




"""simple docstring"""




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

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
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.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
					import faiss






@require_faiss
class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					tempfile.mkdtemp()
							lowerCamelCase			      =					8

							# DPR tok
							lowerCamelCase			      =					[
							    """[UNK]""",
							    """[CLS]""",
							    """[SEP]""",
							    """[PAD]""",
							    """[MASK]""",
							    """want""",
							    """##want""",
							    """##ed""",
							    """wa""",
							    """un""",
							    """runn""",
							    """##ing""",
							    """,""",
							    """low""",
							    """lowest""",
							]
							lowerCamelCase			      =					os.path.join(self.tmpdirname		, """dpr_tokenizer""" )
							os.makedirs(_a		, exist_ok=_a )
							lowerCamelCase			      =					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
							lowerCamelCase			      =					[
							    """l""",
							    """o""",
							    """w""",
							    """e""",
							    """r""",
							    """s""",
							    """t""",
							    """i""",
							    """d""",
							    """n""",
							    """\u0120""",
							    """\u0120l""",
							    """\u0120n""",
							    """\u0120lo""",
							    """\u0120low""",
							    """er""",
							    """\u0120lowest""",
							    """\u0120newer""",
							    """\u0120wider""",
							    """<unk>""",
							]
							lowerCamelCase			      =					dict(zip(_a		, range(len(_a ) ) ) )
							lowerCamelCase			      =					["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
							lowerCamelCase			      =					{"""unk_token""": """<unk>"""}

							lowerCamelCase			      =					os.path.join(self.tmpdirname		, """bart_tokenizer""" )
							os.makedirs(_a		, exist_ok=_a )
							lowerCamelCase			      =					os.path.join(_a		, BART_VOCAB_FILES_NAMES["""vocab_file"""] )
							lowerCamelCase			      =					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    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		, """dpr_tokenizer""" ) )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		, """dpr_tokenizer""" ) )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		, """bart_tokenizer""" ) )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							shutil.rmtree(self.tmpdirname )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					Dataset.from_dict(
							    {
							        """id""": ["""0""", """1"""],
							        """text""": ["""foo""", """bar"""],
							        """title""": ["""Foo""", """Bar"""],
							        """embeddings""": [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
							    } )
							dataset.add_faiss_index("""embeddings"""		, string_factory="""Flat"""		, metric_type=faiss.METRIC_INNER_PRODUCT )
							return dataset





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_dummy_dataset()
							lowerCamelCase			      =					RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size		, question_encoder=DPRConfig().to_dict()		, generator=BartConfig().to_dict()		, )
							with patch("""transformers.models.rag.retrieval_rag.load_dataset""" ) as mock_load_dataset:
										lowerCamelCase			      =					dataset
										lowerCamelCase			      =					RagRetriever(
										    _a		, question_encoder_tokenizer=self.get_dpr_tokenizer()		, generator_tokenizer=self.get_bart_tokenizer()		, )
							return retriever





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_dummy_dataset()
							lowerCamelCase			      =					RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size		, question_encoder=DPRConfig().to_dict()		, generator=BartConfig().to_dict()		, index_name="""custom"""		, )
							if from_disk:
										lowerCamelCase			      =					os.path.join(self.tmpdirname		, """dataset""" )
										lowerCamelCase			      =					os.path.join(self.tmpdirname		, """index.faiss""" )
										dataset.get_index("""embeddings""" ).save(os.path.join(self.tmpdirname		, """index.faiss""" ) )
										dataset.drop_index("""embeddings""" )
										dataset.save_to_disk(os.path.join(self.tmpdirname		, """dataset""" ) )
										del dataset
										lowerCamelCase			      =					RagRetriever(
										    _a		, question_encoder_tokenizer=self.get_dpr_tokenizer()		, generator_tokenizer=self.get_bart_tokenizer()		, )
							else:
										lowerCamelCase			      =					RagRetriever(
										    _a		, question_encoder_tokenizer=self.get_dpr_tokenizer()		, generator_tokenizer=self.get_bart_tokenizer()		, index=CustomHFIndex(config.retrieval_vector_size		, _a )		, )
							return retriever





