




"""simple docstring"""


import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


A_      =    get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
     from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

A_      =    25_60_47
A_      =    25_61_45






@require_sentencepiece
@require_tokenizers
class  lowercase(					_a	,      unittest.TestCase			):







 '''simple docstring'''





 lowercase__			 = NllbTokenizer
 lowercase__			 = NllbTokenizerFast
 lowercase__			 = True
 lowercase__			 = True
 lowercase__			 = {}


 def 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



   super().setUp()

   # We have a SentencePiece fixture for testing
   _snake_case							:		Union[str, Any]								=  NllbTokenizer(__lowerCamelCase,				keep_accents=__lowerCamelCase		)
   tokenizer.save_pretrained(self.tmpdirname		)


 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		str								=  NllbTokenizer(__lowerCamelCase,				keep_accents=__lowerCamelCase		)

   _snake_case							:		Tuple								=  tokenizer.tokenize("""This is a test"""		)
   self.assertListEqual(__lowerCamelCase,				["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]		)

   self.assertListEqual(
       tokenizer.convert_tokens_to_ids(__lowerCamelCase		),				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],				)

   _snake_case							:		Union[str, Any]								=  tokenizer.tokenize("""I was born in 92000, and this is falsé."""		)
   self.assertListEqual(
       __lowerCamelCase,				[
           SPIECE_UNDERLINE + """I""",
           SPIECE_UNDERLINE + """was""",
           SPIECE_UNDERLINE + """b""",
           """or""",
           """n""",
           SPIECE_UNDERLINE + """in""",
           SPIECE_UNDERLINE + """""",
           """9""",
           """2""",
           """0""",
           """0""",
           """0""",
           """,""",
           SPIECE_UNDERLINE + """and""",
           SPIECE_UNDERLINE + """this""",
           SPIECE_UNDERLINE + """is""",
           SPIECE_UNDERLINE + """f""",
           """al""",
           """s""",
           """é""",
           """.""",
       ],				)
   _snake_case							:		str								=  tokenizer.convert_tokens_to_ids(__lowerCamelCase		)
   self.assertListEqual(
       __lowerCamelCase,				[
           value + tokenizer.fairseq_offset
           for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
       ],				)

   _snake_case							:		Optional[Any]								=  tokenizer.convert_ids_to_tokens(__lowerCamelCase		)
   self.assertListEqual(
       __lowerCamelCase,				[
           SPIECE_UNDERLINE + """I""",
           SPIECE_UNDERLINE + """was""",
           SPIECE_UNDERLINE + """b""",
           """or""",
           """n""",
           SPIECE_UNDERLINE + """in""",
           SPIECE_UNDERLINE + """""",
           """<unk>""",
           """2""",
           """0""",
           """0""",
           """0""",
           """,""",
           SPIECE_UNDERLINE + """and""",
           SPIECE_UNDERLINE + """this""",
           SPIECE_UNDERLINE + """is""",
           SPIECE_UNDERLINE + """f""",
           """al""",
           """s""",
           """<unk>""",
           """.""",
       ],				)


 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-nllb""", {})
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
       _snake_case							:		Union[str, Any]								=  self.rust_tokenizer_class.from_pretrained(__lowerCamelCase,				**__lowerCamelCase		)
       _snake_case							:		Optional[int]								=  self.tokenizer_class.from_pretrained(__lowerCamelCase,				**__lowerCamelCase		)

       _snake_case							:		Any								=  tempfile.mkdtemp()

       _snake_case							:		Union[str, Any]								=  tokenizer_r.save_pretrained(__lowerCamelCase		)
       _snake_case							:		Any								=  tokenizer_p.save_pretrained(__lowerCamelCase		)

       # Checks it save with the same files + the tokenizer.json file for the fast one
       self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files		)		)
       _snake_case							:		List[Any]								=  tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f		)
       self.assertSequenceEqual(__lowerCamelCase,				__lowerCamelCase		)

       # Checks everything loads correctly in the same way
       _snake_case							:		Dict								=  tokenizer_r.from_pretrained(__lowerCamelCase		)
       _snake_case							:		List[Any]								=  tokenizer_p.from_pretrained(__lowerCamelCase		)

       # Check special tokens are set accordingly on Rust and Python
       for key in tokenizer_pp.special_tokens_map:
         self.assertTrue(hasattr(__lowerCamelCase,				__lowerCamelCase		)		)

       shutil.rmtree(__lowerCamelCase		)

