


"""simple docstring"""







import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel


class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self		,     snake_case__		,     snake_case__=99		,     snake_case__=13		,     snake_case__=7		,     snake_case__=9		,     snake_case__=True		,     snake_case__=True		,     snake_case__=False		,     snake_case__=32		,     snake_case__=5		,     snake_case__=4		,     snake_case__=37		,     snake_case__=8		,     snake_case__=0.1		,     snake_case__=0.002		,     snake_case__=1		,     snake_case__=0		,     snake_case__=0		,     snake_case__=None		,     snake_case__=None		,     ):






         """simple docstring"""





         lowerCAmelCase     :     str								=							parent
         lowerCAmelCase     :     Union[str, Any]								=							batch_size
         lowerCAmelCase     :     Optional[int]								=							encoder_seq_length
         lowerCAmelCase     :     Optional[int]								=							decoder_seq_length
         # For common tests
         lowerCAmelCase     :     Any								=							self.decoder_seq_length
         lowerCAmelCase     :     List[Any]								=							is_training
         lowerCAmelCase     :     Dict								=							use_attention_mask
         lowerCAmelCase     :     List[Any]								=							use_labels
         lowerCAmelCase     :     List[str]								=							vocab_size
         lowerCAmelCase     :     Optional[Any]								=							hidden_size
         lowerCAmelCase     :     Union[str, Any]								=							num_hidden_layers
         lowerCAmelCase     :     Dict								=							num_attention_heads
         lowerCAmelCase     :     Union[str, Any]								=							d_ff
         lowerCAmelCase     :     Optional[Any]								=							relative_attention_num_buckets
         lowerCAmelCase     :     List[Any]								=							dropout_rate
         lowerCAmelCase     :     List[str]								=							initializer_factor
         lowerCAmelCase     :     Dict								=							eos_token_id
         lowerCAmelCase     :     Any								=							pad_token_id
         lowerCAmelCase     :     Tuple								=							decoder_start_token_id
         lowerCAmelCase     :     List[str]								=							None
         lowerCAmelCase     :     Dict								=							decoder_layers

       def        lowercase__							(						self						):






         """simple docstring"""





         return TaConfig.from_pretrained("google/umt5-base"						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__=None		,     snake_case__=None		,     snake_case__=None		,     snake_case__=None		,     snake_case__=None		,     ):






         """simple docstring"""





         if attention_mask is None:
           lowerCAmelCase     :     Union[str, Any]								=							input_ids.ne(config.pad_token_id						)
         if decoder_attention_mask is None:
           lowerCAmelCase     :     List[Any]								=							decoder_input_ids.ne(config.pad_token_id						)
         if head_mask is None:
           lowerCAmelCase     :     Dict								=							torch.ones(config.num_hidden_layers		,     config.num_attention_heads		,     device=_a						)
         if decoder_head_mask is None:
           lowerCAmelCase     :     List[Any]								=							torch.ones(config.num_decoder_layers		,     config.num_attention_heads		,     device=_a						)
         if cross_attn_head_mask is None:
           lowerCAmelCase     :     Tuple								=							torch.ones(
               config.num_decoder_layers		,     config.num_attention_heads		,     device=_a						)
         return {
             "input_ids": input_ids,
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "decoder_attention_mask": decoder_attention_mask,
             "head_mask": head_mask,
             "decoder_head_mask": decoder_head_mask,
             "cross_attn_head_mask": cross_attn_head_mask,
         }

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							ids_tensor([self.batch_size, self.encoder_seq_length]		,     self.vocab_size						)
         lowerCAmelCase     :     Any								=							ids_tensor([self.batch_size, self.decoder_seq_length]		,     self.vocab_size						)

