





'''simple docstring'''


import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


lowerCAmelCase_   :   List[Any]       =					datasets.logging.get_logger(__name__)


lowerCAmelCase_   :   Dict       =					"\\n@InProceedings{moosavi2019minimum,\n    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n    year = {2019},\n    booktitle = {Proceedings of the 57th Annual Meeting of\n        the Association for Computational Linguistics (Volume 1: Long Papers)},\n    publisher = {Association for Computational Linguistics},\n    address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n    author = {Amit Bagga and Breck Baldwin},\n    title = {Algorithms for Scoring Coreference Chains},\n    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n    year = {1998},\n    pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n    author = {Xiaoqiang Luo},\n    title = {On coreference resolution performance metrics},\n    booktitle = {In Proc. of HLT/EMNLP},\n    year = {2005},\n    pages = {25--32},\n    publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n    title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",\n    author = \"Moosavi, Nafise Sadat  and\n      Strube, Michael\",\n    booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = aug,\n    year = \"2016\",\n    address = \"Berlin, Germany\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P16-1060\",\n    doi = \"10.18653/v1/P16-1060\",\n    pages = \"632--642\",\n}\n\n"

lowerCAmelCase_   :   str       =					"\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn	Type	Description\n1	Document ID	This is a variation on the document filename\n2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3	Word number\n4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.\n5	Part-of-Speech\n6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.\n7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"\n8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.\n9	Word sense	This is the word sense of the word in Column 3.\n10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11	Named Entities	These columns identifies the spans representing various named entities.\n12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN	Coreference	Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n"

lowerCAmelCase_   :   Union[str, Any]       =					"\nCalculates coreference evaluation metrics.\nArgs:\n    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n        Each prediction is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n        Each reference is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    keep_singletons: After extracting all mentions of key or system files,\n        mentions whose corresponding coreference chain is of size one,\n        are considered as singletons. The default evaluation mode will include\n        singletons in evaluations if they are included in the key or the system files.\n        By setting 'keep_singletons=False', all singletons in the key and system files\n        will be excluded from the evaluation.\n    NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n        leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.\n    min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.\n        Minimum spans are determined using the MINA algorithm.\n\nReturns:\n    'mentions': mentions\n    'muc': MUC metric [Vilain et al, 1995]\n    'bcub': B-cubed [Bagga and Baldwin, 1998]\n    'ceafe': CEAFe [Luo et al., 2005]\n    'lea': LEA [Moosavi and Strube, 2016]\n    'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n    >>> coval = datasets.load_metric('coval')\n    >>> words = ['bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -',\n    ... 'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)',\n    ... 'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)',\n    ... 'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -',\n    ... 'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -',\n    ... 'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -']\n    >>> references = [words]\n    >>> predictions = [words]\n    >>> results = coval.compute(predictions=predictions, references=references)\n    >>> print(results) # doctest:+ELLIPSIS\n    {'mentions/recall': 1.0,[...] 'conll_score': 100.0}\n"







def    _lowerCamelCase  (__lowerCamelCase  :     List[str]					,  __lowerCamelCase  :     Any					,  __lowerCamelCase  :     Union[str, Any]=False					,  __lowerCamelCase  :     List[Any]=False					,  __lowerCamelCase  :     Union[str, Any]=True					,  __lowerCamelCase  :     Any=False					,  __lowerCamelCase  :     List[Any]="dummy_doc"     )   ->    Any:
			a__     = {doc: key_lines}
			a__     = {doc: sys_lines}

			a__     = {}

			a__     = 0
			a__     = 0
			a__     = 0
			a__     = 0
			a__     = 0
			a__     = 0

			a__   ,				a__     = reader.get_doc_mentions(__lowerCamelCase					,  key_doc_lines[doc]					,  __lowerCamelCase     )
			key_singletons_num += singletons_num

			if NP_only or min_span:
						a__     = reader.set_annotated_parse_trees(__lowerCamelCase					,  key_doc_lines[doc]					,  __lowerCamelCase					,  __lowerCamelCase     )

			a__   ,				a__     = reader.get_doc_mentions(__lowerCamelCase					,  sys_doc_lines[doc]					,  __lowerCamelCase     )
			sys_singletons_num += singletons_num

			if NP_only or min_span:
						a__     = reader.set_annotated_parse_trees(__lowerCamelCase					,  key_doc_lines[doc]					,  __lowerCamelCase					,  __lowerCamelCase     )

			if remove_nested:
						a__   ,				a__     = reader.remove_nested_coref_mentions(__lowerCamelCase					,  __lowerCamelCase     )
						key_nested_coref_num += nested_mentions
						key_removed_nested_clusters += removed_clusters

						a__   ,				a__     = reader.remove_nested_coref_mentions(__lowerCamelCase					,  __lowerCamelCase     )
						sys_nested_coref_num += nested_mentions
						sys_removed_nested_clusters += removed_clusters

			a__     = reader.get_mention_assignments(__lowerCamelCase					,  __lowerCamelCase     )
			a__     = reader.get_mention_assignments(__lowerCamelCase					,  __lowerCamelCase     )

			a__     = (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

			if remove_nested:
						logger.info(
						    "Number of removed nested coreferring mentions in the key "
						    f'''annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}'''     )
						logger.info(
						    "Number of resulting singleton clusters in the key "
						    f'''annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}'''     )

			if not keep_singletons:
						logger.info(
						    f'''{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '''
						    "files, respectively"     )

			return doc_coref_infos







def    _lowerCamelCase  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     str					,  __lowerCamelCase  :     Dict					,  __lowerCamelCase  :     List[str]					,  __lowerCamelCase  :     Optional[Any]					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     Union[str, Any]     )   ->    List[str]:
			a__     = get_coref_infos(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )