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					Dataset.from_dict(
							    {
							        """id""": ["""0""", """1"""],
							        """text""": ["""foo""", """bar"""],
							        """title""": ["""Foo""", """Bar"""],
							        """embeddings""": [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
							    } )
							dataset.add_faiss_index("""embeddings"""		, string_factory="""Flat"""		, metric_type=faiss.METRIC_INNER_PRODUCT )

							lowerCamelCase			      =					os.path.join(self.tmpdirname		, """hf_bert_base.hnswSQ8_correct_phi_128.c_index""" )
							dataset.save_faiss_index("""embeddings"""		, index_file_name + """.index.dpr""" )
							pickle.dump(dataset["""id"""]		, open(index_file_name + """.index_meta.dpr"""		, """wb""" ) )

							lowerCamelCase			      =					os.path.join(self.tmpdirname		, """psgs_w100.tsv.pkl""" )
							lowerCamelCase			      =					{sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
							pickle.dump(_a		, open(_a		, """wb""" ) )

							lowerCamelCase			      =					RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size		, question_encoder=DPRConfig().to_dict()		, generator=BartConfig().to_dict()		, index_name="""legacy"""		, index_path=self.tmpdirname		, )
							lowerCamelCase			      =					RagRetriever(
							    _a		, question_encoder_tokenizer=self.get_dpr_tokenizer()		, generator_tokenizer=self.get_bart_tokenizer() )
							return retriever