         # we need to clamp the input ids here to avoid having pad token in between
         # this is because for NllbMoe the position_ids are prepared such that
         # all pad tokens have pos id = 2 and rest are between 2..seq_length
         # and the seq_length here is seq_length - num_pad_tokens
         # but when using past, there is no way of knowing if the past input ids had
         # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
         # position_ids being off by num_pad_tokens in past input
         lowerCAmelCase     :     Dict								=							input_ids.clamp(self.pad_token_id + 1						)
         lowerCAmelCase     :     Dict								=							decoder_input_ids.clamp(self.pad_token_id + 1						)

         lowerCAmelCase     :     Dict								=							self.get_config()
         lowerCAmelCase     :     List[Any]								=							config.num_attention_heads
         lowerCAmelCase     :     Optional[Any]								=							self.prepare_inputs_dict(_a		,     _a		,     _a						)
         return config, input_dict

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							self.prepare_config_and_inputs()
         return config, inputs_dict

       def        lowercase__							(						self						):






         """simple docstring"""





         return TaConfig(
             vocab_size=166		,     d_model=self.hidden_size		,     d_ff=self.d_ff		,     d_kv=self.hidden_size // self.num_attention_heads		,     num_layers=self.num_hidden_layers		,     num_decoder_layers=self.decoder_layers		,     num_heads=self.num_attention_heads		,     relative_attention_num_buckets=self.relative_attention_num_buckets		,     dropout_rate=self.dropout_rate		,     initializer_factor=self.initializer_factor		,     eos_token_id=self.eos_token_id		,     bos_token_id=self.pad_token_id		,     pad_token_id=self.pad_token_id		,     decoder_start_token_id=self.decoder_start_token_id		,     )

       def        lowercase__							(						self						):






         """simple docstring"""





         return TaConfig(
             vocab_size=self.vocab_size		,     d_model=self.hidden_size		,     d_ff=self.d_ff		,     d_kv=self.hidden_size // self.num_attention_heads		,     num_layers=self.num_hidden_layers		,     num_decoder_layers=self.decoder_layers		,     num_heads=self.num_attention_heads		,     relative_attention_num_buckets=self.relative_attention_num_buckets		,     dropout_rate=self.dropout_rate		,     initializer_factor=self.initializer_factor		,     eos_token_id=self.eos_token_id		,     bos_token_id=self.pad_token_id		,     pad_token_id=self.pad_token_id		,     decoder_start_token_id=self.decoder_start_token_id		,     )

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							UMTaModel(config=_a						)
         model.to(_a						)
         model.eval()
         lowerCAmelCase     :     Dict								=							model(
             input_ids=_a		,     decoder_input_ids=_a		,     attention_mask=_a		,     decoder_attention_mask=_a		,     )
         lowerCAmelCase     :     Optional[Any]								=							model(input_ids=_a		,     decoder_input_ids=_a						)
         lowerCAmelCase     :     Optional[int]								=							result.last_hidden_state
         lowerCAmelCase     :     int								=							result.past_key_values
         lowerCAmelCase     :     str								=							result.encoder_last_hidden_state

         self.parent.assertEqual(encoder_output.size()		,     (self.batch_size, self.encoder_seq_length, self.hidden_size)						)
         self.parent.assertEqual(decoder_output.size()		,     (self.batch_size, self.decoder_seq_length, self.hidden_size)						)
         # There should be `num_layers` key value embeddings stored in decoder_past
         self.parent.assertEqual(len(_a						)		,     config.num_layers						)
         # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
         self.parent.assertEqual(len(decoder_past[0]						)		,     4						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							UMTaModel(config=_a						).get_decoder().to(_a						).eval()
         # first forward pass
         lowerCAmelCase     :     int								=							model(_a		,     use_cache=_a						)
         lowerCAmelCase     :     str								=							model(_a						)
         lowerCAmelCase     :     List[Any]								=							model(_a		,     use_cache=_a						)

         self.parent.assertTrue(len(_a						) == len(_a						)						)
         self.parent.assertTrue(len(_a						) == len(_a						) + 1						)

         lowerCAmelCase     :     int								=							outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         lowerCAmelCase     :     Any								=							ids_tensor((self.batch_size, 1)		,     config.vocab_size						)

         # append to next input_ids and
         lowerCAmelCase     :     List[str]								=							torch.cat([input_ids, next_tokens]		,     dim=-1						)

         lowerCAmelCase     :     List[str]								=							model(_a						)['last_hidden_state']
         lowerCAmelCase     :     Optional[Any]								=							model(_a		,     past_key_values=_a						)['last_hidden_state']