       # Save tokenizer rust, legacy_format=True
       _snake_case							:		Union[str, Any]								=  tempfile.mkdtemp()

       _snake_case							:		Optional[int]								=  tokenizer_r.save_pretrained(__lowerCamelCase,				legacy_format=__lowerCamelCase		)
       _snake_case							:		Dict								=  tokenizer_p.save_pretrained(__lowerCamelCase		)

       # Checks it save with the same files
       self.assertSequenceEqual(__lowerCamelCase,				__lowerCamelCase		)

       # Checks everything loads correctly in the same way
       _snake_case							:		List[str]								=  tokenizer_r.from_pretrained(__lowerCamelCase		)
       _snake_case							:		Dict								=  tokenizer_p.from_pretrained(__lowerCamelCase		)

       # Check special tokens are set accordingly on Rust and Python
       for key in tokenizer_pp.special_tokens_map:
         self.assertTrue(hasattr(__lowerCamelCase,				__lowerCamelCase		)		)

       shutil.rmtree(__lowerCamelCase		)

       # Save tokenizer rust, legacy_format=False
       _snake_case							:		Tuple								=  tempfile.mkdtemp()

       _snake_case							:		Union[str, Any]								=  tokenizer_r.save_pretrained(__lowerCamelCase,				legacy_format=__lowerCamelCase		)
       _snake_case							:		List[str]								=  tokenizer_p.save_pretrained(__lowerCamelCase		)

       # Checks it saved the tokenizer.json file
       self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files		)		)

       # Checks everything loads correctly in the same way
       _snake_case							:		Optional[int]								=  tokenizer_r.from_pretrained(__lowerCamelCase		)
       _snake_case							:		int								=  tokenizer_p.from_pretrained(__lowerCamelCase		)

       # Check special tokens are set accordingly on Rust and Python
       for key in tokenizer_pp.special_tokens_map:
         self.assertTrue(hasattr(__lowerCamelCase,				__lowerCamelCase		)		)

       shutil.rmtree(__lowerCamelCase		)


 @require_torch
 def 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



   if not self.test_seqaseq:
     return

   _snake_case							:		Optional[int]								=  self.get_tokenizers()
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"		):
       # Longer text that will definitely require truncation.
       _snake_case							:		Optional[Any]								=  [
           """ UN Chief Says There Is No Military Solution in Syria""",
           """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"""
           """ Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"""
           """ will only worsen the violence and misery for millions of people.""",
       ]
       _snake_case							:		Union[str, Any]								=  [
           """Şeful ONU declară că nu există o soluţie militară în Siria""",
           """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"""
           """ Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"""
           """ că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
       ]
       try:
         _snake_case							:		Any								=  tokenizer.prepare_seqaseq_batch(
             src_texts=__lowerCamelCase,				tgt_texts=__lowerCamelCase,				max_length=3,				max_target_length=10,				return_tensors="""pt""",				src_lang="""eng_Latn""",				tgt_lang="""ron_Latn""",				)
       except NotImplementedError:
         return
       self.assertEqual(batch.input_ids.shape[1],				3		)
       self.assertEqual(batch.labels.shape[1],				10		)
       # max_target_length will default to max_length if not specified
       _snake_case							:		List[str]								=  tokenizer.prepare_seqaseq_batch(
           __lowerCamelCase,				tgt_texts=__lowerCamelCase,				max_length=3,				return_tensors="""pt"""		)
       self.assertEqual(batch.input_ids.shape[1],				3		)
       self.assertEqual(batch.labels.shape[1],				3		)

       _snake_case							:		Tuple								=  tokenizer.prepare_seqaseq_batch(
           src_texts=__lowerCamelCase,				max_length=3,				max_target_length=10,				return_tensors="""pt"""		)
       self.assertEqual(batch_encoder_only.input_ids.shape[1],				3		)
       self.assertEqual(batch_encoder_only.attention_mask.shape[1],				3		)
       self.assertNotIn("""decoder_input_ids""",				__lowerCamelCase		)


 @unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece."""		)
 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   pass


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
       _snake_case							:		List[str]								=  [AddedToken("""<special>""",				lstrip=__lowerCamelCase		)]

