"""simple docstring"""





import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
       from transformers.models.rag.configuration_rag import RagConfig
       from transformers.models.rag.tokenization_rag import RagTokenizer


@require_faiss
@require_torch
class      UpperCAmelCase_	(    _a):


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		Tuple       =	tempfile.mkdtemp()
             lowercase__			:		Dict       =	8

             # DPR tok
             lowercase__			:		str       =	[
                 '[UNK]',
                 '[CLS]',
                 '[SEP]',
                 '[PAD]',
                 '[MASK]',
                 'want',
                 '##want',
                 '##ed',
                 'wa',
                 'un',
                 'runn',
                 '##ing',
                 ',',
                 'low',
                 'lowest',
             ]
             lowercase__			:		List[str]       =	os.path.join(self.tmpdirname		,			'dpr_tokenizer'							)
             os.makedirs(a		,			exist_ok=a							)
             lowercase__			:		int       =	os.path.join(a		,			DPR_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]							)							)

             # BART tok
             lowercase__			:		List[Any]       =	[
                 'l',
                 'o',
                 'w',
                 'e',
                 'r',
                 's',
                 't',
                 'i',
                 'd',
                 'n',
                 '\u0120',
                 '\u0120l',
                 '\u0120n',
                 '\u0120lo',
                 '\u0120low',
                 'er',
                 '\u0120lowest',
                 '\u0120newer',
                 '\u0120wider',
                 '<unk>',
             ]
             lowercase__			:		Any       =	dict(zip(a		,			range(len(a							)							)							)							)
             lowercase__			:		Optional[Any]       =	['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
             lowercase__			:		List[Any]       =	{'unk_token': '<unk>'}

             lowercase__			:		Union[str, Any]       =	os.path.join(self.tmpdirname		,			'bart_tokenizer'							)
             os.makedirs(a		,			exist_ok=a							)
             lowercase__			:		str       =	os.path.join(a		,			BART_VOCAB_FILES_NAMES['vocab_file']							)
             lowercase__			:		Tuple       =	os.path.join(a		,			BART_VOCAB_FILES_NAMES['merges_file']							)
             with open(self.vocab_file		,			'w'		,			encoding='utf-8'							) as fp:
                    fp.write(json.dumps(a							) + '\n'							)
             with open(self.merges_file		,			'w'		,			encoding='utf-8'							) as fp:
                    fp.write('\n'.join(a							)							)


      def 		_UpperCAmelCase       (   self							)       ->					DPRQuestionEncoderTokenizer:
             return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,			'dpr_tokenizer'							)							)


      def 		_UpperCAmelCase       (   self							)       ->					BartTokenizer:
             return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		,			'bart_tokenizer'							)							)


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             shutil.rmtree(self.tmpdirname							)


      @require_tokenizers
      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__			:		List[str]       =	os.path.join(self.tmpdirname		,			'rag_tokenizer'							)
             lowercase__			:		Optional[Any]       =	RagConfig(question_encoder=DPRConfig().to_dict()		,			generator=BartConfig().to_dict()							)
             lowercase__			:		Dict       =	RagTokenizer(question_encoder=self.get_dpr_tokenizer()		,			generator=self.get_bart_tokenizer()							)
             rag_config.save_pretrained(a							)
             rag_tokenizer.save_pretrained(a							)
             lowercase__			:		Optional[Any]       =	RagTokenizer.from_pretrained(a		,			config=a							)
             self.assertIsInstance(new_rag_tokenizer.question_encoder		,			a							)
             self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()		,			rag_tokenizer.question_encoder.get_vocab()							)
             self.assertIsInstance(new_rag_tokenizer.generator		,			a							)
             self.assertEqual(new_rag_tokenizer.generator.get_vocab()		,			rag_tokenizer.generator.get_vocab()							)