         # select random slice
         lowerCAmelCase     :     Any								=							ids_tensor((1,)		,     output_from_past.shape[-1]						).item()
         lowerCAmelCase     :     List[str]								=							output_from_no_past[:, -1, random_slice_idx].detach()
         lowerCAmelCase     :     Dict								=							output_from_past[:, 0, random_slice_idx].detach()

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(_a		,     _a		,     atol=1e-3						)						)







       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     str								=							UMTaModel(config=_a						).to(_a						).half().eval()
         lowerCAmelCase     :     Dict								=							model(**_a						)['last_hidden_state']
         self.parent.assertFalse(torch.isnan(_a						).any().item()						)


@require_torch
class       SCREAMING_SNAKE_CASE__				(		SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				str   =(
           (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
       )
       a  :				Union[str, Any]   =(UMTaForConditionalGeneration,) if is_torch_available() else ()
       a  :				Dict   =(
           {
               "conversational": UMTaForConditionalGeneration,
               "feature-extraction": UMTaModel,
               "summarization": UMTaForConditionalGeneration,
               "text2text-generation": UMTaForConditionalGeneration,
               "translation": UMTaForConditionalGeneration,
               "question-answering": UMTaForQuestionAnswering,
           }
           if is_torch_available()
           else {}
       )
       a  :				Optional[Any]   =True
       a  :				str   =False
       a  :				List[Any]   =False
       a  :				str   =True
       a  :				str   =True
       # The small UMT5 model needs higher percentages for CPU/MP tests
       a  :				List[Any]   =[0.8, 0.9]

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							UMTaModelTester(self						)

       @unittest.skip("Test has a segmentation fault on torch 1.8.0"						)
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							self.model_tester.prepare_config_and_inputs()
         lowerCAmelCase     :     str								=							UMTaModel(config_and_inputs[0]						).to(_a						)
         with tempfile.TemporaryDirectory() as tmpdirname:
           torch.onnx.export(
               _a		,     (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])		,     f"""{tmpdirname}/t5_test.onnx"""		,     export_params=_a		,     opset_version=9		,     input_names=["input_ids", "decoder_input_ids"]		,     )

       @unittest.skipIf(torch_device == "cpu"		,     "Cant do half precision"						)
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model_fpaa_forward(*_a						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							['encoder_attentions', 'decoder_attentions', 'cross_attentions']
         lowerCAmelCase     :     Dict								=							self.model_tester.prepare_config_and_inputs()
         lowerCAmelCase     :     Union[str, Any]								=							config_and_inputs[0]
         lowerCAmelCase     :     Optional[Any]								=							UMTaForConditionalGeneration(_a						).eval()
         model.to(_a						)

         lowerCAmelCase     :     Dict								=							{
             'head_mask': torch.zeros(config.num_layers		,     config.num_heads		,     device=_a						),
             'decoder_head_mask': torch.zeros(config.num_decoder_layers		,     config.num_heads		,     device=_a						),
             'cross_attn_head_mask': torch.zeros(config.num_decoder_layers		,     config.num_heads		,     device=_a						),
         }

         for attn_name, (name, mask) in zip(_a		,     head_masking.items()						):
           lowerCAmelCase     :     Tuple								=							{name: mask}
           # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
           if name == "head_mask":
             lowerCAmelCase     :     str								=							torch.ones(
                 config.num_decoder_layers		,     config.num_heads		,     device=_a						)

           lowerCAmelCase     :     Optional[Any]								=							model.generate(
               config_and_inputs[1]["input_ids"]		,     num_beams=1		,     max_length=3		,     output_attentions=_a		,     return_dict_in_generate=_a		,     **_a		,     )
           # We check the state of decoder_attentions and cross_attentions just from the last step
           lowerCAmelCase     :     List[str]								=							out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
           self.assertEqual(sum([w.sum().item() for w in attn_weights]						)		,     0.0						)