       _snake_case							:		List[Any]								=  self.rust_tokenizer_class.from_pretrained(
           __lowerCamelCase,				additional_special_tokens=__lowerCamelCase,				**__lowerCamelCase		)
       _snake_case							:		Optional[int]								=  tokenizer_r.encode("""Hey this is a <special> token"""		)

       _snake_case							:		List[Any]								=  tokenizer_r.encode("""<special>""",				add_special_tokens=__lowerCamelCase		)[0]

       self.assertTrue(special_token_id in r_output		)

       if self.test_slow_tokenizer:
         _snake_case							:		str								=  self.rust_tokenizer_class.from_pretrained(
             __lowerCamelCase,				additional_special_tokens=__lowerCamelCase,				**__lowerCamelCase,				)
         _snake_case							:		Dict								=  self.tokenizer_class.from_pretrained(
             __lowerCamelCase,				additional_special_tokens=__lowerCamelCase,				**__lowerCamelCase		)

         _snake_case							:		Union[str, Any]								=  tokenizer_p.encode("""Hey this is a <special> token"""		)

         _snake_case							:		List[str]								=  tokenizer_cr.encode("""Hey this is a <special> token"""		)

         self.assertEqual(__lowerCamelCase,				__lowerCamelCase		)
         self.assertEqual(__lowerCamelCase,				__lowerCamelCase		)
         self.assertTrue(special_token_id in p_output		)
         self.assertTrue(special_token_id in cr_output		)







@require_torch
@require_sentencepiece
@require_tokenizers
class  lowercase(					unittest.TestCase			):







 '''simple docstring'''





 lowercase__			 = """facebook/nllb-200-distilled-600M"""
 lowercase__			 = [
     """ UN Chief Says There Is No Military Solution in Syria""",
     """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
 ]
 lowercase__			 = [
     """Şeful ONU declară că nu există o soluţie militară în Siria""",
     """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
     """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
     """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
 ]
 lowercase__			 = [
     25_60_47,
     1_62_97,
     13_44_08,
     81_65,
     24_80_66,
     1_47_34,
     9_50,
     11_35,
     10_57_21,
     35_73,
     83,
     2_73_52,
     1_08,
     4_94_86,
     2,
 ]


 @classmethod
 def 			UpperCamelCase_       (    cls:		Any		):




   '''simple docstring'''



   _snake_case							:		NllbTokenizer								=  NllbTokenizer.from_pretrained(
       cls.checkpoint_name,				src_lang="""eng_Latn""",				tgt_lang="""ron_Latn"""		)
   _snake_case							:		List[Any]								=  1
   return cls


 def 			UpperCamelCase_       (    self:		Tuple		):




   '''simple docstring'''



   self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""],				256_001		)
   self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""],				256_002		)
   self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""],				256_057		)


 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  self.tokenizer.batch_encode_plus(self.src_text		).input_ids[0]
   self.assertListEqual(self.expected_src_tokens,				__lowerCamelCase		)


 def 			UpperCamelCase_       (    self:		List[Any]		):




   '''simple docstring'''



   self.assertIn(__lowerCamelCase,				self.tokenizer.all_special_ids		)
   # fmt: off
   _snake_case							:		Optional[int]								=  [RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
   # fmt: on

   _snake_case							:		List[Any]								=  self.tokenizer.decode(__lowerCamelCase,				skip_special_tokens=__lowerCamelCase		)
   _snake_case							:		int								=  self.tokenizer.decode(generated_ids[1:],				skip_special_tokens=__lowerCamelCase		)
   self.assertEqual(__lowerCamelCase,				__lowerCamelCase		)
   self.assertNotIn(self.tokenizer.eos_token,				__lowerCamelCase		)


 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  ["""this is gunna be a long sentence """ * 20]
   assert isinstance(src_text[0],				__lowerCamelCase		)
   _snake_case							:		int								=  10
   _snake_case							:		List[Any]								=  self.tokenizer(__lowerCamelCase,				max_length=__lowerCamelCase,				truncation=__lowerCamelCase		).input_ids[0]
   self.assertEqual(ids[-1],				2		)
   self.assertEqual(ids[0],				__lowerCamelCase		)
   self.assertEqual(len(__lowerCamelCase		),				__lowerCamelCase		)


 def 			UpperCamelCase_       (    self:		Tuple		):




