



from pickle import UnpicklingError

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

from ..utils import logging


a__      :				str   	=			logging.get_logger(__name__)

def   _lowerCAmelCase   (						A__						,  A__	):
      try:
            with open(A__						,  'rb'	) as flax_state_f:
                  lowercase__     =  from_bytes(A__						,  flax_state_f.read()	)
      except UnpicklingError as e:
            try:
                  with open(A__	) as f:
                        if f.read().startswith('version'	):
                              raise OSError(
                                  'You seem to have cloned a repository without having git-lfs installed. Please'
                                  ' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'
                                  ' folder you cloned.'	)
                        else:
                              raise ValueError from e
            except (UnicodeDecodeError, ValueError):
                  raise EnvironmentError(F'''Unable to convert {model_file} to Flax deserializable object. '''	)

      return load_flax_weights_in_pytorch_model(A__						,  A__	)

def   _lowerCAmelCase   (						A__						,  A__	):

      try:
            import torch  # noqa: F401
      except ImportError:
            logger.error(
                'Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'
                ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
                ' instructions.'	)
            raise

      # check if we have bf16 weights
      lowercase__     =  flatten_dict(jax.tree_util.tree_map(lambda A__	: x.dtype == jnp.bfloataa						,  A__	)	).values()
      if any(A__	):
            # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

            # and bf16 is not fully supported in PT yet.
            logger.warning(
                'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
                'before loading those in PyTorch model.'	)
            lowercase__     =  jax.tree_util.tree_map(
                lambda A__	: params.astype(np.floataa	) if params.dtype == jnp.bfloataa else params						,  A__	)

      lowercase__     =  ''

      lowercase__     =  flatten_dict(A__						,  sep='.'	)
      lowercase__     =  pt_model.state_dict()

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

      for flax_key_tuple, flax_tensor in flax_state_dict.items():
            lowercase__     =  flax_key_tuple.split('.'	)

            if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
                  lowercase__     =  flax_key_tuple_array[:-1] + ['weight']
                  lowercase__     =  jnp.transpose(A__						,  (3, 2, 0, 1)	)
            elif flax_key_tuple_array[-1] == "kernel":
                  lowercase__     =  flax_key_tuple_array[:-1] + ['weight']
                  lowercase__     =  flax_tensor.T
            elif flax_key_tuple_array[-1] == "scale":
                  lowercase__     =  flax_key_tuple_array[:-1] + ['weight']

            if "time_embedding" not in flax_key_tuple_array:
                  for i, flax_key_tuple_string in enumerate(A__	):
                        lowercase__     =  (
                            flax_key_tuple_string.replace('_0'						,  '.0'	)
                            .replace('_1'						,  '.1'	)
                            .replace('_2'						,  '.2'	)
                            .replace('_3'						,  '.3'	)
                            .replace('_4'						,  '.4'	)
                            .replace('_5'						,  '.5'	)
                            .replace('_6'						,  '.6'	)
                            .replace('_7'						,  '.7'	)
                            .replace('_8'						,  '.8'	)
                            .replace('_9'						,  '.9'	)
                        )

            lowercase__     =  '.'.join(A__	)

            if flax_key in pt_model_dict:
                  if flax_tensor.shape != pt_model_dict[flax_key].shape:
                        raise ValueError(
                            F'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
                            F'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''	)
                  else:
                        # add weight to pytorch dict
                        lowercase__     =  np.asarray(A__	) if not isinstance(A__						,  np.ndarray	) else flax_tensor
                        lowercase__     =  torch.from_numpy(A__	)
                        # remove from missing keys
                        missing_keys.remove(A__	)
            else:
                  # weight is not expected by PyTorch model
                  unexpected_keys.append(A__	)

      pt_model.load_state_dict(A__	)

      # re-transform missing_keys to list
      lowercase__     =  list(A__	)

      if len(A__	) > 0:
            logger.warning(
                'Some weights of the Flax model were not used when initializing the PyTorch model'
                F''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
                F''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
                ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
                F''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
                ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
                ' FlaxBertForSequenceClassification model).'	)
      if len(A__	) > 0:
            logger.warning(
                F'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
                F''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
                ' use it for predictions and inference.'	)

      return pt_model





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__      :				Optional[int]   	=			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Any   	=			["BlenderbotTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Dict   	=			[
           "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BlenderbotForCausalLM",
           "BlenderbotForConditionalGeneration",
           "BlenderbotModel",
           "BlenderbotPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				List[str]   	=			[
           "TFBlenderbotForConditionalGeneration",
           "TFBlenderbotModel",
           "TFBlenderbotPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				int   	=			[
           "FlaxBlenderbotForConditionalGeneration",
           "FlaxBlenderbotModel",
           "FlaxBlenderbotPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_blenderbot import (
           BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BlenderbotConfig,
           BlenderbotOnnxConfig,
       )
       from .tokenization_blenderbot import BlenderbotTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_blenderbot import (
                  BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BlenderbotForCausalLM,
                  BlenderbotForConditionalGeneration,
                  BlenderbotModel,
                  BlenderbotPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_blenderbot import (
                  TFBlenderbotForConditionalGeneration,
                  TFBlenderbotModel,
                  TFBlenderbotPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_blenderbot import (
                  FlaxBlenderbotForConditionalGeneration,
                  FlaxBlenderbotModel,
                  FlaxBlenderbotPreTrainedModel,
              )

else:
       import sys

       a__      :				Optional[Any]   	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
