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


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





def 	__lowerCamelCase							(UpperCAmelCase__     :							List[str]      , UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							Optional[Any]=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(UpperCAmelCase__  )
										logger.info(F"Loading PyTorch weights from {pt_path}"  )

										SCREAMING_SNAKE_CASE    			=       torch.load(UpperCAmelCase__      , 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(UpperCAmelCase__      , UpperCAmelCase__  )
					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(UpperCAmelCase__      , UpperCAmelCase__  )
					return flax_state_dict





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple[str]      , UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							Dict[str, jnp.ndarray]      , UpperCAmelCase__     :							str      , ):

					def is_key_or_prefix_key_in_dict(UpperCAmelCase__     :							Tuple[str]  ) -> bool:
										return len(set(UpperCAmelCase__  ) & {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(UpperCAmelCase__  ):
										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(UpperCAmelCase__  ):
										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(UpperCAmelCase__  ):
										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(UpperCAmelCase__  ):
										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(UpperCAmelCase__  ):
										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(UpperCAmelCase__  ):
										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 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							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(UpperCAmelCase__  )

					# 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(UpperCAmelCase__  )

					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(
										    UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )

										# 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(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
															SCREAMING_SNAKE_CASE    			=       jnp.asarray(UpperCAmelCase__  )

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

					return unflatten_dict(UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							List[str]      , UpperCAmelCase__     :							Optional[Any]  ):
					import torch

					# Load the index
					SCREAMING_SNAKE_CASE    			=       {}
					for shard_file in shard_filenames:
										# load using msgpack utils
										SCREAMING_SNAKE_CASE    			=       torch.load(UpperCAmelCase__  )
										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(UpperCAmelCase__  )
															random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]  )  )
										else:
															SCREAMING_SNAKE_CASE    			=       flax_model.params
															SCREAMING_SNAKE_CASE    			=       flatten_dict(UpperCAmelCase__  )

										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(
															    UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )
															# 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(UpperCAmelCase__  )
																									continue
																				if "var" in flax_key[-1]:
																									SCREAMING_SNAKE_CASE    			=       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
																				SCREAMING_SNAKE_CASE    			=       jnp.asarray(UpperCAmelCase__  )

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





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							Optional[Any]  ):
					SCREAMING_SNAKE_CASE    			=       os.path.abspath(UpperCAmelCase__  )
					logger.info(F"Loading Flax weights from {flax_checkpoint_path}"  )

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

					# load flax weight dict
					with open(UpperCAmelCase__      , "rb"  ) as state_f:
										try:
															SCREAMING_SNAKE_CASE    			=       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__     :							Optional[int]      , UpperCAmelCase__     :							Dict  ):

					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 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."  )
										SCREAMING_SNAKE_CASE    			=       jax.tree_util.tree_map(
										    lambda UpperCAmelCase__  : params.astype(np.floataa  ) if params.dtype == jnp.bfloataa else params      , UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       flatten_dict(UpperCAmelCase__  )
					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(UpperCAmelCase__  ) not in pt_model_dict:
															# conv layer
															SCREAMING_SNAKE_CASE    			=       flax_key_tuple[:-1] + ("weight",)
															SCREAMING_SNAKE_CASE    			=       jnp.transpose(UpperCAmelCase__      , (3, 2, 0, 1)  )
										elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCAmelCase__  ) 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(UpperCAmelCase__  )

										# 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(UpperCAmelCase__  )
																				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(UpperCAmelCase__  ) if not isinstance(UpperCAmelCase__      , np.ndarray  ) else flax_tensor
																				SCREAMING_SNAKE_CASE    			=       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
					SCREAMING_SNAKE_CASE    			=       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





import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property


@unittest.skipUnless(os.path.exists(a     )				,       """Tatoeba directory does not exist."""     )
class 						lowercase    (     unittest.TestCase     ):




							@cached_property
							def 	__snake_case(						self   :					int      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       tempfile.mkdtemp()
												return TatoebaConverter(save_dir=_UpperCamelCase      )




							@slow
							def 	__snake_case(						self   :					Tuple      )					->				List[Any]:



												'''simple docstring'''

												self.resolver.convert_models(["heb-eng"]      )





							@slow
							def 	__snake_case(						self   :					Any      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.resolver.write_model_card("opus-mt-he-en"	,							dry_run=_UpperCamelCase      )
												assert mmeta["long_pair"] == "heb-eng"