   '''simple docstring'''



   self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]		),				[256_203, 3]		)


 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  tempfile.mkdtemp()
   _snake_case							:		Tuple								=  self.tokenizer.fairseq_tokens_to_ids
   self.tokenizer.save_pretrained(__lowerCamelCase		)
   _snake_case							:		Dict								=  NllbTokenizer.from_pretrained(__lowerCamelCase		)
   self.assertDictEqual(new_tok.fairseq_tokens_to_ids,				__lowerCamelCase		)


 @require_torch
 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  self.tokenizer(
       self.src_text,				text_target=self.tgt_text,				padding=__lowerCamelCase,				truncation=__lowerCamelCase,				max_length=len(self.expected_src_tokens		),				return_tensors="""pt""",				)
   _snake_case							:		Tuple								=  shift_tokens_right(
       batch["""labels"""],				self.tokenizer.pad_token_id,				self.tokenizer.lang_code_to_id["""ron_Latn"""]		)

   self.assertIsInstance(__lowerCamelCase,				__lowerCamelCase		)

   self.assertEqual((2, 15),				batch.input_ids.shape		)
   self.assertEqual((2, 15),				batch.attention_mask.shape		)
   _snake_case							:		Optional[Any]								=  batch.input_ids.tolist()[0]
   self.assertListEqual(self.expected_src_tokens,				__lowerCamelCase		)
   self.assertEqual(__lowerCamelCase,				batch.decoder_input_ids[0, 0]		)  # EOS
   # Test that special tokens are reset
   self.assertEqual(self.tokenizer.prefix_tokens,				[EN_CODE]		)
   self.assertEqual(self.tokenizer.suffix_tokens,				[self.tokenizer.eos_token_id]		)


 def 			UpperCamelCase_       (    self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Dict								=  self.tokenizer(self.src_text,				padding=__lowerCamelCase,				truncation=__lowerCamelCase,				max_length=3,				return_tensors="""pt"""		)
   _snake_case							:		int								=  self.tokenizer(
       text_target=self.tgt_text,				padding=__lowerCamelCase,				truncation=__lowerCamelCase,				max_length=10,				return_tensors="""pt"""		)
   _snake_case							:		str								=  targets["""input_ids"""]
   _snake_case							:		Dict								=  shift_tokens_right(
       __lowerCamelCase,				self.tokenizer.pad_token_id,				decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang],				)

   self.assertEqual(batch.input_ids.shape[1],				3		)
   self.assertEqual(batch.decoder_input_ids.shape[1],				10		)


 @require_torch
 def 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  self.tokenizer._build_translation_inputs(
       """A test""",				return_tensors="""pt""",				src_lang="""eng_Latn""",				tgt_lang="""fra_Latn"""		)

   self.assertEqual(
       nested_simplify(__lowerCamelCase		),				{
           # A, test, EOS, en_XX
           """input_ids""": [[256_047, 70, 7_356, 2]],
           """attention_mask""": [[1, 1, 1, 1]],
           # ar_AR
           """forced_bos_token_id""": 256_057,
       },				)


 @require_torch
 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  True
   _snake_case							:		Tuple								=  self.tokenizer(
       """UN Chief says there is no military solution in Syria""",				src_lang="""eng_Latn""",				tgt_lang="""fra_Latn"""		)
   self.assertEqual(
       inputs.input_ids,				[16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047]		)

   _snake_case							:		Optional[int]								=  False
   _snake_case							:		Dict								=  self.tokenizer(
       """UN Chief says there is no military solution in Syria""",				src_lang="""eng_Latn""",				tgt_lang="""fra_Latn"""		)
   self.assertEqual(
       inputs.input_ids,				[256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2]		)








"""simple docstring"""


import os
from pickle import UnpicklingError
from typing import Dict, Tuple

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

import transformers

from .utils import logging


A_      =    logging.get_logger(__name__)


def  UpperCAmelCase__	(snake_case__							: List[str]		,       snake_case__							: Dict		,       snake_case__							: Union[str, Any]		,       snake_case__							: int=False   ):
  """simple docstring"""

  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        """Loading a PyTorch model in Flax, 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

  if not is_sharded:
    _snake_case							:		Dict								=  os.path.abspath(snake_case__   )
    logger.info(F"Loading PyTorch weights from {pt_path}"   )