			a__     = {}
			a__     = 0
			a__     = 0

			for name, metric in metrics:
						a__   ,				a__   ,				a__     = evaluator.evaluate_documents(__lowerCamelCase					,  __lowerCamelCase					,  beta=1     )
						if name in ["muc", "bcub", "ceafe"]:
									conll += fa
									conll_subparts_num += 1
						output_scores.update({f'''{name}/recall''': recall, f'''{name}/precision''': precision, f'''{name}/f1''': fa}     )

						logger.info(
						    name.ljust(10     )					,  f'''Recall: {recall * 100:.2f}'''					,  f''' Precision: {precision * 100:.2f}'''					,  f''' F1: {fa * 100:.2f}'''					,  )

			if conll_subparts_num == 3:
						a__     = (conll / 3) * 100
						logger.info(f'''CoNLL score: {conll:.2f}'''     )
						output_scores.update({"conll_score": conll}     )

			return output_scores







def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    Any:
			a__     = False
			for line in key_lines:
						if not line.startswith("#"     ):
									if len(line.split()     ) > 6:
												a__     = line.split()[5]
												if not parse_col == "-":
															a__     = True
															break
												else:
															break
			return has_gold_parse






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,_KWARGS_DESCRIPTION     )
class       _lowerCAmelCase  (		datasets.Metric     ):







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				return datasets.MetricInfo(
				    description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
				        {
				            "predictions": datasets.Sequence(datasets.Value("string"		)		),
				            "references": datasets.Sequence(datasets.Value("string"		)		),
				        }		) , codebase_urls=["https://github.com/ns-moosavi/coval"] , reference_urls=[
				        "https://github.com/ns-moosavi/coval",
				        "https://www.aclweb.org/anthology/P16-1060",
				        "http://www.conll.cemantix.org/2012/data.html",
				    ] , )







	def 						__a   (				self			:   List[Any] , lowerCamelCase			:   List[str] , lowerCamelCase			:   List[Any] , lowerCamelCase			:   str=True , lowerCamelCase			:   Any=False , lowerCamelCase			:   Dict=False , lowerCamelCase			:   List[str]=False		):




				'''simple docstring'''
				a__     = [
				    ("mentions", evaluator.mentions),
				    ("muc", evaluator.muc),
				    ("bcub", evaluator.b_cubed),
				    ("ceafe", evaluator.ceafe),
				    ("lea", evaluator.lea),
				]

				if min_span:
							a__     = util.check_gold_parse_annotation(lowerCamelCase		)
							if not has_gold_parse:
										raise NotImplementedError("References should have gold parse annotation to use 'min_span'."		)
										# util.parse_key_file(key_file)
										# key_file = key_file + ".parsed"

				a__     = evaluate(
				    key_lines=lowerCamelCase , sys_lines=lowerCamelCase , metrics=lowerCamelCase , NP_only=lowerCamelCase , remove_nested=lowerCamelCase , keep_singletons=lowerCamelCase , min_span=lowerCamelCase , )

				return score










'''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


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







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]					,  __lowerCamelCase  :     Optional[int]					,  __lowerCamelCase  :     List[str]					,  __lowerCamelCase  :     Union[str, Any]=False     )   ->    Dict:
			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__     = os.path.abspath(__lowerCamelCase     )
						logger.info(f'''Loading PyTorch weights from {pt_path}'''     )

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

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







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

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

			# layer norm
			a__     = 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
			a__     = 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
			a__     = 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
			a__     = 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
			a__     = 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     ):
						a__     = pt_tensor.transpose(2					,  3					,  1					,  0     )
						return renamed_pt_tuple_key, pt_tensor

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

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

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

			return pt_tuple_key, pt_tensor







def    _lowerCamelCase  (__lowerCamelCase  :     int					,  __lowerCamelCase  :     List[Any]     )   ->    Any:
			# convert pytorch tensor to numpy
			a__     = {k: v.numpy() for k, v in pt_state_dict.items()}

			a__     = flax_model.base_model_prefix

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

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

			a__     = {}

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

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

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

						# add model prefix if necessary
						a__     = (model_prefix,) + flax_key in random_flax_state_dict
						if load_base_model_into_model_with_head and require_base_model_prefix:
									a__     = (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__     = 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
									a__     = jnp.asarray(__lowerCamelCase     )

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

			return unflatten_dict(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]					,  __lowerCamelCase  :     Any     )   ->    Union[str, Any]:
			import torch

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

						a__     = 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__     = flax_model.params["params"]

									a__     = flatten_dict(__lowerCamelCase     )
									random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]     )     )
						else:
									a__     = flax_model.params
									a__     = flatten_dict(__lowerCamelCase     )

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

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

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

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







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Tuple     )   ->    Any:
			a__     = os.path.abspath(__lowerCamelCase     )
			logger.info(f'''Loading Flax weights from {flax_checkpoint_path}'''     )

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

			# load flax weight dict
			with open(__lowerCamelCase					,  "rb"     ) as state_f:
						try:
									a__     = 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  (__lowerCamelCase  :     str					,  __lowerCamelCase  :     Tuple     )   ->    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
			a__     = flatten_dict(jax.tree_util.tree_map(lambda __lowerCamelCase     : 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."     )
						a__     = jax.tree_util.tree_map(
						    lambda __lowerCamelCase     : params.astype(np.floataa     ) if params.dtype == jnp.bfloataa else params					,  __lowerCamelCase     )

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

			a__     = (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__     = (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__     = []
			a__     = set(pt_model_dict.keys()     )

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

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

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

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

						if flax_key in special_pt_names:
									a__     = 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__     = np.asarray(__lowerCamelCase     ) if not isinstance(__lowerCamelCase					,  np.ndarray     ) else flax_tensor
												a__     = 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
			a__     = 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



