperCamelCase					       =  ''

      UpperCamelCase					       =  flatten_dict(_UpperCamelCase       ,							sep='.')
      UpperCamelCase					       =  pt_model.state_dict()

      # keep track of unexpected & missing keys
      UpperCamelCase					       =  []
      UpperCamelCase					       =  set(pt_model_dict.keys())

      for flax_key_tuple, flax_tensor in flax_state_dict.items():
            UpperCamelCase					       =  flax_key_tuple.split('.')

            if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
                  UpperCamelCase					       =  flax_key_tuple_array[:-1] + ['weight']
                  UpperCamelCase					       =  jnp.transpose(_UpperCamelCase       ,							(3, 2, 0, 1))
            elif flax_key_tuple_array[-1] == "kernel":
                  UpperCamelCase					       =  flax_key_tuple_array[:-1] + ['weight']
                  UpperCamelCase					       =  flax_tensor.T
            elif flax_key_tuple_array[-1] == "scale":
                  UpperCamelCase					       =  flax_key_tuple_array[:-1] + ['weight']

            if "time_embedding" not in flax_key_tuple_array:
                  for i, flax_key_tuple_string in enumerate(_UpperCamelCase):
                        UpperCamelCase					       =  (
                            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')
                        )

            UpperCamelCase					       =  '.'.join(_UpperCamelCase)

            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
                        UpperCamelCase					       =  np.asarray(_UpperCamelCase) if not isinstance(_UpperCamelCase       ,							np.ndarray) else flax_tensor
                        UpperCamelCase					       =  torch.from_numpy(_UpperCamelCase)
                        # remove from missing keys
                        missing_keys.remove(_UpperCamelCase)
            else:
                  # weight is not expected by PyTorch model
                  unexpected_keys.append(_UpperCamelCase)

      pt_model.load_state_dict(_UpperCamelCase)

      # re-transform missing_keys to list
      UpperCamelCase					       =  list(_UpperCamelCase)

      if len(_UpperCamelCase) > 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(_UpperCamelCase) > 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











from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
       import datasets





class 					A__     (     __snake_case ):

     '''simple docstring'''






     @slow
     @require_torch
     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'   ,						'prajjwal1/bert-tiny' )
           UpperCamelCase					       =  BertTokenizer.from_pretrained('bert-base-uncased' )

           UpperCamelCase					       =  bertabert.config.encoder.vocab_size
           UpperCamelCase					       =  tokenizer.sep_token_id
           UpperCamelCase					       =  tokenizer.cls_token_id
           UpperCamelCase					       =  128

           UpperCamelCase					       =  datasets.load_dataset('cnn_dailymail'   ,						'3.0.0'   ,						split='train[:1%]' )
           UpperCamelCase					       =  datasets.load_dataset('cnn_dailymail'   ,						'3.0.0'   ,						split='validation[:1%]' )

           UpperCamelCase					       =  train_dataset.select(range(32 ) )
           UpperCamelCase					       =  val_dataset.select(range(16 ) )

           UpperCamelCase					       =  4

           def _map_to_encoder_decoder_inputs(_SCREAMING_SNAKE_CASE      :    Tuple ):
                 # Tokenizer will automatically set [BOS] <text> [EOS]
                 UpperCamelCase					       =  tokenizer(batch['article']   ,						padding='max_length'   ,						truncation=_SCREAMING_SNAKE_CASE   ,						max_length=512 )
                 UpperCamelCase					       =  tokenizer(batch['highlights']   ,						padding='max_length'   ,						truncation=_SCREAMING_SNAKE_CASE   ,						max_length=128 )
                 UpperCamelCase					       =  inputs.input_ids
                 UpperCamelCase					       =  inputs.attention_mask

                 UpperCamelCase					       =  outputs.input_ids
                 UpperCamelCase					       =  outputs.input_ids.copy()
                 UpperCamelCase					       =  [
                     [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                 ]
                 UpperCamelCase					       =  outputs.attention_mask

                 assert all(len(_SCREAMING_SNAKE_CASE ) == 512 for x in inputs.input_ids )
                 assert all(len(_SCREAMING_SNAKE_CASE ) == 128 for x in outputs.input_ids )

                 return batch

           def _compute_metrics(_SCREAMING_SNAKE_CASE      :    str ):
                 UpperCamelCase					       =  pred.label_ids
                 UpperCamelCase					       =  pred.predictions

                 # all unnecessary tokens are removed
                 UpperCamelCase					       =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE )

                 UpperCamelCase					       =  sum([int(pred_str[i] == label_str[i] ) for i in range(len(_SCREAMING_SNAKE_CASE ) )] ) / len(_SCREAMING_SNAKE_CASE )

                 return {"accuracy": accuracy}

           # map train dataset
           UpperCamelCase					       =  train_dataset.map(
               _map_to_encoder_decoder_inputs   ,						batched=_SCREAMING_SNAKE_CASE   ,						batch_size=_SCREAMING_SNAKE_CASE   ,						remove_columns=['article', 'highlights']   ,						)
           train_dataset.set_format(
               type='torch'   ,						columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']   ,						)

           # same for validation dataset
           UpperCamelCase					       =  val_dataset.map(
               _map_to_encoder_decoder_inputs   ,						batched=_SCREAMING_SNAKE_CASE   ,						batch_size=_SCREAMING_SNAKE_CASE   ,						remove_columns=['article', 'highlights']   ,						)
           val_dataset.set_format(
               type='torch'   ,						columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']   ,						)

           UpperCamelCase					       =  self.get_auto_remove_tmp_dir()

           UpperCamelCase					       =  SeqaSeqTrainingArguments(
               output_dir=_SCREAMING_SNAKE_CASE   ,						per_device_train_batch_size=_SCREAMING_SNAKE_CASE   ,						per_device_eval_batch_size=_SCREAMING_SNAKE_CASE   ,						predict_with_generate=_SCREAMING_SNAKE_CASE   ,						evaluation_strategy='steps'   ,						do_train=_SCREAMING_SNAKE_CASE   ,						do_eval=_SCREAMING_SNAKE_CASE   ,						warmup_steps=0   ,						eval_steps=2   ,						logging_steps=2   ,						)

           # instantiate trainer
           UpperCamelCase					       =  SeqaSeqTrainer(
               model=_SCREAMING_SNAKE_CASE   ,						args=_SCREAMING_SNAKE_CASE   ,						compute_metrics=_compute_metrics   ,						train_dataset=_SCREAMING_SNAKE_CASE   ,						eval_dataset=_SCREAMING_SNAKE_CASE   ,						tokenizer=_SCREAMING_SNAKE_CASE   ,						)

           # start training
           trainer.train()