    _snake_case							:		Tuple								=  torch.load(snake_case__		,       map_location="""cpu"""   )
    logger.info(F"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()   ):,} parameters."   )

    _snake_case							:		int								=  convert_pytorch_state_dict_to_flax(snake_case__		,       snake_case__   )
  else:
    # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
    _snake_case							:		Dict								=  convert_pytorch_sharded_state_dict_to_flax(snake_case__		,       snake_case__   )
  return flax_state_dict


def  UpperCAmelCase__	(snake_case__							: Tuple[str]		,       snake_case__							: np.ndarray		,       snake_case__							: Dict[str, jnp.ndarray]		,       snake_case__							: str		,       ):
  """simple docstring"""

  def is_key_or_prefix_key_in_dict(snake_case__							: Tuple[str]   ) -> bool:
    return len(set(snake_case__   ) & {key, (model_prefix,) + key}   ) > 0

  # layer norm
  _snake_case							:		Any								=  pt_tuple_key[:-1] + ("""scale""",)
  if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(snake_case__   ):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer mean
  _snake_case							:		Optional[Any]								=  pt_tuple_key[:-1] + ("""mean""",)
  if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(snake_case__   ):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer var
  _snake_case							:		Any								=  pt_tuple_key[:-1] + ("""var""",)
  if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(snake_case__   ):
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  _snake_case							:		Any								=  pt_tuple_key[:-1] + ("""embedding""",)
  if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(snake_case__   ):
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  _snake_case							:		Optional[int]								=  pt_tuple_key[:-1] + ("""kernel""",)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(snake_case__   ):
    _snake_case							:		Dict								=  pt_tensor.transpose(2		,       3		,       1		,       0   )
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  _snake_case							:		List[str]								=  pt_tuple_key[:-1] + ("""kernel""",)
  if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(snake_case__   ):
    _snake_case							:		List[Any]								=  pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  _snake_case							:		List[Any]								=  pt_tuple_key[:-1] + ("""weight""",)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  _snake_case							:		Tuple								=  pt_tuple_key[:-1] + ("""bias""",)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
  _snake_case							:		Optional[Any]								=  None
  if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
    _snake_case							:		Union[str, Any]								=  pt_tuple_key[-2] + """_g"""
  elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
    _snake_case							:		Dict								=  pt_tuple_key[-2] + """_v"""
  if name is not None:
    _snake_case							:		Union[str, Any]								=  pt_tuple_key[:-3] + (name,)
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor


def  UpperCAmelCase__	(snake_case__							: Union[str, Any]		,       snake_case__							: Optional[int]   ):
  """simple docstring"""

  _snake_case							:		Tuple								=  {k: v.numpy() for k, v in pt_state_dict.items()}

  _snake_case							:		int								=  flax_model.base_model_prefix

  # use params dict if the model contains batch norm layers
  if "params" in flax_model.params:
    _snake_case							:		Dict								=  flax_model.params["""params"""]
  else:
    _snake_case							:		List[Any]								=  flax_model.params
  _snake_case							:		Tuple								=  flatten_dict(snake_case__   )

  # add batch_stats keys,values to dict
  if "batch_stats" in flax_model.params:
    _snake_case							:		Union[str, Any]								=  flatten_dict(flax_model.params["""batch_stats"""]   )
    random_flax_state_dict.update(snake_case__   )

  _snake_case							:		Tuple								=  {}

  _snake_case							:		Dict								=  (model_prefix not in flax_model_params) and (
      model_prefix in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
  )
  _snake_case							:		Optional[int]								=  (model_prefix in flax_model_params) and (
      model_prefix not in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
  )

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    _snake_case							:		int								=  tuple(pt_key.split("""."""   )   )

    # remove base model prefix if necessary
    _snake_case							:		Optional[Any]								=  pt_tuple_key[0] == model_prefix
    if load_model_with_head_into_base_model and has_base_model_prefix:
      _snake_case							:		Union[str, Any]								=  pt_tuple_key[1:]

    # Correctly rename weight parameters
    _snake_case		, _snake_case							:		int								=  rename_key_and_reshape_tensor(
        snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )

    # add model prefix if necessary
    _snake_case							:		Dict								=  (model_prefix,) + flax_key in random_flax_state_dict
    if load_base_model_into_model_with_head and require_base_model_prefix:
      _snake_case							:		int								=  (model_prefix,) + flax_key

