

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			:					List[str]						=			logging.get_logger(__name__)





def 	__a     (      A__			:  Any			,  A__			:  List[str]			,  A__			:  List[Any]			,  A__			:  Tuple=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:
        SCREAMING_SNAKE_CASE										=   os.path.abspath(A__       )
        logger.info(F"Loading PyTorch weights from {pt_path}"       )

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

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





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

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

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

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

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

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

    # conv layer
    SCREAMING_SNAKE_CASE										=   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(A__       ):
        SCREAMING_SNAKE_CASE										=   pt_tensor.transpose(2			,  3			,  1			,  0       )
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    SCREAMING_SNAKE_CASE										=   pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(A__       ):
        SCREAMING_SNAKE_CASE										=   pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

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

    # old PyTorch layer norm bias
    SCREAMING_SNAKE_CASE										=   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
    SCREAMING_SNAKE_CASE										=   None
    if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
        SCREAMING_SNAKE_CASE										=   pt_tuple_key[-2] + "_g"
    elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
        SCREAMING_SNAKE_CASE										=   pt_tuple_key[-2] + "_v"
    if name is not None:
        SCREAMING_SNAKE_CASE										=   pt_tuple_key[:-3] + (name,)
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor





def 	__a     (      A__			:  int			,  A__			:  Union[str, Any]       ):
    # convert pytorch tensor to numpy
    SCREAMING_SNAKE_CASE										=   {k: v.numpy() for k, v in pt_state_dict.items()}

    SCREAMING_SNAKE_CASE										=   flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers
    if "params" in flax_model.params:
        SCREAMING_SNAKE_CASE										=   flax_model.params["params"]
    else:
        SCREAMING_SNAKE_CASE										=   flax_model.params
    SCREAMING_SNAKE_CASE										=   flatten_dict(A__       )

    # add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
        SCREAMING_SNAKE_CASE										=   flatten_dict(flax_model.params["batch_stats"]       )
        random_flax_state_dict.update(A__       )

    SCREAMING_SNAKE_CASE										=   {}

    SCREAMING_SNAKE_CASE										=   (model_prefix not in flax_model_params) and (
        model_prefix in {k.split("."       )[0] for k in pt_state_dict.keys()}
    )
    SCREAMING_SNAKE_CASE										=   (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():
        SCREAMING_SNAKE_CASE										=   tuple(pt_key.split("."       )       )

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

        # Correctly rename weight parameters
        SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   rename_key_and_reshape_tensor(
            A__			,  A__			,  A__			,  A__       )

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

            # also add unexpected weight so that warning is thrown
            SCREAMING_SNAKE_CASE										=   jnp.asarray(A__       )

        else:
            # also add unexpected weight so that warning is thrown
            SCREAMING_SNAKE_CASE										=   jnp.asarray(A__       )

    return unflatten_dict(A__       )





def 	__a     (      A__			:  Union[str, Any]			,  A__			:  Optional[int]       ):
    import torch

    # Load the index
    SCREAMING_SNAKE_CASE										=   {}
    for shard_file in shard_filenames:
        # load using msgpack utils
        SCREAMING_SNAKE_CASE										=   torch.load(A__       )
        SCREAMING_SNAKE_CASE										=   {k: v.numpy() for k, v in pt_state_dict.items()}

        SCREAMING_SNAKE_CASE										=   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:
            SCREAMING_SNAKE_CASE										=   flax_model.params["params"]

            SCREAMING_SNAKE_CASE										=   flatten_dict(A__       )
            random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]       )       )
        else:
            SCREAMING_SNAKE_CASE										=   flax_model.params
            SCREAMING_SNAKE_CASE										=   flatten_dict(A__       )

        SCREAMING_SNAKE_CASE										=   (model_prefix not in flax_model_params) and (
            model_prefix in {k.split("."       )[0] for k in pt_state_dict.keys()}
        )
        SCREAMING_SNAKE_CASE										=   (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():
            SCREAMING_SNAKE_CASE										=   tuple(pt_key.split("."       )       )

