




'''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_    	=				logging.get_logger(__name__)




def   lowercase__(			__UpperCamelCase:					Any      ,__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      try:
            with open(__UpperCamelCase      ,'rb'    ) as flax_state_f:
                  SCREAMING_SNAKE_CASE   :      Any    					=						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:					Tuple      ,__UpperCamelCase:					Optional[int]    ):

      """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   :      Optional[int]    					=						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.'    )
            SCREAMING_SNAKE_CASE   :      List[str]    					=						jax.tree_util.tree_map(
                lambda __UpperCamelCase    : params.astype(np.floataa    ) if params.dtype == jnp.bfloataa else params      ,__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      Any    					=						''

      SCREAMING_SNAKE_CASE   :      List[Any]    					=						flatten_dict(__UpperCamelCase      ,sep='.'    )
      SCREAMING_SNAKE_CASE   :      int    					=						pt_model.state_dict()

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

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

            if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
                  SCREAMING_SNAKE_CASE   :      Any    					=						flax_key_tuple_array[:-1] + ['weight']
                  SCREAMING_SNAKE_CASE   :      List[str]    					=						jnp.transpose(__UpperCamelCase      ,(3, 2, 0, 1)    )
            elif flax_key_tuple_array[-1] == "kernel":
                  SCREAMING_SNAKE_CASE   :      int    					=						flax_key_tuple_array[:-1] + ['weight']
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						flax_tensor.T
            elif flax_key_tuple_array[-1] == "scale":
                  SCREAMING_SNAKE_CASE   :      Tuple    					=						flax_key_tuple_array[:-1] + ['weight']

            if "time_embedding" not in flax_key_tuple_array:
                  for i, flax_key_tuple_string in enumerate(__UpperCamelCase    ):
                        SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						(
                            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   :      List[str]    					=						'.'.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
                        SCREAMING_SNAKE_CASE   :      Tuple    					=						np.asarray(__UpperCamelCase    ) if not isinstance(__UpperCamelCase      ,np.ndarray    ) else flax_tensor
                        SCREAMING_SNAKE_CASE   :      List[str]    					=						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
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						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






'''simple docstring'''







import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							tempfile.mkdtemp()
            _lowerCAmelCase     :				List[str]       =							5

            # Realm tok
            _lowerCAmelCase     :				str       =							[
                '[UNK]',
                '[CLS]',
                '[SEP]',
                '[PAD]',
                '[MASK]',
                'test',
                'question',
                'this',
                'is',
                'the',
                'first',
                'second',
                'third',
                'fourth',
                'fifth',
                'record',
                'want',
                '##want',
                '##ed',
                'wa',
                'un',
                'runn',
                '##ing',
                ',',
                'low',
                'lowest',
            ]
            _lowerCAmelCase     :				Union[str, Any]       =							os.path.join(self.tmpdirname   ,						'realm_tokenizer' )
            os.makedirs(snake_case__   ,						exist_ok=snake_case__ )
            _lowerCAmelCase     :				Dict       =							os.path.join(snake_case__   ,						VOCAB_FILES_NAMES['vocab_file'] )
            with open(self.vocab_file   ,						'w'   ,						encoding='utf-8' ) as vocab_writer:
                   vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )

            _lowerCAmelCase     :				List[str]       =							os.path.join(self.tmpdirname   ,						'realm_block_records' )
            os.makedirs(snake_case__   ,						exist_ok=snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,						'realm_tokenizer' ) )







     def a						(			self ):

            '''simple docstring'''

            shutil.rmtree(self.tmpdirname )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							RealmConfig(num_block_records=self.num_block_records )
            return config







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							Dataset.from_dict(
                {
                    'id': ['0', '1'],
                    'question': ['foo', 'bar'],
                    'answers': [['Foo', 'Bar'], ['Bar']],
                } )
            return dataset







