
"""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


_a														=		logging.get_logger(__name__)



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case=False     )							->		Optional[Any]:


					"""simple docstring"""





					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:
										__UpperCamelCase										=  os.path.abspath(__lowerCamelCase     )
										logger.info(F'''Loading PyTorch weights from {pt_path}'''     )

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

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



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case,					)							->		str:


					"""simple docstring"""





					def is_key_or_prefix_key_in_dict(__snake_case     ) -> bool:
										return len(set(__lowerCamelCase     ) & {key, (model_prefix,) + key}     ) > 0

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

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

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

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

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

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

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

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

					return pt_tuple_key, pt_tensor



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Tuple:


					"""simple docstring"""





					__UpperCamelCase										=  {k: v.numpy() for k, v in pt_state_dict.items()}

					__UpperCamelCase										=  flax_model.base_model_prefix

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

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

					__UpperCamelCase										=  {}

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

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

										# Correctly rename weight parameters
										__UpperCamelCase										=  rename_key_and_reshape_tensor(
										    __lowerCamelCase,					__lowerCamelCase,					__lowerCamelCase,					__lowerCamelCase     )

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

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

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

					return unflatten_dict(__lowerCamelCase     )



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Tuple:


					"""simple docstring"""





					import torch

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

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

															__UpperCamelCase										=  flatten_dict(__lowerCamelCase     )
															random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']     )     )
										else:
															__UpperCamelCase										=  flax_model.params
															__UpperCamelCase										=  flatten_dict(__lowerCamelCase     )

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

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

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

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

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



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		List[Any]:


					"""simple docstring"""





					__UpperCamelCase										=  os.path.abspath(__lowerCamelCase     )
					logger.info(F'''Loading Flax weights from {flax_checkpoint_path}'''     )

					# import correct flax class
					__UpperCamelCase										=  getattr(__lowerCamelCase,					'''Flax''' + model.__class__.__name__     )

					# load flax weight dict
					with open(__lowerCamelCase,					'''rb'''     ) as state_f:
										try:
															__UpperCamelCase										=  from_bytes(__lowerCamelCase,					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(__lowerCamelCase,					__lowerCamelCase     )



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		int:


					"""simple docstring"""





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

					__UpperCamelCase										=  flatten_dict(__lowerCamelCase     )
					__UpperCamelCase										=  pt_model.state_dict()

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

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

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

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

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

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

					pt_model.load_state_dict(__lowerCamelCase     )

					# re-transform missing_keys to list
					__UpperCamelCase										=  list(__lowerCamelCase     )

					if len(__lowerCamelCase     ) > 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(__lowerCamelCase     ) > 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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_a														=		logging.get_logger(__name__)

_a														=		{
    """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'biogpt'



		def __init__( self    ,		__a=4_23_84    ,		__a=10_24    ,		__a=24    ,		__a=16    ,		__a=40_96    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=10_24    ,		__a=0.02    ,		__a=1e-12    ,		__a=True    ,		__a=True    ,		__a=0.0    ,		__a=0.0    ,		__a=1    ,		__a=0    ,		__a=2    ,		**__a    ,		)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  scale_embedding
							_UpperCamelCase										=  use_cache
							_UpperCamelCase										=  layerdrop
							_UpperCamelCase										=  activation_dropout
							super().__init__(pad_token_id=__a    ,		bos_token_id=__a    ,		eos_token_id=__a    ,		**__a)