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					1
							lowerCamelCase			      =					self.get_dummy_canonical_hf_index_retriever()
							lowerCamelCase			      =					np.array(
							    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]		, dtype=np.floataa )
							lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					retriever.retrieve(_a		, n_docs=_a )
							self.assertEqual(retrieved_doc_embeds.shape		, (2, n_docs, self.retrieval_vector_size) )
							self.assertEqual(len(_a )		, 2 )
							self.assertEqual(sorted(doc_dicts[0] )		, ["""embeddings""", """id""", """text""", """title"""] )
							self.assertEqual(len(doc_dicts[0]["""id"""] )		, _a )
							self.assertEqual(doc_dicts[0]["""id"""][0]		, """1""" )  # max inner product is reached with second doc
							self.assertEqual(doc_dicts[1]["""id"""][0]		, """0""" )  # max inner product is reached with first doc
							self.assertListEqual(doc_ids.tolist()		, [[1], [0]] )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_dummy_canonical_hf_index_retriever()
							with tempfile.TemporaryDirectory() as tmp_dirname:
										with patch("""transformers.models.rag.retrieval_rag.load_dataset""" ) as mock_load_dataset:
													lowerCamelCase			      =					self.get_dummy_dataset()
													retriever.save_pretrained(_a )
													lowerCamelCase			      =					RagRetriever.from_pretrained(_a )
													self.assertIsInstance(_a		, _a )
													lowerCamelCase			      =					np.array(
													    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]		, dtype=np.floataa )
													lowerCamelCase			      =					retriever.retrieve(_a		, n_docs=1 )
													self.assertTrue(out is not None )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					1
							lowerCamelCase			      =					self.get_dummy_custom_hf_index_retriever(from_disk=_a )
							lowerCamelCase			      =					np.array(
							    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]		, dtype=np.floataa )
							lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					retriever.retrieve(_a		, n_docs=_a )
							self.assertEqual(retrieved_doc_embeds.shape		, (2, n_docs, self.retrieval_vector_size) )
							self.assertEqual(len(_a )		, 2 )
							self.assertEqual(sorted(doc_dicts[0] )		, ["""embeddings""", """id""", """text""", """title"""] )
							self.assertEqual(len(doc_dicts[0]["""id"""] )		, _a )
							self.assertEqual(doc_dicts[0]["""id"""][0]		, """1""" )  # max inner product is reached with second doc
							self.assertEqual(doc_dicts[1]["""id"""][0]		, """0""" )  # max inner product is reached with first doc
							self.assertListEqual(doc_ids.tolist()		, [[1], [0]] )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_dummy_custom_hf_index_retriever(from_disk=_a )
							with tempfile.TemporaryDirectory() as tmp_dirname:
										retriever.save_pretrained(_a )
										lowerCamelCase			      =					RagRetriever.from_pretrained(_a )
										self.assertIsInstance(_a		, _a )
										lowerCamelCase			      =					np.array(
										    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]		, dtype=np.floataa )
										lowerCamelCase			      =					retriever.retrieve(_a		, n_docs=1 )
										self.assertTrue(out is not None )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					1
							lowerCamelCase			      =					self.get_dummy_custom_hf_index_retriever(from_disk=_a )
							lowerCamelCase			      =					np.array(
							    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]		, dtype=np.floataa )
							lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					retriever.retrieve(_a		, n_docs=_a )
							self.assertEqual(retrieved_doc_embeds.shape		, (2, n_docs, self.retrieval_vector_size) )
							self.assertEqual(len(_a )		, 2 )
							self.assertEqual(sorted(doc_dicts[0] )		, ["""embeddings""", """id""", """text""", """title"""] )
							self.assertEqual(len(doc_dicts[0]["""id"""] )		, _a )
							self.assertEqual(doc_dicts[0]["""id"""][0]		, """1""" )  # max inner product is reached with second doc
							self.assertEqual(doc_dicts[1]["""id"""][0]		, """0""" )  # max inner product is reached with first doc
							self.assertListEqual(doc_ids.tolist()		, [[1], [0]] )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_dummy_custom_hf_index_retriever(from_disk=_a )
							with tempfile.TemporaryDirectory() as tmp_dirname:
										retriever.save_pretrained(_a )
										lowerCamelCase			      =					RagRetriever.from_pretrained(_a )
										self.assertIsInstance(_a		, _a )
										lowerCamelCase			      =					np.array(
										    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]		, dtype=np.floataa )
										lowerCamelCase			      =					retriever.retrieve(_a		, n_docs=1 )
										self.assertTrue(out is not None )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					1
							lowerCamelCase			      =					self.get_dummy_legacy_index_retriever()
							lowerCamelCase			      =					np.array(
							    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]		, dtype=np.floataa )
							lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					retriever.retrieve(_a		, n_docs=_a )
							self.assertEqual(retrieved_doc_embeds.shape		, (2, n_docs, self.retrieval_vector_size) )
							self.assertEqual(len(_a )		, 2 )
							self.assertEqual(sorted(doc_dicts[0] )		, ["""text""", """title"""] )
							self.assertEqual(len(doc_dicts[0]["""text"""] )		, _a )
							self.assertEqual(doc_dicts[0]["""text"""][0]		, """bar""" )  # max inner product is reached with second doc
							self.assertEqual(doc_dicts[1]["""text"""][0]		, """foo""" )  # max inner product is reached with first doc
							self.assertListEqual(doc_ids.tolist()		, [[1], [0]] )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_dummy_legacy_index_retriever()
							with tempfile.TemporaryDirectory() as tmp_dirname:
										retriever.save_pretrained(_a )
										lowerCamelCase			      =					RagRetriever.from_pretrained(_a )
										self.assertIsInstance(_a		, _a )
										lowerCamelCase			      =					np.array(
										    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]		, dtype=np.floataa )
										lowerCamelCase			      =					retriever.retrieve(_a		, n_docs=1 )
										self.assertTrue(out is not None )