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             lowercase__			:		str       =	RagTokenizer.from_pretrained('facebook/rag-token-nq'							)
             lowercase__			:		int       =	[
                 'who got the first nobel prize in physics',
                 'when is the next deadpool movie being released',
                 'which mode is used for short wave broadcast service',
                 'who is the owner of reading football club',
                 'when is the next scandal episode coming out',
                 'when is the last time the philadelphia won the superbowl',
                 'what is the most current adobe flash player version',
                 'how many episodes are there in dragon ball z',
                 'what is the first step in the evolution of the eye',
                 'where is gall bladder situated in human body',
                 'what is the main mineral in lithium batteries',
                 'who is the president of usa right now',
                 'where do the greasers live in the outsiders',
                 'panda is a national animal of which country',
                 'what is the name of manchester united stadium',
             ]
             lowercase__			:		Optional[Any]       =	tokenizer(a							)
             self.assertIsNotNone(a							)


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             lowercase__			:		Dict       =	RagTokenizer.from_pretrained('facebook/rag-sequence-nq'							)
             lowercase__			:		Dict       =	[
                 'who got the first nobel prize in physics',
                 'when is the next deadpool movie being released',
                 'which mode is used for short wave broadcast service',
                 'who is the owner of reading football club',
                 'when is the next scandal episode coming out',
                 'when is the last time the philadelphia won the superbowl',
                 'what is the most current adobe flash player version',
                 'how many episodes are there in dragon ball z',
                 'what is the first step in the evolution of the eye',
                 'where is gall bladder situated in human body',
                 'what is the main mineral in lithium batteries',
                 'who is the president of usa right now',
                 'where do the greasers live in the outsiders',
                 'panda is a national animal of which country',
                 'what is the name of manchester united stadium',
             ]
             lowercase__			:		Optional[int]       =	tokenizer(a							)
             self.assertIsNotNone(a							)









"""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_   : Optional[int]   			= logging.get_logger(__name__)



def 					_A			(__a    ,				__a							) ->       Tuple:
    """simple docstring"""






    try:
        with open(__a    ,				'''rb'''							) as flax_state_f:
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		from_bytes(__a    ,				flax_state_f.read()							)
    except UnpicklingError as e:
        try:
            with open(__a							) 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(__a    ,				__a							)





def 					_A			(__a    ,				__a							) ->       Tuple:
    """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 __a							: x.dtype == jnp.bfloataa    ,				__a							)							).values()
    if any(__a							):
        # 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_      :  Optional[Any]    						=		jax.tree_util.tree_map(
            lambda __a							: params.astype(np.floataa							) if params.dtype == jnp.bfloataa else params    ,				__a							)

    SCREAMING_SNAKE_CASE_      :  int    						=		''''''

    SCREAMING_SNAKE_CASE_      :  str    						=		flatten_dict(__a    ,				sep='''.'''							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		pt_model.state_dict()

    # keep track of unexpected & missing keys
    SCREAMING_SNAKE_CASE_      :  str    						=		[]
    SCREAMING_SNAKE_CASE_      :  Any    						=		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_      :  Any    						=		flax_key_tuple_array[:-1] + ['''weight''']
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		jnp.transpose(__a    ,				(3, 2, 0, 1)							)
        elif flax_key_tuple_array[-1] == "kernel":
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		flax_key_tuple_array[:-1] + ['''weight''']
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		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(__a							):
                SCREAMING_SNAKE_CASE_      :  List[str]    						=		(
                    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_      :  Optional[Any]    						=		'''.'''.join(__a							)

        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_      :  Optional[int]    						=		np.asarray(__a							) if not isinstance(__a    ,				np.ndarray							) else flax_tensor
                SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		torch.from_numpy(__a							)
                # remove from missing keys
                missing_keys.remove(__a							)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(__a							)

    pt_model.load_state_dict(__a							)

    # re-transform missing_keys to list
    SCREAMING_SNAKE_CASE_      :  int    						=		list(__a							)

    if len(__a							) > 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(__a							) > 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


