
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


lowercase        =  logging.get_logger(__name__)



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[int]			,				UpperCAmelCase__				:      Tuple			,				UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      str=False       )  ->    Any:
							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_							  =					os.path.abspath(UpperCAmelCase__       )
														logger.info(F'''Loading PyTorch weights from {pt_path}'''       )

														lowerCamelCase_							  =					torch.load(UpperCAmelCase__			,				map_location="""cpu"""       )
														logger.info(F'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()       ):,} parameters.'''       )

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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Tuple[str]			,				UpperCAmelCase__				:      np.ndarray			,				UpperCAmelCase__				:      Dict[str, jnp.ndarray]			,				UpperCAmelCase__				:      str			,				)  ->    (Tuple[str], np.ndarray):

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

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

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

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

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

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

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

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

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

							return pt_tuple_key, pt_tensor



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[Any]			,				UpperCAmelCase__				:      int       )  ->    Dict:
							# convert pytorch tensor to numpy
							lowerCamelCase_							  =					{k: v.numpy() for k, v in pt_state_dict.items()}

							lowerCamelCase_							  =					flax_model.base_model_prefix

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

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

							lowerCamelCase_							  =					{}

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

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

														# Correctly rename weight parameters
														lowerCamelCase_  ,   lowerCamelCase_							  =					rename_key_and_reshape_tensor(
														    UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )

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

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

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

							return unflatten_dict(UpperCAmelCase__       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      Union[str, Any]       )  ->    Dict:
							import torch

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

														lowerCamelCase_							  =					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_							  =					flax_model.params["""params"""]

																					lowerCamelCase_							  =					flatten_dict(UpperCAmelCase__       )
																					random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]       )       )
														else:
																					lowerCamelCase_							  =					flax_model.params
																					lowerCamelCase_							  =					flatten_dict(UpperCAmelCase__       )

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

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

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

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

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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      int       )  ->    int:
							lowerCamelCase_							  =					os.path.abspath(UpperCAmelCase__       )
							logger.info(F'''Loading Flax weights from {flax_checkpoint_path}'''       )

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

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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Tuple			,				UpperCAmelCase__				:      Dict       )  ->    int:

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

							lowerCamelCase_							  =					flatten_dict(UpperCAmelCase__       )
							lowerCamelCase_							  =					pt_model.state_dict()

							lowerCamelCase_							  =					(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_							  =					(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_							  =					[]
							lowerCamelCase_							  =					set(pt_model_dict.keys()       )

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

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

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

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

														if flax_key in special_pt_names:
																					lowerCamelCase_							  =					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_							  =					np.asarray(UpperCAmelCase__       ) if not isinstance(UpperCAmelCase__			,				np.ndarray       ) else flax_tensor
																												lowerCamelCase_							  =					torch.from_numpy(UpperCAmelCase__       )
																												# remove from missing keys
																												missing_keys.remove(UpperCAmelCase__       )
														else:
																					# weight is not expected by PyTorch model
																					unexpected_keys.append(UpperCAmelCase__       )

							pt_model.load_state_dict(UpperCAmelCase__       )

							# re-transform missing_keys to list
							lowerCamelCase_							  =					list(UpperCAmelCase__       )

							if len(UpperCAmelCase__       ) > 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(UpperCAmelCase__       ) > 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_flax_available, is_torch_available


lowercase        =  {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  ['''SpeechEncoderDecoderModel''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  ['''FlaxSpeechEncoderDecoderModel''']

if TYPE_CHECKING:
		from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
		import sys

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





