


"""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


snake_case__						:   Optional[Any]			 = logging.get_logger(__name__)


def    _snake_case  (  _snake_case :	int       ,      _snake_case :	Union[str, Any]       ,      _snake_case :	List[str]       ,      _snake_case :	Optional[Any]=False	):
      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    :		List[str]				=      os.path.abspath(_snake_case	)
            logger.info(f'''Loading PyTorch weights from {pt_path}'''	)

            lowerCAmelCase    :		Dict				=      torch.load(_snake_case       ,      map_location='''cpu'''	)
            logger.info(f'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()	):,} parameters.'''	)

            lowerCAmelCase    :		Union[str, Any]				=      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
            lowerCAmelCase    :		Optional[Any]				=      convert_pytorch_sharded_state_dict_to_flax(_snake_case       ,      _snake_case	)
      return flax_state_dict


def    _snake_case  (  _snake_case :	Tuple[str]       ,      _snake_case :	np.ndarray       ,      _snake_case :	Dict[str, jnp.ndarray]       ,      _snake_case :	str       ,      ):

      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
      lowerCAmelCase    :		str				=      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
      lowerCAmelCase    :		int				=      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
      lowerCAmelCase    :		List[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
      lowerCAmelCase    :		Tuple				=      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
      lowerCAmelCase    :		Union[str, Any]				=      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	):
            lowerCAmelCase    :		Union[str, Any]				=      pt_tensor.transpose(2       ,      3       ,      1       ,      0	)
            return renamed_pt_tuple_key, pt_tensor

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

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

      # old PyTorch layer norm bias
      lowerCAmelCase    :		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
      lowerCAmelCase    :		str				=      None
      if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
            lowerCAmelCase    :		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    :		Optional[Any]				=      pt_tuple_key[:-3] + (name,)
            return renamed_pt_tuple_key, pt_tensor

      return pt_tuple_key, pt_tensor


def    _snake_case  (  _snake_case :	Union[str, Any]       ,      _snake_case :	Optional[Any]	):
      # convert pytorch tensor to numpy
      lowerCAmelCase    :		Any				=      {k: v.numpy() for k, v in pt_state_dict.items()}

      lowerCAmelCase    :		Any				=      flax_model.base_model_prefix

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

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

      lowerCAmelCase    :		int				=      {}

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

            # remove base model prefix if necessary
            lowerCAmelCase    :		int				=      pt_tuple_key[0] == model_prefix
            if load_model_with_head_into_base_model and has_base_model_prefix:
                  lowerCAmelCase    :		int				=      pt_tuple_key[1:]

            # Correctly rename weight parameters
            lowerCAmelCase,					lowerCAmelCase    :		Optional[Any]				=      rename_key_and_reshape_tensor(
                _snake_case       ,      _snake_case       ,      _snake_case       ,      _snake_case	)

            # add model prefix if necessary
            lowerCAmelCase    :		List[Any]				=      (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] or "var" in flax_key[-1]:
                        lowerCAmelCase    :		Optional[int]				=      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
                  lowerCAmelCase    :		List[Any]				=      jnp.asarray(_snake_case	)

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

      return unflatten_dict(_snake_case	)


def    _snake_case  (  _snake_case :	str       ,      _snake_case :	int	):
      import torch

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

            lowerCAmelCase    :		Union[str, 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    :		Any				=      flax_model.params['''params''']

                  lowerCAmelCase    :		str				=      flatten_dict(_snake_case	)
                  random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']	)	)
            else:
                  lowerCAmelCase    :		Optional[Any]				=      flax_model.params
                  lowerCAmelCase    :		Dict				=      flatten_dict(_snake_case	)

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

                  # remove base model prefix if necessary
                  lowerCAmelCase    :		Dict				=      pt_tuple_key[0] == model_prefix
                  if load_model_with_head_into_base_model and has_base_model_prefix:
                        lowerCAmelCase    :		str				=      pt_tuple_key[1:]