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

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

                # also add unexpected weight so that warning is thrown
                SCREAMING_SNAKE_CASE										=   jnp.asarray(A__       )

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





def 	__a     (      A__			:  int			,  A__			:  Tuple       ):
    SCREAMING_SNAKE_CASE										=   os.path.abspath(A__       )
    logger.info(F"Loading Flax weights from {flax_checkpoint_path}"       )

    # import correct flax class
    SCREAMING_SNAKE_CASE										=   getattr(A__			,  "Flax" + model.__class__.__name__       )

    # load flax weight dict
    with open(A__			,  "rb"       ) as state_f:
        try:
            SCREAMING_SNAKE_CASE										=   from_bytes(A__			,  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(A__			,  A__       )





def 	__a     (      A__			:  int			,  A__			:  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
    SCREAMING_SNAKE_CASE										=   flatten_dict(jax.tree_util.tree_map(lambda A__       : x.dtype == jnp.bfloataa			,  A__       )       ).values()
    if any(A__       ):
        # 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."       )
        SCREAMING_SNAKE_CASE										=   jax.tree_util.tree_map(
            lambda A__       : params.astype(np.floataa       ) if params.dtype == jnp.bfloataa else params			,  A__       )

    SCREAMING_SNAKE_CASE										=   flatten_dict(A__       )
    SCREAMING_SNAKE_CASE										=   pt_model.state_dict()

    SCREAMING_SNAKE_CASE										=   (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()}
    )
    SCREAMING_SNAKE_CASE										=   (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
    SCREAMING_SNAKE_CASE										=   []
    SCREAMING_SNAKE_CASE										=   set(pt_model_dict.keys()       )

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        SCREAMING_SNAKE_CASE										=   flax_key_tuple[0] == pt_model.base_model_prefix
        SCREAMING_SNAKE_CASE										=   ".".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:
            SCREAMING_SNAKE_CASE										=   flax_key_tuple[1:]
        elif load_base_model_into_model_with_head and require_base_model_prefix:
            SCREAMING_SNAKE_CASE										=   (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(A__       ) not in pt_model_dict:
            # conv layer
            SCREAMING_SNAKE_CASE										=   flax_key_tuple[:-1] + ("weight",)
            SCREAMING_SNAKE_CASE										=   jnp.transpose(A__			,  (3, 2, 0, 1)       )
        elif flax_key_tuple[-1] == "kernel" and ".".join(A__       ) not in pt_model_dict:
            # linear layer
            SCREAMING_SNAKE_CASE										=   flax_key_tuple[:-1] + ("weight",)
            SCREAMING_SNAKE_CASE										=   flax_tensor.T
        elif flax_key_tuple[-1] in ["scale", "embedding"]:
            SCREAMING_SNAKE_CASE										=   flax_key_tuple[:-1] + ("weight",)

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

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

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

        if flax_key in special_pt_names:
            SCREAMING_SNAKE_CASE										=   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
                SCREAMING_SNAKE_CASE										=   np.asarray(A__       ) if not isinstance(A__			,  np.ndarray       ) else flax_tensor
                SCREAMING_SNAKE_CASE										=   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
    SCREAMING_SNAKE_CASE										=   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)."       )
    else:
        logger.warning(F"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"       )
    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."       )
    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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			:					List[Any]						=			{
    'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
    'processing_git': ['GitProcessor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __A			:					int						=			[
        'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'GitForCausalLM',
        'GitModel',
        'GitPreTrainedModel',
        'GitVisionModel',
    ]

if TYPE_CHECKING:
    from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
    from .processing_git import GitProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_git import (
            GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            GitForCausalLM,
            GitModel,
            GitPreTrainedModel,
            GitVisionModel,
        )

else:
    import sys

    __A			:					List[Any]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)