       @unittest.skip("Does not work on the tiny model as we keep hitting edge cases."						)
       def        lowercase__							(						self						):






         """simple docstring"""





         pass


@require_torch
@require_sentencepiece
@require_tokenizers
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       @slow
       @unittest.skip(
           "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"						)
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							UMTaForConditionalGeneration.from_pretrained("google/umt5-small"		,     return_dict=_a						).to(_a						)
         lowerCAmelCase     :     List[str]								=							AutoTokenizer.from_pretrained("google/umt5-small"		,     use_fast=_a		,     legacy=_a						)
         lowerCAmelCase     :     str								=							[
             'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
             'No se como puedo <extra_id_0>.',
             'This is the reason why we <extra_id_0> them.',
             'The <extra_id_0> walks in <extra_id_1>, seats',
             'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
         ]
         lowerCAmelCase     :     Optional[Any]								=							tokenizer(_a		,     return_tensors="pt"		,     padding=_a						).input_ids
         # fmt: off
         lowerCAmelCase     :     Optional[int]								=							torch.tensor(
             [
                 [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                 [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                 [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
             ]						)
         # fmt: on
         torch.testing.assert_allclose(_a		,     _a						)

         lowerCAmelCase     :     List[Any]								=							model.generate(input_ids.to(_a						)						)
         lowerCAmelCase     :     Optional[Any]								=							[
             '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
             '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
             '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
             '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
             '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
         ]
         lowerCAmelCase     :     List[str]								=							tokenizer.batch_decode(_a						)
         self.assertEqual(_a		,     _a						)










'''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


_UpperCamelCase											=						logging.get_logger(__name__)

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase=False       )      ->      Tuple:
  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:
    __lowerCamelCase       :       str           = os.path.abspath(_lowerCAmelCase       )
    logger.info(F'Loading PyTorch weights from {pt_path}'       )

    __lowerCamelCase       :       Optional[Any]           = torch.load(_lowerCAmelCase       ,map_location='cpu'       )
    logger.info(F'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()       ):,} parameters.'       )

    __lowerCamelCase       :       Tuple           = convert_pytorch_state_dict_to_flax(_lowerCAmelCase       ,_lowerCAmelCase       )
  else:
    # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
    __lowerCamelCase       :       Union[str, Any]           = convert_pytorch_sharded_state_dict_to_flax(_lowerCAmelCase       ,_lowerCAmelCase       )
  return flax_state_dict

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,)      ->      (Tuple[str], np.ndarray):

  def is_key_or_prefix_key_in_dict(_lowerCAmelCase       ) -> bool:
    return len(set(_lowerCAmelCase       ) & {key, (model_prefix,) + key}       ) > 0

  # layer norm
  __lowerCamelCase       :       List[str]           = pt_tuple_key[:-1] + ('scale',)
  if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer mean
  __lowerCamelCase       :       int           = pt_tuple_key[:-1] + ('mean',)
  if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer var
  __lowerCamelCase       :       List[str]           = pt_tuple_key[:-1] + ('var',)
  if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  __lowerCamelCase       :       Tuple           = pt_tuple_key[:-1] + ('embedding',)
  if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  __lowerCamelCase       :       List[str]           = 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(_lowerCAmelCase       ):
    __lowerCamelCase       :       List[Any]           = pt_tensor.transpose(2       ,3       ,1       ,0       )
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  __lowerCamelCase       :       Optional[Any]           = pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    __lowerCamelCase       :       Dict           = pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  __lowerCamelCase       :       Tuple           = pt_tuple_key[:-1] + ('weight',)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  __lowerCamelCase       :       int           = 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
  __lowerCamelCase       :       Optional[int]           = None
  if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
    __lowerCamelCase       :       Union[str, Any]           = pt_tuple_key[-2] + '_g'
  elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
    __lowerCamelCase       :       Tuple           = pt_tuple_key[-2] + '_v'
  if name is not None:
    __lowerCamelCase       :       Any           = pt_tuple_key[:-3] + (name,)
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[int]:
  # convert pytorch tensor to numpy
  __lowerCamelCase       :       Optional[int]           = {k: v.numpy() for k, v in pt_state_dict.items()}