                  # Correctly rename weight parameters
                  lowerCAmelCase,					lowerCAmelCase    :		Tuple				=      rename_key_and_reshape_tensor(
                      _snake_case       ,      _snake_case       ,      _snake_case       ,      _snake_case	)
                  # add model prefix if necessary
                  lowerCAmelCase    :		Any				=      (model_prefix,) + flax_key in random_flax_state_dict
                  if load_base_model_into_model_with_head and require_base_model_prefix:
                        lowerCAmelCase    :		List[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    :		List[str]				=      jnp.asarray(_snake_case	)
                              continue
                        if "var" in flax_key[-1]:
                              lowerCAmelCase    :		List[str]				=      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
                        lowerCAmelCase    :		str				=      jnp.asarray(_snake_case	)

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


def    _snake_case  (  _snake_case :	Optional[Any]       ,      _snake_case :	List[str]	):
      lowerCAmelCase    :		Optional[int]				=      os.path.abspath(_snake_case	)
      logger.info(f'''Loading Flax weights from {flax_checkpoint_path}'''	)

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

      # load flax weight dict
      with open(_snake_case       ,      '''rb'''	) as state_f:
            try:
                  lowerCAmelCase    :		Any				=      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    _snake_case  (  _snake_case :	Tuple       ,      _snake_case :	Dict	):

      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    :		str				=      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.'''	)
            lowerCAmelCase    :		Union[str, Any]				=      jax.tree_util.tree_map(
                lambda _snake_case	: params.astype(np.floataa	) if params.dtype == jnp.bfloataa else params       ,      _snake_case	)

      lowerCAmelCase    :		Union[str, Any]				=      flatten_dict(_snake_case	)
      lowerCAmelCase    :		Any				=      pt_model.state_dict()

      lowerCAmelCase    :		Optional[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    :		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
      lowerCAmelCase    :		Optional[int]				=      []
      lowerCAmelCase    :		Optional[Any]				=      set(pt_model_dict.keys()	)

      for flax_key_tuple, flax_tensor in flax_state_dict.items():
            lowerCAmelCase    :		Dict				=      flax_key_tuple[0] == pt_model.base_model_prefix
            lowerCAmelCase    :		List[str]				=      '''.'''.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[int]				=      (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
                  lowerCAmelCase    :		str				=      flax_key_tuple[:-1] + ('''weight''',)
                  lowerCAmelCase    :		List[Any]				=      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
                  lowerCAmelCase    :		int				=      flax_key_tuple[:-1] + ('''weight''',)
                  lowerCAmelCase    :		List[Any]				=      flax_tensor.T
            elif flax_key_tuple[-1] in ["scale", "embedding"]:
                  lowerCAmelCase    :		List[Any]				=      flax_key_tuple[:-1] + ('''weight''',)

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

            if "batch_stats" in flax_state:
                  lowerCAmelCase    :		Union[str, Any]				=      '''.'''.join(flax_key_tuple[1:]	)  # Remove the params/batch_stats header
            else:
                  lowerCAmelCase    :		List[Any]				=      '''.'''.join(_snake_case	)

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

            if flax_key in special_pt_names:
                  lowerCAmelCase    :		Optional[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    :		Union[str, Any]				=      np.asarray(_snake_case	) if not isinstance(_snake_case       ,      np.ndarray	) else flax_tensor
                        lowerCAmelCase    :		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
      lowerCAmelCase    :		Optional[int]				=      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







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a          =		{
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		["BloomTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		[
           "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BloomForCausalLM",
           "BloomModel",
           "BloomPreTrainedModel",
           "BloomForSequenceClassification",
           "BloomForTokenClassification",
           "BloomForQuestionAnswering",
       ]

if TYPE_CHECKING:
       from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bloom_fast import BloomTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bloom import (
                  BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BloomForCausalLM,
                  BloomForQuestionAnswering,
                  BloomForSequenceClassification,
                  BloomForTokenClassification,
                  BloomModel,
                  BloomPreTrainedModel,
              )

else:
       import sys

       __a          =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