				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							import torch

							lowerCamelCase			      =					1
							lowerCamelCase			      =					self.get_dummy_canonical_hf_index_retriever()
							lowerCamelCase			      =					[[5, 7], [10, 11]]
							lowerCamelCase			      =					np.array(
							    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]		, dtype=np.floataa )
							lowerCamelCase			      =					retriever(_a		, _a		, prefix=retriever.config.generator.prefix		, n_docs=_a )
							lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					(
							    out["""context_input_ids"""],
							    out["""context_attention_mask"""],
							    out["""retrieved_doc_embeds"""],
							)
							self.assertEqual(retrieved_doc_embeds.shape		, (2, n_docs, self.retrieval_vector_size) )
							self.assertIsInstance(_a		, _a )
							self.assertIsInstance(_a		, _a )
							self.assertIsInstance(_a		, np.ndarray )

							lowerCamelCase			      =					retriever(
							    _a		, _a		, prefix=retriever.config.generator.prefix		, n_docs=_a		, return_tensors="""pt"""		, )
							lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					(  # noqa: F841
							    out["""context_input_ids"""],
							    out["""context_attention_mask"""],
							    out["""retrieved_doc_embeds"""],
							    out["""doc_ids"""],
							)
							self.assertEqual(retrieved_doc_embeds.shape		, (2, n_docs, self.retrieval_vector_size) )
							self.assertIsInstance(_a		, torch.Tensor )
							self.assertIsInstance(_a		, torch.Tensor )
							self.assertIsInstance(_a		, torch.Tensor )





				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_dpr_ctx_encoder_tokenizer()
							lowerCamelCase			      =					1
							lowerCamelCase			      =					self.get_dummy_custom_hf_index_retriever(from_disk=_a )
							retriever.set_ctx_encoder_tokenizer(_a )

							lowerCamelCase			      =					[[5, 7], [10, 11]]
							lowerCamelCase			      =					np.array(
							    [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]		, dtype=np.floataa )
							lowerCamelCase			      =					retriever(_a		, _a		, prefix=retriever.config.generator.prefix		, n_docs=_a )

							self.assertEqual(
							    len(_a )		, 6 )  # check whether the retriever output consist of 6 attributes including tokenized docs
							self.assertEqual(
							    all(k in out for k in ("""tokenized_doc_ids""", """tokenized_doc_attention_mask""") )		, _a )  # check for doc token related keys in dictionary.









"""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


lowerCAmelCase							:			Tuple     							=							logging.get_logger(__name__)



def     a__ (				snake_case__       , snake_case__				)						->					Tuple:
			try:
						with open(snake_case__       , """rb"""				) as flax_state_f:
									lowerCamelCase			      =					from_bytes(snake_case__       , flax_state_f.read()				)
			except UnpicklingError as e:
						try:
									with open(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(snake_case__       , snake_case__				)



def     a__ (				snake_case__       , snake_case__				)						->					Tuple:

			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
			lowerCamelCase			      =					flatten_dict(jax.tree_util.tree_map(lambda snake_case__				: x.dtype == jnp.bfloataa       , snake_case__				)				).values()
			if any(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."""				)
						lowerCamelCase			      =					jax.tree_util.tree_map(
						    lambda snake_case__				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params       , snake_case__				)

			lowerCamelCase			      =					""""""

			lowerCamelCase			      =					flatten_dict(snake_case__       , sep="""."""				)
			lowerCamelCase			      =					pt_model.state_dict()

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

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

						if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
									lowerCamelCase			      =					flax_key_tuple_array[:-1] + ["""weight"""]
									lowerCamelCase			      =					jnp.transpose(snake_case__       , (3, 2, 0, 1)				)
						elif flax_key_tuple_array[-1] == "kernel":
									lowerCamelCase			      =					flax_key_tuple_array[:-1] + ["""weight"""]
									lowerCamelCase			      =					flax_tensor.T
						elif flax_key_tuple_array[-1] == "scale":
									lowerCamelCase			      =					flax_key_tuple_array[:-1] + ["""weight"""]

						if "time_embedding" not in flax_key_tuple_array:
									for i, flax_key_tuple_string in enumerate(snake_case__				):
												lowerCamelCase			      =					(
												    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"""				)
												)

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

			pt_model.load_state_dict(snake_case__				)

			# re-transform missing_keys to list
			lowerCamelCase			      =					list(snake_case__				)

			if len(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(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