  __lowerCamelCase       :       str           = flax_model.base_model_prefix

  # use params dict if the model contains batch norm layers
  if "params" in flax_model.params:
    __lowerCamelCase       :       List[Any]           = flax_model.params['params']
  else:
    __lowerCamelCase       :       List[str]           = flax_model.params
  __lowerCamelCase       :       Tuple           = flatten_dict(_lowerCAmelCase       )

  # add batch_stats keys,values to dict
  if "batch_stats" in flax_model.params:
    __lowerCamelCase       :       Optional[int]           = flatten_dict(flax_model.params['batch_stats']       )
    random_flax_state_dict.update(_lowerCAmelCase       )

  __lowerCamelCase       :       str           = {}

  __lowerCamelCase       :       Union[str, Any]           = (model_prefix not in flax_model_params) and (
      model_prefix in {k.split('.'       )[0] for k in pt_state_dict.keys()}
  )
  __lowerCamelCase       :       List[str]           = (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():
    __lowerCamelCase       :       Any           = tuple(pt_key.split('.'       )       )

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

    # Correctly rename weight parameters
    __lowerCamelCase       ,__lowerCamelCase       :       Dict           = rename_key_and_reshape_tensor(
        _lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )

    # add model prefix if necessary
    __lowerCamelCase       :       Dict           = (model_prefix,) + flax_key in random_flax_state_dict
    if load_base_model_into_model_with_head and require_base_model_prefix:
      __lowerCamelCase       :       Dict           = (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]:
        __lowerCamelCase       :       List[Any]           = jnp.asarray(_lowerCAmelCase       )
        continue
      # remove num_batches_tracked key
      if "num_batches_tracked" in flax_key[-1]:
        flax_state_dict.pop(_lowerCAmelCase       ,_lowerCAmelCase       )
        continue

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

    else:
      # also add unexpected weight so that warning is thrown
      __lowerCamelCase       :       str           = jnp.asarray(_lowerCAmelCase       )

  return unflatten_dict(_lowerCAmelCase       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Any:
  import torch

  # Load the index
  __lowerCamelCase       :       Optional[int]           = {}
  for shard_file in shard_filenames:
    # load using msgpack utils
    __lowerCamelCase       :       Dict           = torch.load(_lowerCAmelCase       )
    __lowerCamelCase       :       Dict           = {k: v.numpy() for k, v in pt_state_dict.items()}

    __lowerCamelCase       :       Optional[Any]           = 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:
      __lowerCamelCase       :       str           = flax_model.params['params']

      __lowerCamelCase       :       Tuple           = flatten_dict(_lowerCAmelCase       )
      random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']       )       )
    else:
      __lowerCamelCase       :       Dict           = flax_model.params
      __lowerCamelCase       :       Optional[int]           = flatten_dict(_lowerCAmelCase       )

    __lowerCamelCase       :       List[Any]           = (model_prefix not in flax_model_params) and (
        model_prefix in {k.split('.'       )[0] for k in pt_state_dict.keys()}
    )
    __lowerCamelCase       :       List[str]           = (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():
      __lowerCamelCase       :       Optional[Any]           = tuple(pt_key.split('.'       )       )

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

      # Correctly rename weight parameters
      __lowerCamelCase       ,__lowerCamelCase       :       Any           = rename_key_and_reshape_tensor(
          _lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )
      # add model prefix if necessary
      __lowerCamelCase       :       Tuple           = (model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
        __lowerCamelCase       :       Optional[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]:
          __lowerCamelCase       :       int           = jnp.asarray(_lowerCAmelCase       )
          continue
        if "var" in flax_key[-1]:
          __lowerCamelCase       :       Tuple           = jnp.asarray(_lowerCAmelCase       )
          continue
        # remove num_batches_tracked key
        if "num_batches_tracked" in flax_key[-1]:
          flax_state_dict.pop(_lowerCAmelCase       ,_lowerCAmelCase       )
          continue