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							np.array(
                [
                    b'This is the first record',
                    b'This is the second record',
                    b'This is the third record',
                    b'This is the fourth record',
                    b'This is the fifth record',
                    b'This is a longer longer longer record',
                ]   ,						dtype=snake_case__   ,						)
            return block_records







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							RealmRetriever(
                block_records=self.get_dummy_block_records()   ,						tokenizer=self.get_tokenizer()   ,						)
            return retriever







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.get_config()
            _lowerCAmelCase     :				List[Any]       =							self.get_dummy_retriever()
            _lowerCAmelCase     :				Any       =							retriever.tokenizer

            _lowerCAmelCase     :				Tuple       =							np.array([0, 3]   ,						dtype='long' )
            _lowerCAmelCase     :				Dict       =							tokenizer(['Test question'] ).input_ids
            _lowerCAmelCase     :				int       =							tokenizer(
                ['the fourth']   ,						add_special_tokens=snake_case__   ,						return_token_type_ids=snake_case__   ,						return_attention_mask=snake_case__   ,						).input_ids
            _lowerCAmelCase     :				Tuple       =							config.reader_seq_len

            _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							retriever(
                snake_case__   ,						snake_case__   ,						answer_ids=snake_case__   ,						max_length=snake_case__   ,						return_tensors='np' )

            self.assertEqual(len(snake_case__ )   ,						2 )
            self.assertEqual(len(snake_case__ )   ,						2 )
            self.assertEqual(len(snake_case__ )   ,						2 )
            self.assertEqual(concat_inputs.input_ids.shape   ,						(2, 10) )
            self.assertEqual(concat_inputs.attention_mask.shape   ,						(2, 10) )
            self.assertEqual(concat_inputs.token_type_ids.shape   ,						(2, 10) )
            self.assertEqual(concat_inputs.special_tokens_mask.shape   ,						(2, 10) )
            self.assertEqual(
                tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] )   ,						['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'first', 'record', '[SEP]']   ,						)
            self.assertEqual(
                tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] )   ,						['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'fourth', 'record', '[SEP]']   ,						)







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.get_config()
            _lowerCAmelCase     :				int       =							self.get_dummy_retriever()
            _lowerCAmelCase     :				List[str]       =							retriever.tokenizer

            _lowerCAmelCase     :				List[Any]       =							np.array([0, 3, 5]   ,						dtype='long' )
            _lowerCAmelCase     :				str       =							tokenizer(['Test question'] ).input_ids
            _lowerCAmelCase     :				Union[str, Any]       =							tokenizer(
                ['the fourth', 'longer longer']   ,						add_special_tokens=snake_case__   ,						return_token_type_ids=snake_case__   ,						return_attention_mask=snake_case__   ,						).input_ids
            _lowerCAmelCase     :				Optional[int]       =							config.reader_seq_len

            _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							retriever(
                snake_case__   ,						snake_case__   ,						answer_ids=snake_case__   ,						max_length=snake_case__   ,						return_tensors='np' )

            self.assertEqual([False, True, True]   ,						snake_case__ )
            self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]   ,						snake_case__ )
            self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							self.get_dummy_retriever()
            retriever.save_pretrained(os.path.join(self.tmpdirname   ,						'realm_block_records' ) )

            # Test local path
            _lowerCAmelCase     :				Union[str, Any]       =							retriever.from_pretrained(os.path.join(self.tmpdirname   ,						'realm_block_records' ) )
            self.assertEqual(retriever.block_records[0]   ,						b'This is the first record' )

            # Test mocked remote path
            with patch('transformers.models.realm.retrieval_realm.hf_hub_download' ) as mock_hf_hub_download:
                   _lowerCAmelCase     :				Any       =							os.path.join(
                       os.path.join(self.tmpdirname   ,						'realm_block_records' )   ,						_REALM_BLOCK_RECORDS_FILENAME )
                   _lowerCAmelCase     :				str       =							RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa' )

            self.assertEqual(retriever.block_records[0]   ,						b'This is the first record' )

