





"""simple docstring"""





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


lowerCAmelCase__     			=			logging.get_logger(__name__)


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		try:
				with open(_SCREAMING_SNAKE_CASE  ,	"rb"    ) as flax_state_f:
						UpperCamelCase   =	from_bytes(_SCREAMING_SNAKE_CASE  ,	flax_state_f.read()    )
		except UnpicklingError as e:
				try:
						with open(_SCREAMING_SNAKE_CASE    ) 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(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )






def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		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
		UpperCamelCase   =	flatten_dict(jax.tree_util.tree_map(lambda _SCREAMING_SNAKE_CASE    : x.dtype == jnp.bfloataa  ,	_SCREAMING_SNAKE_CASE    )    ).values()
		if any(_SCREAMING_SNAKE_CASE    ):
				# 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."    )
				UpperCamelCase   =	jax.tree_util.tree_map(
				    lambda _SCREAMING_SNAKE_CASE    : params.astype(np.floataa    ) if params.dtype == jnp.bfloataa else params  ,	_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	""

		UpperCamelCase   =	flatten_dict(_SCREAMING_SNAKE_CASE  ,	sep="."    )
		UpperCamelCase   =	pt_model.state_dict()

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

		for flax_key_tuple, flax_tensor in flax_state_dict.items():
				UpperCamelCase   =	flax_key_tuple.split("."    )

				if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
						UpperCamelCase   =	flax_key_tuple_array[:-1] + ["weight"]
						UpperCamelCase   =	jnp.transpose(_SCREAMING_SNAKE_CASE  ,	(3, 2, 0, 1)    )
				elif flax_key_tuple_array[-1] == "kernel":
						UpperCamelCase   =	flax_key_tuple_array[:-1] + ["weight"]
						UpperCamelCase   =	flax_tensor.T
				elif flax_key_tuple_array[-1] == "scale":
						UpperCamelCase   =	flax_key_tuple_array[:-1] + ["weight"]

				if "time_embedding" not in flax_key_tuple_array:
						for i, flax_key_tuple_string in enumerate(_SCREAMING_SNAKE_CASE    ):
								UpperCamelCase   =	(
								    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"    )
								)

				UpperCamelCase   =	".".join(_SCREAMING_SNAKE_CASE    )

				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
								UpperCamelCase   =	np.asarray(_SCREAMING_SNAKE_CASE    ) if not isinstance(_SCREAMING_SNAKE_CASE  ,	np.ndarray    ) else flax_tensor
								UpperCamelCase   =	torch.from_numpy(_SCREAMING_SNAKE_CASE    )
								# remove from missing keys
								missing_keys.remove(_SCREAMING_SNAKE_CASE    )
				else:
						# weight is not expected by PyTorch model
						unexpected_keys.append(_SCREAMING_SNAKE_CASE    )

		pt_model.load_state_dict(_SCREAMING_SNAKE_CASE    )

		# re-transform missing_keys to list
		UpperCamelCase   =	list(_SCREAMING_SNAKE_CASE    )

		if len(_SCREAMING_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)."    )
		if len(_SCREAMING_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."    )

		return pt_model













"""simple docstring"""





from typing import TYPE_CHECKING

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


lowerCAmelCase__     			=			{
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__     			=			['''BlenderbotTokenizerFast''']

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


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__     			=			[
						    '''TFBlenderbotForConditionalGeneration''',
						    '''TFBlenderbotModel''',
						    '''TFBlenderbotPreTrainedModel''',
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__     			=			[
						    '''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

						lowerCAmelCase__     			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