        # also add unexpected weight so that warning is thrown
        __lowerCamelCase       :       Optional[int]           = jnp.asarray(_lowerCAmelCase       )

      else:
        # also add unexpected weight so that warning is thrown
        __lowerCamelCase       :       Tuple           = jnp.asarray(_lowerCAmelCase       )
  return unflatten_dict(_lowerCAmelCase       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Union[str, Any]:
  __lowerCamelCase       :       Union[str, Any]           = os.path.abspath(_lowerCAmelCase       )
  logger.info(F'Loading Flax weights from {flax_checkpoint_path}'       )

  # import correct flax class
  __lowerCamelCase       :       str           = getattr(_lowerCAmelCase       ,'Flax' + model.__class__.__name__       )

  # load flax weight dict
  with open(_lowerCAmelCase       ,'rb'       ) as state_f:
    try:
      __lowerCamelCase       :       Tuple           = from_bytes(_lowerCAmelCase       ,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(_lowerCAmelCase       ,_lowerCAmelCase       )


def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[Any]:

  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
  __lowerCamelCase       :       Any           = flatten_dict(jax.tree_util.tree_map(lambda _lowerCAmelCase       : x.dtype == jnp.bfloataa       ,_lowerCAmelCase       )       ).values()
  if any(_lowerCAmelCase       ):
    # 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.'       )
    __lowerCamelCase       :       Dict           = jax.tree_util.tree_map(
        lambda _lowerCAmelCase       : params.astype(np.floataa       ) if params.dtype == jnp.bfloataa else params       ,_lowerCAmelCase       )

  __lowerCamelCase       :       Any           = flatten_dict(_lowerCAmelCase       )
  __lowerCamelCase       :       Union[str, Any]           = pt_model.state_dict()

  __lowerCamelCase       :       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()}
  )
  __lowerCamelCase       :       Tuple           = (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
  __lowerCamelCase       :       Any           = []
  __lowerCamelCase       :       Union[str, Any]           = set(pt_model_dict.keys()       )

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    __lowerCamelCase       :       List[str]           = flax_key_tuple[0] == pt_model.base_model_prefix
    __lowerCamelCase       :       Dict           = '.'.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:
      __lowerCamelCase       :       List[Any]           = flax_key_tuple[1:]
    elif load_base_model_into_model_with_head and require_base_model_prefix:
      __lowerCamelCase       :       Optional[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(_lowerCAmelCase       ) not in pt_model_dict:
      # conv layer
      __lowerCamelCase       :       Tuple           = flax_key_tuple[:-1] + ('weight',)
      __lowerCamelCase       :       Tuple           = jnp.transpose(_lowerCAmelCase       ,(3, 2, 0, 1)       )
    elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCAmelCase       ) not in pt_model_dict:
      # linear layer
      __lowerCamelCase       :       Dict           = flax_key_tuple[:-1] + ('weight',)
      __lowerCamelCase       :       str           = flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
      __lowerCamelCase       :       Optional[Any]           = flax_key_tuple[:-1] + ('weight',)

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

    if "batch_stats" in flax_state:
      __lowerCamelCase       :       Tuple           = '.'.join(flax_key_tuple[1:]       )  # Remove the params/batch_stats header
    else:
      __lowerCamelCase       :       Optional[int]           = '.'.join(_lowerCAmelCase       )

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

    if flax_key in special_pt_names:
      __lowerCamelCase       :       int           = 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
        __lowerCamelCase       :       Tuple           = np.asarray(_lowerCAmelCase       ) if not isinstance(_lowerCAmelCase       ,np.ndarray       ) else flax_tensor
        __lowerCamelCase       :       List[str]           = torch.from_numpy(_lowerCAmelCase       )
        # remove from missing keys
        missing_keys.remove(_lowerCAmelCase       )
    else:
      # weight is not expected by PyTorch model
      unexpected_keys.append(_lowerCAmelCase       )

  pt_model.load_state_dict(_lowerCAmelCase       )

  # re-transform missing_keys to list
  __lowerCamelCase       :       int           = list(_lowerCAmelCase       )

  if len(_lowerCAmelCase       ) > 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(_lowerCAmelCase       ) > 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