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
            F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."   )

        # add batch stats if the model contains batchnorm layers
    if "batch_stats" in flax_model.params:
      if "mean" in flax_key[-1] or "var" in flax_key[-1]:
        _snake_case							:		Union[str, Any]								=  jnp.asarray(snake_case__   )
        continue
      # remove num_batches_tracked key
      if "num_batches_tracked" in flax_key[-1]:
        flax_state_dict.pop(snake_case__		,       snake_case__   )
        continue

      # also add unexpected weight so that warning is thrown
      _snake_case							:		List[Any]								=  jnp.asarray(snake_case__   )

    else:
      # also add unexpected weight so that warning is thrown
      _snake_case							:		Optional[Any]								=  jnp.asarray(snake_case__   )

  return unflatten_dict(snake_case__   )


def  UpperCAmelCase__	(snake_case__							: Optional[int]		,       snake_case__							: Dict   ):
  """simple docstring"""

  import torch

  # Load the index
  _snake_case							:		str								=  {}
  for shard_file in shard_filenames:
    # load using msgpack utils
    _snake_case							:		Union[str, Any]								=  torch.load(snake_case__   )
    _snake_case							:		Optional[Any]								=  {k: v.numpy() for k, v in pt_state_dict.items()}

    _snake_case							:		List[str]								=  flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
      _snake_case							:		str								=  flax_model.params["""params"""]

      _snake_case							:		List[Any]								=  flatten_dict(snake_case__   )
      random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]   )   )
    else:
      _snake_case							:		List[Any]								=  flax_model.params
      _snake_case							:		Tuple								=  flatten_dict(snake_case__   )

    _snake_case							:		Tuple								=  (model_prefix not in flax_model_params) and (
        model_prefix in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
    )
    _snake_case							:		Optional[Any]								=  (model_prefix in flax_model_params) and (
        model_prefix not in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
    )
    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
      _snake_case							:		List[str]								=  tuple(pt_key.split("""."""   )   )

      # remove base model prefix if necessary
      _snake_case							:		str								=  pt_tuple_key[0] == model_prefix
      if load_model_with_head_into_base_model and has_base_model_prefix:
        _snake_case							:		Optional[Any]								=  pt_tuple_key[1:]

      # Correctly rename weight parameters
      _snake_case		, _snake_case							:		Optional[Any]								=  rename_key_and_reshape_tensor(
          snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )
      # add model prefix if necessary
      _snake_case							:		List[str]								=  (model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
        _snake_case							:		Any								=  (model_prefix,) + flax_key

      if flax_key in random_flax_state_dict:
        if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
          raise ValueError(
              F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
              F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."   )

            # add batch stats if the model contains batchnorm layers
      if "batch_stats" in flax_model.params:
        if "mean" in flax_key[-1]:
          _snake_case							:		Optional[int]								=  jnp.asarray(snake_case__   )
          continue
        if "var" in flax_key[-1]:
          _snake_case							:		Any								=  jnp.asarray(snake_case__   )
          continue
        # remove num_batches_tracked key
        if "num_batches_tracked" in flax_key[-1]:
          flax_state_dict.pop(snake_case__		,       snake_case__   )
          continue

        # also add unexpected weight so that warning is thrown
        _snake_case							:		List[str]								=  jnp.asarray(snake_case__   )

      else:
        # also add unexpected weight so that warning is thrown
        _snake_case							:		Optional[Any]								=  jnp.asarray(snake_case__   )
  return unflatten_dict(snake_case__   )


def  UpperCAmelCase__	(snake_case__							: List[str]		,       snake_case__							: Optional[Any]   ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  os.path.abspath(snake_case__   )
  logger.info(F"Loading Flax weights from {flax_checkpoint_path}"   )

  # import correct flax class
  _snake_case							:		Union[str, Any]								=  getattr(snake_case__		,       """Flax""" + model.__class__.__name__   )

  # load flax weight dict
  with open(snake_case__		,       """rb"""   ) as state_f:
    try:
      _snake_case							:		Dict								=  from_bytes(snake_case__		,       state_f.read()   )
    except UnpicklingError:
      raise EnvironmentError(F"Unable to convert {flax_checkpoint_path} to Flax deserializable object. "   )

  return load_flax_weights_in_pytorch_model(snake_case__		,       snake_case__   )






def  UpperCAmelCase__	(snake_case__							: Tuple		,       snake_case__							: Optional[int]   ):
  """simple docstring"""

