


'''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


__lowercase	:						Union[str, Any]       =	logging.get_logger(__name__)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   Dict=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:
										__a			:       List[str]					    =  os.path.abspath(SCREAMING_SNAKE_CASE_   )
										logger.info(F"""Loading PyTorch weights from {pt_path}"""   )

										__a			:       List[str]					    =  torch.load(SCREAMING_SNAKE_CASE_						,      map_location='cpu'   )
										logger.info(F"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()   ):,} parameters."""   )

										__a			:       Optional[Any]					    =  convert_pytorch_state_dict_to_flax(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_   )
					else:
										# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
										__a			:       List[Any]					    =  convert_pytorch_sharded_state_dict_to_flax(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_   )
					return flax_state_dict






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Dict						,      ):

					def is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE      :   Tuple   ) -> bool:
										return len(set(SCREAMING_SNAKE_CASE_   ) & {key, (model_prefix,) + key}   ) > 0

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

					# batch norm layer mean
					__a			:       Union[str, Any]					    =  pt_tuple_key[:-1] + ("mean",)
					if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_   ):
										return renamed_pt_tuple_key, pt_tensor

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

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

					# conv layer
					__a			:       Any					    =  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(SCREAMING_SNAKE_CASE_   ):
										__a			:       Optional[int]					    =  pt_tensor.transpose(2						,      3						,      1						,      0   )
										return renamed_pt_tuple_key, pt_tensor

					# linear layer
					__a			:       List[Any]					    =  pt_tuple_key[:-1] + ("kernel",)
					if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE_   ):
										__a			:       List[str]					    =  pt_tensor.T
										return renamed_pt_tuple_key, pt_tensor

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

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

					return pt_tuple_key, pt_tensor






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   Dict   ):
					__a			:       Any					    =  {k: v.numpy() for k, v in pt_state_dict.items()}

					__a			:       Dict					    =  flax_model.base_model_prefix

					# use params dict if the model contains batch norm layers
					if "params" in flax_model.params:
										__a			:       Dict					    =  flax_model.params["params"]
					else:
										__a			:       Any					    =  flax_model.params
					__a			:       Tuple					    =  flatten_dict(SCREAMING_SNAKE_CASE_   )

					# add batch_stats keys,values to dict
					if "batch_stats" in flax_model.params:
										__a			:       Union[str, Any]					    =  flatten_dict(flax_model.params['batch_stats']   )
										random_flax_state_dict.update(SCREAMING_SNAKE_CASE_   )

					__a			:       str					    =  {}

					__a			:       Optional[Any]					    =  (model_prefix not in flax_model_params) and (
					    model_prefix in {k.split('.'   )[0] for k in pt_state_dict.keys()}
					)
					__a			:       Union[str, Any]					    =  (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():
										__a			:       str					    =  tuple(pt_key.split('.'   )   )

										# remove base model prefix if necessary
										__a			:       Tuple					    =  pt_tuple_key[0] == model_prefix
										if load_model_with_head_into_base_model and has_base_model_prefix:
															__a			:       List[Any]					    =  pt_tuple_key[1:]

										# Correctly rename weight parameters
										__a			:       Dict					    =  rename_key_and_reshape_tensor(
										    SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_   )

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

															# also add unexpected weight so that warning is thrown
															__a			:       Union[str, Any]					    =  jnp.asarray(SCREAMING_SNAKE_CASE_   )

										else:
															# also add unexpected weight so that warning is thrown
															__a			:       int					    =  jnp.asarray(SCREAMING_SNAKE_CASE_   )

					return unflatten_dict(SCREAMING_SNAKE_CASE_   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					import torch

					# Load the index
					__a			:       List[Any]					    =  {}
					for shard_file in shard_filenames:
										# load using msgpack utils
										__a			:       Optional[int]					    =  torch.load(SCREAMING_SNAKE_CASE_   )
										__a			:       Any					    =  {k: v.numpy() for k, v in pt_state_dict.items()}

										__a			:       str					    =  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:
															__a			:       str					    =  flax_model.params["params"]

															__a			:       List[Any]					    =  flatten_dict(SCREAMING_SNAKE_CASE_   )
															random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']   )   )
										else:
															__a			:       str					    =  flax_model.params
															__a			:       Union[str, Any]					    =  flatten_dict(SCREAMING_SNAKE_CASE_   )

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

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

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

																				# also add unexpected weight so that warning is thrown
																				__a			:       str					    =  jnp.asarray(SCREAMING_SNAKE_CASE_   )

															else:
																				# also add unexpected weight so that warning is thrown
																				__a			:       List[Any]					    =  jnp.asarray(SCREAMING_SNAKE_CASE_   )
					return unflatten_dict(SCREAMING_SNAKE_CASE_   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   List[Any]   ):
					__a			:       Tuple					    =  os.path.abspath(SCREAMING_SNAKE_CASE_   )
					logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""   )

					# import correct flax class
					__a			:       int					    =  getattr(SCREAMING_SNAKE_CASE_						,      'Flax' + model.__class__.__name__   )

					# load flax weight dict
					with open(SCREAMING_SNAKE_CASE_						,      'rb'   ) as state_f:
										try:
															__a			:       Tuple					    =  from_bytes(SCREAMING_SNAKE_CASE_						,      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(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   Optional[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
					__a			:       int					    =  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 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.'   )
										__a			:       List[Any]					    =  jax.tree_util.tree_map(
										    lambda _SCREAMING_SNAKE_CASE   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params						,      SCREAMING_SNAKE_CASE_   )

					__a			:       Union[str, Any]					    =  flatten_dict(SCREAMING_SNAKE_CASE_   )
					__a			:       Optional[Any]					    =  pt_model.state_dict()

					__a			:       Any					    =  (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()}
					)
					__a			:       str					    =  (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
					__a			:       int					    =  []
					__a			:       str					    =  set(pt_model_dict.keys()   )

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

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

										if "batch_stats" in flax_state:
															__a			:       Tuple					    =  ".".join(flax_key_tuple[1:]   )  # Remove the params/batch_stats header
										else:
															__a			:       List[Any]					    =  ".".join(SCREAMING_SNAKE_CASE_   )

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

										if flax_key in special_pt_names:
															__a			:       Optional[Any]					    =  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
																				__a			:       Dict					    =  np.asarray(SCREAMING_SNAKE_CASE_   ) if not isinstance(SCREAMING_SNAKE_CASE_						,      np.ndarray   ) else flax_tensor
																				__a			:       Tuple					    =  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
					__a			:       int					    =  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).'   )
					else:
										logger.warning(F"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""   )
					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.'   )
					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_tf_available,
    is_torch_available,
)


_snake_case             =     {
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

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

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''TFVisionEncoderDecoderModel''']

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

if TYPE_CHECKING:
   from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
   import sys

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