  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        """Loading a 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
  _snake_case							:		Optional[int]								=  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 the 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."""   )
    _snake_case							:		Optional[int]								=  jax.tree_util.tree_map(
        lambda snake_case__   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params		,       snake_case__   )

  _snake_case							:		Dict								=  flatten_dict(snake_case__   )
  _snake_case							:		Optional[Any]								=  pt_model.state_dict()

  _snake_case							:		Union[str, Any]								=  (pt_model.base_model_prefix in flax_state) and (
      pt_model.base_model_prefix not in {k.split("""."""   )[0] for k in pt_model_dict.keys()}
  )
  _snake_case							:		Optional[int]								=  (pt_model.base_model_prefix not in flax_state) and (
      pt_model.base_model_prefix in {k.split("""."""   )[0] for k in pt_model_dict.keys()}
  )

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

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    _snake_case							:		Tuple								=  flax_key_tuple[0] == pt_model.base_model_prefix
    _snake_case							:		Optional[Any]								=  """.""".join((pt_model.base_model_prefix,) + flax_key_tuple   ) in pt_model_dict

    # adapt flax_key to prepare for loading from/to base model only
    if load_model_with_head_into_base_model and has_base_model_prefix:
      _snake_case							:		List[str]								=  flax_key_tuple[1:]
    elif load_base_model_into_model_with_head and require_base_model_prefix:
      _snake_case							:		Union[str, Any]								=  (pt_model.base_model_prefix,) + flax_key_tuple

    # rename flax weights to PyTorch format
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(snake_case__   ) not in pt_model_dict:
      # conv layer
      _snake_case							:		Union[str, Any]								=  flax_key_tuple[:-1] + ("""weight""",)
      _snake_case							:		Optional[int]								=  jnp.transpose(snake_case__		,       (3, 2, 0, 1)   )
    elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case__   ) not in pt_model_dict:
      # linear layer
      _snake_case							:		Optional[int]								=  flax_key_tuple[:-1] + ("""weight""",)
      _snake_case							:		Union[str, Any]								=  flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
      _snake_case							:		int								=  flax_key_tuple[:-1] + ("""weight""",)

    # adding batch stats from flax batch norm to pt
    elif "mean" in flax_key_tuple[-1]:
      _snake_case							:		Tuple								=  flax_key_tuple[:-1] + ("""running_mean""",)
    elif "var" in flax_key_tuple[-1]:
      _snake_case							:		Optional[int]								=  flax_key_tuple[:-1] + ("""running_var""",)

    if "batch_stats" in flax_state:
      _snake_case							:		int								=  """.""".join(flax_key_tuple[1:]   )  # Remove the params/batch_stats header
    else:
      _snake_case							:		int								=  """.""".join(snake_case__   )

    # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
    _snake_case							:		Optional[Any]								=  {}
    # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
    for key in pt_model_dict:
      _snake_case							:		List[str]								=  key.split("""."""   )
      _snake_case							:		Optional[int]								=  None
      if key_components[-3::2] == ["parametrizations", "original0"]:
        _snake_case							:		int								=  key_components[-2] + """_g"""
      elif key_components[-3::2] == ["parametrizations", "original1"]:
        _snake_case							:		Union[str, Any]								=  key_components[-2] + """_v"""
      if name is not None:
        _snake_case							:		Dict								=  key_components[:-3] + [name]
        _snake_case							:		Dict								=  """.""".join(snake_case__   )
        _snake_case							:		str								=  key

    if flax_key in special_pt_names:
      _snake_case							:		Union[str, Any]								=  special_pt_names[flax_key]

    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
        _snake_case							:		List[str]								=  np.asarray(snake_case__   ) if not isinstance(snake_case__		,       np.ndarray   ) else flax_tensor
        _snake_case							:		List[Any]								=  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
  _snake_case							:		List[str]								=  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)."""   )
  else:
    logger.warning(F"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"   )
  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."""   )
  else:
    logger.warning(
        F"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
        """If your task is similar to the task the model of the checkpoint was trained on, """
        F"you can already use {pt_model.__class__.__name__} for predictions without further training."   )

  return pt_model


